Effects of Transportation Accessibility on Residential ...martinez/PDF/Paper TRB2009.pdf · Effects of Transportation Accessibility on Residential Property Values: ... need to forecast

L. Miguel Martínez and José Manuel Viegas

Effects of Transportation Accessibility on Residential Property Values: A Hedonic

Price Model in the Lisbon Metropolitan Area

L. Miguel Martínez

PhD Candidate

CESUR, Department of Civil Engineering

Instituto Superior Técnico

Lisbon Technical University

Av. Rovisco Pais 1049 - 001 Lisboa, Portugal.

Phone: +351-21-8418425

Fax: +351-21-840 9884

Email: [email protected]

José Manuel Viegas

Professor of Civil Engineering

CESUR, Department of Civil Engineering

Instituto Superior Técnico

Lisbon Technical University

Av. Rovisco Pais 1049 - 001 Lisboa, Portugal.

Phone: +351-21-8418413

Fax: +351-21-840 9884

Email: [email protected]

The total number of words is 8,389 (5,889 words + 4tables + 6figures)

Submitted to the 88th

Annual Meeting of the Transportation Research Board

15th

of November, 2008

L. Miguel Martínez and José Manuel Viegas 1

ABSTRACT

The aim of this paper is to examine the relationship between the availability of transportation

infrastructure and services and the pattern of house prices in an urban area, and to assess

whether public investment in transportation can really modify residential property values.

This study was developed for the Lisbon Metropolitan Area (LMA) as part of a broader study

that intends to develop new value capture financing schemes for public transportation in the

LMA. The paper focuses in three central municipalities (Amadora, Lisbon, Odivelas) where

these effects could be more easily measured due to the existence of a significant variability of

public transportation services.

The paper tries to determine, using different spatial hedonic pricing models, the extent to

which access to transportation infrastructure currently is capitalized into house prices,

isolating the influence of three different transportation infrastructures: metro, rail and road.

The results suggest that the proximity to one or two metro lines leads to significant property

value changes and that the classic hedonic price model (ordinary least squares estimation)

leads to similar coefficient values of the local accessibility dummy variables compared to the

spatial lag model, thus providing a steady basis to forecast the property values changes

derived from transportation investment for the study area in the absence of a significant

property values database.


INTRODUCTION

For decades, there has been considerable discussion about the effects of transportation

accessibility on the housing prices. It is well known that a good public transport system

provides a high level of access to work and other activities for households, and to customers

and employees for businesses. The monetary value of this accessibility will be reflected in

the value of a home or a business, in addition to the value of other features such as the

specific physical attributes of the building and neighbourhood characteristics.

The impact of public transport on property values has been studied from many

perspectives, including analyses of different types of systems (e.g., rapid, commuter, light

rail), of residential versus commercial impacts, and studies that have attempted to isolate

both positive and negative effects. The varied approaches make it difficult to compare the

results of one study to another. Further, some of the contradictory results over the years have

often been due to differing methods of analysis, data quality, and regional differences.

This paper examines the relationship between the availability of transportation

infrastructure and services and the house prices in an urban area, trying to assess the impact

of public investment in transportation on residential property values. This study was

developed for the Lisbon Metropolitan Area (LMA) as part of a broader study that intends to

develop new value capture financing scheme for public transportation in the LMA.

The available data focuses in three central municipalities (Amadora, Lisbon, Odivelas) where

these effects could be more easily measured due to the existence of a significant variability of

public transportation services.

This study presents several hedonic pricing models to assess the relationship between

transportation accessibility and house values, ranging from the classic model to spatial

hedonic price models (spatial lag) and including local and systemwide accessibility

indicators. The results of the different models are assessed and compared having in mind the

need to forecast house prices in subsequent phases of the research project.

LITERATURE REVIEW

In the 1960s, economists like Alonso and Muth developed the theory for determining

residential location in the urban land market (1, 2). The theory illustrates a model where a

household chooses to locate at a point where its bid-rent curve intersects with the actual one,

in which the bid rent curves have a declining gradient with the distance from the residential

location to the central business district (CBD). However it might be necessary to consider the

effect of other variables such as neighborhood characteristics.

The introduction of the hedonic pricing methodology by Rosen (3) led to an easier

way of attributing value to different properties’ features. A number of studies have observed

the integration of physical, neighborhood and accessibility characteristics of the property in

models trying to explain the differences in property values or house prices (4-35). The

hedonic price model is a multivariate regression model for housing values, as well as a

common robust indirect approach to valuation in that its estimates represent the implied

prices that people place on obtaining desirable features of a property and avoiding

undesirable ones (20, 36).

Most commonly, hedonic price models have used ordinary least squares (OLS)

estimation (22, 33, 37-39), but more recently these models have been extended to incorporate


spatial effects in multiple ways: feasible generalized least square estimation (34) and spatial

econometric models (spatial lag and spatial error models) (20, 40).

There are several empirical evidences relating the changes in commercial and

residential property market values and transport investment. Table 1 presents the information

from the Europe, whilst Table 2 does the same for North America.

As can be seen from the tables, the evidence is broadly positive with the widest

difference being found between the residential and commercial markets. Parsons Brinkerhoff

(41) concludes that proximity to rail systems is valued by property owners and there is little

support that this proximity can decrease property values.

Much of the European research (Table 1) has focused mainly on the residential

market, but in the US research (Table 2) where the commercial market has been the main

target. Almost uniformly, the impacts are seen as positive, with some very large percentage

increases particularly in commercial property values. The enormous variability in (positive)

impact points towards either the importance of other factors, or the specificity of results, or

the limitations of the methods used – or a combination of all these factors.

TABLE 1 Property value impacts of public transport proximity in European cities.

Case/Location Impact on Impact Source

Bremen Office rents +50% in most cases (42)

Croydon Tramlink Residential property Some localized positive

impacts (43)

Freiburg Office rent +15-20% (42)

Freiburg Residential rent +3% (42)

Greater Manchester Not stated +10% (42)

Hannover Residential rent +5% (42)

Helsinki Metro Property values +7.5-11% (44)

London Crossrail Residential and commercial

property Positive (45)

London Docklands LRT Residential and commercial

property Positive (44)

London JLE Residential and commercial

property Positive (46, 47)

Manchester Metrolink House Prices Unable to identify (48)

Montpellier Property values Positive (42)

Nantes LRT Commercial property Higher values (42)

Nantes LRT Not stated Small increase (42)

Nantes LRT Number of commercial premises +13% (44)

Nantes LRT Number of offices +25% (44)

Nantes LRT Number of residential dwellings +25% (44)

Newcastle upon Tyne House prices +20% (42)

Orléans Apartment rents None-initially negative due

to noise (42)

Rouen Rent and houses +10% most cases (42)

Saarbrűcken Not stated None (42)

Sheffield Supertram Property values Unable to identify (16, 48)

Strasbourg Office rent +10-15% (42)

Strasbourg Residential rent +7% (42)

Tyne and Wear Metro Property values +2% (49)

Vienna S-Bahn Housing units +18.7% (44)


TABLE 2 Property value impacts of public transport proximity in North American cities.

Case/Location Impact on Impact Source

Atlanta Office rents Positive (8, 50)

Baltimore LRT Not stated Unable to identify (44)

Boston Residential property +6.7% (50, 51)

Buffalo, New York House prices +4-11% (23)

Chicago MTA House prices +20% (52)

Dallas DART Commercial rents +64.8% (53)

Dallas DART Property values +25% (53, 54)

Linden, New Jersey Residential property Positive (55)

Los Angeles Property values Higher values (56)

Miami House prices +5% (7)

New Jersey SEPTA rail House Prices +7.5-8% (57)

New Jersey PACTO rail House Prices +10% (57)

New York Not stated Positive (58)

Pennsylvania SEPTA rail House Prices +3.8% (57)

Portland House prices +10% (42)

Portland Gresham Residential rent >5% (42)

Portland Metropolitan

Express House prices +10.5% (17, 19)

San Diego Trolley Not stated Positive (44)

San Francisco Bay Area

BART Property values Positive (59)

San Francisco Bay Area

BART Residential rent

+10-15%

+15-26% (60, 61)

Santa Clara County Commercial and office property +23-120%% (62)

Santa Clara County House prices +45% (18)

Santa Clara County Residential rent +15% (25)

St. Louis Property values +32% (63)

Toronto Metro House prices +20% (29, 44)

Washington DC Residential rent Positive (50, 64)

DATA DESCRIPTION

The data used in this study are 2007 cross-sectional real estate data from an online

realtor’s database (Imokapa Vector) for Lisbon, Portugal. This database presents the asking

price of residential properties on sale during February of 2007 with a total of 12,488

complete records, 70% inside Lisbon’s Municipality. The real estate data contained

information on their asking sale price, structural attributes and address. The descriptive

statistics of the data is presented in Table 3.

The real estate data was geocoded and imported into geographic information system

(GIS) transportation analysis network map. All the properties were connected with the road

and public transport network in order to measure the several accessibility indicators used.

The spatial distribution of asking prices is presented in Figure 1, where it is easy to

denote that a great part of the most expensive properties are located near the metro and rail

lines.

The dependent variable is the advertised asking selling price at which the owner or

realtor is offering the property in the market. This can be a limitation to the model because

the dependent variable is not directly linked to an equilibrium price, where supply and


demand have cleared the transaction (65). Other studies that relate public transport

accessibility to residential property or land values also have relied on asking prices (11, 16,

24, 64-66).

FIGURE 1 Spatial distribution of property asking prices for the study area

The properties of accessibility to public transport and to the road network were

developed using two different types of characterization: local accessibility and systemwide

accessibility.

Local accessibility indicators were calculated using the network distance to public

transport entry points (walking distance using 4 km/h) and to roads considered in Lisbon

Mobility Plan 2004 (67) road hierarchy at the various levels (distance in meters). The three

different levels of road hierarchy presented represent urban motorways for Network1, urban

arterials as Network2 and collector/distributor roads as Network3.

These accessibility measures were built using two different approaches. The first

approach considers an all-or-nothing influence of proximity to public transport entry points

and to the road network, resulting in a set of dummy variables for each public transport mode

or line and for each road hierarchy level.


The second approach considers a continuous decreasing function of impedance of

proximity to public transport entry points and to the road network. To model this continuous

impedance it was used an inverse logistic function with different parameters for each public

transport line and road hierarchy. The inverse logistic function considered is:

( )( )XXbaY

−−+=

maxexp1

1 (1)

where Xmax is an specific parameter of each public transport line or road hierarchy and a and

b are calibrated by considering two different point in the curve (e.g. 5 minutes walking

distance – Y =0.90, and 15 minutes walking distance – Y =0.10).

An example of comparison between both measuring approaches can be assessed in

Figure 2, where the main differences are in the values observed for accessibility distances

greater than the threshold established for the all-or-nothing measuring approach.

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

0 5 10 15 20 25 30

Accessibility time (min.)

Accessib

ility

indic

ato

r

Continuous Approach All-or-nothing Approach

FIGURE 2 Calibrated β values of the Gravitational model for Lisbon Mobility Plan 2004

These variables and their descriptive statistics are presented in the local accessibility

attributes of Table 3.

The systemwide accessibility indicators were calculated using a gravitational model

calibrated for Lisbon’s Mobility Plan survey of 2004. The model equation is (68):

)exp( ijjjiiij CBDAOF ⋅⋅⋅⋅⋅= β

∑ ⋅⋅⋅=

k

ikkk

iCBD

A)]exp([

1

β;

∑ ⋅⋅⋅=

m

mjmm

jCAO

B)]exp([

1

β (2)


where ijF is the total flow between zone i and j for each mode, iO the total number of trips

with origin in i, jD the total number of trips with destination in j and ijC is the impedance

between zones i and j, measured in travel time between zones. iA and jB are calibration

variables that are needed in a doubly constrained gravitational model calibration.

The calibrated β’s for public transport (PT) and private car (PC) are presented in

Figure 3, where the difference between the calibrated β’s is very significant (approximately 4

times greater for the private car showing a much greater ease of displacement than in public

transport).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 20 40 60 80 100 120 140 160 180 200

Accessibility time Cij (minutes)

Ex

p(-

be

ta.c

(k,j))

Beta PC = -0,03420 Beta PT = -0,00833

FIGURE 3 Calibrated β values of the Gravitational model for Lisbon Mobility Plan 2004

A land use database for the study area was then used for the calculation of the

accessibility indicator. The jD term of the gravitational model equation was replaced by the

land use surface ( jA ) and standardized using the land use surface of the whole study

area∑=

n

j

jA1

. The accessibility indicator results then in the following equation:

∑=

⋅⋅=n

j

jiji ApCAccGr1

)()exp(β ;

∑=

=n

j

j

j

j

A

AAp

1

)( (3)

The descriptive statistics of these accessibility indicators are presented in Table 3.

Some neighbourhood attributes for each property were also calculated in order to

improve to explanatory power of the models. These variables are an Educational Index that

calculates the percentage of undergraduate persons in the population over 20 years old in a

500 radius around the property, and the Entropy Index that measures the mixture of land use

types in a radius of 500 m (69, 70). These descriptive statistics of these neighbourhood

attributes are presented in Table 3.


TABLE 3 Descriptive statistics of the variables (N= 12,488)

Variable Description Mean St Dev

Price Asking price (€) 223,123.11 145,408.15

Ln_Price Natural logarithm of the asking price 12.17 0.533

Structural Attributes

Bedrooms Number of bedrooms 2.393 1.068

House 1 if house 0.027 0.161

Floor Floor number 2.952 2.431

Area Area (sq. meters) 103.789 59.253

Age1 1 if Property age <= 10 years 0.351 0.477

Age2 1 if 10 years < Property age < 50 years 0.327 0.469

Age3 1 if Property age >= 50 years 0.322 0.467

Garage 1 if garage spaces >=1 0.470 0.499

Neighbourhood Attributes

Educational Index Number of undergraduate persons/Population over 20

years old (500 meters radius) 0.197 0.129

Entropy Index

Entropy Index within a walking distance of 500 meters

∑=

⋅=

k

i

ii

k

ppEI

1

500)ln(

)ln((69, 70)

0.220 0.103

Local Accessibility Attributes

Metro 2MAccess10 1 if walk time to access 2 metro lines <=10 minutes 0.048 0.214

2MAccess = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.058 0.163

1MAccess10 1 if walk time to access 1 metro lines <=10 minutes 0.265 0.441

1MAccess = 1/(1+exp(6.812 -0.659 *(17 –walking time))) 0.123 0.240

Road

Network1_1000 1 if distance to Network1 <=1000 meters 0.425 0.494

Network1 = 1/(1+exp(8.789 -0.007 *(2000 –access distance))) 0.266 0.351





Rail

Azambuja10 1 if walk time to Azambuja train station < 10 minutes and

less than 20% of the distance to CBD 0.006 0.078

Azambuja = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.005 0.057

Lisboa10 1 if walk time to Lisbon train station < 10 minutes and less

than 10% of the distance to the CBD 0.014 0.119

Lisboa = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.014 0.094

Nacional10 1 if walk time to Nacional train station < 10 minutes 0.013 0.114

Nacional = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.010 0.082

Sintra10 1 if walk time to Sintra train station < 10 minutes and less

than 20% of the distance to CBD 0.028 0.164

Sintra = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.029 0.131

Fertagus10 1 if walk time to Fertagus train station < 10 minutes and


Fertagus = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.001 0.033

Cascais10 1 if walk time to Cascais train station < 10 minutes and


Cascais = 1/(1+exp(4.394 -0.439 *(20 –walking time))) 0.011 0.078


Variable Description Mean St Dev

Systemwide Accessibility Attributes

Gravitational_PT

Gravitational model accessibility index with β calibrated

for public transport

0.708 0.058

Gravitational_PC

Gravitational model accessibility index with β calibrated

for private car

0.493 0.084

MODELING METHODOLOGY

Six different cross-sectional models were developed in this study. We used three

different specifications for the accessibility effect (local accessibility all-or-nothing, local

accessibility continuous and systemwide accessibility) and two modelling approaches

(ordinary least squares regression model (OLS) and spatial lag regression model). Both

present a semi logarithmic hedonic specification that is widely used in the property value

literature motivated by the fact that it usually produces robust estimates and enables

convenient coefficient interpretation. The general structure of the OLS model is:

iinniii XXXPLn εββββ +++++= '

2

'

21

'

10 ...)(

),0(~ 2IN σε

(4)

where iP is the price of house i , ini XX ...1 are the vectors of the explanatory variables for the

price of house i . The dependent variable is given in the natural logarithmic form; thus the

values of the coefficients represent percentage change. The specifications used for the OLS

models (for each type of accessibility indices) are given by:

ii

'

CSi

'

SNi

'

Ni

'

N

i

'

Ni

'

MAi

'

MA

i

'

EIi

'

LIi

'

GRi

'

AG

i

'

AGi

'

ARi

'

FLi

'

HSi

'

BDi

εCascais10βSintra10β_Networkβ_Networkβ

_NetworkβMAccessβMAccessβ

exEntropyIndβndexEducationIβGarageβAgeβ

AgeβAreaβFloorβHouseβBeedroomsβα)Ln(P

++++

+++

++++

++++++=

25035002

10001101102

3

2

32

112

3

2

(5)

iiCSiSNiNiN

iNiMAiMA

iEIiLIiGRiAG

iAGiARiFLiHSiBDi

CascaisSintraNetworkNetwork

NetworkMAccessMAccess

exEntropyIndndexEducationIGarageAge

AgeAreaFloorHouseBeedroomsPLn

εββββ

βββ

ββββ

βββββα

++++

+++

++++

++++++=

'''

3

'

2

'

1

'

1

'

2

''''

3

'

2

''''

32

112

3

2)(

(6)

iiPCiPT

iEIiLIiGRiAG

iAGiARiFLiHSiBDi

PCnalGravitatioPTnalGravitatio

exEntropyIndndexEducationIGarageAge

AgeAreaFloorHouseBeedroomsPLn

εββ

ββββ

βββββα

++

++++

++++++=

__

3

2)(

''

''''

3

'

2

''''

(7)


The spatial lag models general structure is presented in Equation 8.

iinniiPLni XXXWPLni

εββββρ ++++++= '

2

'

21

'

1

'

0)( ...)(

),0(~ 2IN σε

(8)

where iP is the price of house i , ini XX ...1 are the vectors of the explanatory variables for the

price of house i , ρ is the autoregressive coefficient and )( iPLnW the spatial lagged variable in

order to a NN × spatial weight matrix.

The specifications used for the spatial lag models are given by:

iiCSiSNiNiN

iNiMAiMA

iEIiLIiGRiAGiAG

iARiFLiHSiBDPLni

Cascais10Sintra10NetworkNetwork


exEntropyIndndexEducationIGarageAgeAge

AreaFloorHouseBeedroomsWPLni

εββββ

βββ

βββββ

ββββαρ

++++

+++

+++++

++++++=

'''

3

'

2

'

1

'

1

'

2

''''

3

'

2

''''

)(

250_3500_2

1000_1101102

32

)(

(9)

iiCSiSNiNiN

iNiMAiMA

iEIiLIiGRiAGiAG

iARiFLiHSiBDPLni

CascaisSintraNetworkNetwork




εββββ

βββ

βββββ

ββββαρ

++++

+++

+++++

++++++=

'''

3

'

2

'

1

'

1

'

2

''''

3

'

2

''''

)(

32

112

32

)(

(10)

iiPCiPT

iEIiLIiGRiAGiAG

iARiFLiHSiBDPLni

PCnalGravitatioPTnalGravitatio



εββ

βββββ

ββββαρ

++

+++++

++++++=

__

32

)(

''

''''

3

'

2

''''

)(

(11)

The spatial weight matrix for both spatial lag models was developed assuming

constant spatial dependence between properties until a maximum established distance. The

maximum Euclidean distance used was 1000 m, resulting in a Moran’s I = 0.144 (P-value =

0.000)

MODELING RESULTS AND DISCUSSION

Estimation results from the six different models are presented in Table 4. Using the

Pseudo R2 as goodness-of-fit measure (squared correlation between the predicted and the

observed values of the dependent variable), we can observe a high explanation of the

dependent variable with values ranging from 0.75 and 0.80.

Langrange multiplier (LM) tests were also conducted to assess if the omission of the

spatial lag on the OLS model was erroneous (i.e 0:0 =ρH ). The LM test statistic is given

by (35):


)'(/)'(

1'222

WWWtrMWXbWXb

WyeLM

++⋅

=σσ

(12)

where

=M common residual maker vector )( NN × in OLS estimation,

=e spherical OLS residual vector )1( ×N ,

Nee /'2 =σ ,

=b OLS coefficient vector )1( ×K ,

=tr trace of the matrix )( NN × .

The LM test is approximately 2χ distributed with one degree of freedom. As

presented in Table 4, the LM test for the local accessibility spatial lag models is significant

at 01.0=α , indicating a proper spatial lag specification, whilst the systemwide accessibility

spatial lag model is not significant, indicating that the OLS model in this case is more

appropriate.

Almost all the independent variables used in the six models are significant at

05.0=α (Sintra being the exception for both local accessibility spatial lag models),

independently of the estimation model and of the proxy accessibility variables used. In

addition, the models consistently demonstrate the impact of each independent variable on the

natural logarithm of the asking price and a similar magnitude of the coefficients of the

structural attributes along the different models.

The coefficient estimation for the structural attributes shows that the Area (floor

surface) is the attribute with greater impact in the dependent variable (approximately 0.07%

increase for a 1% square meter increase), followed by the Age and Bedrooms, which present

similar values in all the models.

The neighbourhood attributes are the ones that present higher coefficient variation

among the estimated models. As expected, the spatial lag term “replaces” some of the

explanatory power of the neighbourhood variables and of the constant of the model, although

it does not significantly affect the coefficients of the accessibility attributes, as can be seen in

the local accessibility models in Table 4.

This fact shows the stability of the local accessibility coefficients in the all-or-nothing

approach, the significance of the Sintra attribute being the only one affected. The metro

accessibility attributes coefficients range in the two models between 5.65% and 6.50% for

the accessibility to two metro lines and between 4.25% and 4.28% for the accessibility to a

single metro line, showing a significant impact of the metro proximity over the property

values.

The local accessibility coefficients in the continuous approach present less stability.

The metro accessibility attributes coefficients range in the two models between 7.28% and

11.27% for the accessibility to two metro lines and between 4.06% and 5.44% for the

accessibility to a single metro line, showing again a significant impact of the metro proximity

over the property values.

The rail accessibility attributes coefficients in all the local accessibility models

illustrate a positive impact for the proximity to the Cascais Line with coefficients ranging


between 8.40% and 10.55% for the all-or-nothing accessibility measure approach and

between 10.16% and 18.83% for the continuous approach; and a negative impact for the

proximity to the Sintra Line with coefficients ranging between -4.45% and -1.06% (not

significant for the usual significance levels) for the all all-or-nothing approach and between

-10.16% and 1.04% (not significant for the usual significance levels) for the continuous

approach.

These effects might be explained by the perception of lack of security associated with

the Sintra Line, which prevents the properties of the nearby areas to take full advantage of

the proximity to this high capacity public transport system and the proximity of the Cascais

Line to a very expensive residential area in the Southeast area of Lisbon (Restelo

neighbourhood).

The road accessibility attributes coefficients range in the two models of the all-or-

nothing approach between -9.53% and -10.39% for the accessibility road hierarchy 1,

between 5.89% and 7.16% for the accessibility to road hierarchy 2 and between -3.77% and -

5.90% for the accessibility to the road hierarchy 3.

The road accessibility attributes presents similar impact for the two models of

continuous approach with coefficients ranging between -14.13% and -7.98% for the

accessibility road hierarchy 1, between 1.33% and 9.64% for the accessibility to road

hierarchy 2 and between -1.19% and -4.34% for the accessibility to the road hierarchy 3.

These estimations show that the proximity to the road hierarchy 2 (urban ring roads

and radial network) is the one that presents a positive impact on the property values, whilst

the proximity to the road hierarchy 3 (urban distribution network) and road hierarchy 1

(motorways) present a negative impact.

These results can derive from the congestion and noise externalities perceived by the

population near road hierarchy 1 and the switch to offices centres of buildings located near

road hierarchy 3 in the Lisbon’s city centre, reducing the residential supply in these areas.

The results of the local accessibility continuous spatial lag model show the existence

of a smaller impact of road network attributes on the dependent variable what might be

explained by the significant increase of the SP Lag coefficient of this model in comparison to

the all-or-nothing approach spatial lag model.

We cannot compare directly the coefficients resulting from the local accessibility

continuous models with the all-or nothing accessibility measure models due to fact that the

continuous indicators present continuous values between 0 and 1, not being possible to

derive a percentage of change directly from the coefficient. The percentage of change on the

property selling prices will result from the product between the value of the accessibility

indicator and the coefficient of the same indicator, resulting in a property selling prices

impact distribution rather than a single value.

The systemwide accessibility OLS model presents significant differences in

coefficients of the constant and neighbourhood attributes when compared with the local

accessibility OLS model. This might be due to the significant correlation of the systemwide

accessibility attributes with the neighbourhood attributes (i.e. correlation between

Gravitational_TC and Entropy Index is equal to 0.465 and to 0.235 with the Educational

Index), which can explain the reduction of the neighbourhood attributes coefficients.

This correlation results from the fact that the systemwide accessibility indicators

measure accessibility to activities scattered in the study area, which can be positively


influenced by the presence of a high land use mixture around the property measured by the

Entropy Index.

We can see easily in Figure 4 that the Gravitational_TC indicator measures

simultaneously public transport accessibility and land use activity proximity and their

relation. This fact illustrates that is difficult to isolate with the systemwide accessibility

indicators the changes in property values derived from transportation infrastructures

investment from the neighbourhood land use characteristics.

FIGURE 4 Gravitational_TC indicator spatial distribution for the study area

The systemwide accessibility spatial lag model illustrates also the last statement due

to the non significance of the spatial lag model (see Table 4), because the systemwide

accessibility indicators can already explain part of the spatial dependence of the property

asking price.

Using the Akaike info criterion to rank the models, we can consider the local

accessibility continuous spatial lag model as the best prediction model followed by the

systemwide accessibility OLS model and the local accessibility all-or-nothing spatial lag

model.

We can thus conclude, from the estimates of the developed models, that the local

accessibility models can measure better the isolated effect of transportation investment on


properties selling prices and that the estimates from the OLS models can be sufficiently

accurate in the absence of significant property values database for all the study area (needed

for the calculation of the spatial lag model).

The spatial distribution of the property prices estimates of the local accessibility

continuous spatial lag model is presented in Figure 5 where we can see a similar spatial

distribution to the database asking prices (see Figure 1). Figure 6 present the spatial

distribution of the estimated residuals, where we can denote sub-estimates and over-estimates

scattered along all the study area with some sub-estimates concentrated in the North part of

the study area and in the Expo area in the Northeast Lisbon’s border.

FIGURE 5 Property prices estimates of the local accessibility continuous spatial lag model


FIGURE 6 Unstandardized residuals of the local accessibility continuous spatial lag model


TABLE 4 Results of the OLS and Spatial Lag Models

Ordinary Least Squares (OLS) ML Spatial Lag

Local Accessibility

all-or-nothing Model

Local Accessibility

continuous Model

Systemwide

Accessibility Model

Local Accessibility

all-or-nothing Model

Local Accessibility

continuous Model

Systemwide

Accessibility Model

Coef. Std.

Error Coef.

Std.

Error Coef.

Std.

Error Coef.

Std.

Error Coef.

Std.

Error Coef.

Std.

Error

SP_LAG_LOGPRICE -- -- -- -- -- -- 0.4043 *** 0.0121 0.6264 ***

0.0085 0.2968 *** 0.0143

Constant 11.1146 *** 0.0099 11.1073 *** 0.0104 10.4106 *** 0.0369 6.2840 ***

0.1446 3.6777 *** 0.1010 6.8770 ***

0.1743

Structural attributes Bedrooms 0.0357 *** 0.0033 0.0600 ***

0.0032 0.0380 *** 0.0033 0.0396 ***

0.0032 0.0690 *** 0.0029 0.0410 ***

0.0032

House 0.2037 *** 0.0169 0.0838 *** 0.0165 0.2038 ***

0.0166 0.1738 *** 0.0162 0.0818 ***

0.0148 0.2017 *** 0.0163

Floor 0.0156 *** 0.0010 0.0172 *** 0.0010 0.0184 ***

0.0010 0.0169 *** 0.0010 0.0168 ***

0.0009 0.0183 *** 0.0010

Area 0.0069 *** 0.0000 0.0059 *** 0.0000 0.0070 ***

0.0000 0.0067 *** 0.0000 0.0056 ***

0.0000 0.0069 *** 0.0000

Age2 -0.1291 *** 0.0069 -0.1426 *** 0.0070 -0.1415 ***

0.0067 -0.1203 *** 0.0067 -0.1172 ***

0.0063 -0.1360 *** 0.0066

Age3 -0.0851 *** 0.0073 -0.0963 *** 0.0075 -0.0957 ***

0.0072 -0.0820 *** 0.0071 -0.0895 ***

0.0067 -0.0921 *** 0.0071

Garage 0.1205 *** 0.0064 0.1271 *** 0.0066 0.1279 ***

0.0063 0.1189 *** 0.0062 0.1235 ***

0.0059 0.1235 *** 0.0062

Neighbourhood attributes Educational Index 0.9811 *** 0.0202 1.0407 ***

0.0209 0.7638 *** 0.0193 0.7131 ***

0.0219 0.1972 *** 0.0220 0.5932 ***

0.0219

Entropy Index 0.5430 *** 0.0346 0.4231 *** 0.0257 0.2959 ***

0.0347 0.2466 *** 0.0340 0.2422 ***

0.0231 0.2014 *** 0.0344

Local Accessibility Attributes (all-or-nothing and continuous approach) 2MAccess10 (2MAccess) 0.0565 *** 0.0130 0.1127 ***

0.0171 -- -- 0.0650 *** 0.0126 0.0728 ***

0.0154 -- --

1Maccess10 (1MAccess) 0.0425 *** 0.0061 0.0406 *** 0.0114 -- -- 0.0428 ***

0.0059 0.0549 *** 0.0103 -- --

Network1_1000 (Network1) -0.0953 *** 0.0051 -0.1413 *** 0.0074 -- -- -0.1039 ***

0.0049 -0.0798 *** 0.0067 -- --

Network2_500 (Network2) 0.0716 *** 0.0048 0.0964 *** 0.0070 -- -- 0.0589 ***

0.0047 0.0133 ** 0.0063 -- --

Network3_250 (Network3) -0.0590 *** 0.0048 -0.0434 *** 0.0066 -- -- -0.0377 ***

0.0046 -0.0119 ** 0.0060 -- --

Sintra10 (Sintra) -0.0445 *** 0.0151 -0.1057 *** 0.0180 -- -- -0.0106 0.0146 0.0104 0.0162 -- --

Cascais10 (Cascais) 0.1055 *** 0.0245 0.1883 *** 0.0306 -- -- 0.0840 ***

0.0237 0.1016 *** 0.0274 -- --

Systemwide Accessibility Attributes

Gravitational_PT -- -- -- -- 0.4674 *** 0.0774 -- -- -- -- 0.6923 *** 0.0769

Gravitational_PC -- -- -- -- 0.8084 *** 0.0546 -- -- -- -- 0.4304 *** 0.0561

Pseudo R2 0.753 0.753 0.764 0.773 0.801 0.772

LM statistic 214.670 *** 2019.366 *** -203.435

Log likelihood -656.08 -886.031 -373.817 -426.742 123.652 -475.535

Akaike info criterion 1344.16 1806.06 771.635 889.483 -211.304 977.07

***, **

, and *

denote coefficient significantly different from zero at the 1%, 5%, and 10% level of significance (two-tailed test), respectively.


SUMMARY AND CONCLUSIONS

This paper analyses the effect of transportation accessibility on the properties prices

as part of a broader study that intends to develop new value capture financing scheme for

public transportation in the LMA. Several cross-sectional hedonic price models are estimated

based on an online realtor’s database (Imokapa Vector) of property selling asking price. The

models account for structural, neighbourhood and accessibility attributes of residential

properties, the latest ones structured in two types: local accessibility attributes and

systemwide accessibility attributes.

The main focus of this study is to develop a framework to forecast house prices and

the influence of transportation infrastructure investment in further steps of the research

project.

The estimated models revealed that:

• The local accessibility hedonic price models developed showed the existence of

spatial interactions of sale prices, presenting a spatial autocorrelation with a

significant spatial lag.

• The local accessibility models present a stability of the local accessibility

coefficients estimated, the significance of the Sintra attribute being the only one

affected.

• The metro accessibility attributes coefficients range in the two all-or-nothing

models between 5.65% and 6.50% for the accessibility to two metro lines and

between 4.25% and 4.28% for the accessibility to a single metro line, showing a

significant impact of the metro proximity over the property values.

• The metro accessibility attributes coefficients range in the two continuous models

between 7.28% and 11.27% for the accessibility to two metro lines and between

4.06% and 5.49% for the accessibility to a single metro line.

• The rail accessibility attributes coefficients illustrate a positive impact for the

proximity to the Cascais Line with coefficients ranging between 8.40% and 10.55%,

and a negative impact for the proximity to the Sintra Line with coefficients ranging

between -4.45% and -1.06% (not significant for the usual significance levels) for the

all-or-nothing accessibility measure models.

• The rail accessibility attributes coefficients for the continuous accessibility

measure models present a positive impact for the proximity to the Cascais Line with

coefficients ranging between 10.16% and 18.83%, and a negative impact for the

proximity to the Sintra Line with coefficients ranging between -10.57% and 1.04%

(not significant for the usual significance levels).

• The road accessibility attributes coefficients range in the two all-or-nothing

accessibility measure models between -9.53% and -10.39% for the accessibility road

hierarchy 1, between 5.89% and 7.16% for the accessibility to road hierarchy 2 and

between -3.77% and -5.90% for the accessibility to the road hierarchy 3.

• The road accessibility attributes coefficients range in the continuous accessibility

measure models between -7.98% and -14.13% for the accessibility road hierarchy 1,

between 1.33% and 9.64% for the accessibility to road hierarchy 2 and between -

1.19% and -4.34% for the accessibility to the road hierarchy 3.


• The systemwide accessibility models presents significant differences in

coefficients of the constant and neighbourhood attributes when compared with the

local accessibility models. This indicates the difficulty the isolate the accessibility

effects from the neighbourhood effects over house prices with these models.

• The systemwide accessibility spatial lag model developed is not significant,

which indicates that the systemwide accessibility indicators do also explain also part

of the spatial autocorrelation.

The coefficients resulting from the local accessibility continuous models cannot be

compared directly with the coefficients from the all-or nothing accessibility measure models

due to fact that the continuous indicators present continuous values between 0 and 1, not

being possible to derive a percentage of change directly from the coefficient. The percentage

of change on the property selling prices will result from the product between the value of the

accessibility indicator and the coefficient of the same indicator, resulting in a property selling

prices impact distribution rather than a single value.

Using the Akaike info criterion to rank the models, we can consider the local

accessibility continuous spatial lag model as the best prediction model followed by the

systemwide accessibility OLS model and the local accessibility all-or-nothing spatial lag

model.

The main conclusions that can be drawn from the estimates of the developed models are that

the local accessibility models can better measure the isolated effect of transportation

investment on properties selling prices and that the estimates from the OLS model can be

sufficiently accurate in the absence of a significant property values database for all the study

area (needed for the calculation of the spatial lag model).

ACKNOWLEDGMENTS

This research is being supported by the Portuguese National Science Foundation

(FCT) since 2006. The private company Imokapa Vector has provided support by making

available an online realtor’s database. TIS.pt has also provided support by making available

the LMA Mobility Survey from 2004, and to the software company INTERGRAPH for the

Geomedia Professional 5.2 license.

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