JOURNAL OF REAL ESTATE RESEARCH

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1 JOURNAL OF REAL ESTATE RESEARCH An Official Publication of the American Real Estate Society Editorial Office: Editor Ko Wang Editorial Board Brent Ambrose Randy Anderson Paul Asabere Steven Bourassa Steven Brown Jan Brueckner Dennis Capozza Su Han Chan Peter Chinloy John Clapp Terrence Clauretie Peter Colwell Marsha Courchane David Downs Robert Edelstein Donald Epley Jeffrey Fisher Stuart Gabriel George Gau David Geltner Karen Gibler John Glascock William Goetzmann Jack Goodman Richard Graff Steven Grenadier Karl Guntermann Joseph Gyourko William Hardin David Hartzell Donald Haurin Patric Hendershott Andrew Holmes Martin Hoesli Charles Ingene Donald Jud Michael LaCour-Little Youguo Liang David Ling Hongyu Liu Mark Louargand Kenneth Lusht Christopher Manning Bryan MacGregor John McConnell Issac Megbolugbe Norman Miller Glenn R. Mueller Chris Myers Graeme Newell Kelley Pace Richard Peiser Daniel Quan John Quigley Timothy Riddiough Mauricio Rodriguez Stephen Ross Stephen Roulac Anthony Sanders Arthur Schwartz Eduardo Schwartz Michael Seiler James Shilling Robert Simons G. Stacy Sirmans Tsur Somerville Thomas M. Springer Sheridan Titman Thomas Thibodeau Grant Thrall Kerry Vandell Susan Wachter Nancy Wallace Hongwei Wang James Webb William Wheaton Daniel Winkler Marvin Wolverton Elaine Worzala Tyler Yang Abdullah Yavas Jianping Ye Anthony Yezer Yuqing Zhou Alan Ziobrowski Leonard Zumpano Journal of Real Estate Research, Department of Real Estate, Zicklin School of Business, Baruch College/CUNY 137 East 22nd Street C-406, New York, NY November 9, 2010 Dr. Songtao Wang Room 120, He Shan Heng Building Hang Lung Center for Real Estate Studies Department of Construction Management Tsinghua University Beijing, P.R. China, Re: Manuscript # Dear Dr. Songtao Wang: We are pleased to inform you that your co-authored paper (with Su Han Chan and Bohua Xu) entitled Housing Supply Elasticity across Chinese Cities and their Determinants has been accepted for publication in the Journal of Real Estate Research, assuming that you will be willing to make some minor editorial revisions if our copy editor deems them necessary. You should send me one hard copy of the manuscript and an IBM-compatible diskette containing the final version of the paper in Microsoft Word or WordPerfect formats. Please make sure that you sign the attached copyright release form and return it to me together with the manuscript. We ask that you comply fully with the submission guidelines, which we are enclosing for your convenience. Please pay particular attention to the sections entitled Headings, Exhibits and Endnotes. Your paper will appear in a forthcoming section of the JRER web site ( several days after we receive the information from you. We wish to express our sincere gratitude to you for allowing us an opportunity to review your manuscript. As with all other high quality journals, we rely heavily on the submission of outstanding articles like yours to maintain and improve the reputation and readership of the Journal. Again, we thank you very much for considering the Journal of Real Estate Research as the outlet for your fine research. We look forward to receiving more papers from you in the near future. Sincerely, Ko Wang Editor

2 Style Guidelines Abstracts An abstract of no more than 100 words, summarizing the research purpose, method and findings, is required. Headings Headings should be numbered consecutively as 1, 2, etc. Subheadings should be numbered as 1.1, 1.2, etc. Heading numbers are for editorial purposes only and will be removed before printing. Do not include references to section numbers in the text. Exhibits Tables and figures should be numbered consecutively in the text in Arabic numbers and printed on separate pages. Tables and figures should be self-explanatory and labeled clearly (such as Table 1 and Figure 1). Explanatory paragraphs should be offered to fully explain the table or figure so that the reader does not need to refer to the text. Significant digits should be rounded to no more than two or three numbers. All figures need to be sharp, clear and camera-ready. Mathematical Proofs and Equations Lengthy mathematical proofs and extensively detailed mathematical tables should be placed in an appendix. Equations should be placed on a separate line, centered and numbered consecutively at the right margin. Endnotes Endnotes in the text must be cited consecutively. They should be double-spaced and appear on a separate page. Avoid numerous and lengthy endnotes. Do not use notes if they can be readily included in the text. References References must be presented alphabetically by the last name of the author and be double-spaced. References must be dated, and the citations in the text must agree. Only those references cited within the text should be included. The references must fit the following format: Judge, G. G., W. E. Griffiths, R. C. Hill, H. Lutkepohl and T. C. Lee, The Theory and Practice of Econometrics, second edition, New York: John Wiley, Kinnard, W. N., Tools and Techniques for Measuring the Effects of Proximity to Radioactive Contamination of Soil on Single- Family Residential Sales Prices, Paper presented at the Appraisal Institute Symposium, Mills, E. S., The Value of Urban Land, In H. S. Perloft, (ed.), The Quality of the Urban Environment, Baltimore: Johns Hopkins University, Shilton, L., W. O'Connor, K. Teall and J. R. Webb, Real Estate Taxation and Commercial Loan Underwriting, Decision Sciences, 1992, 23, Acknowledgment Authors may include a brief acknowledgment. It should appear after the references. Keywords Authors should provide one to six keywords that clearly indicate the subject mater of the paper for indexing. Page Proofs Page proofs will be sent to the corresponding author. Proofs should be reviewed carefully. The Editor views the last submitted version as the final copy. No rewrites of any kind are permitted at the page proof stage. Corrections are permitted for errors that occurred as a result of editing or typesetting only. The responsibility for detecting errors lies with the author.

3 AUTHOR S RELEASE FORM The Journal of Real Estate Research All articles published by the American Real Estate Society in the Journal of Real Estate Research become the property of the American Real Estate Society. Each article must represent original work of the author(s) which has not been published, in whole, or in major part, in any other major publication. By signing this form the author is certifying that the author(s) listed on the title page are the researchers responsible for the results and this article is not currently submitted to any other journal. It is the policy of the Journal of Real Estate Research to publish papers only when the data used in the analysis are 1) thoroughly documented, and 2) sufficient details of computations are provided to permit replication. By signing this form the author warrants that the above conditions will be met. Please sign this form immediately and return, as indicated, to permit a timely review and publication. Title of Article: Manuscript Number: Special Issue Author Submitting Article (sign) (print) Address: Telephone: Office: Home: Date: Please return immediately to: Ko Wang, Editor Department of Real Estate Zicklin School of Business Baruch College, The City University of New York 137 East 22nd Street C-406 New York, NY If you have any questions, please feel free to call (646) or fax (646)

4 Estimates of the Price Elasticity of New Housing Supply and Their Determinants: Evidence for China Songtao Wang 1,2, Su Han Chan 3 and Bohua Xu 4 1 Hang lung Center for Real Estate, Tsinghua University, Beijing , P.R. China. 2 Department of Construction Management, Tsinghua University, Beijing , P.R. China. 3 Department of Real Estate, Baruch College, City University of New York, New York, NY 10010, US 4 Department of Landscape Architecture, School of Architecture, University of Southern California, Los Angeles, CA 90007, US Abstract: Despite a growing recognition of the importance of housing supply studies, empirical work on housing supply outside the U.S. is still scarce. This paper adds to this literature by providing a first look at estimates of housing supply elasticities in China, at both the aggregated (national) and disaggregated (city) level. Using a stock adjustment model as in Malpezzi and Maclennan (2001), we estimate the price elasticity of housing supply using panel data for 35 cities in China over a 12 year period from 1998 to 2009, finding that the average price elasticity ranges from 2.82 to Our analysis at the city-level suggests that land availability, urban built-up area and its growth rate, population and its growth rate, housing price as well as governmental regulation are all important determinants of variations in the housing supply elasticity measure across the cities. Our findings have implications to understanding how and why the housing market in China may differ in supply responsiveness from other markets. Keywords: price elasticity of housing supply, housing price, stock-adjustment model, panel data, China 1 Introduction Many cities around the world have experienced a rapid growth in housing prices since the late 1990s, raising the issue of a housing bubble as a major policy concern. To identify the key factors driving up housing prices, an increasing number of studies have attempted to explain the variation in housing price dynamics across countries or metropolitan areas (Glaeser et al., 2008; Wheaton and Nechayev, 2008). Although strong economic growth and 1

5 intensified housing financial support along with other demand side factors played a role in the recent run up in housing prices prior to the financial crisis that started around 2007, these demand side factors alone are hardly sufficient to capture the variations in the regional price dynamics. Hence, an increasing number of supply side studies have begun to surface to shed light on the other side of the coin in explaining housing price dynamics. It is not unusual that the supply side of the housing market has attracted more attention than other commodity markets. One obvious reason is the zero or negligible price elasticity of housing supply in the short run given the lag in construction process. In the medium to long run, however, housing supply becomes more responsive to demand shocks. The magnitude of a change in housing prices as well as the time taken to restore a new level of price equilibrium due to an unexpected shock in housing demand are greatly affected by the price elasticity of housing supply. This explains why early research in the 1960s and 1970s focused on drawing inferences or directly estimating housing supply elasticity from reduced form estimation models (Muth, 1960; Follain, 1979; Whitehead, 1974; Maclennan, 1978; Mayes, 1979). The price elasticity of housing supply, a behavioral parameter, has already become a focal point of housing supply research. Researchers employ various empirical models and use data at the national or metro level to analyze this parameter. The bulk of the research focuses on the U.S. housing market research(dipasquale and Wheaton, 1994; Bramley and Watkins, 1996; Mayer and Somerville, 1996; Malpezzi and Mayo, 1997; Blackley, 1999; Malpezzi and Maclennan, 2001; Evenson, 2001; Harter-Dreiman, 2004; Malpezzi and Wachter, 2005; Green et al., 2005; Glaeser et al., 2006; Glaeser et al., 2008), while only a handful of studies estimate the parameter for non-u.s. housing markets (Peng and Wheaton, 1994; Mayo and Sheppard, 1996; Malpezzi and Mayo, 1997; Malpezzi and Maclennan, 2001; Meen, 2005; Vermeulen and Rouwendal, 2007).Despite a growing body of empirical work on housing supply, the results vary and there is yet to be a consensus, on the estimation method of the price elasticity of housing supply research. Two related research questions stem from the above housing supply elasticity studies. The first concerns the extent to which supply elasticity impacts housing price dynamics (Malpezzi and Wachter, 2005; Wheaton, 2005; Gyourko et al., 2006; Glaeser et al., 2008; Grimes and Aitken, 2010; Davidoff, 2010). Most findings on this issue confirm an inverse relationship, that is, a more elastically supplied housing market tends to have lower price levels as well as smaller price volatilities than a market with less elastic supply. Other studies, 2

6 however, do not find this to be the case (e.g., Aura and Davidoff, 2006; Davidoff, 2010). More recently, some researchers even extended the casual linkage to even broader implications, like the indirect impact from housing supply to economic stability (OECD, 2004), regional employment (Vermeulen and Ommeren, 2009) and social capital investment at the community level (Hilber et al., 2010). The second research question concerns the empirical finding of variations in the estimated price elasticity of housing supply across different countries (Mayo and Sheppard, 1996; Malpezzi and Mayo, 1997; Malpezzi and Maclennan, 2001; Vermeulen and Rouwendal, 2007) or across different regions within a country (Goodman, 1998; Harter-Dreiman, 2004; Green et al, 2005). This variation in supply-side elasticity contrasts with the stability of demand elasticities observed across countries or regions., thereby raising a question as to what are the factors influencing housing supply elasticity. Different researchers have proposed different factors, most of which can be categorized into either institutional, economic or geographical factors. However, none of the past studies has examined all the three groups of determinants simultaneously. Further, there is no consensus on the potential determinants of housing supply elasticity. This question, up till now, is understudied. In this paper, we focus on estimating the price elasticity of housing supply as well as identifying the important determinants of the variations in regional housing supply elasticity in China. We will not examine the linkage between supply elasticity and housing price dynamics since other studies on China (e.g., Wang, 2009) have found a negative connection between the two variables, which is consistent with U.S. results. Instead, this paper will contribute to the international comparative literature on housing supply by providing new insights on how the heterogeneous supply elasticities of different regional markets are shaped. In 1998, China s welfare housing system was substituted by a market- oriented housing system and since then the restrained housing demand is gradually released. Statistics shows that from 1998 to 2008, new immigrants to Chinese cities add up to more than 100 million and the urbanization rate increased from 30.42% to 45.68%. Under such demand shifts, most of the urban housing markets across China enjoyed a sustained price increase over the past decade as the economy boomed. Figure 1 shows the housing price dynamics of Beijing, Shanghai, Guangzhou and Shenzhen, the largest four urban housing markets in China 1. 1 These housing price data are all from China Real Estate Index System (CREIS). The housing price index data bank was constructed in November 1994 and used 1000 as the base point. The indices are transaction based and are designed to take into account the quality variations in the sample. Thus, they are currently the best available indices on China s 3

7 Statistics shows that the annual appreciation rate of Shanghai s housing price index is 12.02% from December 2002 to December 2009, while the annual rate of Beijing is 20.42% from December 2005 to December In mid-2003, fearing that a potential bust in housing prices would undermine the sustained growth of the economy, the central government began to launch a wide range of regulatory policies (including mortgage and reserve rates adjustments, tax rate adjustments, housing price regulation, land use right transaction reform and supply structure regulation 2, 3 ) with the hope to restrain the rising residential prices (Wang and Yang, 2010). The supply side policy measures utilized during the intervention period are aimed at improving the responsiveness of the housing supply environment as well as providing more affordable housing for the low and medium income households. Wang (2009) found that, although demand-side government regulations have rather limited effects, supply side interventions are more successful in curbing the price appreciations Beijing 2700 Shanghai Guangzhou 2200 Shenzhen Figure1. Housing prices of Beijing, Shanghai, Guangzhou and Shenzhen in east China Data source: The housing price data are from China Real Estate Index System housing market. Since the index only covers ten important cities in China, in the empirical part of this paper, we will use another type of housing price index (which is a transaction-based price index for newly built houses) for 35 large scale cities published by the National Bureau of Statistic and National Development and Reform Commission. 2 With regard to land use right, on 31st March 2004, the Ministry of Land and Resources (MLR) of China promulgated Notification No. 71. This notification requested that after 31st August 2004, all state-owned urban land for real estate development should be granted through tender auction, oral auction, or listing auction. Before this date, most of the transactions are conducted through private negotiations. 3 With regard to supply structure, the State Council launched a policy in May 2006, requiring that units with floor area less than 90 square meters must cover 70% of the total floor area in all newly registered or constructed projects. 4

8 Figure2. Housing prices dynamics in east, middle and west China Data source: The housing price data are published by the National Bureau of Statistic and National Development and Reform Commission. Regional housing price is a numerical average of housing price level (in RMB) within a region. Figure3. New housing supply dynamics in east, middle and west China Data source: The housing supply data are published by the National Bureau of Statistic. New housing supply is newly completed floor area of residential housing. Regional housing supply is a numerical average of housing supply level (in square meters) within a region. Apart from some hot regional markets, which have triggered nation-wide government intervention, China s housing market also has an obvious regional pattern across the east, middle and west. Figure 2 illustrates the regional housing price levels from 1998 to We categorized our 35 cities into three groups: East, Middle and West. Wulumuqi, Xining, Lanzhou, Yinchuan, Xi an, Chengdu, Chongqing, Guiyang, Kunming and Nanning are 10 cities in the West group. Huhhot, Taiyuan, Zhengzhou, 5

9 Basically, housing price has a faster price appreciation rate in the East group (18 cities, 35.58%) than in the Middle group (7 cities, 20.13%) and the West group (10 cities, 24.27%). Although the averaging process within each group in figure 2 has masked the potential differences between housing prices of different cities, yet by comparing Figures 1 and 2, we can still conclude that there exist quite different housing price patterns across different regions and cities. Since neighboring cities confront similar demand shocks, different price dynamics shall be a reflection of different environment of housing supply. Figure 3 further shows the regional housing supply dynamics from 1998 to It reveals that after 2004, the housing supply in East group begins to fall while Middle and West groups are still increasing at a high rate, which potentially support the existence of regional variations in housing supply elasticities due to different features in each submarket. The first objective of our paper is to estimate a nation-wide housing supply elasticity for China, which will serve as the basis for cross-country comparisons. To utilize the wealth of data in the regional markets, we employ the panel data technique as in Harter-Dreiman (2004) who imposed a common coefficient on the price elasticity of housing supply across the 76 Metropolitan Statistics Areas (MSAs) in a panel data setting to derive the national supply elasticity. A similar procedure is also employed by Saiz (2010) who imposed a common coefficient on inversed supply elasticity in panel data analysis. In such a way, the estimated elasticity could be deemed as an average price elasticity of housing supply in all the regional markets. For the aggregated level measure, we employ the stock-adjustment model as proposed by Malpezzi and Maclennan (2001).. Kim et al. (2010) point out that such stockflow model is relatively simple and uses the reduced form housing price equation and estimates of demand elasticities to calculate the supply elasticity. In addition, the Malpezzi and Maclennan (2001) model has been widely used in past studies of supply elasticity, and using this model at the aggregated level will foster an identical methodology for crosscountry comparisons. To make the traditional Malpezzi and Maclennan (2001) model more realistic and to get a more accurate estimation, we extend the model by incorporating the real cost of homeownership into the demand equation and introducing lagged housing prices, construction cost as well as capital cost into the housing supply equation. Our sample covers the largest 35 cities in China, most of which are provincial capital cities, (the geographical Hefei, Wuhan, Nanchang and Changsha are 7 cities in the Middle group. The remaining 18 cities are in the East group. The price level data are published by the National Bureau of Statistic and National Development and Reform Commission. 6

10 locations of these cities are presented in Appendix A) and the period from 1998 to We find that the average supply elasticity is within the vicinity of 2.82 to Comparing to the supply elasticity estimates by prior studies for the U.S. and other markets, our finding suggests that China has a moderately elastic housing supply environment. We then explore the determinants of price elasticity of housing supply. The Malpezzi and Maclennan model is not a good choice for estimating the city-level supply elasticity considering the limited observations compared to the high degrees of freedom. As such, the estimated coefficients will suffer from low power of estimation. In addition, in order to explain the determinants of the disaggregated supply elasticity estimates, we need to find point estimates rather than range estimates derived from the Malpezzi and Maclennan (2001) model. To get city-level supply elasticity, we directly estimate new housing constructions in response to changes in housing prices, controlling for important cost shifters as in Topel and Rosen (1988) and Green et al (2005). Using the city-specific point estimate of the price elasticity of housing supply as the dependent variable and related factors as independent variables in the estimation model, we find the land availability, urban built-up area and its growth rate, population and its growth rate, housing price as well as governmental regulation are important determinants of the housing supply elasticities. It is noteworthy that we incorporate geographical information into our model through a developable land ratio variable (as Saiz, (2010) did to explain the housing supply elasticity). By so doing, we can examine the determinants of housing supply simultaneously from geographical, economic and institutional perspectives, thus extending Green et al. s (2005) empirical findings, which excludes a geographic perspective. Generally speaking, the current literature on the China housing market has focused primarily on demand side factors to explain housing price dynamics while largely overlooking housing supply elasticity.. This study fills this gap in the literature and enriches our understanding of the housing market from the supply side. The remaining of the paper is organized as follows. Section two reviews the related literature. Section three discusses the estimation models and the data as well as estimates the nation-wide price elasticity of housing supply. Section four uses a simple cross-sectional regression method to find the important factors that could have an impact on the price elasticity of housing supply. Section five concludes. 2 Previous Research 7

11 2.1 Defining the price elasticity of housing supply In housing economics, the price elasticity of housing supply is an elasticity defined as a numerical measure of the responsiveness of housing supply to a change in housing price. Such a textbook definition is not sufficient to fully understand the feature of housing supply elasticity. At least three more questions need to be answered.. (1) How to measure housing supply? In a broader perspective, all the unit (or measured in housing service) of the total housing stock are potential housing supply, which is a stock variable, but often the housing supply is regarded as the new supply of housing, which is a flow variable. Rydell (1982), Dipasquale and Wheaton (1994), Mayer and Somerville (1996, 2000b) have distinguished a stock price elasticity of housing supply from a flow counterpart. The magnitude of the flow supply elasticity should be larger than the stock supply elasticity. For example, Mayer and Somerville (1996) derived a flow supply elasticity of 6 and a stock supply elasticity of 0.08 for U.S. housing market. In practice, residential investment, newly built number of units and the housing start permit issuance are commonly used variable to depict housing supply. (2) How long is the time horizon to measure the housing supply elasticity? Theoretically speaking, in the immediate short run, the supply elasticity is equal to zero while in a enough long time horizon, the housing supply elasticity could be pretty large. This feature reveals that housing supply elasticity is a time-variant variable and it is important to specify the time horizon when define a supply elasticity. Topel and Rosen (1988) first proposed short-run and long-run supply elasticity concept and define the former to be around 1 year while the latter around 3 years. Evenson (2001) calculated supply elasticity for 47 MSAs in U.S. with time horizon of 3, 6, 9 and 12 years. The estimated supply elasticities increase monotonically over time horizon. Pryce (1999) estimate supply elasticity at consecutive boom and bust period and find housing supply elasticity is noticeably smaller in the boom (0.58) than in the slump (1.03). (3) Is housing supply elasticity a real term or a nominal term? Actually, most of the market indicator shall be in real term to have economic interpretation, so does housing supply elasticity. Housing supply elasticity is derived from both the housing supply variable and housing price variable. When estimating the elasticity, it is essential to utilize the real term variables within the model. In this paper, we examine the real flow supply elasticity with a fixed time horizon of one year which is short-run supply elasticity. 2.2 Estimation methods for the price elasticity of housing supply There are three main different theoretical models for estimating housing supply elasticity with different econometrical techniques. The first type of model is based on the Tobin s q- 8

12 theory which implies that the level of housing investment is a positive function of the ratio of housing prices to construction cost. Houses will be built when the total cost of building a new house is less than the cost of purchasing an existing identical property (replacement cost). Muth (1960), Follain (1979), Follain et al (1993), Vermeulen and Rouwendal (2007), Green et al (2005), Grimes and Aitken (2010) are some examples of using q-theory. Most of empirical settings among the above studies are reduced form equations with price and cost shifters on the right hand side. Typical cost shifters include land cost, material cost, labor cost and various interest cost. The second type of model is based on stock-flow adjustment theory which implies a stock adjustment process to equilibrate housing demand and supply. Newly housing supply adds to meet the increasing housing demand and make up the gap for potential demolishment in the housing stock, however, the current stock adjust to the long run desired housing stock with certain speed (not necessarily clear the market within one year). Topel and Rosen (1988) and Blackley (1999) first incorporated the stock adjustment process (one year lagged housing stock) in their theoretical and empirical research 5. Malpezzi and Mayo (1997) as well as Malpezzi and Maclennan (2001) proposed models to indirectly estimate housing supply elasticity in a flow adjustment and stock adjustment settings based on several demand elasticity and price elasticity. Both models and their extensions have been widely used in international comparative studies, for example, Mayo and Sheppard (1996), Harter-Dreiman (2004), Malpezzi and Watcher (2005), Goodman (2005), Goodman and Thibodean(2008) and Wang (2009). This strand of literature is also reduced form model in nature and there is always housing stock on the right hand side as an independent variable. The third type of model is structural model mainly based on urban spatial theory which explicitly accounts for land as an input of housing construction. Mayer and Somerville (1996, 2000a, 2000b) use models of residential construction based on the theory of urban land development presented in Capozza and Helsey (1989). Dipasquale and Wheaton (1994), Peng and Wheaton (1994) also extend the traditional stock-flow model with urban spatial theory. Aura and Davidoff (2006) and Saiz (2010) separately endogenize land supply in their theoretical models to estimate housing supply elasticity. Among this type of structural models, Poterba (1984) proposed a structural asset market model. Evenson (2001) use conditional vector auto-regression (CVAR) to explain housing price and housing stock simultaneously. When estimating price elasticity of housing supply, another widely discussed issue is whether 5 Although Topel and Rosen (1988) s theoretical model is based on stock-flow theory, yet its empirical model did not include a housing stock proxy, which make it more like a q-theory empirical model. 9

13 it is proper to use housing price level or change in the empirical models. Mayer and Somerville (2000b) argued that since housing price is a stock variable while newly housing supply is a flow one, it is proper to use the price change which is also a flow variable to explain the dynamics of housing supply. Of course, this argument focuses on the time-series property of the data to avoid spurious regression. Grimes and Aitken (2010) conciliated Mayer and Somerville (2000b) s argument with many previous studies using price levels by arguing that the existence of co-integration relationship between housing supply and its explanatory variables are the key issue rather than specify the format of variables. In this paper, we will do co-integration before estimating the supply elasticity. 2.3 Estimated price elasticity of housing supply The earliest attempt to estimate the price elasticity of housing supply was conducted by Muth (1960). He stated that if housing supply is perfect elastic, housing price should be independent from new housing supply, because housing supply will respond to all demand shocks immediately. His empirical study based on reduced model supports this assumption, and finds that U.S. has highly elastic housing supply between the First World War and the Second World War. Follain (1979) used data to get a similar estimation of perfect elasticity while Poterba (1984) gets an estimate between 0.5 to 2.9. Dipasquale and Wheaton (1994) employed urban spatial model to estimate the elasticity of housing supply from 1963 to 1990 in U.S. and found the value lied within the vicinity between 1.0 to1.2 for new housing construction while 1.2~1.4 for housing Stock. Malpezzi and Maclennan (2001) found that the housing supply elasticity of newly built market was 4~10 in U.S. and 1~4 in U.K. before the Second World War, while it was 6~13 in U.S. and 0~1 in U.K. after the Second World War. Blackley (1999) estimated the newly built elasticity of housing supply to be 1.6~3.7. More recently, Harter-Dreiman (2004) proposed VEC model to estimate the range of supply elasticity of U.S. urban housing market to be within the vicinity of 1.8 to 3.2. Green et al (2005) estimate supply elasticity of 45 cities in the U.S. and find a diverged distribution from highest 29.9 in Dallas to lowest in Miami. Goodman (2005) based on 317 U.S. suburban area s data in 1970s, 1980s and 1990s to estimate supply elasticity within the vicinity of Goodman and Thibodean (2008) used similar stock-flow models for 133 U.S. MSAs from 1990 to 2000 and find largest supply elasticity 2.98 in Las Vegas and the smallest supply elasticity in Harrisburg. Saiz (2010) provides the most recent estimates of supply elasticities. Using his topographically-derived estimates of developable 10

14 land ratio along with the local regulation data from Gyourko et al (2008), he provides housing supply elasticity estimation around 1.54 on average for U.S. metropolitan area and a 5.45 upper limit in Wichita while a 0.60 lower limit in Miami. In addition, there are also estimations of housing supply elasticity for counties other than the U.S. Whitehead (1974) found the newly built elasticity of housing supply was from 0.5 to 2 in the U.K during 1955 to Mayo and Sheppard (1996) find Malaysia s supply elasticity is between 0 to 1.5, Thailand near infinite and Korea from 1 to 1.5. Malpezzi and Mayo (1997) s estimate find Malaysia s supply elasticity is between 0 and 0.35, Korea is between 0 to 0.17 while Thailand also near infinite. Vermeulen and Rouwendal (2007) conclude zero elasticity in Netherland both in the short run and long run. Peng and Wheaton (1994) find supply elasticity to be 1.1 in Hong Kong, P.R. China. In general, the above studies gave a wide range of estimation on housing supply elasticity both within a country like U.S. or across different countries. From one side, this is in line with earlier argument that the definition of supply elasticity, specification of estimating model, research time horizon as well as the phase in the market cycle are different. From the other side, this implies great differences embedded in determinants of supply elasticity in different regional markets. 2.4 Determinants of price elasticity of housing supply A majority of literature has focused on the regulatory stringency s impact on housing supply elasticity (Mayo and Sheppard, 1996; Mayer and Somerville, 2000a; Quigley and Raphael, 2005; Green et al, 2005; Malpezzi and Wachter, 2005; Zabel and Paterson, 2006; Vermeulen and Rouwendal, 2007; Glaeser and Ward, 2009). These studies developed different indicators to measure the stringency of government regulation on housing or land market. Malpezzi and Wachter (2005) used a simple addictive index of REGTEST developed by Malpezzi (1996) which incorporated approval time, issue of permits and acreage of land zoned for a single family housing et al. Similar index has also been developed by Gyourko et al (2008) called WRLURI which incorporate more information from a national survey. Evenson (2001) use the number of permit issuing authorities as a measure of the regulatory environment while Quigley and Raphael (2005) use the number of regulation policies as indicator. Mayer and Somerville (2000a) use months to receive subdivision approval, number of growth management technique as well as a development fee dummy variable to depict the regulatory framework. Most of above studies found statistically significant negative effects from tighter regulation on housing supply elasticity. 11

15 Some studies also explore the economic factors of local market on housing supply elasticity. Evenson (2001) found that an area s population densities, historical employment growth rate, region of jurisdiction along with government regulation are important determinants of its supply elasticity and housing price response. Malpezzi and Wachter (2005) found that housing market with more liberal regulatory environments, or less natural constraint or greater access to infrastructure would have bigger price elasticity of housing supply. Green et al (2005) estimated supply elasticities for 45 U.S. metropolitan areas and found through cross section regression that population level, population change, population density, housing price level as well as government regulation are important in shaping regional housing supply elasticity, though some of regressed coefficients have unexpected signs. In current literature, only Saiz (2010) testify that physical land constraints is important in explaining housing supply elasticity by using satellite-generated data on terrain elevation and presence of water bodies. He proposed a model to endogenize developable land ratio with respect to price elasticity of housing supply. Empirical research finds significant impact from developable land ratio on housing supply elasticity. It is clear that geographical factor, along with economic and institutional factors are important in determining the supply elasticity of housing market, but up to now there is no research to examine all the three aspects of housing supply elasticity. This paper will give a thorough consideration of potential determinants from these three dimensions. 3 Estimating the price elasticity of housing supply in China 3.1 Models Stock adjustment model from Malpezzi and Maclennan (2001) Malpezzi and Maclennan (2001) provided a simple stock-adjustment model to estimate housing supply elasticity. We extend his model as follows. * Qdt ( Kt Kt 1) * Kt 0 1HPt 2INCt 3POPt 4OwnCostt Qst 0 1HPt 2HPt 1 3HPt 2 4HPt 3 5ConCostt 6MRate Qdt Qst (1) where, HP is the long-term price level of standard housing service, INC is household income, POP is total population, OWNCOST is the real cost of home ownership, * K is the long-term housing demand reflected by housing service, Kt 1 is the stock of housing service 12

16 with a one period (year) lag, ConCost is the construction cost and MRate is the mortgage interest rate. All the variables in equation (1) are in logarithmic form but OWNCOST and MRate. Therefore 1 is the price elasticity of housing supply. Compare with the standard Malpezzi flow-adjustment model, equation (1) have three extensions. First, it incorporates real cost of homeownership into the demand equation since this variable is quite influential on housing demand. Second, we introduce lagged housing prices in the supply equation since housing construction process is lengthy and historical prices can be more influential to housing supply than current price. In our model, we first arbitrarily assume that housing prices during past three years can impact on current housing supply but will check the detailed lag periods in empirical study. Third, it further incorporates construction cost and capital cost into the housing supply equation since both are important indicators influencing housing supply decision. Equation (1) can be transformed into equation (2). HP INC POP K HP t t t t 1 t HP HP ConCost MRate t 2 t 3 t t (2) Since the parameters on the right side of equation (2) can not be identified directly, Malpezzi estimates 1 indirectly assuming 1 and 2 are given. Equation (2) can be estimated by OLS method as equation (3) when incorporating stochastic term. HP INC POP OwnCost HP t 0 1 t 2 t 3 t 4 t 1 HP HP ConCost MRate K 5 t 2 6 t 3 7 t 8 t 9 t 1 t Compared equation (2) with equation (3), the price elasticity of housing supply 1 is estimated as equation (4) (3) ( ) (4) Where, is the parameter of stock adjustment speed which can be assigned artificially. In Malpezzi and Maclennan (2001) s empirical study, they set the benchmark value of as 0.3 or 0.6, depicting a moderate speed of adjustment. Noticeably, when there is no stock adjustment process in equation (1), the model will be called a flow adjustment model as used in Mayo and Sheppard (1996) as well as Malpezzi and Mayo (1997). Since the market can clear within one period, is equivalent to 1 in equation (4). We believe that housing market are not perfectly efficient, so we adopted stock adjustment model as main results in the following sections. 13

17 As Malpezzi and Mayo (1997) stated, the elasticities of housing demand and housing prices are relatively stable across nations or regions. It makes the Malpezzi s models efficient to estimate the price elasticity of housing supply indirectly with reasonable simplicity. However, as argued by Kim et al (2010), the accuracy of the estimates for supply elasticity depends on the specification of the reduced form house price equation and the estimates of the demand elasticities. In this paper, we extend the Malpezzi and Maclennan (2001) model by incorporating cost of homeownership, cost of housing construction, cost of investment capital and lags in housing prices, which makes the models more realistic. 3.2 Data Data description The data used in this study covers macro-economic indicators as well as housing market variables in 35 major Chinese cities from 1998 to Due to the limited length of timeseries available, major cities are pooled to create a panel dataset. Thus, there are 35 cross sections and 12 years in our panel data, the total observations are 420 in each pooled variable. Actually, in panel data estimation, longer cross sections and shorter time period is good data formation as in Dreiman (2004) where there are 76 MSA cross section and 19 years. The variables and their descriptive statistics are shown in Appendix B.1. HP i is housing price level which is calculated according to Real Estate Price Index of 35 major cities published by National Bureau of Statistics (NBS) and National Development and Reform Commission (NDRC). This housing price index is transaction-based index and is the best available annual housing price data in China for its larger coverage of sample cities as well as its length. HStock i is housing stock estimated by multiplying per capita floor area and residential populations of the year Using the housing completed floor areas as flow amount, the housing stock in the following years is estimated accordingly. disposable income per capita, completed floor area of residential housing, POP i is total residential populations, housing which we use as a proxy for housing demand. INC i is urban household NewStart i is newly SaleArea i is newly sold floor area of residential ConCost i is the construction cost of housing development. We divide the annual value of completed housing units over annual completed floor area to obtain a proxy estimation of construction cost which is a rough reflection of the structure cost. INF i stands for local inflation rate which are calculated from local Consumer Price Indices (CPI). Since all the interest rates are identical cross different 6 Series after the year 1999 is computed by adding newly built floor areas. To make the calculation easy, we assume there is no deterioration in the housing stock. 14

18 urban housing market, we extracted local inflation rate from nominal benchmark lending rate to get MRate i, real rate of five-year lending. Actually, in China, dynamics of real cost of homeownership appreciation 7. We will use MRate i to replace the MRate i can capture the OwnCost t if we neglect the expected housing price OwnCost t in theoretical models in following empirical study. All the nominal variables are also adjusted by local Consumer Price Indices. Our data source includes China Monthly Economic Indicators, China City Statistical Yearbook and Statistic Yearbook in various cities. The data structure is panel data. In our model, we assume there are no structural changes across sections. Therefore, fixed effect panel data model is employed in our empirical study. Unit root test of panel data Table 1 Unit root test of panel data Level First Difference Variables IPS test Order of IPS test Order of stat P value integration stat P value integration ln(hp) I(0) / / / ln(inc) I(1) I(0) ln(pop) I(0) / / / ln(newstart) I(1) I(0) ln(salearea) I(0) / / / ln(hstcok) I(1) I(0) INF I(0) / / / MRate I(0) / / / ln(concost) I(0) / / / Similar to time series model, we should conduct unit root test of panel data to examine whether the data is stable in level or to check the degree of integration. Several panel unit root tests have been published and widely used recently. In our paper, we employ Im et al. (1997) (IPS) test to check the unit root in panel data 9 since IPS test touts particular usefulness for situations in which time-series are short and cross sections are relative plentiful. The test 7 Traditional cost of homeownership is equal to e OwnCost NominalMRate Maintain Pr opertytax Inflation HP / HP. In China, the maintenance cost does not vary much across time and region. There is no enacted property tax during the sample period. If we also assume a constant rate of expected housing price appreciation across time and region, then the real rate of lending (MRate=NominalMRate- Inflation) will fully capture the dynamics of home ownership cost (OwnCost). 9 IPS test is put forwards by Im et al (1997), which relaxed the assumption of cross section unit root homogeneity, and conducts ADF test respectively to each section, assuming there is different unit root on different cross section as the null hypothesis. 15

19 results are demonstrated in Table 1. The results reveal that housing price, population, sale area, local inflation, real mortgage lending rate and housing construction cost are integrated at levels, while the other three series are integrated at the first order. Generally speaking, two panel data series with same degree of integration would be cointegrated (Greene, 1993). However, in order to give robust check on cointegration relationship in panel data, we have to conduct panel cointegration test. 3.3 Estimating the average price elasticity of housing supply First we will estimate the price elasticity ( 1 ) and income elasticity ( 2 ) of housing demand. Then we will estimate the income elasticity ( 1 ) of housing prices. With commonly used arbitrary settings of coefficient, we will get a range estimation of the price elasticity of housing supply ( 1). This method is intrinsically the same as Harter-Dreiman (2004) who also used panel data model to estimate 1. Estimating the price elasticity and the income elasticity of housing demand The price elasticity of housing demand 1 and the income elasticity of housing demand 2 should be given before calculating the housing supply elasticity. In extant literature about Chinese housing market, Zheng (2007) estimated both the coefficients using micro survey data. Her empirical results indicate that the price elasticity is and the income elasticity is for Chinese urban housing market. However, her sample only covers three provinces including Liaoning, Guangdong and Sichuan. In order to acquire more information from panel data of 35 major cities, our study also estimates a reduce-form housing demand function as equation (5). We use sold floor areas, SaleArea, as a proxy for housing demand. ln( SaleArea ) ln( HP ) ln( INC ) ln( POP ) MRate (5) it 0i 1 it 2 it 3 it 4 it it Cointegration test of panel data model is conducted before estimating above formula. We employ the methods of Kao test and Pedroni test to empirically check whether the five variables within equation (5) are cointegrated. The null hypothesis of Kao and Pedroni test are that there is no cointegrated relationship between the variables. The results revealed that the both Kao and Pedroni test reject the null hypothesis 10, so we conclude that the variables used in equation (5) are cointegrated or in other words, they at least have one cointegration vector. Therefore, a Pool Generalized Least Square method is used to estimate the equation taking account of cross-section fixed effect and period fixed effect. Table 2 gives the detailed regression report. 10 The detailed test reports will be provided up on request. 16

20 Table 2 regression result of equation (5) Variable Coefficient Std. Error T-Statistic P value Intercept *** LOG(HP) *** LOG(INC) *** LOG(POP) *** MRate ** Adj. R square D.W. Statistics F-statistic Prob (F-statistic) Notes: Fix effects of each cross section and time dummy are not displayed here. ***significant at the 1% level, **significant at the 5% level, *significant at the 10% level Our results indicate that the price elasticity of housing demand in urban cities is , and the income elasticity is The estimation is basically in accordance with Zheng (2007) s inference. We will use estimations of this paper to proceed with later calculation. Estimating the income elasticity of housing prices In this section, we employ fixed-effect panel data model to estimate equation (3). Cointegration test of panel data model is also conducted beforehand, and both Kao test and Pedroni test indicate that there shall at least have one cointegration vector which is in line with the panel unit root test in table 1. Therefore, a Pool Generalized Least Square method is employed to do the estimation. Table 3 demonstrates the estimated results. Housing Price Model Table 3 Regression result of equation (3) Dependent Variable: log housing price Independent Var I II III 0 ln( INC) : ln( POP) : MRate : 3 1 ln( HP) : 2 t 1 4 ln( HP) : (0.17) ** (2.11) (-0.25) *** (6.52) *** (34.09) t 2 5 / *** (3.22) *** (3.09) (-0.04) *** (5.48) *** (25.16) *** (-9.45) *** (3.73) *** (2.79) (0.95) *** (3.43) *** (19.26) *** (-5.18) 17