Xymax Real Estate Institute

TOPIC REPORTTOPIC REPORT
Analysis of the Economics of Green Buildings in Tokyo’s
Office Market—The Effect of Office Lease Market Conditions and Green Labels on New
Contract Rent—

November 20, 2019

“Do green labels have a positive economic effect on the real estate business?” This question is an important
theme that should be examined in promoting green buildings.
However, the extreme complexity of Tokyo’s office market, with its diverse mix of numerous buildings andcompanies, had made quantitative analysis difficult. Xymax Real Estate Institute (“Xymax REI”) addressed thisproblem by combining the use of market division and quality adjustment to estimate the effect of green labelson new contract rent based on new contract rent data for offices in the 23 wards of Tokyo for 2013 and 2014.
The result was a positive effect of +4.4% (95% confidence interval: +2.1–+6.7%).**The Importance of Environmental Management in the Future Real Estate Market (in Japanese only), published June 1, 2015, ARES Real
Estate Securitization Journal vol. 25
https://soken.xymax.co.jp/results/pdf/201506.pdf

This time we carried out the same analysis using data for 2017 and 2018. In this report, we analyzed theeconomic effects of green labels amid a volatile office market and compared the results with the previous results
to examine the causes of the effects.

<Findings>
The effect of green labels on new contract rent in the Tokyo office market for this analysis period (fromJanuary 2017 to December 2018) was +2.0% (95% confidence interval: +0.5–+3.5%), a positive effecteven as the market shifts from a lessee’s advantage to a lessor’s advantage (Figure 1).

A positive effect of +2.3% (95% confidence interval: +1.0–+3.7%) was observed among large andrecently-built properties, which can obtain green labels more easily. No statistically significant effectwas observed among medium-sized and old properties.
Figure 1: Effect of Green Labels on New Contract Rent (All Samples)

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

1

TOPIC REPORT｜Xymax Real Estate Institute

1. Social background: The connection between environmental
issues and real estate
Today, environmental issues are recognized as the greatest uncertainty and risk factor against thesustainability of the development of humankind.
International measures for tackling environmental issues have been implemented in the past, such as emission
control (direct measure) and environmental tax and emissions trading (economic measures) under the Kyoto
Protocol and the Paris Agreement and. However, these international frameworks have a tendency to develop
into political issues such as conflicts between developed and developing nations and the withdrawal of a major
nation. Furthermore, environmental issues arise from the external diseconomy effect of free economic activities.
Therefore, they seldom lead to voluntary suppression of economic activities by individuals and companies.
A new economic measure that is currently attracting attention is ESG investment. A consensus that ESG
elements (environment, social, governance) are long-term risk factors has already been formed among investors.
Huge pension funds in Japan and abroad, including Japan’s Government Pension Investment Fund (GPIF), havesigned the Principles for Responsible Investment (PRI), while 883 institutions from around the world support thereport by the Task Force on Climate-related Financial Disclosures (TCFD), which was set up at the request of theG20. According to the Global Sustainable Investment Alliance (GSIA), the ESG investment market has reached
31 trillion dollars globally (a 34% growth between 2016 and 2018) and 2.2 trillion dollars in Japan (a 360% growthover the same period).
Amid such circumstances, countries around the world have established programs to assess and certify theenvironmental performance of real estate (green label programs), which are anticipated to serve as a tool thathelps environmentally conscious investors and tenant firms in selecting properties to invest in or rent. They
include LEED (established 1998) and BOMA360 (2009) of the United States, and BREEAM (1990) and GRESB(2009) of the United Kingdom. Japan uses the programs of other countries as well as its own programs such as
CASBEE (2002), DBJ Green Building Certification (2011), and BELS (2014).
From the standpoint of real estate operators, a major concern was whether the advantage gained fromobtaining a green label matched the cost of obtaining them. This is because obtaining a green label not only
requires the gathering of data for green label acquisition and administrative procedures but also additional
investment to conform with green labels. This led to a need for an examination of the economics of green labels,
such as whether they “ensure higher rent,” “stabilize occupancy rates,” “reduce volatility of revenue,” and “ensure
higher evaluation or selling prices.”
Pioneering studies such as those by Miller et al. (2008), Fuerst & McAllister (2011), and Reichardt (2012) have
hitherto provided important insights into the economics of green buildings. Eichholtz, Kok & Quigley (2010)
proved that green labels offer economic premiums after controlling for quality differences of properties. The
economics of green labels have since been actively studied around the world. The existence of economic
premiums to green buildings has been reported by Fuerst et al. (2015) in the United Kingdom, Kahn & Kok(2014) in California, Hyland, et al. (2013) in Ireland, Zheng et al. (2012) in China, and Deng et al. (2012) in
Singapore. In Japan, Yoshida & Shimizu (2010) carried out an analysis of offer prices of condominiums in Tokyo,
while Fuerst & Shimizu (2016) conducted an experimental study on green label premiums, taking into
consideration the income of buyers.
The interest in studies on the economics of green labels is especially strong in the Tokyo office market. Tokyo
remains one of the world’s top-class economic cities with its concentration of companies and people, and its
office market has one of the largest stocks in the global commercial real estate market. It also has a developed
real estate investment infrastructure such as REITs and has an extensive accumulation of data. It has also
experienced a range of socioeconomic events, including the post-war restoration, high economic growth, oilshock, and the birth and burst of the bubble economy, which have led to a wide variety of scale and performanceof office buildings.
Against this background, Sumitomo Mitsui Trust Bank (2015), Xymax REI (2015), and the Development Bank
Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

2

TOPIC REPORT｜Xymax Real Estate Instituteof Japan & Japan Real Estate Institute (2015–2019) have each conducted an empirical analysis of how much rent
is higher at office buildings with a green label than those without. Many of these analyses indicate that greenlabels have a positive effect on rent and prices.
However, with the progress of the analysis of the environmental economic value in the Tokyo office market,
several complicated problems have been revealed.

2. Identifying the problem: A complex market structure
As mentioned earlier, the Tokyo office market is a concentration of a diverse range of office buildings andcompanies. The structure (pricing of rent) of such a market is not always singular. Rather, it is closer to reality tothink of the market as a mixture of multiple markets. The business environment, personnel market, andcustomers significantly differ between small and medium-sized companies that pay a rent of around 10,000 yen
per tsubo for the building they rent and listed companies that pay a rent of more than 40,000 yen per tsubo.
The priorities of the elements the companies require in an office building differ as well, with the former preferring
small and medium-sized office buildings that are flexible even if they are old, and the latter favoring relatively
new and large office buildings that serve as a landmark of the area.
In such a complex market with a mixture of multiple market structures, two problems arise in terms ofquantitative analysis (i.e. quality adjustment and sample selection bias).
The first problem is “quality adjustment.” Figure 2 is the distribution of buildings’ age, gross building area,and new contract rent of the study data for the analysis. The red bars represent office buildings with a greenlabel and the blue bars represent those without. Office buildings with a green label tend to be younger (toward
the left of the chart) and larger in gross building area (toward the right of the chart). In such cases, it is not
appropriate to compare the average new contract rent (the position of the vertical dotted line in Figure 2)
between office buildings with a green label and those without and deem the difference as the economic effectof green labels.
The second problem is “sample selection bias.” Properties with enough performance to qualify for a greenlabel and rented by environmentally conscious tenants have high rent even if they do not have a green label. It
cannot be distinguished whether the rent is high since the property has a green label, or since it has highspecifications, or because it has good tenants.
In order to measure the economic effect of green labels in a market with a complex structure such as Tokyo’s
office market, it is necessary to address these two problems.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

3

TOPIC REPORT｜Xymax Real Estate Institute

Figure 2: Distribution of Age (Top), Gross Building Area (Middle), New Contract Rent (Bottom)(Office Buildings in the 23 Wards of Tokyo)
With green label

Without green label

Age (year)
With green label

Without green label

Gross building area (tsubo)

With green label

Without green label

New contract rent (yen/tsubo)

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

4

TOPIC REPORT｜Xymax Real Estate Institute

3. Analysis process: Propensity score and hedonic approach
These problems arise from analyzing data with different market structures together. Therefore, one solution
would be to divide the samples into groups with a similar market structure. In our analysis, it would be effective
to divide the samples into a group of high-spec properties that attract environmentally conscious tenantcompanies and a group of properties with not such high specifications that attract tenants that are not so
environmentally conscious.
An analysis technique used for collecting samples with a similar market structure as in this case is “propensity
score.” Propensity scores are used in academic papers on such wide-ranging areas as medicine, economy, andpsychology. The score was also used in the field of online surveys (refer to the appendices at the end of thisreport on the theoretical background of propensity scores and sample adjustment using analysis data).
Even within a group with a similar market structure, the location, age, and size of buildings differ slightly.
Therefore, it is necessary to measure the effect of green labels on new contract rent after adjusting for such
attributes (qualities). This problem can be dealt with by measuring the quality-adjusted effect of green labels foreach group using the hedonic approach. The hedonic approach is an analysis method that employs a regression
analysis technique and is widely used for measuring the economic effects of policies and infrastructure
construction. Specifically, the effect of green labels is measured by building the following hedonic model andestimating the partial regression coefficient of a green label dummy (a variable of “1” if the building has a greenlabel and “0” if not).

ln 𝑅𝑖 = α + 𝑔𝑟𝑒𝑒𝑛𝑖 ′ ∙ 𝛽 + 𝑥𝑖 ′ ∙ 𝛾 + 𝜀𝑖
The symbols represent the following:𝑅𝑖𝑖
𝛼𝑔𝑟𝑒𝑒𝑛𝑖
𝛽
𝑥𝑖

:
:
:
:
:
:

𝛾
𝜀𝑖

:
:

New contract rent
Subscript indicating each sample
Constant term
Green label dummy
Effect of existence of green label on new contract rent
Independent variable other than green label dummy[Size] Gross building area, number of stories above ground, standard story
area[Newness] Age, renovation dummy[Performance, equipment] Raised floor dummy, individual air conditioning
dummy, automated security dummy[Location] Time to nearest station, five central Tokyo wards dummy[Contract period] Quarterly dummy
Effect of independent variable x on new contract rent
Error term

As shown above, quality adjustment is carried out for each group after the samples are classified into groups
with a similar market structure. The effect of green labels on new contract rent is thus estimated while taking
into consideration the complex structure of the Tokyo office market.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

5

TOPIC REPORT｜Xymax Real Estate Institute

4. Analysis targets: Changes in market structure due to market
conditions
In this analysis, we estimated the economic effect of green labels using the method described in the previous
chapter over a period between January 2017 and December 2018. The analysis data was compiled from newcontract rent and property attributes of new tenancy cases in office buildings located in the 23 wards of Tokyo.
The attribute concerning green labels is “1” if the building has obtained any of the following comprehensive
ratings by a third party: CASBEE, CASBEE for real estate, and DBJ Green Building Certification, and “0” if it hasnot.
Market conditions can also be cited as a factor that changes market structures. The economic effect of greenlabels may differ between a period when vacancies are difficult to fill and a period when vacancies are scarcedue to strong demand from tenants. The effect may also change depending on how much green labels havepenetrated the market.
Figure 3 shows that while there is no significant change in the property attributes of the samples such as
location, size, and age, the percentage of green label acquisition rose from the previous 5.3% (Period 1: January
2013 to December 2014) to 10.0% (Period 2: January 2017 to December 2018) in this analysis. As the previous
analysis was conducted during the dawn of the green label program for real estate, this result reflects the factthat the program has begun to spread in the market over the four years from the previous analysis. The officelease market conditions have also changed significantly. According to data by Xymax REI, the vacancy rate ofTokyo’s entire office market was relatively high at 5–7% during the previous period but has dropped to the 1%
level during the period for this report due to tenants’ strong demand for offices.

Figure 3: Summary Statistics of Analysis Data
Period 1: Jan. 2013–Dec. 2014
Name of variable

Dependent
variable
Independent
variable

Unit

No. ofsamples

Avg.

Period 2: Jan. 2017–Dec. 2018

Standard
Minimum Maximum
deviation

No. ofsamples

Avg.

Standard
Minimum Maximum
deviation

New contract rent

Yen/
tsubo

6,758

17,090

6,155

6,100

55,0407,129

19,723

7,431

7,000

60,975

Green label dummy(0, 1)

6,758

0.053

0.225

0.000

1.000

7,129

0.100

0.310

0.000

1.000

Gross building area

Tsubo

6,758

5,726

11,327300

114,783

7,129

6,253

12,083300

114,783

Age

Year

6,758

23.73011.8260.000

59.9107,12926.12011.8600.000

60.320No. of stories above ground

Story

6,758

11.690

7.698

3.000

60.0007,12912.250

8.500

3.000

60.000Standard story area

Tsubo

6,758

236

241

30

2,975

7,129

238

257

30

2,975

5 central Tokyo wards
dummy(0, 1)

6,758

0.769

0.422

0.000

1.000

7,129

0.770

0.420

0.000

1.000

Time to nearest station

Minute

6,758

3.358

2.313

0.000

15.000

7,129

3.300

2.240

0.000

19.000Raised floor dummy(0, 1)

6,758

0.685

0.465

0.000

1.000

7,129

0.830

0.380

0.000

1.000

Individual air con dummy(0, 1)

6,758

0.800

0.400

0.000

1.000

7,129

0.820

0.390

0.000

1.000

Automated security dummy(0, 1)

6,758

0.833

0.373

0.000

1.000

7,129

0.950

0.210

0.000

1.000

Renovation dummy(0, 1)

6,758

0.131

0.337

0.000

1.000

7,129

0.260

0.440

0.000

1.000

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

6

TOPIC REPORT｜Xymax Real Estate Institute

5. Analysis result: The effect remains positive in the overallmarketWe will first present the results of the analysis of all samples. The new contract rent for buildings with a greenlabel was higher than that for buildings without a green label by 4.4% in Period 1 (from January 2013 to
December 2014) and 2.0% in Period 2 (from January 2017 to December 2018) (Figure 1). The figures are
statistically significant for both periods, implying that green labels have a positive effect on new contract rent
even when the market shifts to the advantage of lessors.
(Aforementioned) Figure 1: Effect of Green Labels on New Contract Rent (All Samples)

As mentioned earlier, a wide range of market structures coexist in the Tokyo office market, requiring
appropriate market division when conducting quantitative analysis. In this analysis, we estimated the effect ofgreen labels on new contract rent by dividing the market into five groups based on propensity of obtaining
green labels using propensity scores and making quality adjustments to each group using the hedonic approach.
The results of the estimate for each group are summarized in Figure 4. Groups 1 to 3 have been excluded fromthe examination due to the small number of samples with a green label and the lack of statistically significant
results and implications due in part that their samples mainly consist of small and old buildings.
Figure 4: Effect of Green Labels on New Contract Rent in Each Group
Group 1

Group 2

Group 3

Group 4

Group 5Overall

This analysis
Period 2: Jan. 2017–Dec. 2018

(Unable tocalculate)+17.2%

+1.3%

+1.9%

+2.3%

+2.0%

-3.3% – +33.7%-5.1% – +7.8%-1.5% – +5.2%

1.0% – +3.7%

0.5% – +3.5%

Previous analysis
Period 1: Jan. 2013–Dec. 2014

(Unable tocalculate)+12.8%

+6.3%

+9.6%

-1.1%

+4.4%

-17.0% – -42.8%-14.8% – +27.5%+4.1% – +15.0%-3.2% – +1.0%

2.1% – +6.7%*The upper row indicates estimates of partial regression coefficients of the green label dummy. The lower row is the 95% confidence interval range.
*Figures in red frames are statistically significant results.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

7

TOPIC REPORT｜Xymax Real Estate InstituteGroup 4 mainly consists of samples that are relatively medium in size and old. The effect of green labels was+1.9% for the analysis period of this report (Period 2) but was not statistically significant. The effect in theprevious analysis period (Period 1) was +9.6%, which was statistically significant (Figure 5). In this group of
relatively medium-sized and old building samples, the effect of green labels was significantly positive during theperiod when lessees had an advantage in the market and vacancies were abundant but became almost
nonexistent when lessors had an advantage in the market and vacancies were few.
Figure 5: Effect of Green Labels on New Contract Rent in Group 4 (Medium-sized, Old Buildings)

Group 5 mainly consists of samples that are large in size and relatively new. The effect of green labels during
the period of this analysis (Period 2) was +2.3% and statistically significant (Figure 6). On the other hand, theeffect during the previous period (Period 1) was -1.1% but was not statistically significant. In this group of large
and relatively new building samples, the effect of green labels was not observed during the period when lessees
had an advantage in the market, but a positive effect was seen during the period when it was a lessors’ market.
The results were different from Group 4.
Figure 6: Effect of Green Labels on New Contract Rent in Group 5 (Large, Relatively New Buildings)

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

8

TOPIC REPORT｜Xymax Real Estate Institute

6. Insight into the background: Social changes and market cycle
Based on the above analyses, we examine the background to the difference in the effect of green labels on
new contract rent due to the different periods.
First of all, we believe the background to the difference in Group 5 (large-scale, relatively new buildings) to
be structural changes in society. Following the spread of the recognition of SDGs and ESG investment markets,awareness toward environmental issues has been rising among large companies, especially those that operate
on a global scale. These companies are large-scale and highly creditworthy, and have the ability to afford therent of large and relatively new office buildings that would qualify for Group 5. With the progress of globalizationand labor shortage, companies face an increasing need for competent personnel to improve theircompetitiveness. Companies that rent offices that belong to Group 5 are now required to provide a comfortableenvironment to attract competent talents. Indoor comfort, which is one of the assessment items of green labels,
responds to such needs.
With a certain amount of time having passed since the start of the green label program, there has been a risein the number of office buildings with a green label. In Group 5, in particular, the percentage of cases at officebuildings with a green label has significantly increased from approximately 15% in Period 1 to approximately
42% in Period 2. Office buildings with a green label are now included in companies’ search options when theylook for offices. Following such social changes that have intensified in recent years, we believe that tenantcompanies have begun to appreciate green labels, resulting in their positive effect on new contract rent
especially in Group 5, which includes buildings that respond to companies’ appreciation of green labels.
Meanwhile, we believe the background to the difference in Group 4 (medium-sized, old buildings) to simply
be a change in the power balance between lessors and lessees. During Period 1, the vacancy rate was high andlessees (tenants) had many vacancies to choose from. In such a market environment, green labels functioned asa differentiating factor. However, during Period 2 when the vacancy rate was low, lessors had the upper hand.
Even vacancies in less competitive office buildings could be filled quickly if the owner put a call out for tenants.
In this market environment, green labels no longer worked as a differentiator.
Additionally, social changes that we cited as a background to Group 5 are not believed to have been
manifested as strongly in Group 4. At companies that take up tenancy in office buildings belonging to Group 4,
there remains a gap (compared to companies that rent buildings in Group 5 above) between their awareness ofenvironmental issues and the development of a comfortable office environment and their actual actions, due inpart to their lack of financial capacity. Furthermore, although the percentage of office buildings with a greenlabel has risen, it remains at around 7% in Period 2, which is not high enough to allow companies to encountersuch buildings when they look for offices.
Thus, we believe that green labels have ceased to function as a differentiating factor in Group 4 where theimpact of social changes has not been felt strongly, amid a shift of the market to the advantage of lessors.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

9

TOPIC REPORT｜Xymax Real Estate Institute

7. Summary
In this analysis, we conducted an empirical quantitative analysis of the effect of green labels on new contract
rent amid a shift of the market from the lessee’s advantage to the lessor’s advantage. Among large and relatively
new buildings, the effect turned positive on the back of the spread of green buildings and a rise in environmental
awareness mainly among large companies. On the contrary, the effect was no longer observed among mediumsized and old buildings, as buildings have been able to attract tenants even if they were less competitive, due
to the tight supply-demand balance. In terms of the overall market, a positive effect can still be observed.
The future outlook of the Japanese economy is increasingly uncertain. When companies’ demand for offices
drop and vacancies increase, leading to a shift of the market to the lessee’s advantage, green labels will once
again attract attention as a differentiating factor in some markets. Meanwhile, various social changes are also
ongoing, such as the progress of labor shortage, work style reforms, technology, and the aging of building stock.
The penetration of initiatives in environmental issues that involve investors and companies, such as ESG
investment and SDGs, is one of the major social changes.
Going forward, it will be important to determine both the cyclical changes of the market and the irreversible
changes of society separately.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

10

TOPIC REPORT｜Xymax Real Estate Institute[Appendices] Technical paper: Sample adjustment by propensity
score
Appendix 1. BackgroundIn the appendices, we provide technical explanations on the quantitative analysis techniques used for the
analysis. For the analysis data, we use Period 1 of the report (from January 2013 to December 2014).
As in this analysis, an incentive to accurately estimate the causal effect of intervention has long existed.
Statistical causal inference deals with this matter. An important issue in statistical causal inference has been theremoval of the effect of confounding factors. The design of experiments advocated by Fisher, the founder ofmodern inferential statistics, removed the effect of confounding factors by defining a control group withpotentially equal attributes as the treatment group through a random allocation of treatments. However, sincealmost all data that social science handles is observation data, the effect of confounding factors cannot be
removed with the design of experiments.
The hedonic approach advocated by Lancaster (1966) and Rosen (1974) has econometric consistency witheconomic theory backgrounds and has been used in a broad range of areas such as price indices of real estate
as well as for measuring the effect of intervention in the social science field, such as in impact analyses ofenvironmental policies and social capital policies. The hedonic approach considers a good as a bundle of various
attributes and estimates a hedonic model that regresses a good to its various attributes. The marginal value ofintervention can be estimated by using the partial regression coefficient of each attribute derived from theestimation of this hedonic model.
One of the problems that requires the most attention in using the hedonic approach is endogeneity. When
there is a correlation between the independent variable and the error term in an econometric model, it isendogenous. In this case, the estimator derived by the hedonic approach has no consistency but is biased andtherefore cannot accurately assess the effect of intervention. The mechanisms that produce endogeneity include
observational error, omitted variable, simultaneity, and sample selection bias. While all of these are important
issues that should be addressed, in these appendices we focus on sample selection bias, which is often
encountered when using or appropriating an existing data set. A sample selection bias is a bias that emerges
when attributes differ between the treatment group and the control group of the obtained samples due toconstraints and problems when the data was collected. Countermeasures against sample selection bias include
heckit, which was advocated by Heckman (1974), DID (difference in difference), known by the works by Besley
and Case (2000), and propensity score.
In these appendices, we will explain about sample adjustment using propensity scores, with the analysis of
this report as the subject. We will start off with an overview of the theoretical background of propensity scoresin Appendix 2. We will then estimate the propensity score in Appendix 3 using data that was used in the main
text of this report. In Appendix 4, we will explain about matching as the first example of sample adjustmentusing propensity score. In Appendix 5, we will explain about stratification using propensity scores as the second
example of sample adjustment. Appendix 6 is a summary of what we explained in the appendices.

Appendix 2. Theory on propensity score
Analysis using propensity scores was advocated by Rosenbaum and Rubin (1983). It is used as a method to
remove the effect of confounding factors by consolidating several covariates into one propensity score. Since
biased samples can be adjusted under certain assumptions to become as close to random samples as possible
by using propensity scores, propensity score has the potential to be applied as a technique for dealing withsample selection bias, which we mentioned in the previous section, and has attracted attention since the early

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

11

TOPIC REPORT｜Xymax Real Estate Institute2000s. Its range of application is broad, and many studies have been reported in the fields of medicine,
economics, and psychology. There was also a case where propensity score was used in adjustments for online
surveys.
An analysis using propensity scores first sets a model that explains with covariates whether samples can beallocated to the group to which intervention is made (treatment group) and estimates the parameters of themodel. Using the estimate of the estimated parameters, the predictive probability allocated to the treatmentgroup for each sample is calculated, which is deemed the estimate of propensity score. Then, based on theestimated propensity score, two similar groups are created, with the only major difference between them being
the allocation variable: either “1” or “0.” The effect is estimated subsequently. This is the process of the analysis.
Treating data using propensity scores enables to achieve a certain degree of balance in the average covariate
between the two groups, making it possible to address the problem of endogeneity. Furthermore, sincemultidimensional covariates are consolidated into one dimension, it can withstand detailed analysis using
stratification.
We define the elements as follows:𝑌1 :Dependent variable when intervened (𝐷 = 1)

𝑌0 :Dependent variable when not intervened (𝐷 = 0)

𝐷:

Allocation variable (1: When intervened; 0: When not intervened)𝒙:

Attribute observable as a covariate. However, 𝒙 shall be a vector whose element is (𝑥1 , … , 𝑥𝑖 ).

P(𝒙) = 𝑃𝑟(𝐷 = 1|𝒙): Predictive probability of sample with attribute 𝒙 being allocated to 𝐷 = 1

If we deem that the effect of an intervention is the difference between the expected value of the dependent
variable of samples allocated to “D=1” and that of samples allocated to “D=0,” the effect will be expressed by
the following formula.

E(𝑌1 |𝐷 = 1) − E(𝑌0|𝐷 = 0)

(1)

In this case, if the covariate affects the propensity of being allocated to “D=1,” the estimated result may bebiased. Therefore, the effect of intervention is considered as the following formula:∆𝐷=1 (𝒙) = E(𝑌1 − 𝑌0 |𝑃(𝒙), 𝐷 = 1)(2)This formula expresses the expected value of the difference in the dependent variable between whenintervention was made and when supposing that intervention was not made for samples to which intervention
was actually made. Thus, the pure effect of intervention can be derived if the dependent variable of when
supposing that intervention was not made to samples to which intervention was actually made can be identified.
However, since such data cannot usually be observed, we estimate a proxy for them from the dependent variableof samples to which intervention was not actually made, weighted by P(x). When matching this proxy value andthe samples to which intervention was actually made, the estimate of the effect of intervention will be as follows:𝑛1∆̂𝐷=1 (𝑥) =1∑ {𝑌1𝑖 (𝒙𝒊 ) − 𝐸̂ (𝑌0𝑖 |𝑃(𝒙𝒊 ), 𝐷𝑖 = 0)}
𝑛1

(3)

𝑖=1
{𝐷𝑖 =1}

Here, “n1” represents the number of samples to which intervention was actually made. The expected value ofthe dependent variable of the samples to which intervention was not made, which is used as a proxy, is expressed

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

12

TOPIC REPORT｜Xymax Real Estate Instituteby the following formula:𝑛0

𝐸̂ (𝑌0𝑖 |𝑃(𝒙𝒊 ), 𝐷𝑖 = 0) = ∑ 𝑊𝑖 (𝑃(𝒙𝒊 ))𝑌0𝑗
𝑗=1{𝐷𝑗 =0}(4)Here, “n0” represents the number of samples to which intervention was not made and “W” represents theweight of when “P(x)” was given. Nearest neighbor matching is used for weighting when making a comparison.
In nearest neighbor matching, samples to which intervention was made and samples to which intervention was
not made, which have the nearest probability of intervention, are matched, and the dependent variable of thematched samples to which intervention was not made is used as a proxy for the dependent variable of when
supposing that intervention was not made to samples to which intervention was actually made.
To estimate the probability of samples with attribute “x” to be allocated to the group to which intervention is
made, a logistic regression or a probit regression, which is shown below, is often used. Here, “β” represents thevector that expresses the attribute of the property.
𝑥𝛽1𝑧2𝑒𝑥𝑝 (− ) 𝑑𝑧
2−∞ 2𝜋P(𝒙) = Pr(𝐷 = 1|𝒙) = 𝛷(𝒙𝜷) = ∫(5)

Appendix 3. Estimation of propensity score
As explained in the previous section, analysis using propensity scores adopts an approach of removing theeffect of covariates on the independent and dependent variables by creating samples with similar covariates.
There are several ways to create similar samples, and in this study we use the following two: propensity scorematching (Appendix 3) and stratification using propensity score (Appendix 4).
In this study, the office new contract rent function is estimated by specifying the existence of green labels andother independent variables that affect new contract rent of office buildings. New contract rent of an officebuilding is generally expressed as a hedonic price function as below, based on the attribute information andgreen label acquisition status of the office building.

ln 𝑅𝑖 = 𝛼 + 𝑔𝑟𝑒𝑒𝑛𝑖 ′ ∙ 𝛽 + 𝑥𝑖 ′ ∙ 𝛾 + 𝜀𝑖

(6)Here, the left member is the dependent variable and a logarithm of the new contract rent of contract example“i.” The second term is the green label dummy, which is “1” if the building has a green label and “0” if not. “x” isa vector that expresses an attribute of type “n” in contract example “i.”The result of an OLS estimate of the analysis data of this report using a hedonic model of formula (6) isindicated in (1) of Appendix Figure 1. Since the same data is used as in the main text of this report (Period 1),
we will omit explanations on data such as summary statistics. The result of the estimate was positive andsignificant at +0.0439 (0.0115) (the figure in brackets is the standard error). This indicates that the new contract
rent of buildings with a green label is around 4% higher than that of buildings without a green label. However,does this result justify an assertion that obtaining a green label is an economically rational action that can be
expected to lead to greater revenue? We believe that the following points require attention.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

13

TOPIC REPORT｜Xymax Real Estate Institute

Appendix Figure 1: Probit Model that Estimates Propensity Scores and Hedonic Regression Model
before and after Sample Adjustment
(1)

(2)

(3)

Base line
model

Propensity
score
estimation
model

Propensity
score
matching(4)Intercept

7.8684***(0.0318)

Green label dummy

0.0439***(0.0115)Gross building area (log)0.1074***(0.0090)0.4199***(0.1331)-6.0229(6.0873)Age-0.0089***(0.0003)-0.0727***(0.0045)

No. of stories above ground0.0029***(0.0007)

Standard story area (log)-3.9439***(0.5239)-74.6288(86.6478)

(5)

(6)

(7)

(8)

Stratification by propensity score
Group 1

Group 2

7.6919***(0.0785)

Group 3

Group 4

Group 5

7.8393***(0.0781)7.8943***(0.0816)

7.9906***(0.0791)7.9708***(0.0695)

0.2453**(0.1231)-0.0343(0.0950)

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

0.1556***(0.0216)

0.0961***(0.0273)0.0801***(0.0238)

0.0967***(0.0220)0.0767***(0.0167)-0.0628(0.0554)-0.0073***(0.0007)-0.0023**(0.0009)-0.0074***(0.0022)-0.0111***(0.0015)-0.0130***(0.0008)-0.0146*(0.0083)

1.6052(1.6120)-0.0004(0.0018)

0.0027(0.0027)

0.0078***(0.0020)0.0043**(0.0019)0.0034***(0.0010)0.0027(0.0103)-0.0492(0.1522)17.2996(17.8718)-0.0452*(0.0246)-0.0139(0.0309)0.0104(0.0226)-0.0008(0.0230)

0.0382**(0.0187)

Time to nearest station-0.0238***(0.0011)-0.0149(0.0178)0.7583(0.7291)-0.0240***(0.0027)-0.0215***(0.0032)-0.0247***(0.0024)-0.0158***(0.0023)-0.0218***(0.0022)

Raised floor dummy

0.0040(0.0058)

0.0938(0.1368)-2.9774(3.1113)0.0271**(0.0124)-0.0083(0.0134)

0.0147(0.0115)-0.0256*(0.0133)-0.0078(0.0216)

Individual air con dummy

0.0051(0.0066)

0.1596*(0.0970)-11.5097(11.7921)

0.0158(0.0167)-0.0134(0.0175)

0.0116(0.0164)-0.0172(0.0140)-0.0099(0.0119)

Automated security dummy-0.0118*(0.0067)

0.1633(0.1132)

12.8193(13.5811)-0.0495***(0.0151)

0.0078(0.0145)0.0165(0.0166)

0.0025(0.0164)0.0081(0.0136)

Renovation dumy

0.0334***(0.0079)

1.0639***(0.1257)-5.7735(6.2213)0.0202(0.0218)-0.0208(0.0233)

0.0946**(0.0372)

0.0553**(0.0239)0.0397**(0.0182)

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

No. of samples

6,758

6,758

722

1,352

1,351

1,352

1,351

1,352

Percentage of "with green label"

0.05340.50000.00000.00220.00300.02220.2396

Determination coefficient adjusted
for degrees of freedom

0.67700.92130.46690.50340.48520.62180.7684Contract moment dummy
Area dummy-1.6673(1.8874)

Log likelihood-831.1474

AIC

1814.2950*Figures in brackets indicate standard error

First of all, buildings with a green label tend to be large and relatively new buildings that are developed andowned by a major developer or a REIT. Since such players have more financial resources than players of small
and medium-sized building projects, they can afford to pay the necessary costs for obtaining a green label. In
addition, the situation of obtaining a green label should be different depending on the construction period. The
environmental performance of newly built office buildings is improving each year on the back of the progressof construction technologies such as the improvement of exterior thermal insulation and efficiency of equipment.
For old office buildings with lower environmental performance to obtain a green label, it is necessary to spend
costs for prior surveys and renovations. Meanwhile, newly built or recently built office buildings tend to havegreater environmental performance in exterior and equipment and are able to obtain a green label more easily.
Appendix Figure 2 shows the aggregation of the analysis data for this study by existence of a green label.
While the average values of gross building area and age of samples with a green label (green buildings) are
18,670 tsubo and 8.8 years, respectively, samples without a green label (non-green buildings) tend to be smaller
and older at 4,995 tsubo and 24.6 years, respectively. In this study, the distinction between environmental
performance and other building performances may also have been difficult. The estimated coefficient of thegreen label dummy— +4.4%— should be carefully analyzed about whether it is really due to the existence of agreen label or whether it may have been affected size, newness, location, and contract timing.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

14

TOPIC REPORT｜Xymax Real Estate Institute

Appendix Figure 2: Comparison of Major Covariates by Existence of Green Label (Sample Unadjusted)Green Buildings

Non-Green Buildings

No. of samples: 361

No. of samples: 6,397

AverageStandard
deviation

AverageStandard
deviation

New contract rent

25,251

7,122

5,030

1,743

Green label dummy

1.000

0.000

0.000

0.000

Gross building area

18,67024,495

4,995

9,580

Age

8.777

9.775

24.57411.359No. of stories above ground20.17212.50011.211

7.037

464

368

223

224

5 central Tokyo wards dummy

0.856

0.352

0.764

0.425

Time to nearest station

2.983

1.804

3.379

2.336

Raised floor dummy

0.956

0.206

0.669

0.471

Individual a/c dummy

0.812

0.392

0.800

0.400

Automated security dummy

0.873

0.334

0.831

0.375

Renovation dummy

0.125

0.331

0.131

0.338

Standard story area

This problem can be expressed as follows when examined from the perspective of an analysis technique called
the hedonic approach. If the decision-making of obtaining a green label is influenced by the same variables as
for the new contract rent of office buildings, such as size and newness, the method of estimation by
distinguishing an office building’s new contract rent function by the green label dummy may have generated aproblem of endogeneity. Therefore, if we estimated the hedonic function as a simple linear model in the analysis
data for this study, the effects related to size, age, and performance will be reflected in the difference between
the existence of a green label, leading to a greater likelihood for the estimate to be biased.
Regarding how the effect of green labels influence new contract rent, we estimate the probability of officebuildings with office building attribute “x” to obtain a green label by regressing the probit model expressed informula (5).
The result of the estimate is shown in (2) of Appendix Figure 1. The coefficient of building age is estimatedwith significance as negative, reflecting the fact that relatively new buildings are more likely to be given a greenlabel. Since the coefficients of the raised floor dummy, the individual air conditioning dummy, and theautomated security dummy are positive at the 5% level, although not significant, office buildings with better
equipment are thought to be more likely to be granted a green label. The estimated coefficient of gross building
area is positive, reflecting the fact that larger buildings are more likely to be given a green label. The result for
the time to the nearest station is not significant.
The above results indicate that large, relatively new, and high-quality office buildings with good equipment
is more likely to have a green label. In order to meet the comprehensive environment performance requirementsof green labels, it is necessary to establish good management of the property, such as energy-efficient exterior
and air conditioning equipment, water-saving hygiene facilities, greening of the surroundings, appropriate
maintenance, disaster prevention, and BCP. We believe that the background to the results was that only large
office buildings could manage to acquire the site and ensure business profitability upon installing andmaintaining such equipment.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

15

TOPIC REPORT｜Xymax Real Estate Institute

Appendix 4. Propensity score matching
Appendix Figure 3 indicates, by existence of a green label, the descriptive statistics value of samples
extracted by nearest neighbor matching using the estimated propensity score. Since office buildings with agreen label and those without are matched at a 1:1 ratio, the number of samples will each be 361, totaling 722.
When compared with the unadjusted samples of Appendix Figure 2, the average gross building area of nongreen buildings after the matching has significantly closed in on that of the unadjusted samples, at 14,698 tsubo.
The difference in the average age of non-green buildings has also been reduced at 8.4 years. The other variables
have also been adjusted in general, with the difference between the two sample groups reduced due to thematching, creating similar samples.
Appendix Figure 3: Samples after Propensity Score Matching

Green Buildings

Non-Green Buildings

No. of samples: 361

No. of samples: 361

AverageStandard
deviation

AverageStandard
deviation

New contract rent

25,251

7,122

25,207

7,548

Green label dummy

1.000

0.000

0.000

0.000

Gross building area

18,67024,495

14,693

16,958

Age

8.780

9.770

8.360

8.030

No. of stories above ground20.17012.50018.32010.260

464

368

422

339

5 central Tokyo wards dummy

0.856

0.352

0.839

0.368

Time to nearest station

2.983

1.804

2.806

2.163

Raised floor dummy

0.956

0.206

0.928

0.259

Individual a/c dummy

0.812

0.392

0.820

0.385

Automated security dummy

0.873

0.334

0.892

0.311

Renovation dummy

0.125

0.331

0.086

0.281

Standard story area

The result of the estimate by the hedonic approach of samples extracted by the matching is indicated in (3)of Appendix Figure 1. The estimated coefficient of the green label dummy was -1.67 (1.88), which is not
significant. When interpreted as is, this result indicates that green labels do not have an effect on new contract
rent, a different result than the analysis in Appendix 3.
The point that requires attention here is the fact that the extracted 722 samples mainly consist of large andnew office buildings, due to the method of matching 361 green building samples against non-green building
samples with the nearest propensity score out of 6,397 samples of non-green buildings at a 1:1 ratio. This is also
clear in the fact that non-green buildings in Appendix Figure 3 are adjusted to large-scale and relatively newbuildings. In other words, the data of the approximately 6,000 non-green buildings that were not matched has
been discarded.
Therefore, it would be appropriate to interpret the analysis results in this section as meaning that the effectof green labels is almost nonexistent or marginally negative among relatively large and new office buildings butthat the effect among other office buildings is unknown. In an analysis using propensity score matching, thereis the problem of not being able to confirm the effect of green labels in old, small and medium-sized officebuildings.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

16

TOPIC REPORT｜Xymax Real Estate Institute

Appendix 5. Stratification using propensity score
In order to solve the problem of Appendix 4, we divided the samples into five groups based on the size of
their propensity scores. Since buildings with a low propensity score tend to be small and old, analysis can be
expected in groups with medium to low propensity scores. The calculation method of propensity scores is thesame as in Appendix 3. The analysis data is divided into five groups using the estimated propensity scores. The
number of samples of each group has been made roughly the same by using the quintile point as thesegmentation boundary.
Appendix Figure 4 indicates the descriptive statistics value of the samples of Group 4 and Group 5 byexistence of a green label. Even in Group 4, the average gross building area of green buildings is 6,018 tsubo
while that of non-green buildings is 4,439 tsubo, showing a smaller difference between the two compared toAppendix Figure 2, which is before sample adjustment. The difference in average age is also smaller, with 19.3
years for green buildings and 22.2 years for non-green buildings.
In Group 5, the difference in average gross building area has been reduced, with 20,162 tsubo for green
buildings and 13,560 tsubo for non-green buildings. The difference in average age has also been reduced, with
7.3 years for green buildings and 10.7 years for non-green buildings. The other variables have also been adjusted
in general, with the difference between the two groups reduced due to stratification using propensity score as
well, creating similar samples. Furthermore, unlike in matching, stratification enables to obtain samples for which
the covariates have been adjusted in medium-sized and old buildings as in the buildings in Group 4.

Appendix Figure 4: Samples after Stratification using Propensity Score (Groups 4 and 5)
Group 4

Group 5

Green Buildings

Non-Green Buildings

Green Buildings

Non-Green Buildings

No. of samples: 30

No. of samples: 1,321

No. of samples: 324

No. of samples: 1,028

AverageStandard
deviation

AverageStandard
deviation

AverageStandard
deviation

AverageStandard
deviation

New contract rent

18,9505,611

17,196

4,695

25,985

6,922

23,131

6,737

Green label dummy

1.000

0.000

0.000

0.000

1.000

0.000

0.000

0.000

Gross building area

6,018

4,874

4,439

5,875

20,162

25,382

13,56016,158Age19.3278.356

22.194

7.882

7.261

8.444

10.6828.131

No. of stories above ground14.8676.704

11.341

5.940

20.87012.82017.986

10.786

213

113

230

176

493

376

382

280

5 central Tokyo wards dummy

0.700

0.466

0.845

0.362

0.874

0.333

0.841

0.365

Time to nearest station

1.833

0.834

3.528

2.310

3.074

1.836

3.009

2.277

Raised floor dummy

0.733

0.450

0.810

0.393

0.982

0.135

0.941

0.236

Individual a/c dummy

0.567

0.504

0.799

0.401

0.830

0.376

0.793

0.406

Automated security dummy

0.967

0.183

0.894

0.308

0.861

0.346

0.890

0.313

Renovation dummy

0.000

0.000

0.207

0.406

0.133

0.340

0.119

0.324

Standard story area(4)–(8) of Appendix Figure 1 shows the estimates of the hedonic function of each group as in Appendix 3. In
Group 5 (8), which has the highest propensity scores, the figure was a near-zero negative at -0.0058 (0.0105).
The covariates of Group 5 mostly consist of large and relatively new building samples, and this result is consistent
with the result of Appendix 4, which uses propensity score matching. In Group 4 (7), we found that there was asignificantly positive effect at +0.1378 (0.0328). Compared to Group 5, Group 4 tends to have more mediumsized and old office buildings. The reason why the results were not significant for Groups 1–3 was probably due
to the small percentage of green buildings and thus the lack of enough samples to measures the effect.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

17

TOPIC REPORT｜Xymax Real Estate InstituteIn view of the results of Appendix 4 as well, it is implied that the effect of green labels on new contract rent
varies depending on the market segment. Tokyo’s office building market, the target of this study, has one of theworld’s largest stocks. The age of office buildings is broadly distributed and thus their environmental
performance varies due to the city’s experience of high economic growth, the oil shock, and the birth and burstof the bubble economy. In terms of the companies, who are the users of office buildings, the 23 wards of Tokyo
have a concentration of numerous companies diverse in scale. Furthermore, Tokyo is not dependent on certain
industries, but all kinds of industries exist in it, such as manufacturing, telecommunications, finance, restaurant,
and service. Due to these factors, the office market of the 23 wards of Tokyo is huge and diverse, and considered
to consist of multiple segments with different pricing systems of rent. In other words, the effect that green labels
has on new contract rent is not +4.4% generically for all office buildings but varies by the nature of the building.
In particular, the effect is almost nonexistent among large buildings, which are likely to obtain a green label,while it is prominent at approximately +10% among medium-sized buildings.

Appendix 6. Summary
In these appendices, we used propensity scores to estimate the propensity of obtaining a green label andadjusted samples by matching and stratification. By doing so, we indicated that it was possible to collect only
samples with a similar market structure. Using the techniques indicated in this analysis will enable us to estimate
the effect of intervention more precisely in a market with a complex market structure as the Tokyo office market.

Appendix 7. References
Deng, Y., Li, Z., & Quigley, J. M. (2012). Economic returns to energy-efficient investments in the housing
market: evidence from Singapore. Regional Science and Urban Economics, 42(3), 506-515.
Eichholtz, P., Kok, N., & Quigley, J. M. (2010). Doing well by doing good? Green office buildings. American
Economic Review, 100(5), 2492-2509.
Fuerst, F., & McAllister, P. (2011). Eco-labeling in commercial office markets: Do LEED and Energy Star offices
obtain multiple premiums?. Ecological Economics, 70(6), 1220-1230.
Fuerst, F., McAllister, P., Nanda, A., & Wyatt, P. (2015). Does energy efficiency matter to home-buyers? Aninvestigation of EPC ratings and transaction prices in England. Energy Economics. 48, 145-156.
Fuerst, F.D., & Shimizu, C. (2016). The Rise of Eco-Labels in the Japanese Housing Market, Journal of Japaneseand International Economy, 39, 108-122.
Hyland, M., Lyons, R. C., & Lyons, S. (2013). The value of domestic building energy efficiency — evidence from
Ireland. Energy Economics, 40, 943-952.
Kahn, M. E., & Kok, N. (2014). The capitalization of green labels in the California housing market. Regional
Science and Urban Economics, 47, 25-34.
Miller, N, Spivey, J. & Florance, A. (2008). Does Green Pay Off? Journal of Real Estate Portfolio Management,
14, 385-399.

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

18

TOPIC REPORT｜Xymax Real Estate InstituteReichardt, A., Fuerst, F., Rottke, N., & Zietz, J. (2012). Sustainable building certification and the rent premium:a panel data approach. Journal of Real Estate Research, 34(1), 99-126.
Rosenbaum,P.R., & Rubin, D.B. (1983). The central role of the propensity score in observational studies forcausal effects. Biometrika, 70(1), 41-55.
Zheng, S., Wu, J., Kahn, M. E., & Deng, Y. (2012). The nascent market for “green” real estate in Beijing. European
Economic Review, 56(5), 974-984.
Ito, Masato. (2015). Creating opportunities for expanding the spread of green building — focusing on thetrend of ESG investment and economic effect surveys. Green Building Promotion Review Committee.
Ushijima, Koichi. (2016). Identification of Cause and Effect in the Hedonic Approach. Toshi Jutaku Gaku (Urban
Housing Sciences), 2016(92), 25-30.
Obayashi, Hitoshi. (2016). Interpretation of Logistic Regression and Propensity Score Analysis. Tenri Medical
Bulletin, 19(2), 71-79.
Japan Real Estate Institute. (2019). The Economics of ESG Investment. Sustainability and ESG Investment inReal Estate — Announcement of GRESB Rating Results and Outlook of Real Estate ESG Investment.
Hoshino, Takahiro & Shigemasu, Kazuo. (2004). Estimation of Causal Effect and Adjustment of Survey Data
using Propensity Scores. Kodo Keiryogaku (The Japanese Journal of Behaviormetrics), 31(1), 43-61.
Yoshida, Atsushi & Onishi, Junichiro. (2015). Analysis of the Economics of Environment Management — The
Relationship between the Existence of Green Labels and New Contract Rent among Office Buildings in Tokyo 23
Wards: An Analysis using the Hedonic Approach and Propensity Scores. Green Building Promotion Review
Committee.
Yoshida, Jiro & Shimizu, Chihiro. (2010). Impact of Green Buildings on Real Estate Prices: Among Japan’s Newly
Built Condominiums (No. 106). Center for Spatial Information Science, Discussion Paper.

Please contact below for inquiries on this report
Xymax Real Estate Institutehttps://soken.xymax.co.jp

E-MAIL: info-rei@xymax.co.jp

Topic Report: Analysis of the Economics of Green Buildings in Tokyo’s Office Market
The contents of this report are as of the time of writing. Xymax Real Estate Institute does not guarantee their accuracy or completeness. This
report may not be reproduced, cited, transmitted, distributed, or reprinted without prior permission of Xymax Real Estate Institute.
Copyright © 2019 Xymax Real Estate Institute Corporation. All rights reserved.

19