The Economic Value of Green Buildingｓ

The Economic Value of Green Buildingｓ
16/10/2015 The Economic Value of Green Buildingｓ Yongheng Deng National University of Singapore With Hitotsubashi-RIETI International Workshop on Real Estate Market, Productivity, and Prices Tokyo, Japan, October 14, 2016 Junichiro Onishi Xymax Real Estate Institute Chihiro Shimizu Nihon University & NUS IRES Siqi Zheng Tsinghua University

DENG @ Hitotsubashi-RIETI
16/10/2015 Introduction In the past decade, systems for rating and evaluating the sustainability and energy efficiency of buildings have proliferated. Energy Star and LEED (U.S.) BOMA-Best (Canada) BREEAM (UK) HQE (France) CASBEE (Japan) Green Mark (Singapore) Three Star System (China) Buildings account for approximately 40 percent of the consumption of raw materials and energy. In addition, 55 percent of the wood that is not used for fuel is consumed in construction. Overall, buildings and their associated construction activity account for at least 30 percent of world greenhouse gas emissions 14/10/2016 Hitotsubashi-RIETI

Green Label Systems around the World
14/10/2016 Hitotsubashi-RIETI

Emerging Green Label System in China

Initiatives to promote green label system
The United States : Acquisition of LEED obligatory when constructing or renovating federal government-related buildings. In many local governments have imposed benchmarks from Energy Star and reporting obligations. The United Kingdom : Since 2008, practically all buildings must be certified by the Energy Performance Certification (EPC) when they are being purchased, sold, constructed, or leased; from 2018, it will become illegal to lease real estate whose energy efficiency does not reach a certain level. 14/10/2016 Hitotsubashi-RIETI

Initiatives to promote green label system
In Japan, it is mandatory to report greenhouse gas emissions above a certain scale based on the laws : The revised Act on the Rational Use of Energy (2009) The Tokyo Metropolitan Ordinance on Environmental Preservation (2008) In April 2016, Japan enforced the Act on the Improvement of the Energy Consumption Performance of Buildings and imposed the obligation of large-scale nonresidential buildings to conform to energy consumption performance standards Conversely, although Japan has established incentives toward displaying the energy performance of existing buildings, it does not have an enforceable mechanism like the United Kingdom. 14/10/2016 Hitotsubashi-RIETI

Factors of slow adoption and acceptance in Japan
A social system incorporating environmental consideration, including in its real estate market, has already come into existence. 1950s, environmental pollution The same social problems that China is currently experiencing 1970’s, the oil shock Japan has pursued a policy calling for energy conservation. Further environmental considerations would not necessarily lead to market differentiation. Buildings constructed to high specifications in recent years are implicitly meeting or exceeding environmental standards even without green labeling. Earthquake-proofing is given the highest priority. 14/10/2016 Hitotsubashi-RIETI

16/10/2015 Introduction Buildings account for approximately 40 percent of the consumption of raw materials and energy. In addition, 55 percent of the wood that is not used for fuel is consumed in construction. Overall, buildings and their associated construction activity account for at least 30 percent of world greenhouse gas emissions. Consequently, energy represents the single largest and most manageable operating expense in commercial building operations. UN Sustainable Development Goal (SDG) by 2030 SDG 11 – Make cities and human settlements inclusive, safe, resilient and sustainable SDG 13 – Take urgent action to combat climate change and its impacts Buildings account for approximately 40 percent of the consumption of raw materials and energy. In addition, 55 percent of the wood that is not used for fuel is consumed in construction. Overall, buildings and their associated construction activity account for at least 30 percent of world greenhouse gas emissions 14/10/2016 Hitotsubashi-RIETI

Eichholtz, Kok and Quigley (2010)
Doing Well by Doing Good? Green Office Buildings (American Economic Review, 2010) The seminal paper provides the first systematic analysis of the impact of environmentally sustainable building practices upon economic outcomes as measured in the marketplace. They find that buildings with a “green rating” command rental rates that are roughly 3 percent higher per square foot than otherwise identical buildings after controlling for the quality and the specific location of office buildings. Premiums in effective rents are even higher, above 7 percent. Selling prices of green buildings are higher by about 16 percent. The percent increase in rent or value for a green building is systematically greater in smaller or lower-cost regions or in less expensive parts of metropolitan areas. The private market does incorporate the “green” certification information in the determination of rents and asset prices. 14/10/2016 Hitotsubashi-RIETI

Deng, Li and Quigley (2012) Economic Returns to Energy-Efficient Investments in the Housing Market: Evidence from Singapore (Regional Science and Urban Economics, 2012) It provides one of the first analyses of the economics of green building in the residential sector, and the first one analyzing property markets in Asia. They adopt a two-stage research design In the first stage, a hedonic pricing model is estimated based on transactions involving green and non-green residential units in 697 individual projects or estates; In the second stage, the fixed effects estimated for each project are regressed on the location attributes of the projects, as well as control variables for a Green Mark rating. Their results suggest that the economic returns to green building are substantial. The returns vary by Green Mark category – both Platinum and Gold are positive and statistically significant. Green Mark Platinum remains significant using PSM nearest neighbor matching of control and treatment samples. The study provides insight about the operation of the housing market in one country, but the policy implications about the economic returns to sustainable investments in the property market may have broader applications for emerging markets in Asia. 14/10/2016 Hitotsubashi-RIETI

Zheng, Wu, Kahn and Deng (2012)
The Nascent Market for “Green” Real Estate in Beijing (European Economic Review, 2012) Using two unique geo-coded micro data sets to explore the nascent “green housing market” in Beijing, from both the supply side and demand side. Based on Google searches, they construct a sample that contains information whether certain housing complex’s greenness-related characteristics are emphasized during its marketing. Focusing on information that developers wish to convey to potential buyers. The study found nascent “green housing market” does exist in Beijing. On the Supply side – “Greenness” has been adopted as a marketing point in part of the newly-built complexes, which helps gain significant price premium for their developers. Although whether such “greenness” is really effective remains a question. On the Demand Side – Pro-environmental households do have a smaller carbon footprint, which provide potential demands for green housing. An introduction of a standardized official certification program would help ‘‘green’’ demanders to acquire units that they desire and would accelerate the advance of hina’s nascent green real estate market. 14/10/2016 Hitotsubashi-RIETI

Deng and Wu (2014) Economic Returns to Residential Green Building Investment: The Developers’ Perspective (Regional Science and Urban Economics, 2014) The study provides the first evidence of the mismatch that developers face between outlays and benefits in the residential green building sector. This mismatch may impede further development of green residential properties. The study found that the “green price premium” of residential developments arises largely during the resale phase, relative to the presale stage. The market premium of GM-rated units is about 10% at the resale stage, compared to about 4% during the presale stage. This implies that, while developers pay for almost all of the additional costs of energy efficiency during construction, they only share part of the benefits associated with such green investments. The study found no evidence that the development of green housing can immediately and significantly improve the corporate financial performance of Singaporean residential developers. The emerging green real estate markets should be encouraged to introduce innovative business arrangements and financial products that allow residential developers to capture the future benefits associated with green properties. 14/10/2016 Hitotsubashi-RIETI

Wu, Deng, Huang, Morck, and Yeung (2014)
Incentives and Outcomes: China’s Environmental Policy (Capitalism and Society, 2014) “A city government’s spending on environmental improvements is actually significantly negatively related to the odds of its (Communist party) secretary and mayor being promoted,” wrote Professors Wu Jing, Deng Yongheng, Huang Jun, Randall Morck and Bernard Yeung. 1:45PM GMT 26 Feb 2013 14/10/2016 Hitotsubashi-RIETI

Deng, Onishi, Shimizu and Zheng (2016)
The Economic Value of Environmental Consideration in the Tokyo Office Market (Working Paper, 2016) We are among the first to analyze the economic value of green buildings in an office market in Japan ; A social system incorporating environmental consideration has already come into existence. Further environmental considerations would not necessarily lead to market efficiency. Buildings constructed to high specifications in recent years are implicitly meeting or exceeding environmental standards even without green labeling. We use an independent data set, including 6,758 rental properties in Tokyo office market and examine if there is a significant premium from the green label for new contract rent. 14/10/2016 Hitotsubashi-RIETI

Hedonic price function
The new contract rent of office buildings, based on characteristics of office buildings and green label acquisition conditions, is generally expressed as a hedonic price function. 𝑅𝑖 : the new contract rent of completed contract case 𝑖 𝑔𝑟𝑒𝑒𝑛𝑖 : the green label dummy 𝑥𝑖: the vector that expresses the attributes in the completed contract case 𝑖 𝑅 𝑖 =h 𝑔𝑟𝑒𝑒𝑛 𝑖 , 𝑥 𝑖 14/10/2016 Hitotsubashi-RIETI

The model Our specification is set up as following: ln𝑅𝑖 : New contract rent logarithm (dependent variable) α: Constant term β, γ: Vector of the coefficients correspondeing to each independent variable ε : Error term 𝑔𝑟𝑒𝑒𝑛𝑖 : Green label dummy (independent variable) 𝑥𝑖: Vector that expresses the characteristic in the completed contract case 𝑖 (independent variable) ln 𝑅 𝑖 =α+ 𝑔𝑟𝑒𝑒𝑛 𝑖 ′∙𝛽+ 𝑥 𝑖 ′∙𝛾+ 𝜀 𝑖 (2) 14/10/2016 Hitotsubashi-RIETI

Independent variables (1)
Scale ; Gross building area, standard story area, and number of above-ground stories. Age ; Age of the property and whether a renovation had been carried out. Convenience ; Distance to the nearest station and the presence or absence of office specifications that consumers find appealing, such as a raised floor, individual air conditioning, and automated security. 14/10/2016 Hitotsubashi-RIETI

An office area dummy ; A dummy variable obtained by dividing a typical office area in Tokyo’s 23 wards by 50 areas, in order to express the effects of location on rent. In popular areas, rent tends to be high even for properties that are medium scale, small scale, or old. The dummy for the time the contract was completed ; The demand-supply balance in the market at the time the property is offered affects the rent. For quality adjustment at the time the contract was completed, the two-year sample period from January 2013 to December 2014 was divided into eight quarters and a quarterly dummy variable was allocated according to the time the contract was completed. 14/10/2016 Hitotsubashi-RIETI

Green label dummy If a building had acquired any one of the green labels used for the analysis, the green label dummy was given a value of 1. CASBEE, CASBEE Real estate, DBJ Green Building Certification, SMBC Sustainable Building Assessment There were three reasons for selecting these systems. Acquired on the unit of buildings Not the unit of corporations and portfolios Assessing comprehensive environmental performance Not only energy-saving performance A third party organization carries out the assessment based on the standards that these systems have established. 14/10/2016 Hitotsubashi-RIETI

Green labels used in this study

Data We constructed an integrated data on rent and green label acquisition conditions. Rental data ; Data used in this study are the contract case database for office buildings collected by the Xymax Corporation. This dataset contains contract rent (not offered rent) – Tokyo 23 wards 6,758 observations (2,689 buildings) The characteristics of the buildings; Scale, Age, Facility, Location, Contract time Green label dummy ; We collected and arranged the published information. 14/10/2016 Hitotsubashi-RIETI

Variables 14/10/2016 Hitotsubashi-RIETI

Summary Statistics 14/10/2016 Hitotsubashi-RIETI

Estimation results (baseline)
The estimation result of ＋ was positive and significant; The new contract rent for buildings that were granted a green label was approximately 4% higher than for those that were not granted one. Whether acquiring a green label can result in higher returns? Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Caveat In an economic analysis of environmental consideration, there are concerns that factors such as scale and age of the property become proxy variables for a green label. Scale: Many of the buildings that have been granted a green label were developed by major developers and REIT; They are of large scale and are young buildings. They are able to pay the costs necessary to acquire a green label. Their shareholders and investors demand that they make such efforts. Age: The advance in environment and construction technologies for newly developed properties If it is judged from the results of the preliminary survey that there is little prospect of the building obtaining a high score and grade, few owners of old office buildings would actually take the actions necessary to acquire certification. 14/10/2016 Hitotsubashi-RIETI

Summary statistics : with/without green label
The average values for gross building area is 61,718 sqm and age is 8.78 years in our green sample (properties with a green label), showing that these buildings tend to be large scale and newly developed. 14/10/2016 Hitotsubashi-RIETI

Potential endogeneity
When the decision to acquire the green label is also affected by variables common to the newly rented office building, such as size and age, endogeneity may occur in the estimation method by identifying the new rent function using the green label dummy. Therefore, when the hedonic function is estimated as a simple linear model of the data in our analysis, the effects of size, age, and performance reflect the difference in the presence or absence of the green label, there is a potential bias in the measurements. 14/10/2016 Hitotsubashi-RIETI

Potential endogeneity
We adopt the propensity score matching approach to make sure the treatment group and control group have similar value of the covariate. First; we estimate a variable that denotes the ease of which the green label is obtained from the covariate (i.e., the propensity score). Next; based on the estimated propensity score, we create two similar groups, and let the large difference between these groups indicate the existence of a green label. Finally, we proceed with the analysis by estimating the effect of a green label on a new contract rent. 14/10/2016 Hitotsubashi-RIETI

Propensity score analysis (1)
The effect of the green label on a new contract rent, when treated as the difference between office buildings with or without green label; 𝑌 1 : New contract rent, with green label granted 𝑌 0 : New contract rent, with green label not granted D: 1: When green label is granted to an office building 0: When green label is not granted to an office building E 𝑌 1 |𝐷=1 −E 𝑌 0 |𝐷=0 . (3) 14/10/2016 Hitotsubashi-RIETI

If the size or newness of a building affects whether there is green label, it is possible that there is bias in the estimation effect. We consider that the strict green label effect; 𝒙: Characteristics of an office building that can be observed as a covariate, where note that 𝐱 is a vector of the components 𝑥 1 , …,𝑥 𝑖 . P 𝒙 =𝑃𝑟 𝐷=1|𝒙 : Forecasted probability of a green label being granted to an office building with characteristics 𝐱. ∆ 𝐷=1 𝒙 =E 𝑌 1 − 𝑌 0 |𝑃 𝒙 ,𝐷=1 (4) 14/10/2016 Hitotsubashi-RIETI

If we know what the new contract rent of an office building with a green label was if it has not been granted, we can simply extract the effect of the green label. Because it is normally not possible to observe such new contract rents, the value that represents this is assigned a weight by P 𝒙 , and the new rent is estimated from the new rents of office buildings without green labels. 𝑛 1 : the number of samples that have been granted a green label ∆ 𝐷=1 𝑥 = 1 𝑛 1 𝑖=1 𝐷 𝑖 =1 𝑛 1 𝑌 1𝑖 𝒙 𝒊 − 𝐸 𝑌 0𝑖 |𝑃 𝒙 𝒊 , 𝐷 𝑖 =0 (5) 14/10/2016 Hitotsubashi-RIETI

New rents of office buildings that have not been granted a green label, and are used as proxies, will have an expected value of; 𝑛 0 : the number of samples that have not been granted a green label W: the weight assigned by P 𝒙 . In this study, nearest neighbor matching is used to assign weights for comparisons. (Matching is performed on office buildings with the nearest probability of being granted a green label.) The new contract rent of an office building that has not been granted a green label is used as a proxy for new contract rent in the hypothetical case in which an office building that has been granted a green label is an office building that has not been granted a green label. 𝐸 𝑌 0𝑖 |𝑃 𝒙 𝒊 , 𝐷 𝑖 =0 = 𝑗=1 𝐷 𝑗 =0 𝑛 0 𝑊 𝑖 𝑃 𝒙 𝒊 𝑌 0𝑗 (6) 14/10/2016 Hitotsubashi-RIETI

Probit regression is used to estimate the probability of a green label being granted to an office building possessing 𝒙 characteristics. β : a vector of the elements 𝛽 1 ,…, 𝛽 𝑖 ′. office characteristics for determining whether or not a green label is to be granted: gross building area (sqm), standard story area (sqm), above-ground stories (stories), age (years), area dummy, time to nearest station (minutes), raised floor dummy, individual air conditioning dummy, automated security dummy, and time of contract completion dummy. P 𝒙 =Pr 𝐷=1|𝒙 =Φ 𝒙𝜷 = −∞ 𝑥𝛽 1 2𝜋 𝑒𝑥𝑝 − 𝑧 𝑑𝑧 (7) 14/10/2016 Hitotsubashi-RIETI

Estimation results (Probit regression)
The probability of acquiring a green label is higher for high quality office buildings that are new, large, and equipped with facilities. Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Summary statistics (Propensity score matching)
By using propensity score matching, the differences between the two groups were reduced, enabling us to create similar samples. 14/10/2016 Hitotsubashi-RIETI

Estimation results (Propensity score matching)
The estimated coefficient of the green label dummy was (1.8874) and, thus, was not a statistically significant result, was negative, and close to zero. Green label does not have an effect on new contract rent, which differs from Baseline. Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Problems of propensity score matching
The extracted samples by propensity score matching were centered on large, newly constructed office buildings. The averages of gross building area and age for non-green buildings were 48,572 sqm and 8.36 years. The data of the approximately 6000 non-green buildings that were not matched were excluded. Green label has almost no effect, or even a slightly negative effect on comparatively large and new office buildings. However, the effect on other office buildings is unknown. In the analysis of the propensity score matching, it is problematic that the effect of a green label on medium and small office buildings and on old office buildings cannot be confirmed. 14/10/2016 Hitotsubashi-RIETI

Division of samples by clustering
We divided the samples into five cluster based on the value of the propensity score. Since the properties with a low propensity score tend to be smaller and older, we should also be able to analyze the clusters of mid to low propensity scores. Five quantile were used as the boundaries of these clusters, and the number of samples in each cluster is nearly the same (number of observations : 1351 or 1352 ). 14/10/2016 Hitotsubashi-RIETI

Summary statistics (Cluster no.1: low propensity score)

Summary statistics (Cluster no.2: medium low propensity score)

Summary statistics (Cluster no.3: medium propensity score)

Summary statistics (Cluster no.4: medium high propensity score)

Summary statistics (Cluster no.5: high propensity score)

Estimation results (The hedonic function for each cluster)
In cluster no. 5, (the large and new office buildings), the value is negative and nearly zero ( (0.0105)). the results from this cluster are consistent with those used propensity score matching. In cluster no. 4 (the medium-sized and older office buildings), the result was (0.0328), which is a significant positive effect. There were no statistically significant effects in cluster no.1 to 3. The effect of green label on new contract rents is not uniformly +4.3% for all office buildings. Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Robustness check Office buildings that were granted green label had a higher propensity score than office buildings that were not granted green label. Although we have extracted the samples through clustering to those with relatively closer propensity scores, there may still be a possibility to remove the effect caused by the covariates from each cluster. Therefore, we have chosen to conduct further propensity score matching within the clustered sample groups, extract samples, and then estimate the hedonic function. We will be able to verify the effect of green label in samples with greater homogeneity, which are not influenced by covariates. 14/10/2016 Hitotsubashi-RIETI

Estimation results (Propensity score matching within the clustered samples) We extracted the samples and conducted further propensity score matching within the cluster no.4. The coefficient of the green label dummy was (0.0370), a significant estimation result. This result is equivalent, proving the robustness of our analysis. Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Extended model estimation
In the probit regression, we learned that newer buildings are more likely to be granted a green label. Even with clustered data, a certain relation will still exist between the age of the building and the green label dummy. we make our estimate based on a model that adds the green label dummy and the age as cross-terms to equation (2). Where, the age will be a true value instead of a dummy variable, since there are only 30 cases in the data that have been granted green labels within the samples in cluster no.4. 14/10/2016 Hitotsubashi-RIETI

Estimation results (Include the age and green labal dummy as cross-term) In cluster no.4, the coefficient of the green label dummy was (0.0824), showing a significant positive result. And the coefficient of the cross-term comprising the green label dummy and the age of the building was (0.0039), showing a significant negative result. The Effect diminishes with time, and, finally, no further effect exists at about 30 years from the original construction. Notes: Standard errors in the parenthesis. * p < 0.1, ** p < 0.05, *** p < 0.01 14/10/2016 Hitotsubashi-RIETI

Conclusion After performing a quality adjustment of building characteristics using the hedonic approach, green labels showed a significant effect of +4.39% on new contract rents. By estimating the propensity score for the target variable of the presence or absence of a green label, we confirmed that the building characteristics of age and size make it easier to obtain a green label. In order to address the problem of endogeneity, we used samples that were adjusted using the nearest neighbor matching technique, based on the propensity scores. In this case, the effect of a green label was not statistically significant. 14/10/2016 Hitotsubashi-RIETI

Conclusion When we analyzed the samples clustered according to the value of the propensity score, we found that the effect on the stratum in which large and new buildings were concentrated was not statistically significant, at -0.58%. Furthermore, the effect on the stratum in which medium-sized and old buildings were concentrated was %. By conducting further propensity score matching on the clustered samples, we were able to verify the robustness of our analysis results. There was a significant correlation between cross-terms regarding the age of the building and the green label. We verified that, even in the mid-size market, where green labels made a difference in the contract rent, buildings that are 30 years or older are no longer affected by these labels. 14/10/2016 Hitotsubashi-RIETI

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