CSIS Discussion Paper No.155

The Economic Value of Green Office Buildings in Tokyo*
Yongheng Deng,† Junichiro Onishi,‡ Chihiro Shimizu,‡ and Siqi Zheng§
May 18, 2018
Abstract
This study estimates the economic value of green buildings in Tokyo’s office market. We found,on average, a green label gained a premium of about 4% on contracted rent, but this premium
lost its significance after we employed propensity score matching. We also estimated thegreen premiums for Tokyo’s submarkets using clustering analysis. We found that, although
the green label’s premium was weak in a market focused on new, large-scale office buildings
constructed only a few years ago, it had a statistically significant effect, adding
approximately 10% of value, for older, medium-sized office buildings constructed several
years ago. However, the latter drops to around 5.6% and becomes statistically insignificant
after we control for the characteristics of tenants in the buildings. Our findings suggest thata green label does not create an economic premium by itself. Instead, it creates a premium
through a signal to the market. As a result, tenants with high energy and resource efficiency
and companies who have strong sense of social responsibility are willing to pay more for suchlabels, and thus, have a larger probability of moving into a building with a green label.

Keywords; green building, green label, hedonic approach, propensity scorematching, clustering
JEL Codes: G51; M14; D92

We are grateful to Jiro Yoshida, Daniel McMillen, Tien Foo Sing, and the participants at the“Green
Cities in Asia” Workshop, the Hang Lung Center for Real Estate, Tsinghua University. The financial
support from JSPS KAKEN(S) #25220502, Nomura Foundation for this research is gratefully
acknowledged.
†Wisconsin School of Business, University of Wisconsin-Madison.
‡Xymax Real Estate Institute.
‡Correspondence: Nihon University, Setagaya Campus, 34-1 Shimouma 3 chome, Setagaya-ku, Tokyo, 154-8513,
Japan. e-mail: shimizu.chihiro@nihon-u.ac.jp. Research Affiliate of CSIS, The University of Tokyo.
§ Department of Urban Studies and Planning, and Center for Real Estate, Massachusetts Institute of Technology.
*

11. Introduction
Recent decades witness a marked economic growth in East Asian countries, such as
China, and the relationship between economic growth and environmental burden has
also be scrutinized over the last decade. The focus on these issues has become evenstronger as the connection between global warming and increases in carbon
compounds—which have risen sharply in the process of economic growth—has become
clear and as global warming has manifested in the form of visible phenomena such as
the destruction of existing ecosystems and rising sea levels.
The connection between economic growth and environmental burden has long been
debated in the field of environmental economics. When environmental problems areinterpreted as domestic issues, the debate is over environmental destruction andpollution induced health problems. However, in recent years, as economic growth has
accelerated, environmental pollution related crises that used to occur every few decades
have begun to occur every few years, shining a spotlight on this rapid change.
The “rapid economic growth” that occurred in Japan in the 1950s and 60s is often
scorned in comparison to the post-Industrial Revolution economic growth in Britain-- its
associated environmental burden, and the subsequent economic growth in other
European countries. But it is accurate to say that Japan’s growth was concentrated in
an extremely short period of time. Similar pattern was found during the recent economic
growth in East Asian countries, like China.
Amid such conditions, academic researchers and policy makers have begun to point to
the importance of providing green buildings with a low environmental burden and highenvironmental performance (hereafter referred to as “green buildings”), which affectapproximately half of the global carbon compound emissions. This trend resembles thepush for green vehicles in the automobile industry. A great deal of research has been
published debating on the economic efficiency of green buildings (see, for example,
Eichholtz, Kok and Quigley, 2010). In short, if green buildings with high environmental
performance come at a higher premium (i.e., rent and price) than buildings that are notenvironmentally conscious, it becomes possible to convert the stock of real estate into
real estate with high environmental performance through market mechanisms and, asa result, the environmental burden can be reduced, even without strong policy
intervention.
These are the circumstances under which the “environmental certification system” forreal estate has emerged, with standards for environmental performance determined
exogenously and objectively by a third party. Since BREEAM, the world’s first2

environmental valuation system, was presented in the UK in 1990, other systems likethe US’ LEED (1996) and France’s HQE (1996) have also been established. Such systems
have spread globally, with Europe’s pension fund group establishing GRESB, a systemthat evaluates the sustainability-consciousness of real estate companies and theirportfolios, and Japan introducing it into most J-REITS (Japanese real estate investment
trusts).
Since the Ministry of Land, Infrastructure, Transport and Tourism initiative CASBEE
was announced in 2001, Japan has established a variety of environmental certification
systems in the public and private sectors, including CASBEE Real Estate (2009), which
was customized for easy application in real estate developments and transactions, the
DBJ Green Building Certification developed by the Development Bank of Japan, and
SMBC Sustainable Building Evaluation Financing developed by Sumitomo Mitsui Bank.
This study focuses on the office market in Tokyo, a city with one of the highest
concentration of green office buildings in the world. Its goal is to compile a unique andrich micro-data on rents, green labels and green building performance, and provide
rigorous empirical evidence to demonstrate whether the existing environmental
certification system creates an economic premium for office rent. Aside from the city’ssize, this study focuses on Tokyo because we believe it has distinctive historicalcharacteristics that relate to Japanese environmental policy.
As mentioned, Japan achieved miraculous economic growth in the 1950s and 60s.
However, the downside was the growth in discouraging environmental pollution
problems. In recent years, people have pointed to the high levels of air pollutants suchas PM2.5 in China; however, levels in Tokyo at the time were more than 10 times thosein Beijing today, causing extremely serious problems with pollution in the air, soil, rivers,
and seas. Amid such conditions, the environmental issues became socioeconomic issues
and, as environmental technology developed, environmental standards reached thehighest levels, exemplified by green vehicles and the like.
However, while green buildings have received attention in Tokyo for more than adecade since the environmental certification systems were established, the share ofcertified environmentally friendly real estate is only 2% in terms of the number ofbuildings and 12% in terms of area, indicating that we are still in the development stagesof making the overall stock of real estate certified environmentally friendly. We believethat environmental certification has not spread throughout the entire real estate market
because, due to the environmental pollution problems that occurred under rapid
economic growth and energy conservation policies following the oil crisis, anenvironmentally conscious social system had already been constructed. As such,3

substantial progress toward green buildings is being made and some point out thatenvironmental certification evaluation may not serve as a point of differentiation in themarket.
Furthermore, some also note that environmental performance has become secondary
because major earthquakes frequently strike Japan and earthquake resistance inpreparation for large-scale disasters has been emphasized over environmental
performance. As such, although earthquake resistance evaluation is required in theperformance evaluations of structures, environmental performance evaluation isvoluntary.
As this is the case, when conducting a positive analysis to determine whether
environmental certification evaluation creates an economic premium, we first pay
attention to the type of characteristics present in real estate where environmental
certification is prevalent. In other words, if obtaining environmental certification isvoluntary, we must clarify why some building owners pursue this path. Furthermore, as
revealed by Eichholtz, Kok and Quigley (2009), it is important to look not only at overallbuilding characteristics such as earthquake resistance but also at who the tenants willbe. In the analysis of the price formulation of real estate value, some have indicated that
there may be under-formulation bias due to a large number of unmeasurable variables(Ekeland, Heckman and Nesheim (2004)), and such problems need to be carefully
identified.
Taking those problems into consideration, this study aims to empirically determine
whether environmental certification evaluation creates an additional economic premiumin the office market in Tokyo, a city where offices that meet high environmental
standards are already concentrated. If it does create a premium, we will determine what
the market looks like while also taking into account the local characteristics of tenantsin the office buildings with environmental certification and in the buildings without it.
The remainder of the paper is organized as follows: in the next section, we provide athorough literature review; in section 3, we introduce the baseline estimation model anddata; section 4 presents our empirical strategy and the estimation results; and the lastsection concludes.

2. Literature Review
Several pioneering studies have provided important insights into green buildings in4the office market1 The seminal paper by Eichholtz, Kok and Quigley. (2010) provided
the first systematic analysis of the impact of environmentally sustainable building
practices on economic outcomes as measured in the marketplace. They found thatbuildings with a “green rating” could command rental rates that were roughly 3% higher
per square foot than otherwise identical buildings after controlling for the quality andthe specific location of the office buildings. Premiums in effective rents were even higher,
above 7%. Selling prices for green buildings were higher by about 16%. The percent
increase in rent or value for a green building was systematically greater in smaller orlower-cost regions or in less expensive parts of metropolitan areas. Apparently, theprivate market did incorporate the “green” certification information in the determinationof rents and asset prices. Fuerst and McAllister (2011) also used a hedonic approach toconfirm that environmental certification creates an economic premium in terms of rent
and asset value in the US commercial real estate market.
Most of the studies have verified the existence of an economic premium for “greenness.”Many of these use data on the US office market because of the ease of obtaining suchdata. However, this highlights the limitations of the results and raises the question as
to whether they can be generalized to other locations and periods.
Deng, Li and Quigley (2012) and Zheng et al. (2012) were the first studies todemonstrate that environmental certification creates an economic premium in the realestate markets of Asia. In Deng, Li and Qugiley (2012), they adopted a two-stage
research design. In the first stage, a hedonic pricing model was used based ontransactions involving green and non-green residential units in 697 individual projects
or estates. In the second stage, the fixed effects estimated for each project were regressedon the locational attributes of the projects as well as control variables for a Green Mark
(GM) rating. Their results suggested that the economic returns on green buildings were
substantial. The returns varied by Green Mark category – both Platinum and Gold werepositive and statistically significant. Green Mark Platinum remained significant using
propensity score matching (PSM), the nearest neighbor matching of control andtreatment samples. The study provided insight about the operation of the housing
market in one country but the policy implications about the economic returns tosustainable investments in the property market may have broader applications for
emerging markets in Asia.
Zheng et al. (2012) used two unique geo-coded micro-data sets to explore the nascent“green housing market” in Beijing, from both the supply and demand sides. Based on
1See, for example, Eichholtz, Kok and Quigley (2009), (2010), Eichholtz and Quigley (2012), Fuerst
and McAllister (2011), Miller et al. (2008), and Reichardt et al. (2012).

5

Google searches, they built a sample that contained information on whether a certain
housing complex green-related characteristics were emphasized during marketing,focusing on information that developers wished to convey to potential buyers. The study
found that a nascent “green housing market” did exist in Beijing. They suggested thatthe introduction of a standardized official certification program would help ‘‘green’’buyers find units and accelerate the advance of China’s nascent green real estate market.
Based on a China’s housing market data, Deng and Wu (2014) suggested a potential
mismatch that developers face between outlays and benefits in the residential green
building sector and how this mismatch could impede further development of green
residential properties. The study found that the “green price premium” of residential
development comes largely during the resale phase relative to the presale stage. The
premium for GM-rated units was about 10% at the resale stage compared to about 4%
during the presale stage. This implies that while developers pay for almost all of theadditional costs of energy efficiency during construction, they share only part of thebenefits associated with such green investments.
In addition to the above studies, Zheng, Wu and Kahn(2008) and Zheng et al. (2012)
found significant price premiums for green properties in the Chinese housing market.
Moreover, Deng, Li and Quigley (2012) found substantial economic returns on green
buildings in Singapore. Kahn and Kok (2014), as well as Hyland et al. (2013), arrived atsimilar conclusions for the Californian and Irish housing market, respectively.
There are several studies on the green premium in Japan’s housing market. Shimizu(2010, 2013) employs the hedonic model and shows that new condominiums with “greenlabels” in Tokyo command a premium of approximately 5.8% in their offer prices and4.7% in their transacted prices. Such positive premiums are further verified by Yoshida
and Sugiura (2015). Their findings echo the studies on green premiums in othercountries’ residential property markets (Banfi et al., 2005; Fuerst et al., 2015; Brounen
and Kok, 2011; Zheng et al., 2012; Deng et al., 2012; Kahn and Kok, 2014; Hyland et al.,2013).
Fuerst and Shimizu (2016) used a unique transaction database of condominiums in
the Tokyo metropolitan area and a hedonic analytical framework. They found that green
buildings commanded a small but significant premium on both the asking andtransaction prices. As far as they knew, their study was the first one on green buildings’
economic premiums based on a hedonic model incorporating “buyer characteristics.”
However, further analysis revealed that this premium was primarily driven by wealthy
households who exhibited a higher willingness-to-pay for eco-labeled condominiums,
both as a total amount and as a fraction of the total sales price. They, therefore, concluded

6

that eco-labels were perceived as representing luxury goods in the Japanese housing
market rather than a means to save money on lower utility bills.
Based on the prior studies described, our study uses Deng, Li and Quigley (2012) andFuerst and Shimizu (2016) as a starting point to build the following empirical model.
First, there may be systematic differences in building attributes between green and nongreen buildings. Without carefully controlling for such differences, researchers willobtain a biased estimate of the green premium. PSM is widely used to mitigate thisproblem. However, if most of the green buildings are concentrated in a specificsubmarket (for instance, newer and larger-size buildings), the PSM results will mainly
show the green premium in that submarket and other submarkets with less green
buildings will be under-represented. In this study, we use PSM with clustering analysis
to address this issue to reveal the green premiums in various submarkets. We thenconduct PSM again, even in specified clusters where economic premiums were detected,
and also conduct robustness tests.
Second, there may be some systematic differences in dweller attributes between green
and non-green buildings, namely, firms and residents may sort themselves along the
economic performance dimension of the building. Fuerst and Shimizu (2016) estimateda hedonic model that considered household characteristics, such as income. They foundthat the higher the income group, the higher the economic value was. In the office market,
firms with stronger social responsibility or a higher sensitivity to energy expenditure
may have a higher willingness-to-pay for green office space, and thus, we should observea larger share of such firms in green buildings compared to non-green buildings. One
merit of our data set is that we have some information on tenant characteristics. We areable to include such information in our model to investigate whether the green label
creates the premium by itself or whether it triggers tenant sorting so the observed green
premium is the differential of willingness-to-pay among different tenant groups.
With environmental technology continuing to develop, we are confident that this study
demonstrates a new perspective in the field of measuring the economic premium createdby environmental certification of real estate. The study not only looks at the office market
in Tokyo—which boasts the largest stock of office buildings in the world—but also
carefully uses PSM to differentiate between office buildings with and without
environmental certification and verifies the effect of environmental certification while
controlling for tenant attributes.

7

3. Estimation model and data
3.1 Estimation model
We estimate a price function for new contracts to rent office space by specifying thepresence or absence of the green label. The new rent contract for office space is a functionof the characteristics of the office building and the green label certification condition. It
is generally expressed as a hedonic price model, as shown below.

𝑅 = h(𝑔𝑟𝑒𝑒𝑛 , 𝑥 ),(1)

where 𝑅 on the left-hand side of equation (1) represents the new rent contract ofcompleted contract case𝑖; 𝑔𝑟𝑒𝑒𝑛 = (0, 1) is the green label dummy;𝑥′=(𝑥 , 𝑥 , … , 𝑥 ) is the vector that expresses the n characteristics of the completed
contract case 𝑖. In this study, we employ the log-linear function form:ln 𝑅 = α + 𝑔𝑟𝑒𝑒𝑛 ′ ∙ 𝛽 + 𝑥 ′ ∙ 𝛾 + 𝜀 ,(2)where ln 𝑅 (new contract rent, in logarithm) is the dependent variable, α is theconstant term, 𝑔𝑟𝑒𝑒𝑛 and 𝑥 are the independent variables, and 𝜀 is the error term.
Here, the parameters to be estimated are α, β, γ, and β = (𝛽 , … , 𝛽 ); and γ = (𝛾 , … , 𝛾 )are the vectors of the coefficients corresponding to each independent variable.
Next, we describe the independent variable 𝑥 used in equation (2). First, we apply
gross building area, standard story area, and number of above ground stories as thevariables measuring the scale of an office building. Second, we use age and whether arenovation had been carried out as the variables to measure how new the building is.
Additionally, we apply travel time to the nearest metro station and the presence orabsence of office specifications that tenants find appealing such as a higher floor,
individual controlled air conditioning, and automated security, as indicators of
attractiveness.
Further, we add to the model an office area dummy and a dummy for the time thecontract was completed to control for omitted variables in space and time. We use an
office area dummy variable to capture the effects of location on rent, obtained by dividing
a typical office area in Tokyo’s 23 wards into 50 areas. In Tokyo, tenants tend to beconcentrated in developed areas that are convenient and popular office areas. These
include Marunouchi, Otemachi, Shinjuku, Shibuya, Shinagawa, Nihonbashi, Shinbashi,
Toranomon, Akasaka, and Roppongi. In these popular areas, rents tend to be high, even8for properties that are of medium- to small-scale or old. Conversely, in areas that are not
popular as office areas, it is difficult to observe a contract with a high rent, even if theproperty is large and new.
The demand and supply conditions in the market at the time the property is offeredaffects the rent. A two-year sample period, from January 2013 to December 2014, wasdivided into eight quarters and quarterly dummy variables were constructed according
to the time the contract was completed.

3.2 Data
We collect and then merge the data on rents and green labels to create the dataset foranalysis. First, for the rent data, we use the completed contract case database for officebuildings collected by the Xymax Corporation.2 In Japan, most rent data are available
on offered rents. However, there may be a divergence between the offered rent and thecontract rent due to a decision following negotiations between the property owner andthe tenant. To achieve the objective of this study, we consider it preferable not to use theoffered rent but rather the completed-contract rent, which reflects actual market
conditions more accurately. Here, we use such completed-contract rent data on newcontract rentals collected by Xymax. 3 Their database includes many variables
measuring the characteristics of the buildings, including scale, such as gross buildingarea; performance, such as the building’s age and air-conditioning systems; location,such as the time to walk to the nearest metro station and the area in which the building
is located; and the time period in which the contract was completed. Considering thatthe decision structure for the rental is generally determined by the area and the good or
bad conditions of the office market at the time in question, the completed contract cases
we use for analysis were set within Tokyo’s 23 wards, with a gross building area of morethan 300 tsubo, completed between January 2013 and December 2014. As a result, we
extracted data on 2,689 buildings and 6,758 new rent contract cases as our sample.
Next, for the data on the presence or absence of a green label, we survey the homepagesof organizations and the various companies granting the green label and collect andarrange the published information. For the analysis, we select the following green label
systems: CASBEE, CASBEE for real estate, DBJ Green Building Certification, and2
3

Xymax is one of the representative property management companies in Japan. https://www.xymax.co.jp/english/
The data registered in the Xymax Corporation database are rent data based on completed contracts from within theimplementation of real estate management operations. As of the end of December 2014, the company had
accumulated information on around 5,300 properties and approximately 33,000 cases (in Tokyo’s 23 wards, and agross building area of more than 300 tsubo).

9

SMBC Sustainable Building Assessment.
There were three reasons for selecting these systems. First, in these systems, thecertification is acquired by the building. This makes it possible to integrate the rent data
and the green certification data by building, which enables us to conduct the analysis
using the green label as one factor affecting rent. 4 Second, these are certification
systems assessing comprehensive environmental performance, that is, they assess not
only the energy-saving performance of the buildings but also aspects such as reductionof the environmental load, usefulness to users, and management policy.5 Third, in thesecertification systems, a third party organization carries out the assessment based on thestandards that these systems have established.
Our survey of the office buildings located in Tokyo’s 23 wards showed that there eightbuildings were under the SMBC Sustainable Building Assessment, 90 buildings under
DBJ Green Building Certification, 77 buildings under CASBEE, and 36 buildings under
CASBEE for real estate (as of November 2014).
We integrate data on green-label certification and data on the rents to use for the
analysis. Specifically, for the dummy variable (hereafter, the green label dummy), if abuilding has acquired any one of the green labels used for the analysis, the green label
dummy will take the value 1, and 0 otherwise. These dummy variables are merged withthe rental data.
The data used for the analysis are comprised of 6,758 completed contract cases (2,689
buildings). Among them, we record 361 cases as having a green label dummy value of 1,
which accounts for 5.3% of the sample.

4. Estimation strategy and results
4.1

Hedonic model estimation results

Table 1 lists the variables used for the analysis, and Column 1 of Table 2 shows thesummary statistics.
4

Within the green label systems, there are those that carry out assessments and grant certifications on corporations andportfolios. However, as it is difficult to integrate such data with rent data on unit properties, they are excluded fromthe analysis.
5 In Japan, the growth in the use of green label systems is still underway. Therefore, if we used only an individual
certification system, the number of samples would have been insufficient for analysis. This is why we added
information that considers if the building acquired certification from any one of the multiple systems. Although thedetailed assessment items and standards are different in each of the systems used for analysis, they have in common
the fact that they all assess comprehensive environmental performance. Therefore, we did not consider integrating
their data into the analysis as a problem. There are various other systems that only assess energy saving performance.
However, we excluded them from the analysis, as we decided it would become difficult to use them when interpreting
the results.

10

Table 1 Variables and description
Variable

Content

Unit

New contract rent

The contract rent when newly entering into the building (not the offered price when
it was being offered to tenants)

Yen/sqm

Green label dummy

Buildings granted a green label: 1

Gross building area

Gross building area of the building

sqm

Age

Number of years since construction

Year

Number of above-ground stories

Number of above-ground stories in the building

StoriesStandard story area

Standard story area of the building

sqm

Five city-center wards dummy

In the event that the building is located in one of the five city-center wards (Chiyoda
Ward, Chuo Ward, Minato Ward, Shinjuku Ward, and Shibuya Ward): 1 Allothers: 0(0,1)

Time to nearest station

The number of minutes to walk to the building from the nearest station

Raised floor dummy

If a raised floor has been installed in the building: 1 All others: 0(0,1)

Individual air conditioning dummy

If individual air conditioning has been installed in the building: 1 All others: 0(0,1)

Automated security dummy

If automated security has been installed in the building: 1 All others: 0(0,1)

All others: 0(0,1)

Minutes

If the building has been/is being renewed at the time the contract was completed: 1
All others: 0
On preparing dummy variables for each time of contract completion(quarter), If thetime the contract has been completed corresponds to the quarter : 1 All others：0
On preparing dummy variables for each typical office area, in the event that there isa building: 1 All others: 0

Renewal dummyTime of contract completion dummy
Area dummy

(0,1)
(0,1)
(0,1)

Table 2 Summary dataset statistics
Full Sample

Green-Label

Non-Green-Label

Number of Obsarvations Number of Obsarvations Number of Obsarvations= 6,758= 361= 6,397
standard
standard
standard
mean
mean
mean
deviation
deviation
deviation

New contract rent

5,169.73

1,862.02

Green label dummy

0.05

0.22

Gross building area

7,638.37

2,154.42

5,030.42

1,743.04

1.00

-

0.00

-

18,928.33 37,444.15 61,717.76 80,973.86 16,513.61 31,669.82

Age

23.73

11.83

8.78

9.77

24.57

Number of above-ground stories

11.69

7.70

20.17

12.50

11.21

11.36
7.04

Standard story area

780.24795.11

1,533.13

1,217.62

737.76742.05

Five city-center wards dummy

0.77

0.42

0.86

0.35

0.76

0.42

Time to nearest station

3.36

2.31

2.98

1.80

3.38

2.34

Raised floor dummy

0.68

0.46

0.96

0.21

0.67

0.47

Individual air conditioning dummy

0.80

0.40

0.81

0.39

0.80

0.40

Automated security dummy

0.83

0.37

0.87

0.33

0.83

0.38

Renewal dummy

0.13

0.34

0.12

0.33

0.13

0.34

We carry out ordinary least squares (OLS) estimates based on equation (2); Column 1of Table 3 shows the results. Based on our analysis in Section 1, we expect a significantly
positive coefficient for the green label dummy. The estimation result of ＋0.0439 (0.0115)
is positive and significant (the figures in parentheses are the standard errors). This11

shows that the new contract rent for buildings granted green labels was approximately
4% higher than other new rental contracts.
Table 3 Results of hedonic and probit regressions
(1)

(2)

(3)

OLS

ProbitOLS

Baseline
Constant

Green label dummy

Gross building area (logarithm)

Age

Number of above-ground stories

Standard story area (logarithm)

Time to nearest station

Raised floor dummy

Individual air conditioning dummy

Automated security dummyPSM

7.8684***-3.9439***-74.6288(0.0318)(0.5239)(86.6478)

0.0439***-1.6673(0.0115)(1.8874)

0.1074***0.4199***-6.0229(0.0090)(0.1331)(6.0873)-0.0089***-0.0727***-0.0628(0.0003)(0.0045)(0.0554)0.0029***-0.0146*

1.6052(0.0007)(0.0083)(1.6120)0.0027-0.0492

17.2996(0.0103)(0.1522)(17.8718)-0.0238***-0.01490.7583(0.0011)(0.0178)(0.7291)0.00400.0938-2.9774(0.0058)(0.1368)(3.1113)0.00510.1596*-11.5097(0.0066)(0.0970)(11.7921)-0.0118*0.163312.8193(0.0067)(0.1132)(13.5811)

0.0334***1.0639***-5.7735(0.0079)(0.1257)(6.2213)Time of contract completion dummy

Yes

Yes

Yes

Area dummy

Yes

Yes

Yes

6,758

6,758

722

Renewal dummy

Number of samples
Percentage of buildings
with green label
adjusted R-squared

5.34%

50.00%

0.67700.9213Log likelihood-831.1474

AIC

1814.2950

Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01

These results suggest that obtaining the green label was an economically rational

12action that could be expected to earn higher returns. However, we should consider that
there may be other factors that correlate with both the green label and office rents, which
could lead to omitted variable bias.
Many of the buildings granted green labels were developed by major developers and
REITs (Real Estate Investment Trusts); they are generally large in scale and relatively
new. These types of developers have an abundance of financial strength compared todevelopers of medium- or small-scale building projects. Therefore, they are able to pay
the costs necessary to acquire a green label. In some cases, they are willing to get
involved in social responsibility activities and their shareholders and investors also
demand that they make this effort. Therefore, it is possible that the conditions aroundgreen label certification will differ depending on the developer and development
characteristics of the office building.
We also consider that the conditions for green certification would differ depending on
the building’s age. Based on the progress in construction technologies, such as
improvement in insulation for building exteriors and higher equipment efficiency, theenvironmental performance of newly constructed office buildings is improving annually.
An old office building, with inferior environmental performance, would need to carry out
a preliminary survey and refurbishments to acquire a green label, both of which would
incur costs. In addition, from the preliminary survey results, there would be little
prospect of the building obtaining a high score and grade; thus, few owners of old officebuildings would actually take the actions necessary to pursue green certification.
Conversely, office buildings that have been newly constructed or are only a few years old
will already have higher environmental performance in areas such as the building
exterior and equipment. Thus, they would be able to acquire the green labels more easily.
In particular, in the case of newly constructed office buildings, possibly, the design andconstruction may be carried out while keeping the green label certification in mind at
the planning stage. Therefore, the refurbishment costs necessary for existing buildings
would not be incurred. In other words, in an economic analysis of environmental
considerations, a problem may exist where factors such as scale and newness become
proxy variables for a green label.
Table 2, Columns 2 and 3 show the statistics of green and non-green office buildings.
The average values for gross building area and age since construction for samples thatwere granted a green label were 61,717 m2 and 8.78 years, respectively. In contrast, thesevalues were 16,513 m2 and 24.57 years, respectively, for samples that were not granted
a green label, showing that these buildings tended to be of a small scale and older. In

13this study, it is difficult to distinguish between environmental performance and othercharacteristics. The question remains whether the estimation of +4.39% as the greenlabel dummy coefficient is really attributable to the presence or absence of a green label
or whether it comes from a mixture of the effects of scale, newness, location, and theperiod the contract was completed. Therefore, it is necessary to be cautious when
explaining the coefficient of the green label dummy in the basic OLS regression.
We can also understand this issue from the perspective of the hedonic approach. We
consider that the decision whether or not to acquire a green label is also affected byvariables common to the rental hedonic model, such as size and newness, which leads tothe endogeneity issue.
4.2 Estimation using propensity score analysis (1): The whole sample
This study employs the following two methods to address the endogeneity issue
mentioned: (1) PSM (see subsection 3.2) and (2) stratification based on the propensity
score (see subsection 3.3). In PSM, we first estimate a probit model of the probability ofobtaining a green label (i.e., the propensity score). Next, based on the estimatedpropensity score, we create two similar groups, and let the large difference between thesegroups indicate the existence of a green label. Then, we proceed with the analysis by
estimating the effect of a green label on new contract rent.
When processing the data using the propensity score, it is possible to take a constantdegree of balance between the two groups with regard to the average value of thecovariate, thereby making it possible to address endogeneity. In addition, because
covariates of multiple dimensions are combined, this method enables a detailed analysis
using clustering.
The variables are defined as follows:𝑌:

New contract rent, with green label granted;

𝑌:

New contract rent, with green label not granted;

D:

1: When green label is granted to an office building;
0: When green label is not granted to an office building;𝐱:

Characteristics of an office building that can be observed as a covariate,
where 𝐱 is a vector of the components (𝑥 , … , 𝑥 );P(𝒙) = 𝑃𝑟(𝐷 = 1|𝒙): Forecasted probability of a green label being granted to an
office building with characteristics 𝐱.

14

The effect of the green label on new contract rent, when treated as the difference
between office buildings being granted or not granted a green label, is expressed asfollows:(3)

E(𝑌 |𝐷 = 1) − E(𝑌 |𝐷 = 0).

If the size or newness of a building affects whether it has a green label, it is possiblethat there is bias in the estimation effect. In other words, if new contract rent increases
because of high quality and a transaction is concluded, irrespective of whether a greenlabel is granted, the new contract rents in the office buildings that have not been granted
green labels also increase. Therefore, we consider that the strict green label effect can be
expressed as follows:∆(𝒙) = E(𝑌 − 𝑌 |𝑃(𝒙), 𝐷 = 1).

(4)This formula concerns office buildings that have been granted a green label andexpresses the difference in the new contract rent between when a green label is granted
and when it is not. Through this expression, if we know the new contract rent in an officebuilding with a green label, we can simply extract the effect of the green label if it has
not been granted. Because it is normally not possible to observe such “hypothetical” newcontract rents, the value that represents this is assigned a weight by P(𝒙) and the newrent is estimated from the new rents of office buildings without green labels. When thisvalue is matched with the new contract rents of office buildings that have been granted
green labels, the estimated value of the effect of a green label is expressed as follows.

∆

(𝑥) =

𝑌 (𝒙𝒊 ) − 𝐸 (𝑌 |𝑃(𝒙𝒊 ), 𝐷 = 0) ,

∑
{

}

(5)

where 𝑛 denotes the number of buildings that have been granted green labels. New
rents at office buildings that have not been granted green labels, which are used as
proxies, will have an expected value as follows.

𝐸 (𝑌 |𝑃(𝒙𝒊 ), 𝐷 = 0) = ∑𝑊 𝑃(𝒙𝒊 ) 𝑌 ,(6)

where 𝑛 is the number of samples that have not been granted green labels and W is15the weight assigned by P(𝒙). In section 3.2, nearest neighbor matching is used to assign
weights for comparisons. Under nearest neighbor matching, the matching is performed
on office buildings with the nearest probability of being granted a green label, those thathave been granted a green label, and those that have not been granted a green label. The
new contract rent at an office building that has not been granted a green label but has avery similar probability (in terms of having a green label) to a green office building isused as a proxy for new contract rents.
Note that the following probit regression is used to estimate the probability of a greenlabel being granted to an office building possessing 𝐱 characteristics. Here, β is avector of the elements (𝛽 , … , 𝛽 )′.

P(𝒙) = Pr(𝐷 = 1|𝒙) = Φ(𝒙𝜷) = ∫

𝑒𝑥𝑝 −

𝑑𝑧.

(7)

The study of the following office characteristics determines whether a green label is tobe granted: gross building area (m2), standard story area (m2), above-ground stories(stories), age (years), area dummy, time to nearest station (minutes), raised floor dummy,
individual air conditioning dummy, automated security dummy, and time of contract
completion dummy.
The estimation results of the probit regression are reported in Column 2 of Table 3.
The coefficient of age is significantly negative, which reflects that younger buildings are
more likely to be granted green labels. Although the coefficients for raised floor,
individual air conditioning, and automated security are not statistically significant at
the 5% level, they all have a positive sign. Thus, we consider that there is a propensity
for office buildings to be granted green labels when they are equipped with such facilities.
We find that the coefficient of gross building area is positive, reflecting that green labels
are more likely to be granted to larger buildings. The time to nearest metro station was
not statistically significant.
These results indicate that the probability of acquiring a green label is higher for highquality office buildings that are younger, larger, and better equipped. To satisfy thecomprehensive environmental performance requirements for a green label, the exterior
and the air conditioning need to be energy efficient, the plumbing system designed toconserve water, there needs to be greenery around the building, and there must be
appropriate maintenance and good building management with respect to disaster andbusiness continuity planning. We consider that unless the office building is large, it

16

would be difficult to realize a sufficient plot size and business profitability to enable theestablishment and sustainable management of such a building.
Table 4 shows the summary statistics of the datasets extracted from the nearest
neighbor matching, using the propensity score that measures the probability of obtaining
green versus non-green labels. To perform a one-to-one matching of green and non-green
buildings, a sample size of 361 was set for each, for a total sample size of 722.
Before matching, the average value of gross building area was 61,717 m2 for green
buildings and 16,513 m2 for non-green buildings. After matching, the difference wassmaller, with a gross building area of 61,717 m2 for green buildings and 48,572 m2 fornon-green buildings.
With regard to average age, before matching, the average age was 8.78 years for green
buildings and 24.57 years for non-green buildings. After matching, the average age was
8.78 years for green buildings and 8.36 years for non-green buildings. Other variables
were adjusted as well and by using PSM, the differences between the two groups reduced,
enabling us to create similar samples.
Table 4 Summary statistics of dataset (after propensity score matching)

New contract rent

Green-Label

Non-Green-Label

Number of obserrvations = 361

Number of observations = 361

meanstandard
minimum maximum
deviationmean

standard
minimum maximum
deviation

7,638.37

2,154.42

3,025.00

16,649.60

7,625.18

2,283.37

2,420.00

Green label dummy

1.00

0.00

1.00

1.00

0.00

0.00

0.00

13,612.50
0.00

Gross building area

61,717.76

80,973.86

1,785.06

379,447.90

48,572.06

56,058.70

1,100.83

263,996.70
35.50

Age

8.78

9.77

0.00

50.75

8.36

8.03

0.00

Number of above-ground stories

20.17

12.50

5.00

54.00

18.32

10.26

5.00

54.00

1,533.13

1,217.62

182.91

5,054.55

1,394.13

1,120.63

115.746,760.33

Five city-center wards dummy

0.86

0.35

0.00

1.00

0.84

0.37

0.00

1.00

Time to nearest station

2.98

1.80

0.00

10.00

2.81

2.16

0.00

10.00

Standard story area

Raised floor dummy

0.96

0.21

0.00

1.00

0.93

0.26

0.00

1.00

Individual air conditioning dummy

0.81

0.39

0.00

1.00

0.82

0.38

0.00

1.00

Automated security dummy

0.87

0.33

0.00

1.00

0.89

0.31

0.00

1.00

Renewal dummy

0.12

0.33

0.00

1.00

0.09

0.28

0.00

1.00

Column 3 of Table 3 shows the estimation results of the hedonic approach for the
sample, calculated using PSM. The estimated coefficient of the green label dummy is 1.6673 (1.8874) and is not a statistically significant, which means a green label does notbring in additional rent premiums. This result is quite different from what we obtained
from the OLS regression in section 3.1.
It is important to note that the extracted samples are centered on large, newly
constructed office buildings. We believe these are affected by those that matched the 361
green building sample and 6,391 non-green building sample in a one-to-one. Table 2

17

showed the average gross building area as 16,513 m2 and average age of non-green
buildings as 24.57 years. The averages of the same variables for non-green buildings inTable 4, using matching by propensity score, were 48,572 m2 and 8.36 years, respectively.
The data of the approximately 6000 non-green buildings that were not matched were
excluded.
Therefore, we interpret the results in this section as follows: a green label has almost
no effect or even a slightly negative effect on comparatively large and young officebuildings. However, the effect on other office buildings is unknown. In the analysis of thePSM, it is problematic that the effect of a green label on medium- and small-sized andon old office buildings cannot be confirmed.
4.3 Estimation using a propensity score analysis (2): subsamples by clustering
To address the problem raised in the previous section, we divide the sample into fiveclusters based on the value of the propensity score. Since the properties with a low
propensity score tend to be smaller and older, we should also be able to analyze theclusters of mid- to low-propensity scores.
To calculate the propensity score, we used the same method described in section 3.2.
By using the estimated propensity scores, the sample is divided into five clusters. Fivequantiles are used as the boundaries of these clusters and the number of samples in eachcluster is nearly the same.
Columns 1 to 5 of Table 4 show the estimation results of the hedonic function for eachcluster using the same method described in section 3.1. In cluster 5, which has thehighest propensity score, the value is negative and nearly zero (-0.0058 (0.0105)). The
covariates of stratum 5 show that the office buildings in the sample are large and young(in cluster 5, the average gross building area is 50,057 m2 and average age 9.86 years).
In cluster 4, the result is +0.1378 (0.0328), which is a significant and positive effect.
Compared with cluster 5, the medium-sized and older office buildings in this cluster havea high propensity score (in cluster 4, the average gross building area is 14,789 m2 andaverage age 22.13 years). We find there are no statistically significant effects in clusters
1 to 3 because the sample size ratio of green to non-green buildings is small, which means
there are insufficient green buildings to estimate an effect. The effect of a green label on
new contract rents is not uniform for all office buildings. Instead, it depends on thecharacteristics of the office buildings. Larger properties with green labels hardly show
any effect, while mid-sized properties show a significant effect of more than +10%.

18

Table 5 Results of hedonic regressions (PS Clustering sample)

Constant

(1)

(2)

(3)

(4)

(5)

Cluster
no.1

Clusterno.2

Cluster
no.3

Cluster
no.4

Cluster
no.5

7.6919***7.8393***

7.8943***7.9906***7.9708***(0.0785)(0.0781)(0.0816)(0.0791)(0.0695)

0.2453**-0.0343

0.1378***-0.0058(0.1231)(0.0950)(0.0328)(0.0105)

0.1556***

0.0961***0.0801***

0.0967***0.0767***(0.0216)(0.0273)(0.0238)(0.0220)(0.0167)-0.0073***-0.0023**-0.0074***-0.0111***-0.0130***(0.0007)(0.0009)(0.0022)(0.0015)(0.0008)-0.0004

0.00270.0078***0.0043**0.0034***(0.0018)(0.0027)(0.0020)(0.0019)(0.0010)

Green label dummy

Gross building area (logarithm)

Age

Number of above-ground stories

Standard story area (logarithm)

Time to nearest station

Raised floor dummy

Individual air conditioning dummy

Automated security dummy-0.0452*-0.01390.0104-0.0008

0.0382**(0.0246)(0.0309)(0.0226)(0.0230)(0.0187)-0.0240***-0.0215***-0.0247***-0.0158***-0.0218***(0.0027)(0.0032)(0.0024)(0.0023)(0.0022)0.0271**-0.00830.0147-0.0256*-0.0078(0.0124)(0.0134)(0.0115)(0.0133)(0.0216)0.0158-0.0134

0.0116-0.0172-0.0099(0.0167)(0.0175)(0.0164)(0.0140)(0.0119)-0.0495***

0.00780.01650.00250.0081(0.0151)(0.0145)(0.0166)(0.0164)(0.0136)0.0202-0.02080.0946**

0.0553**0.0397**(0.0218)(0.0233)(0.0372)(0.0239)(0.0182)

Time of contract completion dummy

Yes

Yes

Yes

Yes

Yes

Area dummy

Yes

Yes

Yes

Yes

Yes

Number of samples

1,352

1,351

1,352

1,351

1,352

Percentage of buildings
with green label

0.00%

0.22%

0.30%

2.22%

23.96%

adjusted R-squared

0.46690.50340.48520.62180.7684

Renewal dummy

Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01

4.4 Verification of estimation robustness and extended model estimation
Here, we verify whether similar results can be obtained even when analyzing ahomogeneous sample, and how the age of the building affects the impact of the greenlabel over time. When we observe each of the clustered sample groups in subsection 3.3,
we find office buildings with green labels with higher propensity scores than others.
Although we extracted the samples through clustering next to those with relatively

19

closer propensity scores, we may be able to further remove the effect caused by thecovariates from each cluster. Therefore, we choose to conduct further PSM within theclustered sample groups, extract samples, and then estimate the hedonic function. Thus,
we will be able to verify the effect of a green label in samples with greater homogeneity,
which are not influenced by covariates.
Subsequently, we verify the effect of a green label based on the age of the buildings
and how this changes over time. In the probit regression model in subsection 3.2, welearned that newer buildings were more likely to acquire green labels. Therefore, we can
expect that even with clustered data, a certain relation will still exist between the age ofthe building and the green label dummy. We create our estimate based on a model that
adds the green label dummy and the age as an interaction term into equation (2). There
are only 30 cases in the data that have been granted green labels within the samples incluster 4 where the age will be a true value instead of a dummy variable,
Column 2 of Table 6 shows the results of the hedonic function estimate after we extract
the samples and conduct further PSM within cluster 4. The coefficient of the green label
dummy is +0.1297 (0.0370), thereby being a significant result. This result is equivalent
to those in subsection 3.3, proving the robustness of our analysis.
Next, Column 3 of Table 6 shows the estimation result of the model that included theage of the buildings and the green label dummy as interaction term. In cluster 4, thecoefficient of the green label dummy is +0.3888 (0.0824), showing a significant positive
result, and the coefficient of the interaction term of the green label dummy and the ageof the building is -0.0131 (0.0039), showing a significant negative result. From theseresults, we can see newer buildings benefitting from the highly positive effect of being
green labeled but this effect diminishes with time and, finally, there are no further effects
at about 30 years from the original construction.
When we consider this, we believe it is because tenants are inclined to pay higher rent
for comparatively more attractive office buildings. Office buildings that are large andnew are implicitly meeting or exceeding environmental performance even without greenlabeling. Such buildings include various elements (e.g., attractive exteriors, building
function specifications, location, special facilities such as business continuity plans, etc.)
apart from a green label, and tenants are able to recognize good quality office buildings
using this information. Moreover, because about a quarter of all large and new officebuildings on the market have been granted a green label, we consider that the ubiquityof green labels is also a reason for these results. This helps to explain why the greenlabel dummy loses its significance in cluster 5 in the PSM regression.

20

Table 6 Robustness check and expansion model
(1)

(2)

(3)

PSM

Constant(4)

Cross term
(Green×Age)

PSM+TSR

Cluster

Cluster

Cluster

Cluster

no.4

no.4

no.4

no.4

7.9906***(0.0791)

9.0946***(0.3604)

Green label dummy

7.9628***(0.0792)-0.01319.6544***(0.4914)***(0.0039)
Green label dummy × Age

0.1378***(0.0328)0.1191***(0.0366)***

0.0556(0.0824)(0.0380)

0.3888***TSR Score

0.0116(0.0035)

Gross Building Area (logarithm)
Age

***

-0.1295

0.0991***-0.1913(0.0220)(0.1113)(0.0219)(0.1039)

0.0967***0.01-0.0106(0.0015)-0.0111***0.0220***(0.0101)(0.0015)

Number of above-ground stories

0.0043**0.01180.0045

Standard strory area (logarithm)(0.0019)-0.0008(0.0090)0.0222(0.0019)-0.0018(0.0105)-0.0551(0.0230)(0.1104)(0.0229)(0.0936)

Time to nearest station-0.0158***-0.024-0.0158***-0.0077(0.0023)(0.0212)(0.0023)(0.0189)*-0.0159-0.0268**-0.0358(0.0133)(0.0588)(0.0133)(0.0586)-0.0172-0.1664-0.0179(0.0140)(0.1039)(0.0140)-0.2976(0.1139)0.00250.2029*0.0059-0.2327(0.0164)(0.1113)(0.0163)(0.1332)

Raised floor dummy
Individual air conditioning dummy
Automated security dummy
Renewal dummy-0.0256

0.0553**0.0500(0.0239)**(0.0088)0.0205

*

**

*

**

(0.0238)

Time of contract completion dummy

Yes

Yes

Yes

Yes

Area dummy

Yes

Yes

Yes

Yes

Observations
Percentage of buildings with green label

1,351
2.22%

60
50.00%

1,351
2.22%

51
50.98%

0.63640.883

0.63950.9441

Adjusted R

2

Note: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01

On the other hand, when it comes to medium- to small-sized old office buildings, there
are fewer attractive elements that lead to differentiation for tenants. In this case, a greenlabel shows that the office building is of comparatively good value. In this market, officebuildings with a green label have always been rare, and thus, stand out. However, overtime, obtaining a green label would not be sufficient to regain competitiveness. In suchcases, perhaps it would be necessary to consider drastic measures such as rebuilding or
renovating such buildings.

21

4.5 Estimating the hedonic model considering the characteristics of tenants
After clustering according to the propensity score, even in those clusters withenvironmental certifications, as well as pair data analysis matching the same number
due to the propensity scores, we learn there is a premium of about 12%. However, wecannot conclude from these results that environmental certification is related to thepremium. In Fuerst and Shimizu (2016), the results of considering the attributes of homebuyers in the housing market in Tokyo revealed that more high-income earners paid
expensive premiums for environmental certification. Eichholtz et al. (2009) also pointed
out that among the corporations that have office buildings with environmental
certifications, many are retailers with direct contact with consumers, the government
related agencies, or energy-related corporations. If the attributes that position the officebuildings for environmental certification were to change due to tenant characteristics,
the economic premium for the environmental certification would also change due todifferences in those attributes. In that case, the economic premium estimated without
considering tenant characteristics would be overestimated.
Here, regarding cluster 4, which had the largest influence on how the existence of anenvironmental certification affects new rental agreements, an analysis was conductedby expanding this model and including the attributes of current tenants. The attributesof tenants were gathered by collecting information on current tenants in 60 properties
in cluster 4 according to the tendency score matching (30 with environmental
certification and 30 without). 6 The result was 299 tenants (124 tenants withenvironmental certification and 175 tenants without) identified as tenants occupying
office floors. When these tenants were matched with the corporate information of Tokyo
Shoko Research (TSR), which collects corporate and financial information representativeof Japan, we were able to obtain corporate and financial information for 50 corporations
with environmental certification and 60 corporations without it.
In Table 7, the companies are separated into the environmentally certified group(certified) and the non-certified group (non-certified). In addition to quantitative data,such as financials, TSR 7 also looks at scores created by adding the management
quantitative data and various distribution of capital, number of employees, mean value

The data source is Zenrin Co., Ltd. Zenrin investigated the names of the tenants on each floor of each of thebuildings they visited. They conducted the research once every three months in the Tokyo region.
7 The TSR score is created by combining financial data such as capital, sales, number of employees, and revenue. It
also takes into consideration attributes such as social credibility, which cannot be measured by management’s
qualitative attributes and financial information. Ono and Uesugi (2006) applied the TSR score to prioritize
transactions from financial institutions.
622

of sales, standard deviation, and verification of the difference in means (p-value), which
are shown in Figure 1.
Table 7 Summary statistics on corporations
Certified

Non-Certified

p-value

Score

59.04
-6.90756.683-6.961

0.079

Capital14,767,295-51,890,634

20,608,054-124,465,592

0.744

Number of employees

3186.26-11,630.61

950.41-2,392.09

0.187

Sales

205,545,491-805,873,579

115,977,306-585,563,697

0.515

Number of observations

50

60

As can be understood from the test statistics of the difference in mean value and shownin Figure 1, though there is no difference in capital, number of employees, or sales
between the corporate groups in environmentally certified buildings and those in noncertified buildings, there was some difference at the 7% significance level according to
the TSR score.
Eichholtz et al. (2009) focused on the industry differences among tenants occupying
environmentally certified and non-certified office buildings. Here, we examined the ratioof those industries (Figure 2). In manufacturing, the score was 15.1% for the certified
group, around 2.5 times as much as for the non-certified group (6.1%). In the wholesale
and retail trade, the score was 30.2% for the certified group, nearly double the noncertified group’s, which had a score of 16.4%. On the other hand, in information andcommunications, the score was 9.4% for the certified group, representing a significant
difference compared to the non-certified group, which had a score of 24.6%. However, it
was difficult to conclude that there was an overall significant deviation by industry
between the environmentally certified and non-certified groups.

23

Figure 1. Distribution difference in corporate data
35.0%
30.0%
25.0%

Certified
Not-Certified

20.0%
15.0%
10.0%
5.0%
0.0%

Figure 2. Probability of certification by industry

24

Therefore, we made estimates in a model that considers a significantly higher
standard of difference, depending on the existence of environmental certification.8 The
estimated results are shown in Table 5, Column (4). The premiums from environmental
certification were 12.9% in model (2), but when considering the TSR score, we found thatthe result dropped to 5.6% and was not significant. In other words, this means that theprobability of office buildings with environmental certification being selected by more
economically viable corporations was relatively high, which makes it seem as though
they are paying a high premium.
This provides a very important indication. In a study by Yoshida et al. (2016), it wassuggested that the economic premiums for green labels disappear once power and water
bills are taken into consideration. If we draw a conclusion from our estimates, in Japan,
the green label does carry an economic value but this should not be attributed to theeffect of the green certification. The economic value is created because users with astrong sense of responsibility choose buildings that meet high, green building standards.
As a more detailed interpretation, it can be said that the economic value of officebuildings with a green label is created in the following way.
A company with a high TSR score is an excellent enterprise that has a management
team with high social esteem, from the qualitative aspect, and with high operational
efficiency and stable financial health, from a quantitative aspect. Such an enterprise has
a strong sense of social responsibility, and thereby, will most likely choose a building
with green certification and has a higher willingness-to-pay for it. Furthermore, as thiskind of company utilizes natural resources efficiently, it can be interpreted that it ispaying higher rent compared to the market price because it believes that its gain fromenergy savings will exceed the higher rent. After we control for this selection effect, therent premium for green label disappears.

5. Conclusion
This study empirically estimates the rent premium for green office buildings in Tokyo,
where office buildings utilizing advanced environmental technology have been recently
developed. We analyze the economic premium attributable to the presence or absence of
There were 34 properties included in the rent information cluster 4 (environmentally certified: 10 properties, noncertification: 24 properties). The tenant attributes were revealed for 28 of those properties (environmentallycertified: 8 properties, non-certification: 20 properties). For this score, the median (median score for the tenants
currently residing in the building for which corporate information was available) was used to represent the tenant
attributes in each building unit. Additionally, in Table 5 (2), we compared the rent of 30 environmentally certified
buildings and 30 buildings with no environmental certification, but we were only able to match 25 buildings withenvironmental certification and 26 buildings with no environmental certification to the TSR data, reducing eachgroup by about five properties.
825

a green label among a sample of 6,758 office buildings in Tokyo. The findings are
summarized as follows:・ After performing a quality adjustment of building characteristics with the hedonic
approach, green labels showed a significant effect, +4.39% on new rent contracts in
our OLS regression.
・ By estimating the propensity score for the target variable of the presence or absenceof a green label, we confirm that the building characteristics of age and size make iteasier to obtain a green label.

To address endogeneity, we use samples that are

adjusted, using the nearest neighbor matching technique, based on propensity scores.
In this case, the effect of a green label was not statistically significant.
・ When we analyze the samples clustered according to the value of the propensity score,
we find that the effect on the stratum in which large and new buildings areconcentrated is not statistically significant. Furthermore, the effect on the stratum
in which medium-sized and older buildings are concentrated is +13.78% andstatistically significant.
・ By conducting further propensity score matching on the clustered samples, we areable to verify the robustness of our analysis results. In addition, we find that even in
the mid-sized market, where green labels make a difference in the rent contract,buildings that are 30 years or older are no longer affected by these labels.
・ When considering tenant attributes, we find that companies with higher TSR scores(indicating that they have high operational efficiency, stable financial health, andstrong social responsibility) are more likely to move to green office buildings and theyalso have a higher willingness-to-pay for the green label as the gain from energy
savings means more to them. After we control for this selection effect, the rent
premium for green label disappears.
This series of empirical results provide some implications regarding green certificationin the real estate market. It has been empirically shown that the economic value of agreen label itself is not very prominent. On the other hand, in the case of older officebuildings, green certification seems to be a factor differentiating them from otherbuildings with low sustainability, thereby creating economic value.
However, the green label can send a clear signal to the market and companies with astrong sense of social responsibility are more likely to choose a building with a greenlabel, and also are willing to pay higher rents for green buildings. This triggers firm
sorting along this green dimension.

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References
Banfi, S., Farsi, M., Filippini, M., Jakob, M., 2005. Willingness to pay for energy-saving
measures in residential buildings. CEPE Working Paper 41.
Brounen, D., Kok, N., 2011. On the economics of energy labels in the housing market. J.
Environ. Econ. Manage. 62, 166–179.
Deng, Y., Li, Z., Quigley, J.M., 2012. Economic returns to energy-efficient investments in the
housing market: Evidence from Singapore. Reg. Sci. Urban. Econ. 42 (3), 506–515.
Deng, Y and J. Wu., 2014. Economic Returns to Residential Green Building Investment: The
Developers’ Perspective, Reg. Sci. Urban Econ. 47, 35-44.
Ekeland, I., J. J. Heckman and L. Nesheim, 2004, “Identification and Estimation of Hedonic
Models,” J.Pol. Econ., 112, pp.60-109.
Eichholtz, P. Kok, N., Quigley, J., 2009. Why do companies rent green? Real property andcorporate social responsibility. Program on Housing and Urban Policy Working Paper
W09-004.
Eichholtz, P., Kok, N., Quigley, J., 2010. Doing well by doing good? Green office buildings. Am.
Econ. Rev. 100, 2492–2509.
Eichholtz, P., Quigley, J. 2012., Green building finance and investments: Practice, policy andresearch. Eur. Econ. Rev. 56 (5), 903–904.
Fuerst, F., McAllister, P. 2011., Eco-labelling in commercial office markets: Do LEED and
Energy Star offices obtain multiple premiums? Ecol. Econ. 70, 1220–1230.
Fuerst, F., McAllister, P., Nanda, A., Wyatt, P., 2015. Does energy efficiency matter to homebuyers? An investigation of EPC ratings and transaction prices in England. Energy
Econ., 48, 145–156.
Fuerst, F., Shimizu C., 2016. The rise of eco-labels in the Japanese housing market. J. Jap.
Int. Econ. 39, 108–122.
Hyland, M., Lyons, R.C., Lyons, S., 2013. The value of domestic building energy

efficiency—

Evidence from Ireland. Energy Econ. 40, 943–952.
Kahn, M.E., Kok, N., 2014. The capitalisation of green labels in the California housing
market. Reg. Sci. Urban Econ. 47, 25–34.
Miller, N., Spivey, J., Florance, A., 2008. Does green pay off? J. Real Estate Portfolio Manage.
14, 385–399.
Ono, A., Uesugi, I., 2006. Role of collateral and personal guarantees in relationship lending:
Evidence from Japan’s SME loan market. J. Money Credit Bank. 41(5), 935–960.
Reichardt, A., Fuerst, F., Rottke, N., Zietz, J., 2012. The business case of sustainable building
certification: A panel data approach. J. Real Estate Res. 34, 99–126.

27

Shimizu, C., 2010. Will green buildings be appropriately valued by the market? Reitaku
Institute of Political Economics and Social Studies (RIPESS) Working Paper, No. 40.
Shimizu, C., 2013. Sustainable measures and economic value in green housing, Open House
Int. J. 38(3), 57–63.
Yoshida, J., Sugiura, A., 2015. The effects of multiple green factors on condominium prices.
J. Real Estate Fin. Econ. 50(3), 412–437.
Yoshida, J., J. Onishi, and C. Shimizu, (2016), Energy Efficiency and Green Building Markets
in Japan. SSRN: 2844040.
Zheng, S., Wu, J., Kahn, M., 2008. Land and residential property markets in a booming
economy: New evidence from Beijing. J. Urban Econ. 63, 743–757.
Zheng, S., Wu, J., Kahn, M.E., Deng, Y., 2012. The nascent market for “green” real estate in
Beijing. Eur. Econ. Rev. 56(5), 974–984.

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