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Electronic copy available at: https://ssrn.com/abstract=3028183
The Effect of Green Retrofitting on US Office
Properties: An Investment Perspective.
David Geltnera, Lucas Moserb, Alex van de Minnea
aMIT Center for Real Estate, 105 Massachusetts Ave, Cambridge, MA 02139.bDiener Syz Real Estate AG, Asylstrasse 77, 8032 Zurich, Switzerland.
Abstract
Buildings are responsible for over one-third of all resource consumption,greenhouse gas emissions, and energy consumption. Commercial buildingsrepresent approximately half of that total. In mature economies such as theUnited States, new construction annually represents only a small fraction ofthe existing stock of buildings. Hence, retrofitting of commercial buildings,through major renovation projects, is extremely important for sustainabilitywithin the built environment. Most studies of green building economics havefocused on new construction. This paper is one of the first to focus specificallyon retrofit green. Based on a larger sample than most previous studies ofnew construction, we quantify the magnitude of value enhancement createdby green retrofit of US office buildings. This paper is also the first to con-sider the subsequent investment price dynamics effects of such sustainability.Methodologically, we introduce an innovative way to control for other effectsto isolate the value impact and the investment risk and return impact ofgreen retrofitting. We do this by applying a repeat-sales model of only (andall) buildings which will ultimately be retrofitted (in our sample). By usingnew real estate price indexing methodology, namely a structural time seriesmodel employing a hierarchical repeat-sales (HRS) specification, we can buildstatistically rigorous comparative price indexes of retrofit green, versus non-green, office buildings in the US, quarterly for the 2005-2014 period, evenwith relatively scarce transaction price data (441 pairs). We find substantialvalue enhancement in green retrofit projects (between +10% and +20%),and we find evidence that retrofitted green buildings provide investors withlower asset price volatility. But during and just after the Financial Crisis thepremium dropped temporarily to near zero, suggesting that the demand forgreen property investment is income-elastic.
Electronic copy available at: https://ssrn.com/abstract=3028183
Keywords: commercial real estate , sustainability , retrofit construction ,structural time series , green buildings.JEL-codes: R3 , C32 , Q5.
1. Introduction and Background
The construction industry, buildings and their behavior have significanteconomic, environmental and social impact. Globally, the construction andoperation of buildings consumes about 40% of all energy, 25% of water, and40% of other resources. Buildings account for approximately one third ofglobal greenhouse gas emissions (United States Forum for Sustainable andResponsible Investment, 2014). The design and operation of so-called ‘green’buildings has therefore been gaining increasing attention, gradually, since thenotion of sustainability was first introduced by Brundlandt (1987).
Government and industry have embraced this development, and manygreen building certification and consulting agencies have made it their goal toimplement sustainable thinking with property developers, owners and occu-pants by setting up transparent and standardized evaluation systems. Manyof these agencies are private and non-profit organizations. The currentlymost widely used evaluation systems are certifications supplied by variousagencies around the world, such as the US Green Building Council’s (US-GBC) Leadership in Energy and Environmental Design (LEED) system inthe United States, and the Building Research Establishment Environmen-tal Assessment Method (BREEAM) in the United Kingdom (Zuo and Zhao,2014). The popularity of building ‘green’ is also reflected in the number ofproperties with such a green label. For example, in 2009 less than 0.5% ofall properties in the 30 largest US cities had a LEED certificate. In 2014 thispercentage ballooned to 3.5% (though still obviously a small fraction).
It is therefore not surprising that a large literature has grown in greenbuilding research. Most of the research has focused on the physical engi-neering, architectural and design questions. But more recently, academicshave undertaken significant research on the economics of green buildings, in-cluding questions of interest to real estate investors. For the most part, thisliterature suggests favorable economics for green building construction, evenwithout counting government subsidies. A summary of some major recentpapers quantifying asset value impacts, as well as the effect of certificationon occupancy rates, is given in Table 5, which will be discussed in Section
2
Electronic copy available at: https://ssrn.com/abstract=3028183
4.1. Green labeling of office properties appears to result in a value premiumin the range of 8-25% for new construction. This reflects the fact that greenbuildings reduce operating costs, and they tend to achieve higher rents andoccupancies. They also may tend to fetch higher asset prices per dollar ofcurrent net income (lower yields). Such findings reflect the demand for greenbuildings on the part of both tenants (occupants) in the space market andlandlords (investors in the asset market).
The previous literature on green building economics focuses on new con-struction, the development of new buildings. But in mature economies likethat of the United States, the vast bulk of buildings that will exist in the nearto intermediate future already exist, and in fact most of them are already intheir ‘middle age’. The Commercial Building Energy Consumption Survey(CBECS) of the US Energy Information Administration (EIA) finds that asof 2012, 85% of US commercial buildings (by floor area) were over 10 yearsold, and 50% were over 30 years old.1 Binkley (2007) suggests that 75% ofthose 30-year-old buildings today will still be standing in 40 years. Bokhariand Geltner (2017) find that the average commercial building in the US lasts100 years.
Therefore, it is clear that major progress in ‘greening’ the building stock inmature economies like that of the US will require ‘retrofitting’, that is, capitalexpenditures to produce renovations and upgrades of the building systems.Recognizing the importance and ubiquity of this type of green construction,as well as the physical and economic difference between new constructionversus renovation, there is a separate LEED certification process specificallyfor retrofits (or renovation projects). This is the type of green constructionthat we focus on in the present paper. This paper is among the first researchto focus on the economics specifically of retrofit green construction.2
We find evidence that green retrofitting is normally associated with abouta 10% to 20% higher property valuation, within our sample of almost 350US office properties (though this premium temporarily disappeared duringthe Financial Crisis).
It is also important to recognize that, particularly in mature economieslike the US, many commercial buildings are owned as investments. They areevaluated as such, and decisions about the management and operation of
1For more information see the webite of the EIA.2Binkley (2007) is an exception.
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these buildings are taken from an investment perspective. Fundamental tothis perspective is the nature of the dynamics that govern the future risk andreturn of the property asset as an investment. Yet no one has yet examinedthe price dynamics of green buildings compared to otherwise similar non-green buildings. Does green retrofitting make a building less, or more, risky(in terms of price volatility)? We therefore focus on modeling the pricedynamics, including historical risk and return behavior, of our sample ofgreen retrofit office buildings in the US, in direct comparison with a controlgroup of not-yet-green buildings.
This type of comparison is only possible with recent advances in com-mercial property transaction price data availability, and innovations in theeconometrics of real estate price indexing. We use the Real Capital Analyt-ics (RCA) database of US commercial property transactions, which collectsnearly all sales of properties over $2,500,000.
Even with such data improvements, we still face the classical challenge ofreal estate valuation, especially for commercial properties: scarce data, theheterogeneity of commercial real estate, and the potential for omitted variablebias. This is of particular concern in analysis of green buildings, which area new and still small phenomenon overall. And green properties are a veryspecific subset of properties within the commercial property market; Greenproperties tend to be large (both in dollar value and in physical size), highquality structures in relatively central locations. These properties are in theirown market. Indeed, Geltner and van de Minne (2017) showed that high pricepoint properties exhibit unique price dynamics, yielding less (total) returncompared to the overall market, and more risk. Therefore, one cannot simplyestimate a price index based on green property values and compare it withan overall market index such as the widely cited NCREIF Property Index.
To address this, we introduce a novel approach, in which we analyze onlyproperties that ultimately (by the end of the sample period) obtained a greenretrofit certification. Thus, all the properties in our sample are effectively inthe same type of market for our purposes, in that they all ultimately weregreen retrofitted. We estimate a price index for properties that have beenretrofitted green (LEED certificate), and compare it with an index estimatedon the properties that have not yet been (but would subsequently be) greenretrofitted. Also, by estimating the indexes in a repeat sales framework, wecircumvent other omitted variable bias as well.
Although our sample is large in the context of the green commercialbuilding economics literature, it is small in the statistical context of real
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estate price indexing. And the repeat-sales framework, though advantageousfor specification and control, certainly does not help with the small samplechallenge, as it means we can only use properties that sold at least twice.After merging transaction price data from RCA with LEED certification datafrom the USGBC, we end up with less than 450 pairs of office propertiesbetween 2000 and 2015 (345 different properties, as some sold more thantwice).
To address the small sample problem, we develop a variant of the Hierar-chical Repeat Sales model introduced by Francke and Van de Minne (2017).We enhance the hierarchical approach by estimating the model in a struc-tural time series framework. This allows the two indexes (green index versusnon-green) to be estimated simultaneously, while informing each other via acommon trend. Both green and non-green indexes are specified as random-walk deviations from that common trend.
We also introduce an time-interacted dummy variable which captures theone-time asset price ‘bump’ associated with the green retrofit. This allowsus to track the value of this green retrofit price premium across our history.
Altogether, this innovative indexing methodology allows the productionof well behaved, statistically rigorous price indexes with small samples ofdata. This is what enables us to develop directly comparable green and‘non-green’ price indexes, quarterly, for the 2005-14 period.
We find that, during that historical period, the retrofit green proper-ties displayed lower price volatility than the non-green buildings. And theprice premium time-dummies reveal that, while normally the green valuepremium is around 15%, during and immediately after the Financial Crisisthe premium virtually disappeared. We tested for retrofit effects on buildingoccupancy as well as price, and found similar results. This suggests that thedemand for ‘green’ is income-elastic, as the Crisis cut into incomes and raisedbasic economic well being fears in building occupants and investors.
Overall, we view our findings as good news for investors who have tojustify capital expenditures on green retrofit renovations. At least in non-recessionary times, such investment costs appear to be at least partially offsetby the value premium generated by going green, as well as by (or partly inrecognition of) a reduction in asset value volatility going forward.
In summary, this paper is the first to more closely examine the value side(though not the cost side) of the economics of green retrofitting of commer-cial buildings, and the price dynamics of green buildings, and we do so byintroducing a new sample control methodology as well as by developing a
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state-of-the-art price indexing econometric methodology. We find interestingsuggestions about income elasticity of demand for green. In addition to itscontribution to the ‘green buildings’ literature, this paper also relates to priceindexing econometrics, especially research on structural time series, such asGoetzmann (1992); Francke (2010); Francke and Van de Minne (2017).
The paper is organized as follows. Section 2 presents the analysis frame-work and modeling approach used in the study, including the basic repeat-sales model and the new variant we have developed specifically for this study.Section 3 documents the data sources and summarizes the statistics of theUSGBC label data as well as the examined repeat-sales sample set. The re-sults of the green property repeat-sales models with respect to price and occu-pancy rates are presented and discussed in Section 4, including an extensivesection on robustness of the results. The paper ends with some concludingremarks in Section 5.
2. Model
In this section we describe our modeling approach, including our strategyfor identifying the effect of green retrofitting controlling for other effects, andour model specification for price indexing in a scarce data environment.
2.1. Identification Strategy
As noted earlier, green properties tend to be a very specific subset ofall commercial properties. The value of green properties are on the highend of the price spectrum. These are high quality assets in top locations.There can be significant differences in the price dynamics of commercial realestate across regions and sectors (property usage types Francke and Van deMinne, 2017), and across price points (Geltner and van de Minne, 2017),due to differences in both the space and asset markets (Geltner et al., 2014).Therefore, estimating an index for green buildings and comparing it to a‘normal’ or general price index for commercial real estate is likely to givespurious results.3
3Eichholtz et al. (2013); Chegut et al. (2014), among others, use propensity scoreweighting to try to minimize the selection bias between certified and non-certified build-ings, by differentiating based on individual building characteristics. But the effectivenessof this approach in many commercial property data samples is questionable, including inour data, because of the scarcity of hedonic data. Commercial real estate is notoriously
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The approach we propose to deal with this problem is to consider onlyproperties that will, by the end of our sample period, obtain a green certifi-cate. Thus, all of our properties are in market segments that are amenableto ‘greening’. Furthermore, we estimate repeat-sales price indexes based onthat subset of properties. Repeat-sales price indexes are based only on theprice change within the same properties between the buy and the sell. Thismethodology should substantially accomplish two things: control for spuri-ous differences between green and non-green properties; and minimize thepotential for model misspecification or omitted variables problems in thehedonic data, so as to avoid the ‘apples vs oranges’ problem in comparingprices of different properties across time (the classic real estate price indexconstruction problem).
Within this framework, we have three types of repeat sales observations:(1) properties that were bought and sold without a label (‘non-green’ proper-ties in our index, getting their certificate only after their last sale); (2) prop-erties that were bought without a label, but sold with a label (the retrofits,which will exhibit the one-time conversion green price premium, the associ-ated non-temporal price ‘bump’); and (3) properties that were both boughtand sold with a green label (these will purely demonstrate the retrofit greenprice dynamics characterizing properties once they are already green).
To formalize our price modeling approach, let’s begin with the classichedonic price model with time dummy variables (pooled regression), as givenby
pit = µt +Xitβ + Zitγ + εit, (1)
where: pit is the log transaction price of property i, i = 1, . . . , nt at time t,t = 1, . . . , T ; Xit are observed characteristics and Zit are unobserved charac-
heterogeneous (Francke and Van de Minne, 2017), and hedonic models for such real estatetend to be very noisy. (Previous studies do not report the RMSE of their models.) Ofcourse, the earlier papers do not focus on the price dynamics, but rather only on the greenpremium via cross-sectional analysis. Our interest in price dynamics adds to the challenge.Also, propensity scoring still results in biases if the markets are not integrated, as we as-sume. (Propensity scoring only makes the estimates more efficient.) Another approach islaid down by Reichardt et al. (2012), who essentially use a hedonic ‘diff-in-diff’ strategy toidentify the effect of ‘greenness’ on rents. Their control group are properties in the samemarket. Our point that this is not sufficient to control for the differences between greenand non-green properties (both due to market segmentation and omitted variable biases)still remains here.
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teristics with corresponding hedonic coefficients (‘shadow prices’) β and γ;µt is a time varying constant capturing the log price movement in the marketvalue over time (the subject of a relevant price index); and the error termεit is typically assumed to be independently normally distributed with meanzero and variance σ2
ε . Note that, apart from the µt term, the elements onthe RHS of (1) represent essentially cross-sectional and idiosyncratic compo-nents of price dispersion in the transaction sample. Thus, µt represents thelongitudinal dispersion in the aggregate market value.
The classical repeat-sales model is then derived by differencing (1) acrossthe first (‘buy’) and second (‘sell’) transactions within the same properties.Under the assumption that the ‘quantities’ of both the observed and unob-served hedonic attributes X and Z do not change over time, this gives, forpairs of sales of the same properties:
rits = pit − pis = µt − µs + εit − εis, (2)
where s represents the time of buy (as opposed to the time of resale t, withs < t), and µ1 = 0 for identification. Please note that we follow the broadstrand of literature by assuming pair fixed effects, instead of a property fixedeffects, leading to zero correlation between pairs of sales of the same property.Finally, in subsequent sections of the paper, we will relax the assumption that∆Xβ = 0, when we include the change in ”greenness” of the property.4
2.2. Structural Time Series Repeat Sales model
Though the repeat-sales model has the above noted advantages in ourcontext, a disadvantage is that only properties sold more than once are in-cluded in the analysis sample: all single-sales are left out. When databasesmature, this is not so much of an issue, as practically all properties will
4It should also be noted that, for purposes of constructing a property price index,the assumption of constant hedonic attribute quantities can be ignored so long as it isunderstood that the resulting price index will combine the effect of pure constant-quantityprice change with the effect of changes in hedonic quantities and shadow prices. Thisis not generally a problem if the resulting ‘value’ (price X quantity) index is meant tosimply reflect the price change ‘experienced’ directly by investors in the properties thatwere bought and sold (that is, simply the difference between the price they sold theirproperty for compared to the price they bought it for). For example, the index reflectsthe effect of both capital improvement expenditures and capital depreciation on the valueof the properties
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eventually get sold more than once. However, certification of green proper-ties is a relative recent phenomena, thus reducing the number of repeat-salesobservations.
Traditionally, the specification of the aggregate time effect in Eq.(2), theµt−µs, is simply a dummy variable approach with fixed parameters µt. Con-ditional on the pair fixed effect, the expectation of the estimate of µt reflectsthe average selling price purely at time t. This means that the estimate ofµt does not depend on preceding and subsequent periods. However, the esti-mate of µt is sensitive to transaction price dispersion (‘noise’), in particular insmall samples when the number of transactions per period is low. This hap-pens, for example, with local price indexes (‘granularity’), short time periods(high frequency indexes), and/or in case of severe outliers when the transac-tion price differs from its true market value by a large amount. And it willbe an issue with our green retrofit index. The resulting price indexes maythen display a spurious volatility (with a ‘spikey’ or sawtooth appearanceFrancke, 2010).
To address this issue, Francke (2010) generalized the classic time-dummyformula by assuming that the trend component of the aggregate price followsa local linear trend, and by providing the loglikelihood function `(p̃;σ2
η, σ2ε)
to estimate the signal σ2η (‘true volatility’) and noise σ2
ε (‘error’ dispersion)directly (for example by maximum likelihood). This avoids the somewhatad hoc two-step procedure proposed by Goetzmann (1992). The local lineartrend repeat sales model is given by
rits = µt − µs + εit − εis, εit ∼ N(0, σ2ε), (3)
µt+1 = µt + κt + ηt, ηt ∼ N(0, σ2η), (4)
κt+1 = κt + ζt, ζt ∼ N(0, σ2ζ ). (5)
Note that the local linear trend is very flexible and includes different spec-ifications, like random walk (σ2
ζ = 0 and κ1 = 0), random walk with drift(σ2
ζ = 0) (Goetzmann, 1992), and pure smoothed trend (σ2η = 0) (determin-
istic pricing). (See Harvey, 1989, for a description of the different functionalforms.)5
5Note that the local linear trend can result in an I(2) process. When forecasting isyour main interest, it is not recommended to use such structures. One alternative is theAR process assumed by Francke et al. (2017).
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The local linear trend repeat sales model is an example of a structuraltime series model. In general, a structural time series model is a model inwhich the trend, error terms, and other relevant components are modeledexplicitly. In contrast to the dummy variable approach, the structural timeseries model enables the prediction of the price level based on preceding andsubsequent information. This means that even for particular time periodswhere no observations are available, an estimate of the price level can beprovided. It also means that many fewer parameters need to be estimated,and this can greatly reduce the noise in the resulting price index. In additionto repeat-sales, such ‘State Space’ models have also been successfully usedto estimate hedonic price indexes, see Schwann (1998) and Francke and Vos(2004).
The structural time series model described above could be used to esti-mate a separate index for both green properties (bought green and sold green)and non-green properties (bought non-green and sold non-green), by subdi-viding the data into two sub samples. However, we lose a lot of informationby that approach, for three reasons.
1. There are properties that were bought as a non-green property and soldas a green property (and theoretically vice verse). As these propertiesaren’t purely green or non-green, these ‘in between’ properties wouldbe omitted from both sub samples. Thus, we lose sample data in total,and;
2. There might be some information in the returns of non-green propertiesthat could help us estimate the returns of the green properties and viceverse. Indeed, as all properties in our data are both green and non-greenat some point in time, you would expect some ‘cross-over’ information.
3. We would lose the ability to estimate the retrofit green price premium inthe assets, the ‘bump’ in value that occurs only at the time of conversion(that is, only in the ‘in between’ omitted sample noted above).
With the above in mind, we propose a modified version of the HierarchicalRepeat Sales model (HRS), introduced by Francke and Van de Minne (2017).The main benefit of using a hierarchical structure is that a larger aggregatesample of transactions can be used to estimate indexes, enabling applicationat a more granular level. More information is used, more effectively.
In their application, Francke and Van de Minne (2017) estimate indexesfor single family houses in Amsterdam at a 4-digit ZIP code level of granular-ity. The ZIP code specific indexes are specified as deviations from a common
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index underlying all the ZIP codes combined.It is quite easy to see how we could use such a framework: we have
two sub-indexes, green and non-green, which may be specified as deviationsfrom a common underlying aggregate index (green and non-green combined).However, we need to add two components to the model.
First, note that our indexes are overlapping. In contrast to regional/sectorindexes - which are non-overlapping - a property can be both green and non-green in our data, at different points in time, due to retrofitting. Our modelneeds to be able to account for that. We do this by ‘tricking’ the model tothink that the property got sold as a ‘non-green property’ in period τ1, andsubsequently got bought as a ‘green property’ in period τ2, with τ2 ≥ τ1.(We will discuss the exact timing of τ1 and τ2 later in Section 3.) Thus forthe ‘in-between’, retrofit properties we get four important points in time; (1)the time of buy when it is non-green (t), (2) time of sell when it is non-green(τ1), (3) time when the property gets bought as green (τ2) and finally, (4)when the property gets sold as green (s). Note that we only observe thetransaction prices at t and s.
Second, there is a non-temporal return component associated with thechange in status from not-green to green, the asset ‘price premium’ associatedwith the green retrofit. This non temporal return should be captured by adummy variable which equals 1 if the property was converted from non-greento green between the first and second sales (within the repeat-sale observationpair), and 0 otherwise.6
Our Hierarchical Repeat Sales model is now given by;
λjt+1 =λjt + ςt, ςt ∼N(0, σ2ς ), (6)
rits =µt − µs + λGreent − λGreen
τ2+ (7)
λnGreenτ1
− λnGreens + ωi + εit − εis, εit ∼N(0, σ2
ε). (8)
The vectors of sub-index specific trends λjt , j = {Green,Non-green} are mod-eled as (random walk) deviations from the common trend µt. To get the indexfor green properties we add the sub-index specific trend for green propertiestogether with the common trend: µt + λGreen
t . The non-green index is simi-larly: µt+λ
nGreent . The common trend is specified as a local linear trend model
6We will enhance this dummy variable later by interacting it with time, to see how thegreen price premium varied across our history.
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as given in Eqs. (4)–(5). We impose µ1 = λj1 = 0 for identification. Finally,dummy ω ‘catches’ the price premium, the non-temporal return associatedwith the change from non-green to green. Some details on the estimation ofthe model, and the software we used, are given in Appendix C.
The expected return (r̄) of retrofits therefore consists of three compo-nents;
r̄st = r̄Greent,τ2
+ r̄nGreenτ1,s
+ r̄Retrofitτ2,τ1
(9)
with;
r̄Greent,τ2
= µ̄t − µ̄τ2 + λ̄Greent − λ̄Green
τ2
r̄nGreenτ1,s
= µ̄τ1 − µ̄s + λ̄nGreenτ1
− λ̄nGreens
r̄Retrofitτ2,τ1
= µ̄τ2 − µ̄τ1 + ω̄.
If a property was bought green and sold green, then r̄nGreenτ1,s
= r̄Retrofitτ2,τ1
= 0,and τ2 = s. When it was bought and sold as non-green, r̄Green
t,τ2= r̄Retrofit
τ2,τ1= 0,
with τ1 = t. Note that τ2 − τ1 ≥ 0, which means that retrofitting is notinstantaneous per se. During this ‘limbo’ period the property is neithergreen, nor non-green. The temporal effect during this period is ‘caught’ bythe common trend. We discuss our definitions of ‘green’, ‘non-green’ and the‘retrofit’ period next, in Section 3.
3. Data and Descriptives
To analyze the effect of certification of existing (office) buildings (that is,green retrofitting) on property values and price dynamics, we need at leastthe following. First, we need transaction data, including sales dates, ways toidentify properties, and of course sales prices. Second, we need a data sourcethat gives us the date of certification (and type of certificate) with - again -a way to identify the properties.
Transaction (repeat sales) data of office properties in the US is providedto us by Real Capital Analytics. RCA’s database is one of the most extensiveand intensively documented national databases of commercial property pricesin the US (Geltner and Pollakowski, 2007). Its objective is to include, ona timely basis, all transactions of commercial property transactions greaterthan $2,500,000 US dollars. RCA estimates they achieve a capture rate of
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generally at least 90%. In total, we observe approximately 8,000 office repeatsales in the RCA data that we have for this study.
Information about LEED certificates is provided to us by the USGBC.Any basic information about the registration and certification of projectsis stored in the LEED Online project directory which is the interface toenter project information by the associated teams and reviewers during thecertification process. We observe approximately 15,000 individual propertiesin the USGBC data, of which 5,000 are office properties.
We have merged these two datasets based on the street addresses. Themerged database consists of repeat sales of office properties in the US whichobtained a LEED green retrofit certificate at some time before the end ofour sample period. In many cases, we have repeat sales of properties, beforethey got the LEED certificate. We also have properties that were boughtand sold as green buildings (both sales in the repeat-sale observation pairwere ‘green’). And properties that were bought as non-green buildings andsold as green gives us a way to identify the non-temporal price premium, theone-time ‘bump’ in value.
Thus, in our application, we use LEED retrofit certificates as indicationsof when properties became green. First introduced in 1998, the LEED cer-tification process is considered to be part of the first generation of greencertification systems assessing the green performance of buildings. Origi-nally focusing mainly on ecology and energy consumption of buildings, sincethe World Summit in 2005, LEED certification systems also factor in: socialand economic considerations as well as ecology, for a broader conception of‘sustainability’. LEED certified commercial buildings account for over 4.9billion square feet (as of end 2015). For more details on the history of theLEED certificate and the actual process of certification, please consult thewebsite of the USGBC.
We exclude properties that were awarded certificates in the Construction&/or Interior Design (ID+C) family of LEED certificates. The Constructioncertificates (‘NC’ category) apply to new construction as distinct from theretrofit projects that are our focus.7 The interior design label is governed by adifferent motive than the other LEED labels, introduced to address the needof tenants to get their individually rented space within a larger property
7Thus, even our Green⇒Green sample of properties ‘green’ before the first sale consistsonly of retrofit-green properties.
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labeled. It is therefore likely that in most cases the Interior Design labelwould not substantially contribute to the ‘greenness’ of the whole property.
It is also important to note that there is a difference between the registra-tion date for the LEED certificate of a property, and when the certificate isactually officially approved and granted. Figure 1d gives a histogram of thenumber of quarters between the registration and certification of the proper-ties in our data. On average it takes five quarters (see Table 3 as well) togo from registration to certification.8 For us, a property is only consideredto be ‘green’ from the point in time when it is officially certified, whichdate is denoted τ2. Properties are classed as ‘non-green’ prior to the date ofregistration, which date is denoted τ1. In our application, this means thatwithin the ‘non-green’ time series (λnGreen
t ) of property i, the date when itwas registered is treated as if it were the date of a second ‘sale’ (effectivelyclosing out a ‘pseudo’ repeat-sale ‘pair’). Then, when the property gets cer-tified, the ‘green’ time series (λGreen
t ) of the same property treats that datelike the date of a first ‘sale’ in a repeat-sale pair (effectively opening another‘pseudo’ pair observation). When a property is in between these two dates, itis classed as neither green nor non-green. We call this the retrofit period.9
Thus, properties that were bought after registration and sold before certi-fication are omitted from the data, as they contain no information for eitherthe green or non-green index (and not even for the non-temporal return).
After merging the data and applying various traditional filters, we endup with 441 repeat sale observation pairs, representing 345 unique properties(as some properties were sold more than twice). As noted earlier, we losemany observations, as ‘green’ certification is a relatively recent phenomenon,and it takes time for repeat sales to enter the data. Still, this number liesabove other sample sizes mentioned in previous literature on green commer-cial buildings (Eichholtz et al., 2013; Fuerst and McAllister, 2011; Eichholtzet al., 2010). (See Table 5 in Section 4.1 for more details.)
Table 1 gives some descriptive statistics of the merged database. Forcomparison, Table 2 gives the same statistics for our entire RCA office sample
8It takes 1,000 days to go from registration to certification on average in the entireUSGBC data. But this high average is mainly caused by new construction properties. Forthe retrofit properties in our estimation sample the average time between registration andcertification is 5 quarters, see Figure 1d.
9We will examine the implications of modifications of this definitional scheme in ourdiscussion of robustness in Section 4.2.
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Table 1: Descriptive statistics green properties
mean median std. dev.
(log) Price difference 0.195 0.247 0.402Holding period (yrs) 7.477 7.500 3.038Price $ 131,887,875 $ 81,375,000 $ 173,533,321Year built 1980 1985 23Age 30 25 23Size (Square foot) 433,038 329,780 348,501Price per square foot $ 292 $ 253 $ 176Occupancy 0.872 0.920 0.170CBD 0.604 1.000 0.490Obs 441
Table 2: Descriptive statistics of ALL office properties in RCA data
mean median std. dev.
(log) Price difference 0.060 0.160 0.538Holding period (yrs) 5.612 5.000 3.116Price $ 33,359,850 $ 11,100,000 $ 80,571,524Year built 1976 1984 27Age 32 25 27Size (Square foot) 151,559 81,800 208,287Price per square foot $ 211 $ 164 $ 191Occupancy 0.830 0.930 0.253CBD 0.249 0.000 0.432Obs 8,864
(after 2000). The log price difference refers to the gross difference betweenthe second sale minus the first sale (not per period).
The average sales price of green properties is a factor 4 times higher thanthe average property in the RCA sample. The difference is even more starkin the median: $81M and $11M for green properties versus all propertiesrespectively. The properties are also a lot bigger. Green properties are almost3 times as big as the ‘average’ property. This also means that the price persquare foot is higher (almost 40% higher). This is not surprising, consideringthat more than 60% of our green properties are located in CBDs (comparedto just 25% on average). This is also apparent from Figure Appendix A.1in Appendix A, which gives the distribution of LEED certified properties inthe US. The ‘green’ properties are clearly centered around major US metros.
15
Table 3: (log) Price difference of retrofits versus non retrofits and statistics on the retrofitperiod
Transformation mean median std. dev. Obs.
Green ⇒ Green 0.212 0.194 0.211 22nGreen ⇒ nGreen 0.223 0.269 0.395 277nGreen ⇒ Green 0.139 0.169 0.433 142
Retrofit period (in quarters) 5.268 5.000 2.562 142
The holding period between the two sales (from s to t) also seems longer forour green properties. On average, green properties are held for 7.5 years,compared to 5.5 years for the average property in our RCA sample.
The difference between the ‘green’ dataset and the entire dataset for theother variables (age/year built and occupancy) is less pronounced. Occu-pancy is about 5% higher for green certified properties, and the propertiesare 2 years younger on average. The average age of ‘green’ properties is 30years, which is comparable to the average age in the entire data.
Table 3 gives the gross return statistics (log of ‘sell’/‘buy’ price ratio, notdivided by years held) for the retrofits, the non-retrofits, and the transitionproperties. Properties that were bought and sold with the LEED certificate(Green ⇒ Green), properties that were bought and sold without the LEEDcertificate (nGreen⇒ nGreen), and thirdly properties that were bought with-out the LEED certificate but were sold with one (nGreen ⇒ Green), i.e. theretrofits. The bottom row gives the mean, median and standard deviationof the retrofit period in quarters. The retrofit period is 5 quarters in themedian.
As shown in Table 3, we only observe 22 properties that were both boughtand sold with the label. This is too few to build a meaningful index withoutuse of the HRS method. (We demonstrate this in Section 4.) There aremany more properties that obtained the certificate between the sales. Weuse these pairs to estimate the non-temporal return, and to infer informationto estimate the ‘green’ price index.
In total (log) price difference, there does not seem to be a big difference inthe properties with or without the LEED certificate (first two rows in Table3). However, the properties that got converted (nGreen ⇒ Green) show alower return, albeit with a high standard deviation (and though not shown,the returns are positively skewed).
16
Fre
quen
cy
−1.0 −0.5 0.0 0.5 1.0 1.5
010
2030
4050
60
(a) Gross Price Return distribution.
200001 200204 200407 200610 200901 201104 201307 201510
non−Green TransactionGreen Transaction
010
2030
40(b) Distribution of log Price Returns perholding period in quarters.
200001 200204 200407 200610 200901 201104 201307 201510
non−Green TransactionGreen Transaction
010
2030
40
(c) Distribution of green versus non greensales by quarter (individual transactions).
Fre
quen
cy
5 10 15
05
1015
2025
30
(d) Distribution of the retrofit period inquarters.
Figure 1: Descriptive statistics of our Data.
Figure 1a gives the gross (log) returns distribution, whereas Figure 1bgives the distribution of returns per quarter in the merged dataset (somethinglike the rate of return), all in terms of log price differences. The gross returnsin Figure 1a are the simple log price differences in the repeat-sale pairs, thesale log price minus the buy log price. The returns per quarter are the gross
17
returns divided by the number of quarters in the holding period between thebuy and the sell. Figure 1a seems to contain some outliers, but these arelargely caused by long holding periods.
Our data starts in 2000, but as noted, ‘green’ certification is only donerelatively recently. This means that there is a correlation between time of saleand certification. Figure 1c shows the number of transactions per quarterbetween 2000 and 2016, for both green and non-green properties in our data.Before 2009, virtually no transactions had a LEED certificate. However,registration and certification did start before that date, around 2005 – 2007.The certified properties ‘simply’ did not transact until 2009. In the finalperiod(s), the opposite occurs. Most properties were already certified bythat date.
With this in mind, we analyze the estimated indexes only between mid-2005 and 2014 in the main body of text. We do use the data before and afterthis period for the estimation of our indexes (note that structural time seriesallows us to estimate indexes even in periods without data), but we only usethe indexes between 2005 and 2014 to calculate the metrics of interest, suchas: mean return, volatility, autocorrelation, etc.
4. Results
The main results of our empirical analysis of the effect of green retrofittingin US office properties are described in this Section, together with someanalysis of the robustness of the results.
4.1. Main Results
We have estimated four models:
1. The first model is the Local Linear Trend model (denoted LLT), intro-duced by Francke (2010). The model is given by Eqs. (3) – (5). In thisframework, we must estimate the green and non-green indexes sepa-rately, on two non-overlapping subsets of data. For the ‘non-green’ in-dex, the estimation subset only includes repeat sales of properties thatwere both bought and sold without a green certificate (277 obs). Con-versely, the ‘green’ index is estimated on data that only includes prop-erties that were both bought and sold with a green certificate (22 obs).In this framework we cannot identify the effect of the non-temporalprice premium associated with the retrofitting (the ω term). We show
18
the results from this estimation primarily simply to illustrate the diffi-culty of this approach in a scarce data environment, where at least oneof the sub-indexes has insufficient data.
2. The second model is the hierarchical trend model introduced in Eqs.(3) – (8), denoted HRS. This model is estimated on the entire dataset(441 repeat-sales observations). Importantly, this estimation data in-cludes the retrofits, that is, the properties that were retrofitted be-tween the first and second sales of the repeat-sales pair. (As noted, allproperties in the the entire estimation data ultimately are retrofitted.)With this HRS approach, we can not only estimate separate ‘green’ and‘non-green’ price indexes, but also the non-temporal price premium, or‘bump’, associated with the certification (ω). The common trend (µt)is informed by all the properties. The two sub-trends (λjt) are for:(1) green certified properties and (2) properties without yet a greenregistration.
3. Third, we estimate the same model as in (2), but allow the retrofitdummy (ω) to be time-varying (ωt).
10 We allow for a random walkstructure on the parameter, so ωt = ωt−1 + φt−1. Where φt is assumedto be normally distributed. The timing (t) of the retrofit is set to thedate of certification. There is a lot of anecdotal evidence from industrythat the demand for ‘green’, whether on the part of tenants and/orinvestors, is ‘income elastic’. When times are good and incomes arehigh, people may care more about sustainability and the environment.Some evidence of this phenomena has been found in empirical literatureas well, especially in rents (Reichardt et al., 2012). We call this thirdmodel THRS (Time-Varying HRS).
4. Finally, we - again - estimate the same model as in (2), but we furthersubdivide the green index in to two sub-indexes and the non temporaldummy into two separate dummies, one for properties that are ‘slightly’green and one for properties that are ‘more’ green. We identify theamount of ‘greenness’ by looking at the actual labels. ‘Certified’ and‘Silver’ (C/S) certified properties are in the first group, and ‘Gold’ and‘Platinum’ (G/P) properties are in the latter group. In our data, 163of the 441 properties ended up with either a ‘Certified’ or ‘Silver’ (C/S)
10Here we use the t subscript generically for time. In the scheme noted in , the relevanttime subscript on ω would be τ2, the date of certification.
19
label, and the other 278 properties ended up with a ‘Gold’ or ‘Platinum’(G/P) label. We expect that the non-temporal price premium will behigher for the G/P properties, compared to the C/S properties. Wedenote this fourth model AHRS (Augmented HRS).
The resulting indexes can be seen in Figure 2. Some statistics on the returns(quarterly log differences) and the fit of the model are presented in Table 4.As expected, the green index from the LLT model without the hierarchicalestimation, based solely on only the 22 the green repeat-sales, does not appearto be a very meaningful index (and it is quite inconsistent with the otherestimates of the ‘green’ index). There is simply insufficient data to estimatethe green index this way. On the other hand, the LLT ‘non-green’ index ismore in line with the other models.
As noted, with the HRS Model we can use all information, including fromthe properties that were registered and certified between the two sales inthe repeat-sales pair observations. This adds another 142 observations, andallows us to estimate the asset price premium ‘bump’ dummy which capturesthe non-temporal return associated with the certification. The indexes (bothgreen and non green) look more believable compared to the LLT results.
We find a positive coefficient of 0.155 on this dummy (ω), meaning thatthe price of a property goes up about 17% (exp(0.155) − 1) in associationwith it getting certified. Compared to other literature - see Table 5 - this pre-mium appears realistic. Indeed, it suggests that the property value increaseassociated with green retrofits is of similar magnitude to what is obtained ingreen new construction.11
It is important to keep several points in mind when considering the natureand meaning of the 17% jump that we find associated with green retrofitting.First, although this finding is likely ‘good news’ for the value of such invest-ment, it is only part of the story. We have no information on the cost of thegreen retrofit projects. The analysis in this paper is of green value effects,the ‘demand’ side of the ‘market for green’. Complete green benefit-costanalysis is beyond our scope. But presumably, profit-maximizing landlordsand investors would not undertake capital improvement projects whose net
11Keep in mind that the price indexes in Figure 2 all start arbitrarily at a value level of‘100’. They depict only the price dynamics, longitudinal changes in relative values, anddo not themselves reveal the non-temporal price premium in the green-retrofit properties,though they may reflect its effect in values over time.
20
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Figure 2: Estimated Green versus Non-green indexes (exponentiated log levels), quarterlymid 2005 to 2014 (2005Q2 = 100
present value (NPV) are less than zero, at least in terms of expectations.Assuming rational expectations, this suggests that the costs would at least
21
tend to be no greater than the value benefits we are quantifying.12
Secondly, we have no way of saying how much of the ω effect is due strictlyto the ‘green’ nature of the capital improvement projects that implementedthe green retrofits, as opposed to other aspects of any such renovation orimprovement projects.13
Thirdly, as defined above, the ω effect actually captures not just purelythe non-temporal price ‘bump’ associated with the retrofit project, but alsoany other price trend other than the common trend µt that occurs duringthe interval of time between when the property registered for the LEED cer-tificate and when the certificate was actually approved (the interval duringwhich the property was neither in the ‘green’ nor the ‘non-green’ sub-index).As noted in Section 3, this time interval in our merged dataset averages alittle over a year. If the price trend during this ‘retrofit period’ is structurallydifferent from the common trend, it might bias the component of the indi-cated retrofit effect that we attribute to the non-temporal bump. However,in Section 4.2 we check for this possibility and show that the ω result islargely insensitive to alternative definitions of the ‘green’ conversion timingdesignation.
Beyond the (largely) one-time bump in value reflected in the ω coeffi-cient, the real estate price indexes depicted in Figure 2 and summarized inthe top panel of Table 4 give an indication of the price dynamics of greenretrofit properties compared to non-green properties within our sample. Theestimated ‘green’ indexes from the HRS models show less return overall pricegrowth trend, and notably, a greater price drop in the financial crisis. Butthey also display generally less risk as measured by the quarterly volatilityof the returns.
One explanation as to how the green properties apparently suffered agreater value drop during the financial crisis is given in Figure 3a, whichgives the time varying retrofit price premium dummy of the THRS modelduring the crisis. The retrofit premium is relatively stable over the sample,except during the crisis, when the premium evaporates. The premium islowest in 2010Q3, whereas the trough of the indexes is in 2010Q2. Thissuggests income-elastic demand for ‘green’. The implication is that when
12See (Chegut et al., 2015) for evidence regarding the benefit-cost consideration of greenfor new construction.
13It would often be very difficult to disentangle the ‘green’ versus ‘non-green’ aspects ofa given improvement project.
22
times are good and incomes are high, people care more about sustainabilityand the environment, and vice verse. During the financial crisis incomes fellor were threatened, removing the ‘green premium’ in value, resulting in alarger drop in the value of existing green properties, well into 2010 (Figure2).
Turning to the analysis of different degrees of ‘greenness’, Figure 2d andTable 4 reveal results consistent with the previous, only more pronounced.Not only is the ω dummy coefficient on the properties with a ‘Gold’ and‘Platinum’ (G/P) label higher compared to properties with a ‘Certified’ or‘Silver’ (C/S) label (which gives us confidence in the results), the differencesin the indexes also seem consistent. However, the results for the difference involatility are inconclusive, in that the resulting volatilities are more or lessequivalent between the C/S and G/P labeled properties.
−0.
050.
000.
050.
100.
15
200801 200810 200907 201004 201101 201110 201207
(a) Price premium.
−0.
020.
000.
020.
040.
06
200801 200810 200907 201004 201101 201110 201207
(b) Occupancy premium.
Figure 3: Exponent of time varying retrofit dummy (ωt) during the crisis.
The estimated indexes appear reasonably smooth, and display high au-tocorrelation in the quarterly returns, which is to be expected in privateproperty markets, and suggests that the indexes are relatively free of noise.14
The autocorrelation of the index returns is not appreciably different betweenthe ‘green’ and ‘non-green’ indexes, suggesting that predictability is not dif-
14The ‘footprint’ of noise is negative first-order autocorrelation in returns.
23
ferent between the indexes.15 It makes sense that the quarterly correlationbetween the two sub-indexes would be high, as all the properties in our sam-ple are presumably in the same general type of property market (reflected inthe fact that they all ultimately obtained green retrofits).
Note that none of our indexes reached their pre-crisis peaks by the endof our sample period (first quarter of 2014). The price levels are 13% belowthe previous peak for non green properties, and 26% for the green properties(in the HRS model). This is very consistent with the Commercial PropertyPrice Index (CPPI) published by RCA on their website as of mid-2017. Theyshow that in the first quarter of 2014, office prices were 12% below the pre-crisis peak in the US. The fact that we find slightly less price growth isconsistent with Geltner and van de Minne (2017), who find that larger pricepoint properties have less price growth in general.
From a technical perspective, we note that the four chains used in theBayesian estimation process converge nicely, as the R̄ values in Table 4 areway below the pre-defined threshold value of 1.100. (See Lunn et al. (2013) formore information on the R̄-statistic.) In MCMC analysis, model fit is usuallymeasured by the deviance information criterion (DIC). Lower values equalsbetter model fit. Unfortunately, we cannot compare the DIC of the LLTmodels with the each other, or to the HRS models, because the underlyingdatasets are different. (Note that the more well known Akaike informationcriterion (AIC) has the same caveat.) We can compare the DIC between theHRS, THRS and AHRS though. The THRS model renders the best model fit.The difference with the second best model (HRS) is 5 points, indicating thatthe difference is seen as ‘considerable’. The difference between the HRS andAHRS is within 5 points, indicating that the difference between these modelsis not ‘considerable’. Mixing is also best for the THRS model, which youcan tell by comparing the effective sample sizes.16 (Lower effective sample
15We also ran a Granger Causality test, to see whether or not you can forecast one indexof the other. However, we found no evidence for this.
16The effective sample size (ESS) is computed as follows;
ESS =n
1 + 2∑∞
k=1 ρ(k), (10)
where n is the number of samples and ρ(k) is the correlation at lag k. You get a differentESS for every variable. Thus, the difference between the effective sample size and theactual sample size, gives you a measure on how independent the draws are.
24
size equals worse mixing.)Although a full causal analysis of why properties might display less risk
and less return after green retrofitting is beyond the scope of this paper, wecan supply some relevant additional analysis. As shown in Section 3, wealso have data on occupancy of the property (as % of total rentable space).Thus, we redo the same analysis as before, using difference in occupancy (notlogged) as the dependent variable, in stead of log price returns. A summaryof these results is found in Table 6. Because we only have occupancy dataon 334 pairs (of the 441 in our original data) we skip the LLT model. (Ifinterested in the full results, including the ‘occupancy indexes’ please contactthe authors.) The time varying retrofit dummy of the THRS for occupancyis given in Figure 3b.
The results show that there is a one time 1.5% increase in occupancy (forexample, vacancy reduced from 8% to 6.5%) after a property gets certified.This figure is in the range of findings from previous research (see Table 5),albeit a bit low in the spectrum. However, we do find that this premiumwas higher in the pre-crisis era (Figure 3b). More specific, the occupancypremium is on average 6% before the crisis and close to 1% after the crisis.Still, these results should be taken with a ‘pinch of salt’ as the credibleintervals are relatively high. This is unsurprising, knowing that the numberof observations is low. (These results are not reported here, but availableupon request.)
Differentiating between ‘Certified’ / ‘Silver’ (C/S) properties and ‘Gold’ /‘Platinum’ (G/P) properties, the occupancy results are consistent as before.The occupancy increases 4% if the property gets G/P certified, comparedto ‘only’ 0.5% for C/S certified properties. Interesting though, is that theoccupancy trend was the most negative for the G/P properties in our ana-lyzed period. However, the trends in occupancy are very small in general,and not enough to offset the one-time positive effect associated with gettingthe ‘Gold’/‘Platinum’ label.
Higher occupancy would be indirectly suggestive of greater and morestable income. This could help explain why we find less volatility associatedwith green retrofit.
4.2. Robustness
In this section we present some analysis and discussion of the robustnessof our empirical results.
25
First, as noted, the general appearance and positive autocorrelation andcross-correlation within and among the estimated indexes seem reasonable,and their general pattern is similar to published commercial property priceindexes of US office property. The premiums for green that we have found inboth property value and occupancy are in line with findings in the previousliterature (although that is for new construction whereas we are examin-ing retrofits). These general characteristics provide some confidence in thefindings.
It must be noted that the variance parameter of the noise (σε in Table4) is quite high, estimated at 0.378. The 5% credible intervals on our indexlevels are also relatively wide.17 However, this in itself is not a surprise, aswe are working with little data. And it is important to note that goodnessof fit measures, such as credible intervals and RMSEs, are not necessarilyvery good metrics for the quality of estimated indexes. After all, we are nottrying to predict the values of individual properties. See Guo et al. (2014);Francke et al. (2017). The more relevant question to ask is whether, if wereplicated our study in the future, would we be likely to get very differentresults even if the underlying ‘true’ price effects were the same.
With this in mind, in order to provide additional perspective on therobustness of our findings, we will consider two types of checks. First, weuniformly draw without replacement 60% of our data twice, and estimatethe model on the two separate subsets of data. We also do two such runson 40% of the data. After sampling 60% (40%) of our data, we end upwith 265 (176) observations. Obviously, the data is scarce to begin with, soit is expected that the indexes will change at least moderately. However,we are specifically interested in whether the same ‘pattern’ emerges evenfrom randomly different data samples.18 In particular, do we still find lessvolatility and growth for the green indexes? The indexes themselves arefound in Figure 4, and Table B1 in Appendix Appendix B compares theresulting index statistics.
As expected, the indexes change after running our model on different
17The credible intervals vary somewhere between 10 and 20 index points from the indexlevels. This is large enough to conclude that the green and non-green index levels arenot significantly different from each other. (Not shown here for the sake of brevity, butavailable upon request.)
18Of course, with this method there is randomly some overlap in the two comparisonsamples. This is not a complete ”out of sample” test.
26
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Figure 4: Running the HRS on a subset of the data.
(and very small) subsets of the data. Still, the overall ‘pattern’ we werelooking for remains. Indeed, the average growth of the non-green index isgreater than the green index in all four runs. Also, the volatility is lower forthe green properties, compared to their non-green counterparts. Finally, thecoefficient on the retrofit dummy is very consistent as well. The premiumlies somewhere between the +10% and +20% (we found +17% on the fulldataset).
27
Finally, we conduct a second type of robustness analysis. We change thedate at which we declare properties to be green. Since it is not possible toknow exactly what happens during the period between when the property isregistered green and when it is certified green, we test for the effect of thetwo extreme possibilities in this regard. We redefine the ‘green’/‘non-green’distinction for our modeling purposes in the following two alternative ways.
1. Green Only at Certification: In this variant we treat the propertyas being non-green all the way until the date of certification. We defineaway the in-between ’limbo’ period. Technically (in the mathematicsof our modeling in Eqs.(6)– (8)), at the date of certification (date indi-cated preciously by τ2), the property is ‘sold’ as non-green and ‘bought’as a green property.
2. Green Completely at Registration: Similar to the previous variantonly now the other extreme, the property is assumed to become greenat the time of registration (τ1). So, at date τ1 the property is treatedin the model as ‘sold’ non-green and ‘bought’ as a green property.
Note that in both cases, there is no retrofit period anymore. Rather, theretrofit happens instantaneously, i.e. τ2 − τ1 = 0.
There is something to say for both definitions of ‘greenness’ (as there isfor the main definition we have used elsewhere in this paper). The main pur-pose of this exercise is to give confidence in the estimated indexes. With ourprevious labeling convention, which created a time period when the proper-ties were neither ‘green’ nor ‘non-green’, the model was in effect implicitlyassuming that asset pricing followed the common trend during this retrofitperiod. To the extent that the actual trend during the retrofit period is dif-ferent than the common trend, the model treats this difference to be noise(i.e. some times the residual is negative, sometimes positive). By switchingthe trend during the retrofit period (for example, with the ‘Green Only atCertification’ definition the model treats the properties as following the greenindex during the retrofit period), we can see how sensitive the estimated in-dexes are. In this test, we only re-estimate the HRS model. The resultingindexes are given in Figure 5. Some return statistics and the parameter ofinterest (ω) are given in Table 7.
The overall ‘feel’ of the indexes remains similar in this robustness testas well. There are still higher returns for the non-green indexes, with alsomore volatility. And the green indexes go down more following the crisis.The coefficient on the retrofit dummy changes, but it remains within an
28
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Figure 5: The effect of changing the timing of when properties are ‘green’.
acceptable range. If you declare properties green after registration, we find a13.4% non temporal bump. If you declare properties green after certification,the bump is 18.5%. It is not surprising to find that the effect of the retrofitdummy is higher at certification (compared to registration). Indeed, somecapital expenditures are still being made during this ‘retrofit period’. (Insome cases existing properties may be registered before the retrofit projecthas actually been started, and some may get the certificate before all ‘green’construction was concluded.) All in all, our robustness analyses show thatthe one-time non temporal return associated with retrofitting lies somewherebetween +10% and +20%, except during times of severe recession when thepremium may dissipate.
5. Concluding Remarks
Commercial buildings are a responsible for a large share of resource andenergy consumption, and greenhouse gas emissions worldwide. In matureeconomies such as the United States, new construction annually representsonly a small fraction of the existing stock of buildings, as individual buildingslast on average 100 years. The majority of buildings that will exist in suchcountries in 30 years are already built, and a large fraction of them are alreadyover 10 years old. Hence, retrofitting of commercial buildings, through major
29
renovation projects, is extremely important for sustainability within the builtenvironment in such countries. Yet, almost all the research to date regardingthe economics of green building focuses only on new construction.
In this paper we have focused on the green retrofitting of existing of-fice buildings in the US. We quantify the magnitude of value enhancementassociated with green retrofit construction, as well as the subsequent pricedynamics effects of such sustainability.
In terms of methodology, we introduce an innovative way to control forother effects, in order to isolate the value premium and price dynamics spe-cific to green retrofitting. We do this by applying a repeat-sales modeling ofonly (and all) buildings which will ultimately be retrofitted in our sample.By using new real estate price indexing methodology, namely a structuraltime series model employing a hierarchical repeat-sales (HRS) specification,we can build statistically rigorous comparative price indexes of retrofit green,versus non-green, office buildings in the US, quarterly for the 2005-2014 pe-riod, even with relatively scarce transaction price data (441 repeat-sale ob-servations on 345 separate properties, which is nevertheless a large samplecompared to previous studies of green building economics).
It should be noted that while in the present application we use this modelspecifically to identify the effect of green retrofitting, the procedure is of gen-eral interest. It is suitable for many other questions where one is interestedin a similar ‘diff-in-diff’ approach.
We find substantial value enhancement in green retrofit projects (in therange of +10% to +20%), and we find evidence that retrofitted green build-ings provide investors with lower asset price volatility. We also show thatthe premium was not constant over time, but rather followed the economiccycle, virtually disappearing during and right after the downturn. Indeed,during the Financial Crisis we do not observe a premium associated with ourgreen properties. This suggests that demand for green is income-elastic.
Acknowledgment
The authors greatly appreciate the input of the participants of the 2017Real Estate Research Symposium at the University of North Carolina. Es-pecially the comments by Spencer Robinson, Gary Pivo and Eric Ghyselswere very useful. An earlier version of this paper was also presented at aMIT seminar. Steve Malpezzi is acknowledged for his comments given there
30
as well. Finally, we would like to thank our data providers; Real CapitalAnalytics and the US Green Building Counsel.
31
Tab
le4:
Mai
nre
sult
sof
Loca
lL
inea
rT
ren
d(L
LT
)an
dH
iera
rch
ical
Tre
nd
Mod
el(H
RS
)b
etw
een
2005
–2014
qu
art
erly
retu
rnst
atis
tics
(log
diff
eren
ces)
.
I.LLT
II.HRS
III.
THRS
IV.AHRS
Green
non
Green
Green
non
Green
Green
non
Green
Green
(C/S)
Green
(G/P)
non
Green
Ret
urn
Sta
tist
ics
mea
n-0
.0056
0.0
046
-0.0
030
0.0
027
-0.0
006
0.0
024
-0.0
015
-0.0
028
0.0
026
mea
n*
-0.0
081
0.0
043
-0.0
033
0.0
023
-0.0
008
0.0
021
-0.0
017
-0.0
031
0.0
023
|mea
n|
0.0
563
0.0
189
0.0
192
0.0
222
0.0
170
0.0
209
0.0
188
0.0
191
0.0
218
sd.
0.0
718
0.0
243
0.0
219
0.0
266
0.0
203
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249
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215
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218
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261
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446
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442
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39
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35
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32
Table 5: Price and Occupancy (Occ.) premium for green properties in previous literature.
Paper Price Prem. (%) Occ .Rate (%) LEED Period # Trans.
Eichholtz et al. (2013) 11.1 2.4 Yes 2004–2009 638Fuerst and McAllister (2011) 25.0 n/a Yes 1999–2008 686Eichholtz et al. (2010) 11.3 11.0 Yes 2004–2007 199Pivo and Fisher (2010) 8.5 1.3 No 1999–2008 1,099Miller et al. (2008) 9.9 4.2 Yes 2003–2007Chegut et al. (2014) 14.7 n/a No 2000–2009 68Average 13.4 4.7 538
This paper (HRS) 17.0 1.4 Yes 2001–2016 882
Price Prem. is the estimated price premium (or ω in our models). Occ. Rate is the estimated increase inoccupancy for green properties compared to non green properties. LEED indicates whether or not thegreen label is measured by LEED certification. And # Trans. gives the number transactions. Note thatin case of a repeat sales model, the number of transactions is twice the number of observations (pairs),see Section 2. All papers are done in the US, with exception of Chegut et al. (2014), which is in the UK.Also this selection of literature used CoStar data to get transaction prices, except for Pivo and Fisher(2010) who use NCREIF data (therefore they have appraised values, and not transaction values).
Table 6: Summary of the results of the occupancy model.
HRS THRS AHRSGreen non Green Green non Green Green (C/S) Green (G/P) non Green
trend 0.001 0.000 0.002 0.000 0.003 -0.004 0.000
retrofit dummy
ω 0.014 Figure 3b
ω(C/S) 0.005
ω(G/P) 0.039
The trend is computed by taking the average (periodic) growth trend of the indexes that are estimatedfor the occupancy model. The indexes themselves are not presented here to conserve space, but areavailable upon request.
33
Table 7: Summary of the results for the indexes with different timing of when to call itgreen.
Green at Registration Green at CertificationGreen non Green Green non Green
mean -0.0018 0.0039 -0.0030 0.0025mean* -0.0020 0.0036 -0.0033 0.0022sd. 0.0205 0.0248 0.0226 0.0250ω 0.1260 0.1700p. DIC 417.88 420.97
mean∗ is the Geometric mean, in contrast to the arithmetic mean. sd stands for thestandard deviation of the returns.
34
Appendix A. Distribution of LEED certified properties in the US
(a) All LEED properties in the US.
(b) LEED properties in our sample.
Figure Appendix A.1: Geographic distribution of LEED properties in the US.
35
Appendix B. Full results of our robustness analysis
Table B1: Results of HRS model on randomly sampled subsets of data.
Run 1; sample 60% Run 2; sample 60% Run 1; sample 40% Run 2; sample 40%Green non Green Green non Green Green non Green Green non Green
Return Statistics
mean -0.0012 0.0037 -0.0025 0.0042 -0.0030 0.0016 0.0002 0.0042mean* -0.0015 0.0033 -0.0027 0.0039 -0.0032 0.0013 0.0000 0.0039|mean| 0.0203 0.0228 0.0169 0.0196 0.0170 0.0180 0.0193 0.0209sd. 0.0243 0.0267 0.0212 0.0233 0.0206 0.0230 0.0231 0.0252max 0.0515 0.0622 0.0502 0.0641 0.0448 0.0567 0.0485 0.0589ACF1 0.6956 0.6540 0.7487 0.6851 0.6553 0.5715 0.6989 0.6728ACF2 0.6361 0.5020 0.5697 0.3603 0.4483 0.2523 0.4789 0.3972Cor. w. Green 0.9392 0.9129 0.9307 0.9703
Hyperparameters
σε 0.377 0.369 0.385 0.395ση 0.044 0.039 0.046 0.047σζ 0.022 0.021 0.022 0.023σς 0.036 0.035 0.039 0.035ω 0.140 0.184 0.183 0.096
Model Fit
R̄ 1.000 1.000 1.000 1.000DIC 233.75 222.27 162.92 172.02p.DIC 254.42 242.47 181.50 189.17Eff. size 9,579 8,008 9,607 12,351nr. Chains 4 4 4 4Obs. 265 265 176 176
mean∗ is the Geometric mean, in contrast to the arithmetic mean. sd stands for the standard deviationof the returns, and ACF1 and ACF2 is the serial correlation with either 1 or 2 lags respectively. DIC isthe Deviance Information Criterium, and p.DIC is its penalized version. Eff.size stands for the mean ofthe Effective sample size. The value of the convergence diagnostic R̄ given in the Table, represents theaverage of the R̄s of all variables.
36
Appendix C. Technical Description of the Estimation
The estimation method is full Bayesian inference, where non-informativepriors are specified for all variance parameters as σ−2 ∼ Gamma(0.001; 0.001).We use a Markov Chained Monte Carlo (MCMC) algorithm to estimate ourmodel. More specifically we use Gibbs sampling using JAGS (Just AnotherGibbs Sampler) software, see Plummer (2003). The main reason is the flexi-bility and ease of using the program; it only requires the model specification(in the ‘first difference’ representation (8). All the presented estimation re-sults are from JAGS, which we run from R.
In our initial runs we experienced a lot of autocorrelation in the chains.Indeed, even after sampling 10 million times, we could end up with an effec-tive sample size in the double digits for the estimated (log) indexes. This isa known problem, as the time series (i.e. the indexes) are not independentlydrawn from each other. We therefore used the so-called ‘Matt trick’. Inshort, this means we estimate price increments (α) and not price levels. So,for our sub-trends (and for the time varying retrofit dummy in the THRSmodel), we model λjt = λjt−1 +αjt−1ςj. We specify the (non informative) prioron the increments as αt ∼ Normal(0, 1). We model the increments - insteadof the levels - for the other time trends (µt and κt) in a similar fashion aswell, ensuring independently drawn index points. Using the ‘Matt trick’ weare able to increase the effective sample size considerably, with only 300,000iterations (of which 50,000 were used as a warm-up) for each of the 4 parallelchains. Finally, to circumvent running out of memory we only store the valueof every other draw (or in MCMC terms, we set the thin parameter to 2).
37
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