CSIS Discussion Paper 133 (The University of Tokyo)

Green Luxury Goods? The Economics of Eco-Labels in theJapanese Housing Market∗
Franz Fuerst†, Chihiro Shimizu‡
5th September, 2014
Abstract
This paper aims to extend the existing evidence on the investment value of green buildings
to international markets, speciﬁcally the residential market in Japan. Using a uniquetransaction database of condominiums in the Tokyo metropolitan area and a hedonic analytical framework, we ﬁnd that green buildings command a small but signiﬁcant premiumon both the asking and transaction prices. This ﬁnding is consistent with results fromother countries. As far as we are aware, this is also the ﬁrst study of green buildings’
economic value based on a hedonic model incorporating buyer characteristics. However,further analysis reveals that this premium is primarily driven by wealthy households thatexhibit a higher willingness-to-pay for eco-labelled condominiums, both as a total amountand as a fraction of the total sales price. We therefore conclude that eco-labels are perceived as a luxury good in the Japanese housing market rather than a way to save money
on lower utility bills.
Key Words:
Green building; green label; hedonic approach; oﬀer price; bid price; market price function; omitted variable bias.
Journal of Economic Literature Classiﬁcation Numbers:
G51; M14; D92.

1 Introduction
Sustainability research in real estate has reached a critical juncture. The seminal studies(Miller, Spivey, Florance, 2008[17], Fuerst and McAllister, 2011[9], Reichardt, Fuerst and∗ Franz Fuerst wishes to acknowledge the continuous and generous support of the Cambridge University

Land Society (CULS) in enabling his research activities. Financial assistance was also provided by the
RICS Research Trust.
† University of Cambridge, Department of Land Economy, Cambridge CB3 9EP, UK, ﬀ274@cam.ac.uk‡ Reitaku University, Department of Economics, Kashiwa Ciba 2770062, Japan, cshimizu@reitaku-u.ac.jp.
& Visisting researcher, Center of Spatial Information and Science, The University of Tokyo.

1

Zietz, 2012[12] , Eichholtz, Kok and Quigley, 2010[6], 2011[7] and Eichholtz and Quigley
2012[5] to name just a few) provided ﬁrst valuable insights into the pricing of sustainable realestate. However, these studies are also characterised by important limitations. Firstly, they
typically focus on speciﬁc sectors, in speciﬁc countries and over speciﬁc timeframes whichmeans that their results may not be readily generalisable to other sectors, places and time
periods. This is particularly relevant as the majority of studies were conducted using datafrom the US oﬃce market, possibly because of data availability. Secondly, these studies rely
on a very small number of data sources (notably from the CoStar Group) which provide agreat wealth of information on property characteristics but are rather limited regarding theenvironmental performance and general sustainability indicators.
Perhaps surprisingly, the residential sector has attracted a much smaller number of academic
studies in this topic area, despite its large size and obvious relevance for both the general
economy and sustainable development. The reasons for this lack of empirical evidence are notclear. Larger fragmentation of investors and a lower fraction of professional or institutional
investment in the market driving the discourse around ’green value’ may be a contributing
factor. Also, housing markets are highly regulated and prone to ineﬃciencies in many countries
which makes it more diﬃcult to measure the contribution of sustainability and energy eﬃciency
to prices and rents. Despite the by now widely accepted proposition that pricing incentives are
more eﬀective in reducing environmental harm than ‘command and control’ policies, (Requate
and Unold (2003)[20], the housing market seems to be lagging behind other sectors in theestablishment of green markets.’Green’ ﬁnancial instruments are also still not used widelyin the residential sector which makes capitalization into the lump-sum house price the only
channel for economic rewards of sustainability in many cases. As this poses a signiﬁcant risk
for any upfront investment in energy eﬃciency, ’green value’ might not be readily observablein housing markets.
According to Kotchen (2006)[15], green markets can principally be understood as a form ofa private provision of a public good and as such can have either beneﬁcial or detrimental
eﬀects depending on technology, individual wealth levels and the initial level of the public
good. This proposition has been evaluated empirically, for example by Jacobsen, Kotchen and
Vandenbergh (2012)[20] in the context of residential electricity demand.
Despite these apparent obstacles, the existing evidence of the residential market points to asigniﬁcant green premium. An early study by Dian and Miranowski (1989)[4] showed that
increasing energy eﬃciency increases housing prices. Banﬁ et al. (2005)[1] have publishedresearch ﬁndings indicating that rental housing tenants are prepared to pay up to 13% higher
rent for buildings that have adopted energy-saving measures. Similarly, Fuerst et al (2013)[11]
found a price eﬀect of higher energy performance in the British housing market for a large
sample of sales transactions in the 1995-2011 time period, indicating a 14% premium of thehighest band of the Energy Performance Certiﬁcate (EPC) over the lowest band. They also
ﬁnd that this eﬀect tends to be larger for terraced dwellings and ﬂats compared to detachedand semi-detached houses. Earlier, Brounen and Kok (2011)[2] had examined the relationship between EPC ratings and sale price for 31,993 residential sale prices in 2008-9 in the
Netherlands and report signiﬁcant premiums for more energy-eﬃcient buildings. Although
their dataset contains a large number of control variables, the adoption rate of EPCs in the
Dutch housing market was relatively low at the time (7-25% depending on the year) which
may limit their ﬁndings. Similarly, Zheng and Kahn (2008)[28] and Zheng, Kahn and Deng(2012)[29] ﬁnd signiﬁcant price premia for ’green’ properties in the Chinese housing market
and a study by Deng, Li and Quigley (2012)[3] ﬁnds substantial economic returns to green
buildings in Singapore. Kok and Kahn (2012)[16] as well as Hyland et al (2013)[13] arrive at2

similar conclusions for the Californian and the Irish housing market respectively.
This paper examines ’green value’ in the Japanese housing market. Using a unique transaction
database of contains roughly 50,000 housing transactions in the Tokyo condominium market,
we seek to establish whether an eco-label carries a signiﬁcant premium in asking and/ortransaction prices.
There are a number of existing studies on the Japanese market for green buildings, for example Shimizu (2010)[22] (2013) [23] who have conducted an analysis focusing on the newcondominium market using asking prices and transaction prices. However, due to the small
size of their sales transactions sample, the results did not reach a satisfying level of statisticalreliability. A larger study was conducted by Yoshida and Sugiura (2013)[27]. Using a sample
of roughly 35,000 condominiums the authors ﬁnd that eco-labelled condominiums were soldat a discount, rather than a premium and oﬀer a number of empirical and methodologicalexplanations for this result. The present study seeks to clarify the conﬂicting ﬁndings regarding the Tokyo market and contributes to the body of evidence by applying the largest
and most comprehensive dataset to date in this investigation of ’green value’ in the Japanesecontext. Crucially, it contains information on the property development company as a proxyfor quality as well as buyer characteristics which were possible omitted variables in the earlier
studies cited above.

2 DataThe principal source for the sales transactions database applied in this case study is theTokyo Association of Real Estate Appraisers (2010)[26] which collects transaction prices for
new condominiums and used condominiums. Green labels are currently only awarded in theTokyo market for new builds and not for used condominiums. While our use of new builds only
may limit the application of our ﬁndings to the general housing market, it avoids the reported
problems arising from any discontinuities that may exist in how property characteristics are
priced in the new-build and re-sale markets.
The dataset was collected using a survey of house price and attribute information. The
most important piece of information concerns prices per unit, both the asking price (whichis the producer’s oﬀer price) and the recorded transaction price . Further, in order to ensure
consistency with the hedonic theory model, data relating to buyer characteristics such as
income, household size, etc., was gathered. The questionnaire survey was conducted by the
Recruit Housing Institute, starting in November 2011. Surveys were conducted in writing,
via submissions from a large number of home buyers. Contract data were also used to collectaccurate transaction prices. In addition, information on freehold/leasehold and the form ofmanagement were recorded by the questionnaire survey, i.e. is the building managed throughvisits, (called ‘patrols’), through day shifts (a manager works in the administrative oﬃce
during the day time only), or by having a permanent presence on site (a manager works inthe administrative oﬃce and is present on a 24-hour basis). The intuition behind gathering
this information was that the quality and availability of management services is said to be
reﬂected in condominium prices. More importantly, it can also be viewed as a proxy for otherunobservable quality characteristics that might otherwise be captured by the green label which,in the worst case, could lead to omitted variable bias and overstated green premiums. Standard
hedonic characteristics such as the total number of condominium units, lot area, and overallbuilding area were also included. Moreover, we assume that price diﬀerentials may also arise3

based on the developer’s and (main) construction company’s reputation and brand power.*1
Market conditions and dynamics are an important control variable in any hedonic analysis.
Therefore, we included the ﬁrst-month contract rate as a proxy. The ﬁrst-month contract rate
reﬂects the percentage of units sold within the ﬁrst month of marketing a particular property.
. It is thought that a higher ﬁrst-month contract rate reﬂects better general market conditions
but a higher relative rate (i.e. relative to the market average at the time) also indicates how
aﬀordable the housing unit prices are in relation to the condominium’s features.
In addition to the Recruit Housing Institute’s survey data, the Japanese Real Estate Economic Institute’s database was used. Along with the developer’s asking price, the following
key variables were drawn from the Real Estate Economic Institute’s database: name of thedevelopment company, development overview (development scale), location characteristics (coordinates, address, nearest station, distance to nearest station), and building characteristics(building area, land area, building structure). This information was matched to the data
gathered by the Recruit Housing Institute. Appendix 2 contains a complete overview of thevariables used in the analysis. Using these sources, a large database was assembled for the
10-year period from 2001 to 2011.
With regard to markers of ’green buildings’, data for labelling based on Tokyo Metropolitan
Government’s Green Labelling System for Condominiums were used. This is based on theGreen Building Program which was introduced in June 2002 and mandates that all large-scaleconstruction or major refurbishment projects exceeding 10,000m2 submit an environmental
plan at the time of planning as well as a completion notice. Additionally, in October 2005,
the Green Labelling System for Condominiums was started which required the gathering andpublishing of information based on four environmental evaluation items. The four evaluation
items are: a) quality of building insulation which addresses reduction in the building’s heatload; b) facility energy-saving performance, which addresses energy-saving systems; and c)lifespan extension and d) greening of the building, which address lifespan extension, etc., andgreening. The evaluation results for the respective items are expressed as a number of starsymbols ranging from one to three stars. In addition, in order to increase recognition among
consumers, condominium buildings under the obligation to submit an environmental plan
document had to indicate the scores of the evaluation items. Moreover, from January 2010
onward, the system was changed to cover not only owner-occupied condominium buildings
but also rental condominium buildings and the ﬂoor space for which notiﬁcation is required
was lowered to 5,000m2 in total. This change also stipulated that owners of smaller buildings
were also permitted to apply for this label at their own discretion.
The hedonic model used for this analysis includes a dummy variable for buildings with two
or more stars for either a) building insulation (covering reduction in the building’s heat load)or facility energy-saving performance (covering energy-saving systems) and 0 otherwise. The
dummy variable is not applied to buildings which have only one star under the Green Labelling
System for Condominiums as this was deemed too low to qualify as a credible ’green’ product.
Moreover, with regard to building performance evaluation, the existence of a Housing Design
Performance Evaluation Document and Housing Construction Performance Evaluation Document based on the Housing Quality Assurance Act is also considered in our analysis. This
*1 A dummy variable was created to distinguish: leading construction companies (1) Takenaka Corporation,(2) Obayashi Corporation, (2) Kajima Corporation, (4) Shimizu Corporation, and (5) Taisei Corporation; second-tier construction companies (6) Kumagai Gumi, (7) Toda Corporation, (8) Penta-Ocean
Construction, (9) Konoike Construction, (10) Sato Kogyo, (11) Mitsui Construction, (12) Mitsubishi
Construction, (13) Sumitomo Construction, (14) Nishimatsu Construction, and (15) Haseko Corporation; and (16) other.

4

is done to ensure that the measured price contribution of a green label is separated from theeﬀect of conventional Housing Performance Evaluation and quality assurance documents.
Adequate location controls are essential in any attempt to disentangle the factors contributing
to property prices. In the present analysis we use a ﬁne-grained 500m × 500m mesh blockin which the condominium is located as a unit. Speciﬁcally, the characteristics are built-up
area, average ﬂoor space, standard deviation of ﬂoor space, number of ﬂoors for each building
and the standard deviation for the number of ﬂoors. Next, area-based information on the
proportion of the population aged 65 and over and the proportion of oﬃce workers in thepertaining census mesh block were added. To account for unobserved spatial characteristics,
we also generate a local administrative district dummy. A further set of dummy variables
indicates proximity to a railway line and the time required to Tokyo Station from the nearest
station was also included as a regressor.
Buyer characteristics are an important feature of our analysis as outlined above. The following variables were considered: home buyers’ annual income, age, occupation, household size,
number of children and identiﬁer for ﬁrst time buyers. With regard to occupation , diﬀerencesby employment status,*2 work category,*3 and industry category*4 were examined.
We ﬁrst investigate the distributions and descriptive statistics of the underlying dataset. The
average asking price has a value of 45.49 million yen, the average value for the actual transaction price was approximately 1.5 million yen lower, at 43.91 million. The ﬂoor space ranges
from 10m2 studio condominiums to large-scale condominiums exceeding 200m2 . The walking
time to the nearest public transit station is 7 minutes on average, while the average time toTokyo Central Station is 23 minutes which shows that these properties are generally well servedby public transportation. Looking at housing buyer characteristics, the average age of buyers
was 37 and the average number of people in the household was 2.3, demonstrating that thesebuyers are typical Japanese households and could hence be considered as representative ofJapanese home buyers in general. However, an important caveat is that the household head’s
average income was 8.51 million, a level that is about twice the Japanese average income.

3 Model speciﬁcation
Following the overall research strategy outlined in the previous section and taking into accountbuyer characteristics which are limiting conditions for the bid price function, we specify thefollowing model:
P(i,j,t) = f (Gi , X(i,j) , N Ek , HH(i,j) )*2 With regard to employment status, the survey was conducted using the following classiﬁcation: 01.

permanent employee, 02. contract employee, 03. civil servant/public organisation employee, 04. selfemployed, 05. physician/lawyer/tax accountant/accountant/etc., 06. part-time/casual, 07. homemaker,
08. student, and 09. unemployed. There were no samples corresponding to contract worker, part-timeworker, homemaker, or student.
*3 The survey was conducted using the following classiﬁcation for employment category: 01. clerical job,
02. sales job, 03. technical job, 04. service/retail job, 05. construction/manufacturing job, 06. specialized job, 07. management job, and 08. company executive.
*4 The following items were surveyed as industry categories: 01. agriculture, forestry, and ﬁshing, 02.
construction, 03. manufacturing, 04. transportation/warehousing, 05. ﬁnance/securities/insurance, 06.
advertising/publishing/broadcasting, 07. printing/typesetting, 08. fashion-related, 09. travel/hotel/
leisure, 10. restaurant/bar, 11. housing/real estate, 12. trading/wholesaling, 13. retail, 14. software/
information services, 15. beauty, 16. medical/welfare, 17. education, 18. creative professions, and 19.
other.

5

P(i,j,t) :Gi :X(i,j) :N Ek :
HH(i,j) :New condominium price of condominium i and dwelling j at time t (1: asking
price, 2: transaction price)
Green label of condominium i
Building characteristics of condominium i & dwelling j
Location characteristics of region k
Buyer characteristics of condominium i and dwelling j

This speciﬁcation has a number of desirable properties as compared to previously estimatedhedonic functions. Firstly, with regard to the price (P(i,j,t) ), both the asking and the recordedtransaction prices are known in each individual case, allowing us to investigate whether sellers
have unrealistically high expectations of the market value of a ’green label’. Secondly, withregard to housing prices, in general, a price diﬀerential is generated (X(i,j) ) based on diﬀerencesin condominium (i) features such as building structure and the size of the lot area, as well asfeatures related to the dwelling (j), such as the ﬂoor space, the unit’s position (whether or not
it is a corner unit), etc. In terms of the condominium building’s features (i), it has increasingly
been pointed out that a price diﬀerential is generated by the condominium developer or thedeveloper’s brand (the developer’s reliability and quality assurance, which is diﬃcult to observe
visually) and by the construction company. With regard to these variables, developer andconstruction company information was also gathered and incorporated.
Further to these kinds of building and dwelling characteristics, the characteristics of the surrounding environment, such as the streetscape of the area (k), the commercial density, etc.,
have a major eﬀect on housing prices. This is known as the neighbourhood eﬀect (N Ek ).
The neighbourhood eﬀect includes not only the living environment but also the ease of commuting to work or school and the ease of shopping, which are represented by transportation
convenience (accessibility of nearest station, time to central business district, etc.).
Moreover, as shown in hedonic theory, it is also to be expected that a price diﬀerential will
be generated via changes in the bid price function based on buyer characteristics (HH(i,j) ).
The required ﬂoor space and housing features change in accordance with the buyer’s annual
income and household size, and if they are not linear, these characteristics have to be taken into
account. In particular, since it is to be expected that factors such as a building’s environmental
performance will change considerably according to housing buyers’ preferences, it may be toomuch to assume that there is a homogenous utility function (Shimizu, Nishimura, and Karato,
2014[24]).
Based on this kind of model analysis, the following three estimation models were set.
Here, factoring in the time element, the hedonic price function is estimated focusing on thecondominium price (P(i,j,t) ) at time t.
First, as a standard model, the following model was taken as a starting point (Model 1).
∑m
log P(i,j,t) = a0 + a1 T(i,j) + a2 Gi + a3 Gi T(i,j) +am
4 X(i,j)+∑n

an5 N Ekn +∑tm

at6 Dt + ε(i,j)(1)

T(i,j) is a transaction dummy (1 in the case of the transaction price; 0 in the case of the asking
price), while Dt (t = 2001 to 2011) is a time dummy. With regard to the green label eﬀect(Gi ), it is to be expected the degree to which the eﬀect appears will change depending on theasking price (which is the producer’s oﬀer price) or the transaction price (which is linked tothe bid price). Accordingly, the diﬀerence between the two has been incorporated by inserting6

a cross-term (Gi × Tj ) with the transaction price dummy (Tj ), which is 1 for the transaction
price and 0 for the asking price.
Next, it was expanded into a hedonic function factoring in buyer characteristics, which intheoretical terms should normally be considered, but which were diﬃcult to incorporate into
the model due to data limitations (Model 2).
∑mam
log P(i,j,t) = a0 + a1 T(i,j) + a2 Gi + a3 Gi T(i,j) +4 X(i,j)+∑n

an5 N Ekn +

∑
s

s
+
as6 HH(i,j)∑
tm

at7 Dt + ε(i,j)(2)

Moreover, how the green label eﬀect (Gi ) changed in accordance with the passage of time wasanalyzed (Model 3).
∑mam
log P(i,j,t) = a0 + a1 T(i,j) + a2 Gi + a3 Gi T(i,j) +4 X(i,j)+∑n

an5 N Ekn +∑ss
as6 HH(i,j)
+

∑
t

m

at7 Dt +∑t

at8 Gi Dt + ε(i,j)(3)

4 Estimation Results
The estimation results for the three models are outlined in Table 1. The baseline estimation(Model 1) reveals that the average asking price for a condominium with a green label (twoor three stars out of three) is 6% higher compared to a similar condominium without a label.
In other words, the developers of condominiums with superior environmental performanceoﬀered them at a marginally but signiﬁcantly higher price. However, the actual achieved
transaction prices are more relevant to our central research hypothesis about the existence ofa green premium. The general transaction price variable indicates that transaction prices wereon average 3.5% lower than asking prices in the observed period 2001-2011. Green-labelledproperties transacted with another marginal discount of 0.9%, with the green transaction
price being 4.3% less than the asking price for labelled properties. The total green premium
actually observed in the residential sales market therefore reduces to 1.6% (6%−3.5% −0.9%).
Although relatively small in magnitude, this price premium is statistically signiﬁcant.
A more detailed list of coeﬃcient estimates is contained in Appendix 1 which reveals a number of additional insights into the pricing of green and non-green condominiums. In cases
where a Housing Design Performance Evaluation Document (Part A)*5 exists, condominium
prices are marginally higher (0.6%) while the presence of a Housing Performance Construction Evaluation Document (Part B) entails a 0.5% premium. These evaluation document
variables are important for isolating the ’pure’ eﬀect of the green label eﬀect from other typesof quality evaluation of newly built properties. Next, similarly distinguishing buildings basedon management costs, maintenance/renovation investments, etc., shows that such costs andtransaction prices are positive related. Furthermore, the price was lower when the type ofland ownership was ’general leasehold’ or considerably lower for ’ﬁxed-term leasehold’. The*5 The Housing Performance Indication System is based on the Housing Quality Assurance Act that wasenacted on April 1, 2000. It evaluates housing performance based on ﬁxed standards, such as complying
with the obligatory 10-year defects liability period for basic structural areas of new housing. Under thissystem, Housing Performance Evaluation Documents are issued, which are divided into Housing Design
Performance Evaluation Documents and Housing Construction Performance Evaluation Documents.

7

price was 2.2% higher when the dwelling is a corner unit. The coeﬃcients of all other control
variables including the time-period dummy variables exhibited the expected signs.
Model 2 in Table 1 reports the estimation results using robust regression, an estimation technique which gives proportionally less weight to inﬂuential outliers, thereby reducing the potential bias that a small group of properties with extreme or unusual prices and other attributes
may introduce to the results. However, the results are only slightly diﬀerent to the baselinemodel with a 5.9% asking price premium and a 1.8% transaction price premium for greenlabelled properties.
Next, we examine the impact of buyer characteristics on the model (Models 3-6 of Table 1 andAppendix 1). To this aim, we divide buyers’ incomes into quartiles and estimate the impactof all price determinants separately for each income quartile. As expected, green asking pricepremia (as a fraction of the total price) are found to progress with increasing incomes ofbuyers (from 4% to nearly 8%). Similarly,we ﬁnd that the average price premium observed inrecorded transaction prices (as opposed to asking prices) is mainly driven by households withabove-average incomes.paid for green-labelled properties , Given that these are percentages
on the total price, a base which is higher for more aﬄuent buyers buying more expensive
properties, the spread in terms of absolute monetary values of these price premia is even more
pronounced. This ﬁnding is signiﬁcant in that it demonstrates for the ﬁrst time that ’green’
features are more likely to attract higher-income buyers despite arguments to the contrary
that claim energy eﬃciency and the resulting lower utility bills are a larger concern for moreincome-constrained households.
Further interesting ﬁndings (Appendix 1) in terms of buyer characteristics are that ﬁrst-timebuyers exhibit generally a lower willingness to pay, particularly in the lower income segments.
This may be taken as an indication of ﬁrst-time buyers acting more cautiously on the housing
market regardless of current income, possibly because of their relatively lower asset possessions
compared to buyers who already own a property and seek to ’trade up’. A price diﬀerential
also occurs based on occupation. Independent of current income and age, it is possible that
this variable acts as a proxy for future income or the certainty (stability) of that income. The
fact that annual income and employment generate diﬀerences in housing prices supports ourearlier proposition that prices of both green and non-green properties cannot solely explainedby property characteristics but are also a function of socio-economic buyer characteristics.

8

Table 1. Hedonic regression results

Trans:
Transaction
price discountGreen:
Green asking
price premium1
trgreen:
Green transaction pricediscount2
S:
Space

Floor

(1) baselineOLS
lp(2) Robust
reglp(3) Income
Q1lp(4) Income
Q2lp(5) Income
Q3lp(6) Income
Q4
lp−0.0347∗∗∗−0.0316∗∗∗−0.0359∗∗∗−0.0354∗∗∗−0.0337∗∗∗−0.0343∗∗∗(−27.87)(−26.54)(−11.72)(−15.94)(−16.16)(−13.37)

∗∗∗

∗∗∗

∗∗∗

∗∗∗

∗∗∗

0.0777∗∗∗0.06090.05860.04080.03980.0702(18.66)(18.31)(3.63)(6.74)(13.12)(12.45)∗∗∗−0.0158−0.00692−0.00936−0.00975(−2.40)(−2.59)(−1.16)(−1.08)(−1.50)(−1.41)

∗∗∗

∗∗∗

∗∗∗

∗∗∗

∗∗∗

0.0146∗∗∗−0.00918

0.0243−0.00948

0.02440.01600.01430.0135(77.74)(115.41)(97.94)(98.75)(98.93)(98.12)∗∗∗7.505(329.53)

∗∗∗

7.508
(390.22)

∗∗∗

7.533
(144.48)

∗∗∗

7.757
(154.72)

∗∗∗

7.861
(219.17)7.954∗∗∗(250.75)

Property &condo
attributes

Yes

Yes

Yes

Yes

Yes

Yes

Developer
ﬁxed eﬀects

Yes

Yes

Yes

Yes

Yes

Yes

Locationcontrols

Yes

Yes

Yes

Yes

Yes

Yes

Managementﬁxed eﬀects

Yes

Yes

Yes

Yes

Yes

Yes

Buyer characteristics

Yes

Yes

Yes

Yes

Yes

Yes

Time ﬁxed
eﬀects

Yes

Yes

Yes

Yes

Yes

Yes

48740
0.808
0.807
−60598.0−59296.5

48740
0.818
0.818
.
.

6940
0.799
0.795
−9091.1−8071.2

12896
0.727
0.723
−18262.5−17120.4

15328
0.716
0.714
−20896.4−19720.3

13576
0.783
0.781
−14952.0−13787.0

Constant

N
R2
adj. R2AIC
BIC

t statistics in parentheses∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001
1: Green asking price premium on equivalent unlabelled condominiums.
2: The dummy variable ’transaction price discount’ indicates the average discount observed relative to theequivalent asking price. The dummy variable ’green transaction price discount’ indicates the additional discountapplied to the transaction of a green-labelled condominium. Both the general transaction price discount andthe green transaction price discount have to be subtracted from the green asking price premium to arrive at thegreen transaction price premium. For example, in Model 1 the total green premium paid in transactions is 1.6%
9(6% - 3.5% - 0.9%).

Next, we examine how the added economic value of green buildings has changed over time
(Table 2). For simplicity, only the estimates for the variables of interest are shown and theresults for the large number of control variables are suppressed in this table. First, for the baseasking price, the premium rose over time from 5.1% in 2005 to over 7.4% in 2009 but declined
considerably in 2010 and 2011. When estimating the transaction discounts relative to asking
prices, we ﬁnd that there were signiﬁcant discounts from 2008-2011 but no additional eﬀect isfound for green transactions. This suggests that the relationship between asking prices andtransaction prices was similar for labelled and non-labelled properties when analysed on a
year-to-year basis.
Table 2: Hedonic regression results of the ’green premium’ over timeYeartrans: Transaction
discount

green: Green asking
price premium−0.037−0.037−0.034−0.070∗∗∗−0.078∗∗∗−0.029∗∗∗−0.025∗∗∗
48740
0.807
0.806
−60227.9−58776.8

0.051∗∗
0.044∗∗∗
0.066∗∗∗
0.063∗∗∗
0.074∗∗∗
0.040∗∗∗
0.0191

2005
2006
2007
2008
2009
2010
2011
N
R2
adj. R2
AIC
BIC

trsgreen: Greentransaction
price discount−0.046−0.001
0.009
0.001
0.013
0.003
−0.015t statistics in parentheses∗
p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001’Transaction discount’ refers to the average discount in transaction prices compared to asking prices in eachyear. ’Green asking price premium’ indicates the average asking price premium of green-labelled condominiums
compared to the asking prices for non-labelled condominiums in each year. ’Green transaction price discount’refers to the additional eﬀect of transactions of green-labelled condominiums above and beyond the general’transaction discount’.

5 Conclusions
This paper set out to test whether obtaining a green label adds value to residential properties
in the Japanese housing market using a unique dataset of new condominium transactions in
the Tokyo market. Based on our analysis, this question can be answered in much the same
way as previous research has done across the world.
The hedonic analysis shows a clear price premium for green-labelled condominiums both inasking and transaction price. Although the eﬀect for the latter is rather small (around 1.7%of the transaction price). Taking into account buyer characteristics (Model 2), we ﬁnd that
wealthier buyers are willing to pay a higher premium for green-labelled properties, both inabsolute and in relative terms. It appears that eco-labelled condominiums in Tokyo are aluxury good that is oﬀered primarily to high-income households who are able and willing to
pay a premium for owning and occupying a green-labelled property.
In addition, if one looks at temporal changes in the premium, we ﬁnd that the eﬀect ofgreen labels became larger over time before declining again in the ﬁnal two years of the study
10period. In terms of the possible reasons for this, it may be the case that the awareness of green
buildings has increased in the Japanese residential market in the years 2006-2010 and thatthe buyer segment actively seeking to invest in their value is expanding. Further analysis isrequired to ascertain whether the absence of the premium in the most recent year (2011) is acontinued trend that marks the end of a ’green premium era’ or is simply a one-oﬀ occurrence.
A number of caveats remain for this analysis. First, one could point out the problem of
accuracy with regard to the green labels used as variables in order to distinguish properties withsuperior environmental performance. The current labelling system is based on applications
from developers, and it does no more than indicate buildings’ hypothetical environmental
performance at the time of development. Buyers may be reluctant to pay signiﬁcant premia
for energy eﬃciency and cost savings that are not proven in operation.
In addition, unless the added economic value of green buildings oﬀsets or exceeds the added
development costs, developers are unlikely to develop many green buildings unless they receive
subsidies to make up for the shortfall. It is possible that the premium we measured may still
be too low in comparison to the added development expenses which may be an obstacle tomore widespread adaptation of green buildings in the Japanese market.
Furthermore, it is also uncertain how the green building submarket will be embedded in the
broader housing market that comprises mainly existing stock. Under the current system,
green labels only cover newly developed buildings, but for green building policies to be more
eﬀective the application of labels to existing stocks will have to be considered. Notably, whenit comes to a buyer’s choice of home, the decision is typically made under considerable budget
restrictions. With the rapid changes in Japanese demographic structure, the population ofpeople in their 30s and 40s – which is the home-buyer segment that generates the greatest
demand for housing – is decreasing signiﬁcantly. In this context, it will be necessary tokeep monitoring whether there continues to be an added value and price premiums for green
buildings. Finally, the economic value of green buildings will also be impacted upon by more
stringent environmental regulations are implemented in future (Takagi and Shimizu, 2010[25])but it is diﬃcult to foresee how Japanese eco labels for buildings will adopt to these changing
market conditions.

11References[1] Banﬁ, S., Farsi, M., Filippini, M., and Jakob, M. (2005), Willingness to Pay for EnergySaving Measures in Residential Buildings,” CEPE Working Paper, No. 41.
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Appendix13

14

0.0586∗∗∗(18.31)−0.00948∗∗(−2.59)0.0244∗∗∗(115.41)−0.0000649∗∗∗(−43.89)−0.00937∗∗∗(−54.42)−0.0386∗∗∗(−11.24)−0.00214∗∗∗(−6.87)0.0609∗∗∗(18.66)−0.00918∗(−2.40)0.0243∗∗∗(77.74)−0.0000632∗∗∗(−27.92)−0.00962∗∗∗(−51.06)−0.0358∗∗∗(−9.59)−0.00182∗∗∗(−5.27)green

trgreen

S

ts

bus

2.61e-08(1.21)
0.00689∗∗(3.17)
0.00321(1.89)−7.83e-09(−0.32)0.00679∗∗(2.89)

0.00486∗∗(2.60)

TA

ISP1

ISP2

busTS

S squared−0.0316∗∗∗(−26.54)−0.0347∗∗∗(−27.87)(2) Robust
reg
lp

trans

(1) baseline
OLS
lp0.0104∗(2.06)

0.0109(1.66)−0.000000128(−1.64)−0.000897(−1.35)−0.0283∗∗∗(−3.67)−0.00867∗∗∗(−19.56)

0.0160∗∗∗(97.94)−0.0158(−1.16)0.0408∗∗∗(3.63)−0.0359∗∗∗(−11.72)(3) Income
Q1
lp

0.00842∗(2.55)

0.00316(0.71)

3.36e-08(0.69)−0.00225∗∗∗(−4.27)−0.0172∗∗(−3.05)−0.00827∗∗∗(−24.63)0.0143∗∗∗(98.75)−0.00692(−1.08)0.0398∗∗∗(6.74)−0.0354∗∗∗(−15.94)(4) Income
Q2
lp

0.000887(0.28)−0.00413(−1.13)0.00272(0.60)−0.000000167∗∗∗(−3.83)0.000000185∗∗∗(4.30)
0.00387(1.03)−0.00486∗∗∗(−4.50)−0.0578∗∗∗(−5.84)−0.0101∗∗∗(−23.97)

0.0146∗∗∗(98.12)−0.00975(−1.41)0.0777∗∗∗(12.45)−0.0343∗∗∗(−13.37)(6) Income
Q4
lp−0.00156∗(−2.39)−0.0468∗∗∗(−6.34)−0.00735∗∗∗(−22.17)0.0135∗∗∗(98.93)−0.00936(−1.50)0.0702∗∗∗(13.12)−0.0337∗∗∗(−16.16)(5) Income
Q3
lp

Table A1. Full regression results of Tokyo case study

0.00371(1.96)

0.00687∗∗(2.89)−2.75e-08(−1.10)−0.00213∗∗∗(−5.94)−0.0366∗∗∗(−9.47)−0.00934∗∗∗(−48.92)

0.0155∗∗∗(233.33)−0.00918∗(−2.37)0.0644∗∗∗(19.43)−0.0347∗∗∗(−27.40)(7) Time
eﬀectslp

150.0233(1.92)−0.0449∗∗∗(−5.76)0.0216∗∗∗(16.46)−0.0117∗(−2.42)−0.000141∗∗∗(−5.17)−0.00197∗∗∗(−18.90)−0.00000557(−0.93)0.0146∗∗∗(7.45)−0.0317∗∗∗(−18.40)−0.0000455∗∗∗(−17.23)−0.0000230∗∗∗(−5.55)0.0135∗∗(2.76)
0.000293∗∗∗(30.47)−0.0402∗∗∗(−4.62)0.0213∗∗∗(15.59)−0.00894(−1.71)−0.000152∗∗∗(−5.58)−0.00190∗∗∗(−15.91)−0.0000116(−1.63)0.0143∗∗∗(6.68)−0.0330∗∗∗(−17.70)−0.0000486∗∗∗(−17.94)−0.0000223∗∗∗(−5.28)0.0165∗∗(3.15)

0.000296∗∗∗(28.51)RL2cornerstructure

TR

tt

FAR

comm

industrental

old

oﬃcew

strc2

0.0280∗∗∗(7.92)−0.00805∗∗∗(−4.68)−0.00909∗∗∗(−5.02)RL1

0.000198∗∗∗(7.27)

0.0324∗∗(2.59)−0.0000263∗(−2.51)−0.0000251∗∗∗(−3.42)−0.0314∗∗∗(−6.12)0.0225∗∗∗(4.34)−0.0000318(−1.73)−0.00213∗∗∗(−7.38)−0.000173∗∗(−2.74)−0.0285(−1.64)−0.00000228(−0.00)0.0247∗∗∗(3.30)

0.0232∗∗∗(9.80)

0.0238∗∗∗(9.09)

MG2−0.00673(−1.46)0.000758(0.42)

0.00126(0.67)

MG1

0.000316∗∗∗(16.06)0.0213(1.75)−0.0000172∗(−2.25)−0.0000450∗∗∗(−8.97)−0.0217∗∗∗(−7.22)0.0103∗(2.47)−0.0000109(−0.82)−0.00169∗∗∗(−7.93)−0.000253∗∗∗(−5.36)0.00594(0.49)

0.0188∗∗∗(7.61)−0.0432∗∗(−3.09)−0.00490(−1.44)0.0239∗∗∗(5.09)

0.00158(0.50)

0.000268∗∗∗(15.27)−0.00330(−0.43)−0.00000433(−0.61)−0.0000406∗∗∗(−8.91)−0.0253∗∗∗(−8.11)0.0129∗∗∗(3.50)−0.0000224(−1.72)−0.00154∗∗∗(−7.82)−0.0000641(−1.30)−0.0283∗∗∗(−3.69)0.0162∗∗∗(6.90)−0.0355∗∗(−2.60)−0.00573(−1.95)0.0204∗∗∗(4.63)

0.00206(0.62)

0.000255∗∗∗(12.16)0.00733(1.00)

0.00000189(0.20)−0.0000646∗∗∗(−11.56)−0.0388∗∗∗(−9.22)0.00580(1.37)

0.00000324(0.25)−0.00111∗∗∗(−4.62)−0.0000874(−1.41)−0.0279∗∗∗(−3.90)0.00959∗∗∗(3.55)

0.00643(0.23)−0.00999∗∗(−2.63)0.0158∗∗(3.13)

0.00412(1.02)

0.000295∗∗∗(27.97)0.0138∗∗(2.59)−0.0000173∗∗∗(−4.03)−0.0000485∗∗∗(−17.51)−0.0319∗∗∗(−16.98)

0.0126∗∗∗(5.81)−0.0000114(−1.58)−0.00178∗∗∗(−14.91)−0.000133∗∗∗(−4.79)−0.0113∗(−2.15)0.0178∗∗∗(12.81)−0.0386∗∗∗(−4.54)−0.00928∗∗∗(−5.08)0.0265∗∗∗(9.94)

0.00253(1.33)

16
0.191∗∗∗(49.13)0.158∗∗∗(42.17)0.164∗∗∗(51.80)0.164∗∗∗(46.54)0.180∗∗∗(37.02)0.156∗∗∗(38.84)0.162∗∗∗(48.56)

0.163∗∗∗(46.76)

year11

year12

year13

year140.156∗∗∗(16.35)0.137∗∗∗(11.38)0.189∗∗∗(13.46)

0.0964∗∗∗(8.57)

0.147∗∗∗(22.47)0.147∗∗∗(20.18)0.164∗∗∗(18.51)0.145∗∗∗(20.23)0.0681∗∗∗(12.09)childdummy−0.00517(−1.45)−0.00110(−0.42)−0.00448∗∗∗(−3.50)0.158∗∗∗(44.08)0.155∗∗∗(39.14)

year100.0712∗∗∗(8.75)

0.0116∗(2.00)−0.00526∗∗(−2.81)0.0574∗∗∗(18.92)

0.0535∗∗∗(16.18)year90.0216∗∗(2.85)−0.0233∗∗∗(−4.50)

number

0.00565(1.90)

0.00652∗(2.08)

year8−0.0128(−1.81)−0.0140∗∗(−2.93)0.000736∗∗∗(4.18)−0.0200∗∗∗(−7.24)−0.0218∗∗∗(−7.48)year7−0.00377(−0.58)−0.0261∗∗∗(−6.13)0.000695∗∗∗(4.38)−0.00816∗∗(−3.13)−0.00897∗∗∗(−3.32)year6−0.0301∗∗∗(−5.22)−0.0174∗∗∗(−4.37)age−0.0211∗∗∗(−9.38)−0.0232∗∗∗(−10.02)

year5−0.0221∗∗∗(−4.01)0.00342∗∗∗(3.64)

0.153∗∗∗(24.26)−0.0135∗∗∗(−6.28)−0.0145∗∗∗(−6.89)year4

0.00320∗(2.53)

0.147∗∗∗(15.09)0.00528∗∗∗(11.24)0.00589∗∗∗(11.07)cost−0.00230(−0.90)−0.00262∗(−2.20)0.000222(1.36)

0.157∗∗∗(25.89)0.141∗∗∗(24.56)0.140∗∗∗(20.63)0.163∗∗∗(19.15)0.143∗∗∗(21.17)0.0485∗∗∗(8.13)

0.00608(1.14)−0.0221∗∗∗(−4.37)−0.0127∗∗(−2.74)−0.0186∗∗∗(−4.60)−0.0132∗∗∗(−3.74)0.00501∗∗∗(6.19)

0.00536(1.70)−0.00343∗(−2.30)0.00000781(0.04)

0.177∗∗∗(24.35)0.155∗∗∗(23.59)0.155∗∗∗(18.86)0.177∗∗∗(19.20)0.163∗∗∗(21.95)0.0448∗∗∗(6.39)

0.0159∗(2.39)−0.0112(−1.72)−0.00387(−0.66)−0.0348∗∗∗(−7.01)−0.0175∗∗∗(−3.89)0.00713∗∗∗(6.12)

0.161∗∗∗(45.23)0.155∗∗∗(45.17)0.149∗∗∗(36.28)0.173∗∗∗(35.52)0.150∗∗∗(37.78)0.0503∗∗∗(15.03)0.00507(1.60)−0.0264∗∗∗(−8.89)−0.0149∗∗∗(−5.45)−0.0291∗∗∗(−12.39)−0.0152∗∗∗(−7.06)0.00575∗∗∗(10.61)17

0.00295(0.57)
0.00849(0.79)

0.0133(1.57)
0.0369∗∗(2.87)
0.00666(0.92)

wd8

wd9

yd6

7.505∗∗∗(329.53)

48740
0.808
0.807
−60598.0−59296.5

cons

N
R2
adj. R2
AIC
BIC

48740
0.818
0.818
.
.

6940
0.799
0.795
−9091.1−8071.2

7.533∗∗∗(144.48)−0.00113(−0.33)0.0151∗∗(2.97)

wd7

7.508∗∗∗(390.22)0.00667(0.43)

0.0716∗∗∗(4.93)

hd6

12896
0.727
0.723
−18262.5−17120.4

7.757∗∗∗(154.72)−0.00442(−0.77)0.00752(1.95)

0.0160∗∗(3.27)

0.00526(0.81)

hd4

15328
0.716
0.714
−20896.4−19720.3

13576
0.783
0.781
−14952.0−13787.0

7.954∗∗∗(250.75)

0.00850∗∗(2.76)

0.00767∗(2.37)
7.861∗∗∗(219.17)0.0183∗∗(3.00)

0.00779∗(2.53)

0.00827∗(2.41)
0.0190∗(2.36)

0.0146∗∗∗(3.67)

0.0467∗∗∗(6.88)−0.00105(−0.24)−0.117∗∗∗(−6.35)−0.00675∗(−2.32)0.00143(0.38)

0.0423∗∗∗(4.93)−0.153∗∗∗(−4.55)−0.0832∗(−2.27)0.0557∗(2.07)

invest0.000582(0.18)−0.0148∗∗∗(−3.67)−0.0359∗∗∗(−6.88)

ﬁrst

48740
0.801
0.801
−58949.5−57656.7

7.788∗∗∗(368.49)

Table A2. Variable names and data sources for Tokyo case study
SymbolVariablegreen

Green label
dummy

trans

Content

Unit

SourceGreen building = 1
Other building = 0(0, 1)

Tokyo Metropolitan Government

Transaction
price dummy

Transaction price = 1
Asking price = 0(0, 1)RECRUITS

Floor space

Floor space of building/square metersm2Real Estate Economic InstituteTS

Distance tothe nearest
stationDistance to thenearest station.

mReal Estate Economic InstituteBus

Bus dummy

bus-transportation
area = 1
walk-transportation
area = 0(0, 1)

Real Estate Economic InstituteTT

Time to
CBD(Tokyo
station)Average travel time fromthe nearest rail transit
station to Tokyo Central
Station during daytime
hours

min.

VAL Institute

TU

Total unit

Total units ofcondominium

unit

Real Estate Economic InstituteLand

Site area

Site area ofcondominium

m2

Real Estate Economic InstituteTA

Total buildingarea

Total building areaof condominium

m2

Real Estate Economic Institute

Cost

Management
Cost

Property Management
Cost

U10K
/mo.

RECRUIT

ISP 1

With Housing
performance
evaluation
report A
dummy

With Housing performance evaluation
report A = 1
Without Housing
performance evaluation report A = 0(0, 1)RECRUIT

ISP 2With Housing
performance
evaluation
report B
dummy

With Housing performance evaluation
report B = 1
Without Housing
performance evaluation report B = 0(0, 1)RECRUIT

18

Symbol

Content

Unit

Management
type(A)
dummy
Management
type(B)
dummy

Management typ is
A= 1
other = 0
Management typ is
B= 1
other = 0(0, 1)RECRUIT(0, 1)RECRUIT

Cornercornerdummy

The location of unit iscorner = 1
Other location = 0(0, 1)

Real Estate Economic
Institute

ST D

Studio type
dummy

Floor space 30m2 orless = 1
Floor space over 30m2
=0(0, 1)

Real Estate Economic
Institute

RL1

Leasehold(A)dummyLand right is leasehold(Type A) = 1
other = 0(0, 1)RECRUIT

RL2

Leasehold(B)dummyLand right is leasehold(Type B) = 1
other = 0(0, 1)RECRUITTR

Rate of Sales

Rate of sales in ﬁrst
month

%

Real Estate Economic
InstituteLUg(g = 0, . . . , G)Land Use
regulation
dummy

g-th Land use regulation area = 1 other = 0,
(residential, oﬃce, industrial)(0, 1)

Real Estate Economic
InstituteHDh(h = 0, . . . , H)

Employment
statusdummy

h-th Employment
status of Head ofhousehold = 1,other = 0(0, 1)RECRUITW Di(i = 0, . . . , I)

Job typedummy

i-th job type = 1,other = 0(0, 1)RECRUITY Dj(j = 0, . . . , J)

Businesstype dummy

j-th business type
= 1, other = 0(0, 1)RECRUITLDk(k = 0, . . . , K)

Location(ward) dummy

k-th administrative
district = 1,
Other district = 0.

(0, 1)

Real Estate Economic
InstituteRDl(l = 0, . . . , L)

Railway line
dummy

l-th railway line = 1
Other railway line
= 0.

(0, 1)

Real Estate Economic
InstituteDm(m = 0, . . . , M )Time dummy(yearly)m-th year = 1
Other year = 0.

(0, 1)RECRUIT

M G1
M G2

Variable

19

Source