1

Shale Gas Development and Housing Values over a Decade Evidence from the Barnett Shale

Jeremy G Weber

US Department of Agriculture Economic Research Service

J Wesley Burnett

Assistant Professor West Virginia University Division of Resource Management

Irene M Xiarchos

Natural Resource Economist US Department of Agriculture Office of Energy Policy and New Uses Office of

the Chief Economist

Jeremy G Weber Research Economist United States Department of AgricultureEconomic Research

Service 1400 Independence Ave SW Washington DC 20250-1800 Telephone 202-694-5584 Fax

202-694-5774 Email jeweberersusdagov

Abstract

Extracting natural gas from shale formations can create local economic benefits such as public

revenues but also disamenities such as truck traffic both of which change over time We study

how shale gas development affected zip code level housing values in Texasrsquo Barnett Shale which

splits the Dallas-Fort Worth region in half and is the most extensively developed shale formation

in the US We find that housing in shale zip codes appreciated more than nonshale zip codes

during peak development and less afterwards with a net positive effect of five to six percentage

points from 1997 to 2013 The greater appreciation in part reflects improved local public

finances the value of natural gas rights expanded the local tax base by $82000 per student

increasing school revenues and expenditures Within shale zip codes however an extra well per

square kilometer was associated with a 16 percentage point decrease in appreciation over the

study period

Keywords Natural gas property values Barnett Shale

JEL Codes Q32 Q33 H71 Q51

The views expressed are those of the authors and should not be attributed to the US Department of Agriculture

or the Economic Research Service

2

1 INTRODUCTION

The US has become the global leader in natural gas production by drilling in shale formations (EIA

2013) Drilling has created jobs and generated public revenues for local and state governments in a time of tight

budgets (Weber 2012 Pless 2012) Yet environmental and quality of life concerns have led several states and

cities to impose a moratorium on the key technology used to extract natural gas from shale In 2013 New York

North Carolina Maryland and Vermont had a moratorium on hydraulic fracturing (ldquofrackingrdquo) ndash the method of

injecting a mix of water and chemicals underground at high pressure to create fissures in the shale The support

for moratoriums reflects a range of concerns such as the eye-sore of natural gas infrastructure groundwater

polluted by fracking fluids and the boom-bust nature of energy development Muehlenbachs Spiller and

Timmins (2012 2013) for example found that at least in the short term properties in Pennsylvania dependent on

groundwater lost value when an unconventional gas well was located nearby

We study the medium to long-term effects of shale gas development on zip code level housing values

over the 1997 to 2013 period in Texasrsquo Barnett Shale In doing so we connect the natural resource economics

literature on the effects of extractive industries to the public economics literature on local public finances and

property values The two literatures intersect in our study in Texas the value of oil and gas rights enter the

property tax base once drilling begins generating revenue for local schools and governments

The Barnett Shale has had more wells drilled over a longer time and in a smaller area than any other shale

formation in the US Extensive drilling started in the early 2000s in some areas of the Barnett allowing us to

observe the effect of development on housing values over a decade By 2009 the Barnett Shale had 13740 wells

about ten times more than in Pennsylvaniarsquos Marcellus Shale ndash the location of prior studies on shale gas

development and housing values (Railroad Commission of Texas 2014 PA DEP 2014)

The Barnett Shale also conveniently splits the Dallas-Fort Worth area in half with all of the drilling

occurring on the western side and none occurring on the eastern side The clear demarcation of shale and nonshale

areas all within the Dallas-Fort Worth regional economy provides spatial variation in drilling determined by

geological endowments alone and therefore aids in separating the effect of development from confounding

factors

3

Our zip code-level analysis is well suited to reveal how the net effect of development on local property

values evolves over time Our study period covers a decade of development including a period of frenetic drilling

in the mid-2000s followed by a slow down after 2008 when natural gas prices fell by 50 percent Development

may create broadly-felt disamenities that change over time The noise and truck traffic from drilling eventually

subside but leave the landscape with an eyesore of pipes and tanks The economic stimulus from the industry may

also change as drilling slows but production and royalties begin to flow

We find that shale zip codes appreciated relative to nonshale zip codes from 2005 to 2008 the period of

peak drilling This switched in the 2009-2013 period when nonshale zip codes appreciated more though to a

lesser degree Over the entire 1997-2013 period shale zip codes appreciated 5 to 6 percentage points more than

nonshale zip codes The greater appreciation in part reflects the incorporation of natural gas rights into the

property tax base which increased local public revenues By the 2009-2012 period the assessed value of oil and

gas revenues had expanded the tax base by $82000 per student in shale school districts This in turn increased

school revenues expenditures and fund balances To the extent that housing values fully capture the economic

cost of disamenities from development the results suggest that up to 2013 improved local public finances have

more than offset the disamenties for the typical homeowner The finding may not hold over a longer study period

or in states such as Pennsylvania or Oklahoma where oil and gas rights are not taxed as property

2 WHAT WE KNOW AND WHAT WE MAY LEARN FROM THE BARNETT SHALE

21 Past Literature

An extensive literature explores the local economic impacts from energy development (Merrifield 1984

Isserman and Merrifield 1987 Summers and Branch 1984 Smith et al 2001 Black et al 2005) We add to a

more recent and growing literature on the various consequences of extracting oil and gas from shale which

requires methods that are more disruptive and resource-intensive than traditional methods This literature

addresses a variety of outcomes from effects on income and employment property values infant health to

surface water (Weber 2012 Hill 201 Muehlenbachs Spiller and Timmins 2012 2013 Gopalakrishnan and

Klaiber 2013 Olmstead et al 2013 Weber 2013)

4

Several studies have used housing values to measure how households value having an unconventional gas

well nearby Muehlenbachs Spiller and Timmins (2012) and Gopalakrishnan and Klaiber (2013) both use data

on housing transactions for Washington County Pennsylvania They both find that proximity to natural gas wells

lowers the value of properties dependent on groundwater but has the opposite effect for houses dependent on

public water The authors suggest that the positive effects reflect a combination of lease and royalty payments in-

migration and overall greater economic activity supported by development

Similarly Muehlenbachs Spiller and Timmins (2013) use property-level sales data from Pennsylvania

(and New York border counties) to estimate the heterogeneous effects of shale gas wells on housing values based

on the proximity to wells and dependence on ground water Most relevant for our analysis are their findings for

the effect of shale gas development on housing values in a broad geographic area They find that the number of

wellbores drilled has a positive effect on home values however the effect is only for bores drilled within a one

year period Bores drilled in excess of one year have no impact which the authors interpret as a bust aspect of

well drilling

In contrast to the prior three studies which focus on the more recently developed Marcellus Shale in

Pennsylvania we study the Barnett Shale which has a longer drilling history and covers a more densely

populated area The longer drilling history allows us to separate the 1997-2013 period into four phases of

development pre-drilling modest drilling peak drilling and slowdown Moreover by cutting the Dallas-Fort

Worth region in half the geography of the Barnett aids in identification since it exogenously creates two groups

of zip codes one that is over the shale and has experienced extensive drilling and another without drilling or the

potential for it This contrasts with comparisons on the Pennsylvania-New York border since natural gas

companies leased land for drilling in New York border counties prior to the Statersquos moratorium and will likely

drill there if it is lifted

A second difference is that we use zip code data which are well suited for estimating how extensive

drilling affects the value of the typical zip code residence Our housing value measure ndash the Zillow Home Value

Index (ZHVI) ndash is a measure of the median housing value and reflects information on all single family and

condominium housing in a zip code

5

A third difference is that existing studies have largely ignored how development may affect housing

values through local public finances This partly reflects the location of prior studies ndash Pennsylvania ndash a state that

does not tax oil and gas rights as property Several oil and gas producing states such as Texas require the owners

of oil and gas rights to pay property taxes on the assessed value of their rights (Kent et al 2011)

The public economics literature has extensively studied the link between local public finances local

schools and property values The capitalization of local fiscal variables into housing values has long been

theoretically asserted (Oates 1969 Edel and Sclar 1974 Yinger 1982) Empirically Oates (1969) found that

higher property taxes decrease property values while more school spending per student increase them two

findings that subsequent studies have largely supported (Bradbury Mayer and Case 2001 Barrow and Rouse

2004 Lang and Jian 2004) Still other research establishes a strong link between school quality and housing

values (Black 1999 Fack and Grenet 2010) If shale gas extraction expands the local property tax base all else

constant we would expect property values to increase if any one of three events occur a decline in property tax

rates an increase in public services or an improvement in school finances Although we do not explore changes

in property tax rates or public service provision we show that greater tax revenues in shale school districts

increased school spending and fund balances

22 The Barnett Shale and Its Development

The Barnett Shale formation received its name in the early twentieth century when geologists found an

organic rich shale deposit near the Barnett Stream (Railroad Commission of Texas 2013) The Shale covers many

counties but its most productive areas are near its eastern boundary which divides the Dallas-Fort Worth region

As Figure 1 illustrates this is where most drilling has occurred

Texas led the advancement of hydraulic fracturing (Rahm 2011) which was needed to free gas from the

Barnettrsquos hard shale Much of the impetus for development of the Barnett and the advancement of hydraulic

fracturing came from George Mitchell the founder of Mitchell Energy The company experimented with

hydraulic fracturing there throughout the 1990s but with mixed success As of 1998 the US Geological Survey

still estimated that the Barnett held only modest amounts of gas and as late as 2000 the president of Mitchell

6

Energy described it as a black tombstone in an interview on CNBC But by 2001 Mitchellrsquos wells had proven

sufficiently fruitful for Devon Energy to buy the company at a premium In the following years the industryrsquos

wide-spread skepticism of the Barnett evaporated as natural gas prices increased and justified the more expensive

horizontal wells that would prove most successful in drawing gas from the Barnett (Zuckerman 2013)

Companies must obtain permits prior to drill so permit submissions indicate drilling intentions The

number of submitted permits for five key Barnett Shale counties (for which Zillow zip code housing value data

are available) shows that less than 75 permits were submitted in each year from 1997 to 1999 (Figure 2) There

was a small uptick in 2000 and then a larger increase in 2001 with 736 permits submitted The growth continued

until 2008 when the industry submitted more than 4500 permits in that year alone Submissions fell in 2009 along

with the price of natural gas They continued to decline and in 2013 companies submitted fewer than 650 permits

Of the five counties considered Denton County was the earliest to experience an increase in permitting

and drilling As Figure 2 indicates the small increase in permits submitted in 2000 was almost entirely accounted

for by Denton County in that year Denton accounted for 84 percent of the permits submitted in the five counties

From 2003 onwards the other four counties accounted for most or all of the increase in permits

Anderson and Thodori (2009) interviewed local key informants in the Barnett shale area about the

consequences of shale drilling in their communities Interviewing people such as mayors judges policeman and

journalists they found a perception that development had ldquostimulated economic prosperity in their communitiesrdquo

including increases in city revenue property values and household income At the same time informants

mentioned other consequences such as a deterioration of local roads excessive truck traffic concerns about

waste-water injection wells and air pollution The authors suggested that an analysis of housing prices would

provide a good assessment of how development has affected the region

Consistent with the perceptions of the people interviewed by Anderson and Thodori (2009) a descriptive

look at the Zillow Housing Value Index across shale and nonshale zip codes suggests that development increased

housing values For each year we calculate the average of the log value of the Zillow Housing Value Index for

shale and nonshale zip codes (described in detail below) In Figure 2 we graph the difference in mean values

across shale and nonshale zip codes along with the number of permits submitted for shale counties From 1997 to

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

3

Our zip code-level analysis is well suited to reveal how the net effect of development on local property

values evolves over time Our study period covers a decade of development including a period of frenetic drilling

in the mid-2000s followed by a slow down after 2008 when natural gas prices fell by 50 percent Development

may create broadly-felt disamenities that change over time The noise and truck traffic from drilling eventually

subside but leave the landscape with an eyesore of pipes and tanks The economic stimulus from the industry may

also change as drilling slows but production and royalties begin to flow

We find that shale zip codes appreciated relative to nonshale zip codes from 2005 to 2008 the period of

peak drilling This switched in the 2009-2013 period when nonshale zip codes appreciated more though to a

lesser degree Over the entire 1997-2013 period shale zip codes appreciated 5 to 6 percentage points more than

nonshale zip codes The greater appreciation in part reflects the incorporation of natural gas rights into the

property tax base which increased local public revenues By the 2009-2012 period the assessed value of oil and

gas revenues had expanded the tax base by $82000 per student in shale school districts This in turn increased

school revenues expenditures and fund balances To the extent that housing values fully capture the economic

cost of disamenities from development the results suggest that up to 2013 improved local public finances have

more than offset the disamenties for the typical homeowner The finding may not hold over a longer study period

or in states such as Pennsylvania or Oklahoma where oil and gas rights are not taxed as property

2 WHAT WE KNOW AND WHAT WE MAY LEARN FROM THE BARNETT SHALE

21 Past Literature

An extensive literature explores the local economic impacts from energy development (Merrifield 1984

Isserman and Merrifield 1987 Summers and Branch 1984 Smith et al 2001 Black et al 2005) We add to a

more recent and growing literature on the various consequences of extracting oil and gas from shale which

requires methods that are more disruptive and resource-intensive than traditional methods This literature

addresses a variety of outcomes from effects on income and employment property values infant health to

surface water (Weber 2012 Hill 201 Muehlenbachs Spiller and Timmins 2012 2013 Gopalakrishnan and

Klaiber 2013 Olmstead et al 2013 Weber 2013)

4

Several studies have used housing values to measure how households value having an unconventional gas

well nearby Muehlenbachs Spiller and Timmins (2012) and Gopalakrishnan and Klaiber (2013) both use data

on housing transactions for Washington County Pennsylvania They both find that proximity to natural gas wells

lowers the value of properties dependent on groundwater but has the opposite effect for houses dependent on

public water The authors suggest that the positive effects reflect a combination of lease and royalty payments in-

migration and overall greater economic activity supported by development

Similarly Muehlenbachs Spiller and Timmins (2013) use property-level sales data from Pennsylvania

(and New York border counties) to estimate the heterogeneous effects of shale gas wells on housing values based

on the proximity to wells and dependence on ground water Most relevant for our analysis are their findings for

the effect of shale gas development on housing values in a broad geographic area They find that the number of

wellbores drilled has a positive effect on home values however the effect is only for bores drilled within a one

year period Bores drilled in excess of one year have no impact which the authors interpret as a bust aspect of

well drilling

In contrast to the prior three studies which focus on the more recently developed Marcellus Shale in

Pennsylvania we study the Barnett Shale which has a longer drilling history and covers a more densely

populated area The longer drilling history allows us to separate the 1997-2013 period into four phases of

development pre-drilling modest drilling peak drilling and slowdown Moreover by cutting the Dallas-Fort

Worth region in half the geography of the Barnett aids in identification since it exogenously creates two groups

of zip codes one that is over the shale and has experienced extensive drilling and another without drilling or the

potential for it This contrasts with comparisons on the Pennsylvania-New York border since natural gas

companies leased land for drilling in New York border counties prior to the Statersquos moratorium and will likely

drill there if it is lifted

A second difference is that we use zip code data which are well suited for estimating how extensive

drilling affects the value of the typical zip code residence Our housing value measure ndash the Zillow Home Value

Index (ZHVI) ndash is a measure of the median housing value and reflects information on all single family and

condominium housing in a zip code

5

A third difference is that existing studies have largely ignored how development may affect housing

values through local public finances This partly reflects the location of prior studies ndash Pennsylvania ndash a state that

does not tax oil and gas rights as property Several oil and gas producing states such as Texas require the owners

of oil and gas rights to pay property taxes on the assessed value of their rights (Kent et al 2011)

The public economics literature has extensively studied the link between local public finances local

schools and property values The capitalization of local fiscal variables into housing values has long been

theoretically asserted (Oates 1969 Edel and Sclar 1974 Yinger 1982) Empirically Oates (1969) found that

higher property taxes decrease property values while more school spending per student increase them two

findings that subsequent studies have largely supported (Bradbury Mayer and Case 2001 Barrow and Rouse

2004 Lang and Jian 2004) Still other research establishes a strong link between school quality and housing

values (Black 1999 Fack and Grenet 2010) If shale gas extraction expands the local property tax base all else

constant we would expect property values to increase if any one of three events occur a decline in property tax

rates an increase in public services or an improvement in school finances Although we do not explore changes

in property tax rates or public service provision we show that greater tax revenues in shale school districts

increased school spending and fund balances

22 The Barnett Shale and Its Development

The Barnett Shale formation received its name in the early twentieth century when geologists found an

organic rich shale deposit near the Barnett Stream (Railroad Commission of Texas 2013) The Shale covers many

counties but its most productive areas are near its eastern boundary which divides the Dallas-Fort Worth region

As Figure 1 illustrates this is where most drilling has occurred

Texas led the advancement of hydraulic fracturing (Rahm 2011) which was needed to free gas from the

Barnettrsquos hard shale Much of the impetus for development of the Barnett and the advancement of hydraulic

fracturing came from George Mitchell the founder of Mitchell Energy The company experimented with

hydraulic fracturing there throughout the 1990s but with mixed success As of 1998 the US Geological Survey

still estimated that the Barnett held only modest amounts of gas and as late as 2000 the president of Mitchell

6

Energy described it as a black tombstone in an interview on CNBC But by 2001 Mitchellrsquos wells had proven

sufficiently fruitful for Devon Energy to buy the company at a premium In the following years the industryrsquos

wide-spread skepticism of the Barnett evaporated as natural gas prices increased and justified the more expensive

horizontal wells that would prove most successful in drawing gas from the Barnett (Zuckerman 2013)

Companies must obtain permits prior to drill so permit submissions indicate drilling intentions The

number of submitted permits for five key Barnett Shale counties (for which Zillow zip code housing value data

are available) shows that less than 75 permits were submitted in each year from 1997 to 1999 (Figure 2) There

was a small uptick in 2000 and then a larger increase in 2001 with 736 permits submitted The growth continued

until 2008 when the industry submitted more than 4500 permits in that year alone Submissions fell in 2009 along

with the price of natural gas They continued to decline and in 2013 companies submitted fewer than 650 permits

Of the five counties considered Denton County was the earliest to experience an increase in permitting

and drilling As Figure 2 indicates the small increase in permits submitted in 2000 was almost entirely accounted

for by Denton County in that year Denton accounted for 84 percent of the permits submitted in the five counties

From 2003 onwards the other four counties accounted for most or all of the increase in permits

Anderson and Thodori (2009) interviewed local key informants in the Barnett shale area about the

consequences of shale drilling in their communities Interviewing people such as mayors judges policeman and

journalists they found a perception that development had ldquostimulated economic prosperity in their communitiesrdquo

including increases in city revenue property values and household income At the same time informants

mentioned other consequences such as a deterioration of local roads excessive truck traffic concerns about

waste-water injection wells and air pollution The authors suggested that an analysis of housing prices would

provide a good assessment of how development has affected the region

Consistent with the perceptions of the people interviewed by Anderson and Thodori (2009) a descriptive

look at the Zillow Housing Value Index across shale and nonshale zip codes suggests that development increased

housing values For each year we calculate the average of the log value of the Zillow Housing Value Index for

shale and nonshale zip codes (described in detail below) In Figure 2 we graph the difference in mean values

across shale and nonshale zip codes along with the number of permits submitted for shale counties From 1997 to

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

4

Several studies have used housing values to measure how households value having an unconventional gas

well nearby Muehlenbachs Spiller and Timmins (2012) and Gopalakrishnan and Klaiber (2013) both use data

on housing transactions for Washington County Pennsylvania They both find that proximity to natural gas wells

lowers the value of properties dependent on groundwater but has the opposite effect for houses dependent on

public water The authors suggest that the positive effects reflect a combination of lease and royalty payments in-

migration and overall greater economic activity supported by development

Similarly Muehlenbachs Spiller and Timmins (2013) use property-level sales data from Pennsylvania

(and New York border counties) to estimate the heterogeneous effects of shale gas wells on housing values based

on the proximity to wells and dependence on ground water Most relevant for our analysis are their findings for

the effect of shale gas development on housing values in a broad geographic area They find that the number of

wellbores drilled has a positive effect on home values however the effect is only for bores drilled within a one

year period Bores drilled in excess of one year have no impact which the authors interpret as a bust aspect of

well drilling

In contrast to the prior three studies which focus on the more recently developed Marcellus Shale in

Pennsylvania we study the Barnett Shale which has a longer drilling history and covers a more densely

populated area The longer drilling history allows us to separate the 1997-2013 period into four phases of

development pre-drilling modest drilling peak drilling and slowdown Moreover by cutting the Dallas-Fort

Worth region in half the geography of the Barnett aids in identification since it exogenously creates two groups

of zip codes one that is over the shale and has experienced extensive drilling and another without drilling or the

potential for it This contrasts with comparisons on the Pennsylvania-New York border since natural gas

companies leased land for drilling in New York border counties prior to the Statersquos moratorium and will likely

drill there if it is lifted

A second difference is that we use zip code data which are well suited for estimating how extensive

drilling affects the value of the typical zip code residence Our housing value measure ndash the Zillow Home Value

Index (ZHVI) ndash is a measure of the median housing value and reflects information on all single family and

condominium housing in a zip code

5

A third difference is that existing studies have largely ignored how development may affect housing

values through local public finances This partly reflects the location of prior studies ndash Pennsylvania ndash a state that

does not tax oil and gas rights as property Several oil and gas producing states such as Texas require the owners

of oil and gas rights to pay property taxes on the assessed value of their rights (Kent et al 2011)

The public economics literature has extensively studied the link between local public finances local

schools and property values The capitalization of local fiscal variables into housing values has long been

theoretically asserted (Oates 1969 Edel and Sclar 1974 Yinger 1982) Empirically Oates (1969) found that

higher property taxes decrease property values while more school spending per student increase them two

findings that subsequent studies have largely supported (Bradbury Mayer and Case 2001 Barrow and Rouse

2004 Lang and Jian 2004) Still other research establishes a strong link between school quality and housing

values (Black 1999 Fack and Grenet 2010) If shale gas extraction expands the local property tax base all else

constant we would expect property values to increase if any one of three events occur a decline in property tax

rates an increase in public services or an improvement in school finances Although we do not explore changes

in property tax rates or public service provision we show that greater tax revenues in shale school districts

increased school spending and fund balances

22 The Barnett Shale and Its Development

The Barnett Shale formation received its name in the early twentieth century when geologists found an

organic rich shale deposit near the Barnett Stream (Railroad Commission of Texas 2013) The Shale covers many

counties but its most productive areas are near its eastern boundary which divides the Dallas-Fort Worth region

As Figure 1 illustrates this is where most drilling has occurred

Texas led the advancement of hydraulic fracturing (Rahm 2011) which was needed to free gas from the

Barnettrsquos hard shale Much of the impetus for development of the Barnett and the advancement of hydraulic

fracturing came from George Mitchell the founder of Mitchell Energy The company experimented with

hydraulic fracturing there throughout the 1990s but with mixed success As of 1998 the US Geological Survey

still estimated that the Barnett held only modest amounts of gas and as late as 2000 the president of Mitchell

6

Energy described it as a black tombstone in an interview on CNBC But by 2001 Mitchellrsquos wells had proven

sufficiently fruitful for Devon Energy to buy the company at a premium In the following years the industryrsquos

wide-spread skepticism of the Barnett evaporated as natural gas prices increased and justified the more expensive

horizontal wells that would prove most successful in drawing gas from the Barnett (Zuckerman 2013)

Companies must obtain permits prior to drill so permit submissions indicate drilling intentions The

number of submitted permits for five key Barnett Shale counties (for which Zillow zip code housing value data

are available) shows that less than 75 permits were submitted in each year from 1997 to 1999 (Figure 2) There

was a small uptick in 2000 and then a larger increase in 2001 with 736 permits submitted The growth continued

until 2008 when the industry submitted more than 4500 permits in that year alone Submissions fell in 2009 along

with the price of natural gas They continued to decline and in 2013 companies submitted fewer than 650 permits

Of the five counties considered Denton County was the earliest to experience an increase in permitting

and drilling As Figure 2 indicates the small increase in permits submitted in 2000 was almost entirely accounted

for by Denton County in that year Denton accounted for 84 percent of the permits submitted in the five counties

From 2003 onwards the other four counties accounted for most or all of the increase in permits

Anderson and Thodori (2009) interviewed local key informants in the Barnett shale area about the

consequences of shale drilling in their communities Interviewing people such as mayors judges policeman and

journalists they found a perception that development had ldquostimulated economic prosperity in their communitiesrdquo

including increases in city revenue property values and household income At the same time informants

mentioned other consequences such as a deterioration of local roads excessive truck traffic concerns about

waste-water injection wells and air pollution The authors suggested that an analysis of housing prices would

provide a good assessment of how development has affected the region

Consistent with the perceptions of the people interviewed by Anderson and Thodori (2009) a descriptive

look at the Zillow Housing Value Index across shale and nonshale zip codes suggests that development increased

housing values For each year we calculate the average of the log value of the Zillow Housing Value Index for

shale and nonshale zip codes (described in detail below) In Figure 2 we graph the difference in mean values

across shale and nonshale zip codes along with the number of permits submitted for shale counties From 1997 to

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

5

A third difference is that existing studies have largely ignored how development may affect housing

values through local public finances This partly reflects the location of prior studies ndash Pennsylvania ndash a state that

does not tax oil and gas rights as property Several oil and gas producing states such as Texas require the owners

of oil and gas rights to pay property taxes on the assessed value of their rights (Kent et al 2011)

The public economics literature has extensively studied the link between local public finances local

schools and property values The capitalization of local fiscal variables into housing values has long been

theoretically asserted (Oates 1969 Edel and Sclar 1974 Yinger 1982) Empirically Oates (1969) found that

higher property taxes decrease property values while more school spending per student increase them two

findings that subsequent studies have largely supported (Bradbury Mayer and Case 2001 Barrow and Rouse

2004 Lang and Jian 2004) Still other research establishes a strong link between school quality and housing

values (Black 1999 Fack and Grenet 2010) If shale gas extraction expands the local property tax base all else

constant we would expect property values to increase if any one of three events occur a decline in property tax

rates an increase in public services or an improvement in school finances Although we do not explore changes

in property tax rates or public service provision we show that greater tax revenues in shale school districts

increased school spending and fund balances

22 The Barnett Shale and Its Development

The Barnett Shale formation received its name in the early twentieth century when geologists found an

organic rich shale deposit near the Barnett Stream (Railroad Commission of Texas 2013) The Shale covers many

counties but its most productive areas are near its eastern boundary which divides the Dallas-Fort Worth region

As Figure 1 illustrates this is where most drilling has occurred

Texas led the advancement of hydraulic fracturing (Rahm 2011) which was needed to free gas from the

Barnettrsquos hard shale Much of the impetus for development of the Barnett and the advancement of hydraulic

fracturing came from George Mitchell the founder of Mitchell Energy The company experimented with

hydraulic fracturing there throughout the 1990s but with mixed success As of 1998 the US Geological Survey

still estimated that the Barnett held only modest amounts of gas and as late as 2000 the president of Mitchell

6

Energy described it as a black tombstone in an interview on CNBC But by 2001 Mitchellrsquos wells had proven

sufficiently fruitful for Devon Energy to buy the company at a premium In the following years the industryrsquos

wide-spread skepticism of the Barnett evaporated as natural gas prices increased and justified the more expensive

horizontal wells that would prove most successful in drawing gas from the Barnett (Zuckerman 2013)

Companies must obtain permits prior to drill so permit submissions indicate drilling intentions The

number of submitted permits for five key Barnett Shale counties (for which Zillow zip code housing value data

are available) shows that less than 75 permits were submitted in each year from 1997 to 1999 (Figure 2) There

was a small uptick in 2000 and then a larger increase in 2001 with 736 permits submitted The growth continued

until 2008 when the industry submitted more than 4500 permits in that year alone Submissions fell in 2009 along

with the price of natural gas They continued to decline and in 2013 companies submitted fewer than 650 permits

Of the five counties considered Denton County was the earliest to experience an increase in permitting

and drilling As Figure 2 indicates the small increase in permits submitted in 2000 was almost entirely accounted

for by Denton County in that year Denton accounted for 84 percent of the permits submitted in the five counties

From 2003 onwards the other four counties accounted for most or all of the increase in permits

Anderson and Thodori (2009) interviewed local key informants in the Barnett shale area about the

consequences of shale drilling in their communities Interviewing people such as mayors judges policeman and

journalists they found a perception that development had ldquostimulated economic prosperity in their communitiesrdquo

including increases in city revenue property values and household income At the same time informants

mentioned other consequences such as a deterioration of local roads excessive truck traffic concerns about

waste-water injection wells and air pollution The authors suggested that an analysis of housing prices would

provide a good assessment of how development has affected the region

Consistent with the perceptions of the people interviewed by Anderson and Thodori (2009) a descriptive

look at the Zillow Housing Value Index across shale and nonshale zip codes suggests that development increased

housing values For each year we calculate the average of the log value of the Zillow Housing Value Index for

shale and nonshale zip codes (described in detail below) In Figure 2 we graph the difference in mean values

across shale and nonshale zip codes along with the number of permits submitted for shale counties From 1997 to

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

6

Energy described it as a black tombstone in an interview on CNBC But by 2001 Mitchellrsquos wells had proven

sufficiently fruitful for Devon Energy to buy the company at a premium In the following years the industryrsquos

wide-spread skepticism of the Barnett evaporated as natural gas prices increased and justified the more expensive

horizontal wells that would prove most successful in drawing gas from the Barnett (Zuckerman 2013)

Companies must obtain permits prior to drill so permit submissions indicate drilling intentions The

number of submitted permits for five key Barnett Shale counties (for which Zillow zip code housing value data

are available) shows that less than 75 permits were submitted in each year from 1997 to 1999 (Figure 2) There

was a small uptick in 2000 and then a larger increase in 2001 with 736 permits submitted The growth continued

until 2008 when the industry submitted more than 4500 permits in that year alone Submissions fell in 2009 along

with the price of natural gas They continued to decline and in 2013 companies submitted fewer than 650 permits

Of the five counties considered Denton County was the earliest to experience an increase in permitting

and drilling As Figure 2 indicates the small increase in permits submitted in 2000 was almost entirely accounted

for by Denton County in that year Denton accounted for 84 percent of the permits submitted in the five counties

From 2003 onwards the other four counties accounted for most or all of the increase in permits

Anderson and Thodori (2009) interviewed local key informants in the Barnett shale area about the

consequences of shale drilling in their communities Interviewing people such as mayors judges policeman and

journalists they found a perception that development had ldquostimulated economic prosperity in their communitiesrdquo

including increases in city revenue property values and household income At the same time informants

mentioned other consequences such as a deterioration of local roads excessive truck traffic concerns about

waste-water injection wells and air pollution The authors suggested that an analysis of housing prices would

provide a good assessment of how development has affected the region

Consistent with the perceptions of the people interviewed by Anderson and Thodori (2009) a descriptive

look at the Zillow Housing Value Index across shale and nonshale zip codes suggests that development increased

housing values For each year we calculate the average of the log value of the Zillow Housing Value Index for

shale and nonshale zip codes (described in detail below) In Figure 2 we graph the difference in mean values

across shale and nonshale zip codes along with the number of permits submitted for shale counties From 1997 to

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

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Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

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Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

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Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

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Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

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Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

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Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

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Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

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Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

7

2004 the difference in housing values for shale and nonshale zip codes had no clear increasing or decreasing

trend From 2004 to 2011 the differenced tripled increasing from about 006 log points to 018 log points In 2011

and 2012 it shrank to 014 points as drilling slowed

23 A MODEL OF SHALE GAS DEVELOPMENT AND HOUSING VALUES

We expand on an intertemporal model of housing services developed originally by Schwab (1982) We

follow the original notation but expand the model to consider expected disamenity and amenity effects from

drilling activities Disamenities may include health risks (perceived or actualized) as well as general declines in

the quality of life from greater truck traffic and noise Amenities may include indirect financial remuneration from

drilling activities through increased local tax revenues for schools and infrastructure Greater tax revenues could

also reduce the householdrsquos tax burden on housing service by reducing the mill rate

Consider a consumer who purchases a house in which she will live for two periods The two periods

constitutes the lifecycle (or planning horizon) of a purchasing decision At the beginning of the first period the

consumer purchases a stock of Z units of housing The flow of housing services is a constant proportion of that

stock and we will assume no physical depreciation of the housing stock outside of damages from drilling which

we treat explicitly The consumerrsquos utility in each period depends on her consumption of housing services and a

composite good The intertemporal utility function is the discounted present value where the discount rate is the

pure rate of time preference δ of utility in the two periods as given by

1 2

1( ) ( )

1V U C Z U C Z

(1)

C1 and C2 represent consumption of the composite good in period 1 and 2

The consumer receives a stream of real income of Y1 and Y2 where Y1 includes all of the consumerrsquos

wealth at the beginning of the problem The consumer wishes to have an exogenously determined stock of wealth

W at the end of the second period We assume zero inflation to simplify the exposition The purchase price per

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

8

unit of housing is denoted by P The composite consumption good is the numeraire good giving it a price of one

in both periods

The consumer finances the purchase of the house with a standard level payment mortgage for 100 percent

of the purchase price PZ Like Schwab (1982) we assume that the mortgage has an infinite term and the

consumer repays the entire principal as a balloon payment when the house is sold at the end of the second period

Letting ρ denote the real interest rate the interest payment in each period is ρ middotPZ

We assume that all drilling occurs in the first period Although no drilling occurs in the second period the

disamenity or amenity effects may persist and evolve We specify the total disamenity and amenity effects in each

period as a proportion of the initial housing purchase price PZ with and representing the proportional

disamenity and amenity effect A proportional effect has an intuitive appeal greater traffic and noise arguably

causes a larger absolute decline in the value of five bedroom luxury home than that of an outdated two bedroom

home Similarly a proportional amenity effect fits with the scenario of greater public revenues causing a

reduction in the property tax mill rate which would give a decline in tax burden proportional to the assessed value

of the home

The intertemporal budget constraint requires that the present value of income less the present value of

expenditures on the composite good and housing meets or exceeds the wealth target W We define R1 and R2 as

the present value of housing expenditure per dollar of mortgage principal in the first and second periods

1 1

1 1

R

(2)

2 2

2

1

(1 )R

(3)

Note that amenity and disamenity effects have different subscripts because we allow the effects to differ across

the two periods R2 is the difference between the mortgage payment ρ middotPZ and the proceeds from the sale of the

house net of the mortgage repayment The uncertainty whether ρ + γ2 is greater or smaller than 1 + 2 (ie whether

R2 is negative or positive) motivates households to set aside precautionary savings in the first period

The budget constraint can then be written as

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

9

2 2 21 1 1 2

(1 ) (1 ) (1 ) (1 )

Y C R WY C R PZ

(4)

It shows that an increase in the expected rate of the disamenities γ raises the cost of housing in both periods a

greater rate of amenities has the opposite effect

1

0 12(1 )

t

t

Rt

(5)

1

0 12(1 )

t

t

Rt

(6)

We further assume that households save in the first period to meet second-period housing and consumption

expenditures and the wealth target Thus

1 1 1 0Y C R PZ (7)

A lack of first period borrowing supposes that banks are unwilling to provide unsecured loans or loans secured by

expected equity from future appreciation

The consumer maximizes her utility in (1) subject to the budget constraint in (4) and the implicit

borrowing limit in (7) Schwab (1982) defines (7) as an ldquoeffectiverdquo constraint because capital market

imperfections limit consumerrsquos present and future spending behavior In our case we envision the constraint as

reflecting uncertainty about the consequences of natural gas development on housing values as captured by the net

effect of γ and This uncertainty motivates precautionary savings in the first period We provide the first order

conditions and their interpretations in the Appendix

We explore and interpret the derivative of the demand for housing with respect to expected disamenity

and amenity effects from drilling activities Consider the change in the demand for housing from changes in

expenditures R1 and R2 which are incurred to access Z units of housing stock Total differentiation of the

consumerrsquos demand function for housing yields

1 2

1 2

Z Z

dZ dR dRR R

(8)

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

10

Let ω denote the intensity of drilling in the consumerrsquos area If the change in γt is dγt then dR1 must be

1 1 1R d and dR2 must be 2 2 2( ) R d The same applies to the amenity effect t Substituting the

terms into (8) is analogous to the chain rule in calculus and gives the effect of drilling intensity on housing

demand

sum(

)

(

)

Equation (9) illustrates the two competing effects of drilling on housing demand the effect on the

expected rate of disamenities and the effect on amenities The mechanics of the two effects are similar In the first

period drilling creates a disamenity represented by 1 Although drilling only occurs in the first period it

creates a disamenity in the second period 2 The same applies to the amenity effect

The sign of Z is unclear According to Schwab (1982) 1Z R and 2Z R are analogues to

price slopes and are presumably negative However t tR and t tR have different signs causing the first

term on the right-hand side of (9) to be negative and the second term to be positive for each period Which term

dominates is an empirical question whose answer may change with time The net effect may be positive in the

initial period (amenity effect outweighs disamenity effect) but turn negative in the second period or vice versa In

our model the consumer correctly anticipates the effects of drilling on housing values In practice knowledge

about the amenitity and disamenity effects will evolve with drilling and as it does so will the difference in

housing values across areas with and without drilling

3 COMPARISONS AND IDENTIFICATION

We compare appreciation in housing across shale and nonshale zip codes where shale zip codes are

defined to have more than 99 percent of their area in the shale and nonshale zip codes have less than 1 percent

The basis for our empirics is that nonshale zip codes particularly when selected for certain characteristics

provide a credible counterfactual ndash how housing in shale zip codes would have appreciated in the absence of

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

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Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

11

drilling Because we have housing values prior to the boom in drilling we can test and account for any differences

in initial appreciation trends across the two groups

We perform our regression analysis with the full sample of shale and nonshale zip codes and with

subsamples selected for greater comparability Limiting the analysis to treatment (shale) and control (nonshale)

observations that share the same covariate space can make causal inference less model-dependent and more

accurate and efficient (Ho et al 2007 Crump et al 2009) As Imbens (2014) shows linear regression methods

can give excessive influence to treatment observations in an area of the covariate space lacking control

observations or vice versa Examples of using matching as a precursor to regression when estimating causal

effects are Ravallion and Chen (2005) and Pender and Reeder (2011)

Creating a more homogenous subsample addresses a potential threat to identification shocks to housing

values unrelated to drilling that affected shale and nonshale zip codes differently Shocks such as changes in

housing demand arguably affect similar neighborhoods similarly ndash at least more so than if the neighborhoods had

different demographics and types of houses and so forth Limiting the sample to the most comparable shale and

nonshale zip codes makes it more likely that two groups experienced similar shocks over the study period Indeed

Perdomo (2011) and Paredes (2011) use this assumption as the basis for using matching to study the effects of

programs on housing values

Our first approach to creating a more comparable subsample is to match each shale zip code with a

nonshale zip based on the propensity score The propensity score is the conditional probability of assignment to a

particular treatment given a set of observed covariates (Rosenbaum and Rubin 1983) A perfect predictor of being

a shale zip code is an indicator variable for being in the Barnett Shale however using only socioeconomic

variables to estimate the propensity to be a shale zip code captures the extent that shale and nonshale zip codes

have different characteristics As in Rubin (2006) and Imbens and Wooldridge (2008) we match without

replacement thereby creating a nonshale group that is non-repeating and is as observationally equivalent to the

shale zip codes (as measured by the propensity score) as possible without dropping observations from the shale

group

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

12

Our second approach is to exclude zip codes with extreme values of the propensity score Because the

propensity score is a one-dimensional summary of the observable differences between shale and nonshale zip

codes trimming on it removes the shale zip codes that have characteristics substantially different from any

nonshale zip codes and vice versa Cutoffs of 010 and 090 have been employed as a rule of thumb (Angrist and

Pischke 2009) however Crump et al (2009) and Imbens (2014) provide a method to calculate optimal cutoffs

The cutoffs are based on the asymptotic efficiency bound of the average treatment effect which will presumably

have greater variance in areas of the covariate space with a large disparity in the number of treatment versus

control observations Following Imbens (2014) the optimal cutoff is given by

radic

where minimizes

the function

(sum )

sum The function is a function of the estimated

propensity score and is defined as

4 DATA AND DESCRIPTIVE COMPARISONS

41 Data

For housing values we use the monthly Zillow Home Value Index (ZHVI) Prior studies have used Zillow

data including Sanders (2012) who estimated the effects of the Toxics Release Inventory report on housing

values and Huang and Tang (2012) who studied the effect of land constraints on housing prices The ZHVI is a

hedonically adjusted price index that uses information about properties collected from public records including

their size and number of bedrooms and bathrooms (A similar approach is used by Guerrieri et al (2010) to

construct zip code housing value indices) It is the three-month moving average of the median Zestimate valuation

of single family residences condominiums and cooperative housing in the specified area and period The

Zestimate for each house is an estimate of what it would sell for in a conventional non-foreclosure arms-length

sale Zillow estimates the Zestimate using sales prices and home characteristics

Bun (2012) shows that the distribution of actual sale prices for homes sold in a given period match the

distribution of Zillow estimated sale prices for the same set of homes implying that the Zestimate does not

systematically under or overstate sale prices The median Zestimate the ZHVI is also robust to the changing mix

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

13

of properties that sell in different periods because it involves estimating a sales price for every home By

incorporating the values of all homes in an area ndash not just those homes that sold ndash it avoids the bias associated

with median sale prices (Dorsey et al 2010 Bun 2012)

Unlike the ZHVI the SampPCase-Shiller Home Price Index only uses information from repeat-sales

properties and is value weighted giving trends among more expensive homes greater influence on overall

estimated price changes (SampPCase Shiller Winkler 2013) The SampPCase-Shiller index nonetheless is well-

known and widely used In the aggregate the ZHVI and the SampPCase-Shiller index track closely with a Pearson

correlation coefficient of 095 and median absolute error of 15 percent (Humphies 2008) Three other studies

find similar results when comparing various versions of the two indexes finding correlations of 092 or higher

(Guerrieri et al 2010 Schintler and Istrate 2011 Winkler 2013) We compare the ZHVI for Dallas-Fort Worth

with the corresponding SampPCase-Shiller index and find a correlation of 095

For matching we use 14 zip code-level variables from the 2000 Census (US Census Bureau 2013)

Variables include demographic characteristics (the share of the population that is white the population share by

age group the share with a college education or more) income measures (the share by income class and median

household income) and housing-related characteristics (the share of the zip code area that is urban population

density the share of housing that is vacant the median age of housing and median real estate taxes) A full list of

variables with definitions is in Appendix Table A1

42 Comparing Shale and Nonshale Zip Codes

Because little drilling occurred within the Fort Worth city limits we focus on zip codes in the Dallas-Fort

Worth region where less than 75 percent of the area was urban as defined by the 2000 Census Zillow housing

data is available for zip codes in the shale counties of Denton Hood Johnson Parker and Tarrant counties and

the nonshale counties of Collin Dallas Ellis Hunt Kaufman Rockwall (and the nonshale part of Denton

County) Figure 3 shows the location of the shale and nonshale zip codes used in the analysis with counties

labeled in bold

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

14

The 2000 Census shows that the typical suburban zip code in the Dallas-Fort Worth area had a

predominately white high income population in the average zip code roughly 8 of 10 people were white and the

median household had more than $55000 in income Of greater interest is comparing shale and nonshale zip

codes Testing for statistical differences in means using a t-test has the disadvantage of depending on the sample

size ndash the larger the sample the more likely that two means are statistically different from each other even though

the difference may be economically small A more informative measure is the normalized difference calculated

as the difference in means for the two groups divided by the sum of their standard deviations As a rule of thumb

Imbens and Wooldridge (2009) suggest that linear regression may be misleading when there are normalized

differences larger than 025 standard deviations

We calculate the normalized difference across shale and nonshale zip codes for 14 variables The average

normalized difference was generally small with an average absolute value of 0169 (Table 1) Only one of the 14

variables ndash the share of the population that is white ndash had a normalized difference larger than 025 standard

deviations (the fifth column in Table 1)

When going from the full to the more comparable subsamples we exclude shale zip codes in Denton

County because of the earlier timing of drilling there We then estimate the propensity score by entering all 14

variables linearly into a bivariate Probit model1 Dropping Denton shale zip codes and applying propensity score

matching reduces the normalized difference in most dimensions giving an average absolute normalized difference

of 0096 Using the method outlined in the prior section we calculate the optimal thresholds for trimming on the

propensity score which are 0059 and 0941 Trimming based on the propensity score drops two zip codes that

were in the matched sample and adds four zip codes excluded from it Doing so provides further improvement in

comparability as measured by the normalized difference the average absolute difference is now 0073 standard

deviations with the largest difference being 0323 (column 7 in Table 1)

1Imbens (2014) provides a data-driven method to identify a more flexible specification arguing that some higher order terms

could capture important interactions between variables We apply the method outlined in Appendix A of Imbens (2014)

which gave a specification with five linear terms and three higher order terms For both the matched and trimmed samples

using the propensity score based on this specification gave less comparable shale and nonshale zip code groups as measured

by the average absolute value of the normalized difference

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

15

5 HOUSING VALUE EMPIRICS

51 Empirical Model

We estimate how housing appreciated across Barnett Shale zip codes and nonshale zip codes over time

Our dependent variable is the difference in the log of the ZHVI from one month to the next We specify the

empirical model as

(10)

where Shalei is a dummy variable that equals one if a zip code is as shale zip code as defined previously and zero

otherwise

The shale dummy variable is well-suited to our focus ndash estimating the net effect of drilling on the value of

a typical residence over time It is also suited for our study area where many wells were drilled throughout a

small area thus the effects of the industry will be broadly felt As Figure 1 shows a deluge of drilling occurred on

a band around the western edge of the Fort Worth city limit (A richer visual can be obtained through Google

Maps find Fort Worth and zoom to the west What look like squares of sand generally indicate natural gas wells)

Natural gas wells compressor stations injection wells and other features of the industry will be scattered across

the landscape It would be difficult to control for them or to estimate their unique effects in a finer analysis The

shale indicator variable also has the advantage of being based entirely on a geological feature deep underground

Period and Month are vectors of period and month dummy variables and tt denotes a continuous variable

that starts with the value of one for the first month in the study period and increases by one with each passing

month We create the period variables by dividing the 1997-2013 study period into four periods 1997-2000 (pre

drilling) 2001-2004 (modest drilling) 2005-2008 (drilling boom) and 2009-2013 (modest drilling but peak

production) We specify the time trend function as

(11)

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

16

where the continuous time trend variable t and its square is interacted with the vector of period dummy variables

allowing for distinct nonlinear time trends in the different periods The month and period dummy variables give

additional flexibility by shifting the intercept of the time trend depending on the month or period

The first differenced model and fixed effects model (a dependent variable in levels but accounting for a

zip code specific effect) both control for unobserved variables that are time invariant and zip code specific Under

the strict exogeneity assumption ndash each independent variable in each period is uncorrelated with the error term in

its corresponding period and in every other period ndash both models give consistent coefficient estimates However

when the temporal dimension of the dataset is large and the cross-sectional dimension is small the fixed effect

model relies on assuming normality in the error term for drawing inference because asymptotic conditions are

unlikely to hold (In our application we have 76 zip codes and 201 month-year combinations) First-differencing

can turn a persistent time series into a weakly dependent process meaning that as more time separates two

observations of the same zip code the values become less and less correlated (or almost independent) One can

therefore appeal to the central limit theorem and assume that the parameter estimates are asymptotically normally

distributed (Wooldridge 2002)

A weakly dependent time series may nonetheless still exhibit serial correlation We calculate Huber-

White standard errors clustered by zip code which allow for arbitrary heteroskedasticity and serial correlation

within the same zip code over time

52 Appreciation by Period

Using the coefficients from the model we calculate and display the average difference in appreciation

rates between shale and nonshale zip codes over the four periods (Figure 4) The second bar in the graph (from the

left) for example is the sum of the coefficient on Shale and the coefficient on the interaction between Shale and

the second period dummy variable Looking at the full matched and trimmed samples on average shale zip

codes appreciated at a rate similar to nonshale zip codes in the 1997-2000 period bolstering confidence in their

comparability Appreciation rates then started to diverge in the 2001-2004 period especially in the matched and

trimmed samples The largest difference was during the boom period when shale zip codes appreciated roughly 15

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

17

percentage points faster than nonshale zip codes The trend reversed in the 2009-2013 period when shale zip

codes appreciated 5 to 10 percentage points less Table 2 shows the exact coefficient estimates behind Figure 4

and their associated standard errors

53 Total Appreciation 1997-2013

We now estimate the difference in appreciation across shale and nonshale zip codes over the entire study

period This change represents the net effect of drilling on housing values from prior to development to when

development slowed to pre-2001 levels as evidenced by submitted well permits We regress the log difference in

the annual average ZHVI from 1997 to 2013

on a constant the shale indicator

variable and the same control variables as before In all comparisons we find that over the study period housing

appreciated five to six percentage points more in shale zip codes than in nonshale zip codes (top section in Table

3)

54 Comparing ZHVI and Census-Based Estimates

Because the Zillow Housing Value Index has been used in few research studies we compare estimates of the

average difference in appreciation using the ZHVI with those using Census Bureau data For our beginning period

value we take the zip code median home value of owner-occupied housing from the 2000 Decennial Census The

long form of the census questionnaire which collected housing values was eliminated after the 2000 Census For

the end period we therefore use the median housing value as measured by the American Community Survey 2012

five-year estimate which is based on data collected from 2008 to 2012 Similar to the analysis in the prior section

we take the log difference and estimate the average difference in appreciation across shale and nonshale zip codes

For comparison we estimate the same model but using as the dependent variable the log difference between the

2000 ZHVI and the 2008-2012 average ZHVI

The ZHVI and Census Bureau data give very similar estimates (second and third sections of Table 3) For

the matched and trimmed samples both data give estimates that shale zip codes appreciated about 11 percentage

points more than nonshale zip codes over the decade The standard errors from the Census Bureau data are about

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

18

35 times larger than for the ZHVI which is unsurprising given that the American Community Survey collects

data on a sample whereas the ZHVI incorporates information from all housing in a zip code

55 Did Shale and Nonshale Zip Codes Experience Different Demographic Changes

A common concern of studies of housing values over time is that consumer preferences change as the

local population evolves and sorts into neighborhoods based on preferences In our study area people who are

indifferent to the disamenities of drilling may have sorted into drilling zip codes The empirical model assumes

that shale gas development is the only difference between shale and nonshale zip codes that changed over time

and affected housing values Sorting implies that another variable namely consumer preferences may have also

changed across shale and nonshale zip codes

We compare the mean change in seven demographic variables using the 2000 Decennial Census and the

2008-2012 American Community Survey five-year average We consider the change in the percent of the

population that is white the percent that has a college education or better the percent that is 60 or older and the

percent that is age 20 to 40 We also look at the change in the log of median household income the log of the

median real estate tax and the log of the year when the median age house was built The age of the housing stock

could indicate whether drilling potentially affected housing values by influencing where new housing

development occurred

Shale and nonshale zip codes experienced similar demographic and economic changes over the majority

of the study period The difference in the change is generally small and always statistically insignificant The

largest changes with the smallest standard errors are for median household income and median real estate taxes

On average shale zip codes had a 67 percentage point decline in median real estate taxes paid compared to

nonshale zip codes and a 48 percentage point increase in median household income

56 Drilling Intensity and Total Appreciation

An advantage of the shale dummy variable is that it is based on subsurface geology not surface

characteristics or human decisions An alternative measure of development the number of wells drilled in a zip

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

19

code is more likely to be correlated with local unobservable characteristics since it reflects the decisions of

landowners drilling companies and local policy makers At the same time the number of wells drilled speaks to

the intensive margin relationship between drilling and housing values We therefore proceed with caution and

estimate the relationship between the number of wells drilled in a zip code and total appreciation over the study

period

We use Texas Railroad Commission data to calculate the number of wells drilled per square kilometer

from 1997 to 2012 in each zip code Data are available for 29 of our 34 shale zip codes Using the total wells

drilled from 1997 to 2012 the average zip code had a well density of 32 wells per squared kilometer with 23 of

the 29 zip codes having a density of 1 well or more per square kilometer If wells were drilled uniformly across

space all of the housing in these 23 zip codes would have been within a kilometer of a well by 2012 if not

before By comparison house-level studies of natural gas wells and housing values have looked at the effect of

having a well within 1 to 2 kilometers of the house (Muehlenbachs Spiller and Timmins 2012 2013

Gopalakrishnan and Klaiber 2013)

We regress the log difference in the annual average ZHVI from 1997 to 2013

on a constant and the number of wells drilled with and without controlling for the covariates used in

prior regressions Because of the limited degrees of freedom we estimate the model controlling for a subset of the

original list of 14 covariates and for the full set

An additional well per square kilometer was associated with a 16 percentage point decline in appreciation

from 1997 to 2013 The effect is precisely estimated considering the small number of observations It is also

robust to controlling for a variety of zip code characteristics This result is in line with Gopalakrishnan and

Klaiberrsquos (2013) finding that properties surrounded by agricultural lands suffered greater depreciation from

drilling presumably because agricultural lands are easy to access and are therefore more likely to be drilled in the

future

The negative relationship between drilling intensity and housing values is not necessarily inconsistent

with our finding that shale zip codes appreciated 5 to 6 percentage points more than nonshale zip codes over the

study period More drilling means more localized disamenities one well can imply several thousand more truck

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

20

trips on local roads Zip code well density however likely has a weaker correlation with amenity effects Greater

county tax revenues from industry activity for example may fund better local public services (or lower tax rates)

that improve housing values throughout the county The Shale indicator variable would capture such broadly

distributed effects better than the zip code well density variable

6 WHAT EXPLAINS GREATER APPRECIATION IN SHALE ZIP CODES

There are at least three possible explanations for the increase in housing appreciation in shale versus

nonshale zip codes as drilling and production expanded An intuitive but unlikely explanation is that housing

values capitalize the value of natural gas rights In Texas a long tradition of split estates (separating surface and

mineral rights) has resulted in many residential property owners no longer owning the rights to the subsurface

(Admittedly the extent of split estates is unknown data on the topic are scarce) Moreover when the rights have

substantial value the owners tend to retain them when selling the property in which case the sales price would

not reflect the value of the rights This has been particularly true of new homes where builders and developers

have been retaining mineral rights across the United States a practice also identified in the Dallas-Fort Worth area

(Conlin and Grow 2013) the Dallas-Fort Worth area (Conlin and Grow 2013) Finally land is leased before

drilling If the increased value of gas rights associated with residential properties caused the greater appreciation

we would expect to see more appreciation when the leases were signed Instead we see the greatest appreciation

during the period of peak drilling which came after the period of peak leasing

An alternative explanation is that drilling stimulated economic activity increasing the demand for labor

and with it the demand for housing Natural gas development is associated with greater local income and

employment (Weber 2012 2013) and the explanation likely causes housing appreciation in rural settings Our

suburban context makes it unlikely that greater labor demand would create localized appreciation but to probe

this potential explanation we compare the average change in the log of the total adult population and the log of

total employment across shale and nonshale zip codes Looking at the change from the 2000 Decennial Census to

the American Community 5-year average of 2008-2012 shale zip codes had slightly higher population and

employment growth but the differences in growth were statistically insignificant We also compare total

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

21

appreciation for Johnson and Ellis counties whose common border more or less matches the boundary of the

shale Both counties arguably form part of the same labor market the county seat of both counties is less than a

half an hour drive from the Dallas-Fort Worth beltway The proximity to the Dallas-Fort Worth suburbs would

provide an ample supply of housing within commuting distance to Johnson County yet on average zip codes in

Johnson County (shale area) appreciated 67 percentage points more than those in Ellis County from 1997 to

2013 a difference that is statistically significant

A third explanation is that residents indirectly benefited from an expansion in the property tax base

improved the finances of local governments and schools In Texas and other states property tax law treats the

rights to subsurface resources as property on which taxes are assessed Property taxes in turn fund local

governments and schools Improved local finances and especially school finances may then be capitalized into

house values

61 Evidence of an Expanded Local Tax Base

In Texas oil and gas rights form part of the property tax base once production begins in the area covered

by the leased rights A third party assessor specialized in valuing oil and gas rights will then determine their

market value using information such as data from nearby wells and projections of energy prices The owners of

the rights then pay property taxes on the assessed value Values are reassessed annually to reflect changes in

production prices and any other factors affecting their market value2

Data from the Texas Education Agencyrsquos Public Education Information Management System provides

property tax data from the 19981999 school year to the 20112012 year for public school districts Using the

spatial data on school districts we defined shale and nonshale school districts using the area in the Barnett Shale

and the degree of urbanization ndash the same as in the zip code sample3 The school district sample has 40 shale

districts and 44 nonshale districts which are mapped in Figure 6

2 More details on oil and gas property tax assessment can be found through the Tarrant Appraisal District website

(wwwtadorg) and in particular httpswwwtadorgftp_dataDataFilesMineralInterestTermsDefinitionspdf 3 School district spatial data were obtained on the website of the Texas Education Agency

httpritterteastatetxusSDLsdldownloadhtml Property tax data are available at

httpwwwteastatetxusindex2aspxid=2147494789ampmenu_id=645ampmenu_id2=789

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

22

The value of the property tax base is reported for five categories of property Land Commercial Oil and

Gas Residential and Other We estimate how the tax base per enrolled student evolved over time in shale and

nonshale zip codes by estimating

(12)

where is the dollars of property tax base per student for different categories of property As before is

a vector of binary variables indicating a period Except for beginning the first period in 1998 and ending the last

period in 2011 we use the same periods as in the section on appreciation by period

In the 1998-2000 period the total tax base per student was about 10 percent higher in shale zip codes

(about $21000 more) but the difference was statistically insignificant (Table 6) Over the next three periods the

tax base consistently expanded more in shale zip codes than in nonshale zip codes By the 2009-2011 period the

difference between shale and nonshale zip codes in the tax base per student had increased to $193000 More than

40 percent of the increase came from the value of oil and gas rights The difference between shale and nonshale

zip codes in the tax assessed value of rights increased from about $5000 in the 1998-2000 period to $82000 in

the 2009-2011 period The rest of the increase in the tax base was split among land commercial and residential

property

The change in the residential property tax base provides additional support for our earlier findings based

on the ZHVI From the first to the last period the residential property tax base increased by about $18000 in shale

zip codes relative to nonshale zip codes which represents about 12 percent of the average value over the study

period Unlike with oil and gas property residential property is generally only reassessed once every three years

This means that the assessed value of residential property in the 2009-2011 period most heavily reflects property

assessments in the 2007-2009 period This matches our ZHVI analysis which found the greatest appreciation in

shale zip codes relative to nonshale zip codes in the 2005-2008 period

62 School Finances

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

23

The Texas Education Agency Public Education Information Management System also provides data that

allow us to explore how the expanded tax base affected school district finances We estimate the same model as in

(13) but where is one of five school district financial outcomes all on a dollars per student basis local

revenues state revenues total revenues operating expenditures and the school district fund balance

School districts in shale areas had roughly similar financial situations in the 1998-2000 and 2001-2004

periods (Table 7) This changed in the 2005-2008 period when local tax revenue per student increased by $529

more per student in shale areas and operating expenditures increased by $324 per student The difference widened

further in the 2009-2011 period but a decline in state funding offset three-quarters of the increase in local tax

revenue The decline in state funding reflects the statersquos so-called ldquoRobbin Hoodrdquo or ldquoShare the Wealthrdquo policy

(Chapter 41 of the Texas Education Code Equalized Wealth Level) which defines districts as either property

wealthy or property poor and then transfers funds from rich districts to poor districts (Texas Education Agency

2014)

Despite less state funding total revenues per student increased in shale school districts compared to

nonshale districts By the 2009-2011 period greater revenues translated into roughly $460 in greater operating

expenditures per student The expenditure increase was smaller than the revenue increase creating a surplus that

fed into the fund balance ndash the districtrsquos assets less liabilities Better fund balances mean better credit ratings

lower interest rates and easier access to financing They also help districts cushion variation in expenditures and

generate further revenues through interest income

Multiple studies estimate the elasticity of housing prices with respect to changes in school spending per

student (Bradbury Mayer and Case 2001 Downes and Zabel 2002 Brasington and Haurin 2006) The

estimates are generally around 050 By the 2009-2011 period total school revenues per student had increased

about $600 more in shale zip codes relative to nonshale zip codes an increase that represents about 9 percent of

the beginning shale zip code level Assuming that all of the increase in revenues is eventually spent and applying

an elasticity of 050 implies greater housing appreciation of 45 percent This suggests that improved school

finances accounts for much of the greater appreciation of shale zip codes (5 to 6 percentage points) over the study

period

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

24

7 CONCLUSION

Shale zip codes appreciated more than nonshale zip codes from the beginning of large-scale development

of the Barnett Shale to the end of 2013 when development had largely ceased Although development increased

housing values as reflected in the Zillow Home Value Index and Census median housing values it is possible that

drilling and hydraulic fracturing in particular created disamenities for some or perhaps most residents Indeed

within shale zip codes greater well density was associated with less appreciation Our findings imply that to the

extent that housing values fully capture the economic cost of disamenities from development for the typical

housing property the positive economic effects such as improved local public finances more than offset the

negative externalities up to 2013 We note however that housing values may not fully reflect the value of all

disamenities Currie et al (2012) show that the health effects of a toxic plant are observed over a larger area than

housing market effects

Our finding may not appear in other regions since the perceived risks of oil and gas development may

vary by region and affect the discount that potential homebuyers place on living near a well or other oil and gas

infrastructure Risk perceptions and subsequent housing capitalization reflect what information is available and

several studies show that households underestimate risk exposure prior to information provision (Sanders 2012

Mastromonaco 2012 Oberholzer-Geeand Mitsunari 2006 Davis 2004) Especially with newer technologies

people in different regions of the country may have different information or prior beliefs about risks Moreover

the differences may reflect actual differences in risks it may have taken drilling companies from Texas several

years to learn how to adapt their practices to conditions in Pennsylvania

The link between oil and gas development local public finances and property values may be the

primarily channel through which development affects property values in the long term and is an area ripe for

research We present direct evidence that increases in the value of natural gas rights expanded the property tax

base and generated greater local public revenues but property tax policies vary by state The law in many other

major producing states like Arkansas Colorado Louisiana Utah and Ohio dictates that oil and gas rights are

included in property tax assessments but states like Pennsylvania and Wyoming are notable exceptions and

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

25

details vary from state to state (Kent et al 2011) Furthermore our study of housing values over a decade of

development is still a medium term analysis in certain aspects In the last period of our analysis shale zip codes

appreciated less than nonshale zip codes on average The expanded tax base will not remain large for many years

on account of natural gas rights alone and the industry may have created public liabilities that become more

apparent in time

Acknowledgements

We thank Camille Salama Amanda Harker and Elaine Hill for their input and assistance

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

26

FIGURES

Figure 1 The Geography and Development of the Barnett Shale

Source US Energy Information Administration (2011)

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

27

Figure 2 Drilling Permits and Housing Values 1997-2013

Source Railroad Commission of Texas and Zillow

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

28

Figure 3 Shale and Nonshale Zip Codes

Source Elaboration by the authors using data from the 2000 Decennial Census and a shape file from the Energy Information

Administration County names are in bold

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

29

Figure 4 Mean differences in appreciation by period

Note The mean differences are from the coefficients on a regression controlling for various time effects

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

14

16

18

Full Sample Matched Trimmed

Mea

n D

iffe

ren

ce i

n A

pp

reci

ati

on

()

Sh

ale

- N

on

shale

1997-2000 2001-2004 2005-2008 2009-2013

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

30

Figure 5 Shale and Nonshale School Districts

Note The same counties used in the zip code analysis are used in the school district analysis Similarly we focus on districts

that are less than 75 percent urban Shale school districts have more than 99 percent of their area in the Barnett Shale

nonshale districts have less than 1 percent of their area in the shale

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

31

TABLES

Table 1 Descriptive Statistics for Shale and Nonshale Zip Codes

Full Sample Descriptive Statistics Normalized Differences

Shale Zip Codes Nonshale Zip Codes Sample

Variable mean sd mean sd Full Matched Trimmed

Race Age and Education

Share white 0873 0111 0791 0194 0522 0406 0323

Share age 20-40 0293 0057 0290 0049 0056 0058 0071

Share age 40-60 0273 0045 0265 0033 0206 0003 0000

Share age 60 or older 0115 0050 0116 0043 -0024 0074 0072

Share with some college or more 0144 0087 0139 0089 0064 -0002 -0062

Income

Share 20K-40K 0196 0078 0210 0077 -0186 -0127 -0040

Share 40K-75K 0345 0059 0334 0053 0186 0031 0018

Share 75K or more 0325 0153 0309 0158 0104 0069 0009

Median household income 57705 16487 55221 16975 0148 0064 -0008

Urbanization

Share urban 0204 0232 0171 0208 0152 0060 -0044

Population density 578 672 437 471 0243 0087 -0016

Real Estate

Share vacant 0069 0047 0071 0041 -0041 0139 0145

Median year built 1985 6 1984 7 0198 0073 0088

Median real estate taxes 1967 949 1734 935 0248 0155 0133

Source The 2000 Decennial Census The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes in Denton County

(because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes based on the propensity score The trimmed sample has the 54

observations whose propensity score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

32

Table 2 Differences in Appreciation Rates for Shale and Nonshale Zip Codes through Time

Full Sample Matched Trimmed

Shale 00002 00001 00000

(00003) (00004) (00004)

Shale x Period 2001-2004 00002 00010 00011

(00004) (00006) (00005)

Shale x Period 2005-2008 00015 00015 00015

(00005) (00005) (00005)

Shale x Period 2009-2013 -00007 -00011 -00010

(00004) (00005) (00005)

Period 2002-2004 -00135 -00097 -00135

(00085) (00107) (00102)

Period 2005-2008 00036 00263 00361

(00158) (00188) (00192)

Period 2009-2012 02297 02373 02350

(00203) (00221) (00225)

Intercept 00018 -00037 -00097

(00357) (00397) (00406)

Control for initial charactersitics Y Y Y

Month dummies Y Y Y

Time trend interacted with period dummies Y Y Y

Number of zip codes 76 52 54

Number of observations 15200 10800 10400

Adjusted R-squared 0413 0399 0396

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by

zip code are in parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes

shale zip codes in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique

nonshale zip codes based on the propensity score The trimmed sample has the 54 observations whose propensity

score is between 0059 and 0941 with 24 and 30 shale and nonshale zip codes

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

33

Table 3 Total Appreciation for Shale and Nonshale Zip Codes Using Zillow and Census Data

Full Sample Matched Trimmed

ZHVI 1997-2013

Shale 0052 0057 0058

(0022) (0024) (0023)

Intercept 8177 5462 6836

(7167) (9253) (8984)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0568 0338 0439

ZHVI 2000 - Average(2008-2012)

Shale 0084 0108 0108

(0015) (0016) (0015)

Intercept 8682 6559 5024

(5694) (6224) (5815)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0693 0650 0735

Census Median Value of Owner Occupied Housing 2000 -

Average(2008-2012)

Shale 0062 0110 0110

(0041) (0053) (0053)

Intercept 61482 47077 45070

(13139) (16905) (17115)

Control for initial characteristics Y Y Y

Number of observations 76 52 54

Adjusted R2 0469 0491 0463

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The full sample has 34 shale and 42 nonshale zip codes The matched sample excludes shale zip codes

in Denton County (because of earlier drilling) and has 26 shale zip codes matched to 26 unique nonshale zip codes

based on the propensity score The trimmed sample has the 54 observations whose propensity score is between

0059 and 0941 with 24 and 30 shale and nonshale zip codes

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

34

Table 4 Changes in Zip Code Characteristics

Share White

Share

some

college or

more

Share older

than 60

Share age

20-40

Median

income

Real

estate

taxes

Year built

Shale -0007 0014 0000 -0009 0048 -0067 -0000

(0019) (0026) (0009) (0008) (0032) (0055) (0001)

Intercept -0032 0182 0024 0051 -0040 0321 0004

(0014) (0018) (0005) (0006) (0020) (0033) (0000)

Number of observations 76 76 76 76 76 76 76

Adjusted R2 -0012 -0010 -0014 0001 0017 0007 -0013

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis The dependent variable is the change

from the 2000 Decennial Census to the American Community 5-year average of 2008-2012 For median income real estate taxes and year built the dependent

variable is the log difference in the variable The negative adjusted R-squared indicates a low (unadjusted) R-squared and that adding the shale variable provides

insufficient explanatory power to justify to overcome the penalty that the adjusted R-squared formula imposes for adding an independent variable

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

35

Table 5 Drilling Intensity and Total Appreciation 1997-2013

Model 1 Model 2 Model 3

Wells per Square Kilometer -0016 -0013 -0018

(0006) (0004) (0008)

Intercept 0451 18414 30008

(0024) (9429) (12178)

Only some control variables N Y N

All control variables N N Y

Number of observations 29 29 29

Adjusted R2 0179 0554 0517

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in

parenthesis The initial characteristics controlled for in Model 2 are Share white Share age 60 or more Share some

college or more Median household income Share urban Share vacant Median year built and Median real estate

taxes The covariates in Model 3 are the 14 variables used in the results in Tables 2 and 3

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

36

Table 6 The Average Change in the Tax Base Per Student by Property Type 1998-2011

Total Tax Base Commercial Land Oil and Gas Residential Other

Shale 21594 24519 1744 5236 -9753 -152

(27930) (14444) (6087) (1478) (17670) (502)

Shale x Period 2001-2004 4363 2951 1713 11743 -12563 519

(11835) (4099) (2339) (6382) (7238) (329)

Shale x Period 2005-2008 79729 28407 8473 53319 -10531 62

(25028) (11816) (6072) (15912) (11089) (424)

Shale x Period 2009-2011 171899 55173 15906 82008 18129 683

(30474) (14451) (7937) (17310) (10735) (444)

Period 2001-2004 62892 10456 7959 -8 43214 1272

(7228) (2196) (1137) (7) (6572) (200)

Period 2005-2008 128176 25489 18378 39 83201 1068

(11380) (4562) (3331) (32) (9335) (340)

Period 2009-2012 120852 26915 16003 63 77872 -2

(9488) (4441) (4434) (48) (7124) (345)

Intercept 189740 43061 40395 24 103353 2906

(20323) (7365) (3263) (11) (14704) (357)

Number of school districts 84 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176 1176

Adjusted R-squared 0210 0114 0055 0208 0081 0032

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors are in parenthesis All dependent variables are in 2000

dollars and are measured on a per student basis The excluded period is the 1998-2000 period

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

37

Table 7 School Financial Outcomes across Shale and Nonshale Districts 1998-2011

Local Tax Revenue State Revenue Total Revenue

Operating

Expenses

Fund

Balance

Shale 376 -297 -23 -173 -102

(346) (317) (178) (209) (184)

Shale x Period 2001-2004 65 -173 -93 19 -6

(147) (135) (126) (171) (143)

Shale x Period 2005-2008 529 -409 310 324 523

(245) (177) (184) (180) (201)

Shale x Period 2009-2011 1590 -1190 595 460 454

(316) (242) (207) (198) (237)

Period 2001-2004 994 461 1853 148 26

(92) (101) (96) (160) (108)

Period 2005-2008 1476 598 2940 228 68

(126) (120) (112) (167) (170)

Period 2009-2012 1516 1611 3948 1038 322

(131) (152) (115) (165) (164)

Intercept 2361 3716 6501 6431 1161

(238) (223) (124) (175) (151)

Number of school districts 84 84 84 84 84

Number of observations 1176 1176 1176 1176 1176

Adjusted R-squared 0187 0084 0576 0149 0061

Note indicate statistical significance at the 1 5 and 10 percent levels Robust standard errors clustered by school district are in parenthesis All

dependent variables are in 2000 dollars and are measured on a per student basis The excluded period is the 1998-2000 period

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

38

REFERENCES

Anderson B J and Theodori G L 2009 Local leaderrsquos perceptions of energy development in the

Barnett shale Southern Rural Sociology 24(1) 113-129

Barrow L and Rouse C E 2004 Using market valuation to assess public school spending Journal of

Public Economics 88(9) 1747-1769

Black S E 1999 Do better schools matter Parental valuation of elementary education The Quarterly

Journal of Economics 114(2) 577-599

Black D McKinnish T and Sanders S 2005 The economic impact of the coal boom and bust The

Economic Journal 115 449-476

Bradbury K L Mayer C J and Case K E 2001 Property tax limits local fiscal behavior and

property values Evidence from Massachusetts under Proposition 212 Journal of Public

Economics 80(2) 287-311

Brasier K and Ward M 2010 ldquoAccelerating Activity in the Marcellus Shale An Update on Wells

Drilled and Permittedrdquo State College PA Penn State Extension Office Available at

extensionpsuedu April

Brasington D and Haurin D R 2006 Educational Outcomes and House Values A Test of the value

added Approach Journal of Regional Science46(2) 245-268

Bun Y (2012) Zillow Home Value Index Methodology Zillow Real Estate Research

httpwwwzillowblogcomresearch20120121zillow-home-value-index-methodology (last

accessed 2272014)

Conlin M and Grow B 2013 Special Report US builders hoard mineral rights under new homes

Reuters Wed Oct 9 2013 httpwwwreuterscomarticle20131009us-usa-fracking-rights-

specialreport-idUSBRE9980AZ20131009 (last accessed 2272014)

Crump RK Hotz JV Imbens GW and Mitnik OA 2009 Dealing with limited overlap in the

estimation of average treatment effects Biometrika 96(1) 187-199

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

39

Currie J Davis L Greenstone M and R Walker 2012 Do Housing Prices Reflect Environmental

Health Risks Evidence from More than 1600 Toxic Plant Openings and Closings MIT

Department of Economics Working Paper No 12-30 Massachusetts Institute of Technology

Davis L W 2004 The effect of health risk on housing values evidence from the cancer cluster

American Economic Review 94 1693-1704

Dorsey RE Hu H Mayer WJ and Wang H 2010 Hedonic versus repeat-sales housing price

indexes for measuring the recent boom-bust cycle Journal of Housing Economics 19 (2) 75-93

Downes T A and Zabel J E 2002 The impact of school characteristics on house prices Chicago

1987ndash1991 Journal of Urban Economics 52(1) 1-25

Edel M and Sclar E 1974 Taxes spending and property values Supply adjustment in a Tiebout-Oates

model The Journal of Political Economy 941-954

Energy Information Administration (EIA) 2013 International Energy Statistics ndash Gross Natural Gas

Production by Country

httpwwweiagovcfappsipdbprojectIEDIndex3cfmtid=3amppid=3ampaid=1 (last accessed

2272014)

Fack G and Grenet J 2010 When do better schools raise housing prices Evidence from Paris public

and private schools Journal of Public Economics 94(1) 59-77

Gopalakrishnan S and Klaiber H A 2013 Is the shale energy boom a bust for nearby residents

Evidence from housing values in Pennsylvania American Journal of Agricultural Economics

forthcoming

GuerrieriV Hartley D and Hurst E 2010 Endogenous Gentrification and Housing Price Dynamics

NBER Working Paper No 16237 National Bureau of Economic Research

Hill E L 2013 Unconventional Natural Gas Development and Infant Health Evidence from

Pennsylvania Charles H Dyson School of Applied Economics and Management Working Paper

2012-12

40

Ho D Imai K King G and Stuart E 2007 Matching as nonparametric preprocessing for reducing

model dependence in parametric causal inference Political Analysis 15 199ndash236

Humphies S 2008 Zillow Home Value Index Compared to OFHEO and Case-Shiller Indexes Market

Trends Zillow Blog March 18 2008 httpwwwzillowblogcom2008-03-18zillow-home-

value-index-compared-to-ofheo-and-case-shiller-indexes (last accessed 2272014)

Imbens GM and Wooldridge JM 2008 Recent Developments in the Econometrics of Program

Evaluation No w14251 National Bureau of Economic Research

Imbens GM 2014 Mathcing Methods in Practice Three Examples NBER Working Paper 19959

Isserman AM and Merrifield JD 1987 Quasi-experimental control group methods for regional

analysis An application to an energy boomtown and growth pole theory Economic Geography

63(1) 3-19

Kent C Eastham E Hagan E 2011 Taxation of Natural Gas A Comparative Analysis Marshall

University Center for Business and Economic Research

Lang K and Jian T 2004 Property taxes and property values evidence from Proposition 212 Journal

of Urban Economics 55(3) 439-457

Mastromonaco R 2012 Do Environmental Right-to-know Laws Affect Markets Capitalization of

Information in the Toxic Release Inventory Mimeo

Merrifield J 1984 Impact mitigation in western energy boomtowns Growth and Change 15(2) 23-28

Muehlenbachs L Spiller E and Timmins C 2012 Shale Gas Development and Property Values

Differences Across Drinking Water Sources NBER Working Paper 18390

Muehlenbachs Spiller E and Timmins C 2013 The Housing Market Impacts of Shale Gas

Development Resources for the Future Discussion Paper 13-39

Oates W E 1969 The effects of property taxes and local public spending on property values An

empirical study of tax capitalization and the Tiebout hypothesis The Journal of Political

Economy 77(6) 957

41

Oberholzer-Gee F and Mitsunari M 2006 Information regulation Do the victims of externalities pay

attention Journal of Regulatory Economics 30 141-158

Olmstead SM Muehlenbachs LA Shih JS Chu Z and Krupnick A 2013 Shale Gas

Development Impacts on Surface Water Quality in Pennsylvania Proceedings of the National

Academy of Sciences

Paredes DJC 2011 A methodology to compute regional housing price index using matching estimator

methods Annals of Regional Science 46(1) 139-157

Pless J 2012 Oil and Gas Severance Taxes States Work to Alleviate Fiscal Pressures Amid the Natural

Gas Boom National Conference of State Legislatures httpwwwncslorgresearchenergyoil-

and-gas-severance-taxesaspx (last accessed on 2252014)

Pender J and Reeder R J 2011 Impacts of Regional Approaches to Rural Development Initial

Evidence on the Delta Regional Authority US Department of Agriculture Economic Research

Service ERR 119

Pennsylvania Department of Environmental Protection (PA DEP) 2014 Oil and Gas Reports ndash Wells

Drilled By County Available at

httpwwwportalstatepausportalserverptcommunityoil_and_gas_reports20297

Perdomo JA 2011 A methodological proposal to estimate changes in residential property value Case

study developed in Bogota Applied Economics Letters 18(16-18) 1577-1581

Railroad Commission of Texas 2012 Barnett Shale Information Available at

httpwwwrrcstatetxusbarnettshaleindexphp (accessed 2272014)

Railroad Commission of Texas 2014 Barnett Shale Information Available at

httpwwwrrcstatetxusbarnettshaleindexphp (accessed 482014)

Rahm D 2011 Regulating hydraulic fracturing in shale gas plays The case of Texas Energy Policy 39

2974-2981

Ravallion M and Chen S 2005 Hidden impact household saving in response to a poor-area

development project Journal of Public Economics 89 2183-2204

42

Rosenbaum PR and Rubin DB 1983 The central role of the propensity score in observational studies

for causal effects Biometrika 70(1) 45-55

Rubin DB 1980 Bias reduction using Mahalanobis-metric matching Biometrics 36 293-298

Rubin DB 2006 Matched Sampling for Causal Effects Cambridge University Press Cambridge UK

Sanders NJ 2012 Toxic assets How the Housing Market Responds to Environmental Information

Shocks College of William and Mary Department of Economics Working Paper Number 128

httpnjsanderspeoplewmedupaper-TRI-housingpdf (last accessed 2272014)

Schintler L and Istrate E 2011 Tracking the housing bubble across metropolitan areasndashndash spatio-

temporal comparison of house price indices Cityscape 14(1)

Schwab RM 1982 Inflation and the demand for housing American Economic Review 72(1) 143-153

Smith LB KT Rosen and G Fallis 1988 Recent developments in economic models of housing

markets Journal of Economic Literature 26(1) 29-64

Smith MD Krannich RS and Hunter LM 2001 Growth decline stability and disruption A

longitudinal analysis of social well-being in four western rural communities Rural Sociology

66(3) 425-450

SampPCase Shiller 2014 ldquoSampP Case-Shiller Home Prices Indices Methodologyrdquo

httpwwwspindicescomindex-familyreal-estatesp-case-shiller (last accessed 2272014)

Summers GF and Branch K 1984 Economic development and community social change Annual

Review of Sociology 10 141-166

Texas Education Agency 2014 Chapter 41 Wealth Equalization Available at

httpwwwteastatetxusindex2aspxid=6796

US Census Bureau 2013 Census 2000 Gateway Retrieved from

wwwcensusgovmainwwwcen2000html Accessed October 2013

Weber JG 2012 The effects of a natural gas boom on employment and income in Colorado Texas and

Wyoming Energy Economics 34(5) 1580-1588

43

Weber JG 2013 A decade of natural gas development The makings of a resource curse Resource and

Energy Economics forthcoming

Winkler A 2013 The House Price Indices Insights American Action Forum August 27 2013

httpamericanactionforumorginsightsthe-house-price-indices (last accessed 2272014)

Wooldridge J 2002 Econometric Analysis of Cross Section and Panel Data Massachusetts Institute of

Technology Press Cambridge Mass

Yinger J 1982 Capitalization and the theory of local public finance The Journal of Political Economy

917-943

Zillow 2013 Dallas Fort Worth Zillow Market Overview Real Estate Zillow Real Estate Research

httpfileszillowstaticcomresearchpublicrealestateZHVIDallas-Fort20Worth394514pdf

(last accessed 2272014)

44

APPENDIX

1 Additional Tables

Table A1 Variables Used in Matching and as Control Variables

Description

Variable Name Race Age and Education

Share white Share of the population that is white

Share age 20-40 Share of population 20-40 years old

Share age 40-60 Share of population 40-60 years old

Share age 60 or more Share of population equal to or greater than 60 years old

Share some college or more Share of population with some college or more in education

Income

Share 20K-40K Share of population with income $ 20000-40000 per year

Share 40K-75K Share of population with income $ 40000-75000 per year

Share 75K or more Share of population with income equal to or greater than $75000

Median household income Median household income

Urbanization

Share urban Share of the zip code area that is urban

Population density Population per square mile

Real Estate

Share vacant Percent of housing units that are vacant

Median year built Median year when housing unit was built

Median real estate taxes Median real estate taxes

Source 2000 Decennial Census US Census Bureau

2 First Order Conditions for the Theoretical Model

Assuming an interior solution to the utility maximization problem and that (7) from above holds

with weak inequality we derive the following first order conditions

1 21

1

C CU U

(A1)

where 1

1 1( ) CU U C Z C and

1 2 1 2

1 2

1 2 1

1 2

1 1

1 1

1 1

1 1

Z Z C C

Z Z C

U U U R P U R P

U U U R R P

(A2)

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period

41

Oberholzer-Gee F and Mitsunari M 2006 Information regulation Do the victims of externalities pay

attention Journal of Regulatory Economics 30 141-158

Olmstead SM Muehlenbachs LA Shih JS Chu Z and Krupnick A 2013 Shale Gas

Development Impacts on Surface Water Quality in Pennsylvania Proceedings of the National

Academy of Sciences

Paredes DJC 2011 A methodology to compute regional housing price index using matching estimator

methods Annals of Regional Science 46(1) 139-157

Pless J 2012 Oil and Gas Severance Taxes States Work to Alleviate Fiscal Pressures Amid the Natural

Gas Boom National Conference of State Legislatures httpwwwncslorgresearchenergyoil-

and-gas-severance-taxesaspx (last accessed on 2252014)

Pender J and Reeder R J 2011 Impacts of Regional Approaches to Rural Development Initial

Evidence on the Delta Regional Authority US Department of Agriculture Economic Research

Service ERR 119

Pennsylvania Department of Environmental Protection (PA DEP) 2014 Oil and Gas Reports ndash Wells

Drilled By County Available at

httpwwwportalstatepausportalserverptcommunityoil_and_gas_reports20297

Perdomo JA 2011 A methodological proposal to estimate changes in residential property value Case

study developed in Bogota Applied Economics Letters 18(16-18) 1577-1581

Railroad Commission of Texas 2012 Barnett Shale Information Available at

httpwwwrrcstatetxusbarnettshaleindexphp (accessed 2272014)

Railroad Commission of Texas 2014 Barnett Shale Information Available at

httpwwwrrcstatetxusbarnettshaleindexphp (accessed 482014)

Rahm D 2011 Regulating hydraulic fracturing in shale gas plays The case of Texas Energy Policy 39

2974-2981

Ravallion M and Chen S 2005 Hidden impact household saving in response to a poor-area

development project Journal of Public Economics 89 2183-2204

42

Rosenbaum PR and Rubin DB 1983 The central role of the propensity score in observational studies

for causal effects Biometrika 70(1) 45-55

Rubin DB 1980 Bias reduction using Mahalanobis-metric matching Biometrics 36 293-298

Rubin DB 2006 Matched Sampling for Causal Effects Cambridge University Press Cambridge UK

Sanders NJ 2012 Toxic assets How the Housing Market Responds to Environmental Information

Shocks College of William and Mary Department of Economics Working Paper Number 128

httpnjsanderspeoplewmedupaper-TRI-housingpdf (last accessed 2272014)

Schintler L and Istrate E 2011 Tracking the housing bubble across metropolitan areasndashndash spatio-

temporal comparison of house price indices Cityscape 14(1)

Schwab RM 1982 Inflation and the demand for housing American Economic Review 72(1) 143-153

Smith LB KT Rosen and G Fallis 1988 Recent developments in economic models of housing

markets Journal of Economic Literature 26(1) 29-64

Smith MD Krannich RS and Hunter LM 2001 Growth decline stability and disruption A

longitudinal analysis of social well-being in four western rural communities Rural Sociology

66(3) 425-450

SampPCase Shiller 2014 ldquoSampP Case-Shiller Home Prices Indices Methodologyrdquo

httpwwwspindicescomindex-familyreal-estatesp-case-shiller (last accessed 2272014)

Summers GF and Branch K 1984 Economic development and community social change Annual

Review of Sociology 10 141-166

Texas Education Agency 2014 Chapter 41 Wealth Equalization Available at

httpwwwteastatetxusindex2aspxid=6796

US Census Bureau 2013 Census 2000 Gateway Retrieved from

wwwcensusgovmainwwwcen2000html Accessed October 2013

Weber JG 2012 The effects of a natural gas boom on employment and income in Colorado Texas and

Wyoming Energy Economics 34(5) 1580-1588

43

Weber JG 2013 A decade of natural gas development The makings of a resource curse Resource and

Energy Economics forthcoming

Winkler A 2013 The House Price Indices Insights American Action Forum August 27 2013

httpamericanactionforumorginsightsthe-house-price-indices (last accessed 2272014)

Wooldridge J 2002 Econometric Analysis of Cross Section and Panel Data Massachusetts Institute of

Technology Press Cambridge Mass

Yinger J 1982 Capitalization and the theory of local public finance The Journal of Political Economy

917-943

Zillow 2013 Dallas Fort Worth Zillow Market Overview Real Estate Zillow Real Estate Research

httpfileszillowstaticcomresearchpublicrealestateZHVIDallas-Fort20Worth394514pdf

(last accessed 2272014)

44

APPENDIX

1 Additional Tables

Table A1 Variables Used in Matching and as Control Variables

Description

Variable Name Race Age and Education

Share white Share of the population that is white

Share age 20-40 Share of population 20-40 years old

Share age 40-60 Share of population 40-60 years old

Share age 60 or more Share of population equal to or greater than 60 years old

Share some college or more Share of population with some college or more in education

Income

Share 20K-40K Share of population with income $ 20000-40000 per year

Share 40K-75K Share of population with income $ 40000-75000 per year

Share 75K or more Share of population with income equal to or greater than $75000

Median household income Median household income

Urbanization

Share urban Share of the zip code area that is urban

Population density Population per square mile

Real Estate

Share vacant Percent of housing units that are vacant

Median year built Median year when housing unit was built

Median real estate taxes Median real estate taxes

Source 2000 Decennial Census US Census Bureau

2 First Order Conditions for the Theoretical Model

Assuming an interior solution to the utility maximization problem and that (7) from above holds

with weak inequality we derive the following first order conditions

1 21

1

C CU U

(A1)

where 1

1 1( ) CU U C Z C and

1 2 1 2

1 2

1 2 1

1 2

1 1

1 1

1 1

1 1

Z Z C C

Z Z C

U U U R P U R P

U U U R R P

(A2)

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period

42

Rosenbaum PR and Rubin DB 1983 The central role of the propensity score in observational studies

for causal effects Biometrika 70(1) 45-55

Rubin DB 1980 Bias reduction using Mahalanobis-metric matching Biometrics 36 293-298

Rubin DB 2006 Matched Sampling for Causal Effects Cambridge University Press Cambridge UK

Sanders NJ 2012 Toxic assets How the Housing Market Responds to Environmental Information

Shocks College of William and Mary Department of Economics Working Paper Number 128

httpnjsanderspeoplewmedupaper-TRI-housingpdf (last accessed 2272014)

Schintler L and Istrate E 2011 Tracking the housing bubble across metropolitan areasndashndash spatio-

temporal comparison of house price indices Cityscape 14(1)

Schwab RM 1982 Inflation and the demand for housing American Economic Review 72(1) 143-153

Smith LB KT Rosen and G Fallis 1988 Recent developments in economic models of housing

markets Journal of Economic Literature 26(1) 29-64

Smith MD Krannich RS and Hunter LM 2001 Growth decline stability and disruption A

longitudinal analysis of social well-being in four western rural communities Rural Sociology

66(3) 425-450

SampPCase Shiller 2014 ldquoSampP Case-Shiller Home Prices Indices Methodologyrdquo

httpwwwspindicescomindex-familyreal-estatesp-case-shiller (last accessed 2272014)

Summers GF and Branch K 1984 Economic development and community social change Annual

Review of Sociology 10 141-166

Texas Education Agency 2014 Chapter 41 Wealth Equalization Available at

httpwwwteastatetxusindex2aspxid=6796

US Census Bureau 2013 Census 2000 Gateway Retrieved from

wwwcensusgovmainwwwcen2000html Accessed October 2013

Weber JG 2012 The effects of a natural gas boom on employment and income in Colorado Texas and

Wyoming Energy Economics 34(5) 1580-1588

43

Weber JG 2013 A decade of natural gas development The makings of a resource curse Resource and

Energy Economics forthcoming

Winkler A 2013 The House Price Indices Insights American Action Forum August 27 2013

httpamericanactionforumorginsightsthe-house-price-indices (last accessed 2272014)

Wooldridge J 2002 Econometric Analysis of Cross Section and Panel Data Massachusetts Institute of

Technology Press Cambridge Mass

Yinger J 1982 Capitalization and the theory of local public finance The Journal of Political Economy

917-943

Zillow 2013 Dallas Fort Worth Zillow Market Overview Real Estate Zillow Real Estate Research

httpfileszillowstaticcomresearchpublicrealestateZHVIDallas-Fort20Worth394514pdf

(last accessed 2272014)

44

APPENDIX

1 Additional Tables

Table A1 Variables Used in Matching and as Control Variables

Description

Variable Name Race Age and Education

Share white Share of the population that is white

Share age 20-40 Share of population 20-40 years old

Share age 40-60 Share of population 40-60 years old

Share age 60 or more Share of population equal to or greater than 60 years old

Share some college or more Share of population with some college or more in education

Income

Share 20K-40K Share of population with income $ 20000-40000 per year

Share 40K-75K Share of population with income $ 40000-75000 per year

Share 75K or more Share of population with income equal to or greater than $75000

Median household income Median household income

Urbanization

Share urban Share of the zip code area that is urban

Population density Population per square mile

Real Estate

Share vacant Percent of housing units that are vacant

Median year built Median year when housing unit was built

Median real estate taxes Median real estate taxes

Source 2000 Decennial Census US Census Bureau

2 First Order Conditions for the Theoretical Model

Assuming an interior solution to the utility maximization problem and that (7) from above holds

with weak inequality we derive the following first order conditions

1 21

1

C CU U

(A1)

where 1

1 1( ) CU U C Z C and

1 2 1 2

1 2

1 2 1

1 2

1 1

1 1

1 1

1 1

Z Z C C

Z Z C

U U U R P U R P

U U U R R P

(A2)

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period

43

Weber JG 2013 A decade of natural gas development The makings of a resource curse Resource and

Energy Economics forthcoming

Winkler A 2013 The House Price Indices Insights American Action Forum August 27 2013

httpamericanactionforumorginsightsthe-house-price-indices (last accessed 2272014)

Wooldridge J 2002 Econometric Analysis of Cross Section and Panel Data Massachusetts Institute of

Technology Press Cambridge Mass

Yinger J 1982 Capitalization and the theory of local public finance The Journal of Political Economy

917-943

Zillow 2013 Dallas Fort Worth Zillow Market Overview Real Estate Zillow Real Estate Research

httpfileszillowstaticcomresearchpublicrealestateZHVIDallas-Fort20Worth394514pdf

(last accessed 2272014)

44

APPENDIX

1 Additional Tables

Table A1 Variables Used in Matching and as Control Variables

Description

Variable Name Race Age and Education

Share white Share of the population that is white

Share age 20-40 Share of population 20-40 years old

Share age 40-60 Share of population 40-60 years old

Share age 60 or more Share of population equal to or greater than 60 years old

Share some college or more Share of population with some college or more in education

Income

Share 20K-40K Share of population with income $ 20000-40000 per year

Share 40K-75K Share of population with income $ 40000-75000 per year

Share 75K or more Share of population with income equal to or greater than $75000

Median household income Median household income

Urbanization

Share urban Share of the zip code area that is urban

Population density Population per square mile

Real Estate

Share vacant Percent of housing units that are vacant

Median year built Median year when housing unit was built

Median real estate taxes Median real estate taxes

Source 2000 Decennial Census US Census Bureau

2 First Order Conditions for the Theoretical Model

Assuming an interior solution to the utility maximization problem and that (7) from above holds

with weak inequality we derive the following first order conditions

1 21

1

C CU U

(A1)

where 1

1 1( ) CU U C Z C and

1 2 1 2

1 2

1 2 1

1 2

1 1

1 1

1 1

1 1

Z Z C C

Z Z C

U U U R P U R P

U U U R R P

(A2)

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period

44

APPENDIX

1 Additional Tables

Table A1 Variables Used in Matching and as Control Variables

Description

Variable Name Race Age and Education

Share white Share of the population that is white

Share age 20-40 Share of population 20-40 years old

Share age 40-60 Share of population 40-60 years old

Share age 60 or more Share of population equal to or greater than 60 years old

Share some college or more Share of population with some college or more in education

Income

Share 20K-40K Share of population with income $ 20000-40000 per year

Share 40K-75K Share of population with income $ 40000-75000 per year

Share 75K or more Share of population with income equal to or greater than $75000

Median household income Median household income

Urbanization

Share urban Share of the zip code area that is urban

Population density Population per square mile

Real Estate

Share vacant Percent of housing units that are vacant

Median year built Median year when housing unit was built

Median real estate taxes Median real estate taxes

Source 2000 Decennial Census US Census Bureau

2 First Order Conditions for the Theoretical Model

Assuming an interior solution to the utility maximization problem and that (7) from above holds

with weak inequality we derive the following first order conditions

1 21

1

C CU U

(A1)

where 1

1 1( ) CU U C Z C and

1 2 1 2

1 2

1 2 1

1 2

1 1

1 1

1 1

1 1

Z Z C C

Z Z C

U U U R P U R P

U U U R R P

(A2)

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period

45

Condition (A1) implies in equilibrium that an increase in utility from consumption of the composite

good in the first period yield no net increase or decrease in discounted utility from consumption of the

composite good in the second period Condition (A2) implies that if the consumer increases her

consumption of housing (left-hand side of the equality) she must sacrifice 1R P units of the composite

good in the first period and 2R P units of the composite good in the second period By combining (A1)

and (A2) the interpretation changes slightly so that if the consumer considers increasing her consumption

of housing then she must sacrifice the sum of 1R P and (the current value of) 2R P units of the composite

good in the first period