中国住房市场波动的影响因素研究——基于租金收益率的方差分解
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摘要
基于资产定价的视角,本文通过将标准的动态戈登增长模型和传统的住房使用成本模型相结合,建立了一个关于住房市场租金收益率的动态住房使用成本模型。该模型将租金收益率分解为购房的预期资金成本、预期购房相对于租房的风险溢价和预期未来租金增长率三个部分的现值之和。进一步,本文将该模型应用于京沪广深四大城市的季度数据,并使用方差分解方法来考察国内住房市场动态波动的影响因素及其相对重要性。本文结果表明,资金成本变动在四大城市的住房市场波动中为最重要的影响因素,而租金在住房市场波动中虽然存在着一定的影响作用,但并不如资金成本显著。此外,本文还发现,不能直接观测得到的购房相对于租房的风险溢价也是影响国内住房市场的一个不可忽视的重要因素。值得注意的是,近年四大城市居民租房面临的风险相对于购房正日益上升。
Housing prices in China's major cities have been surging since 2003. Given that this may be one of the main macro risks to the real economy in China,great effort has gone into establishing the economic forces driving the large swings in China's housing market. Asset pricing theory typically tries to answer this question by relating an asset's price to its future cash flow uncertainty,usually in the form of a present-value statement.Among the various measures of valuation,the rental yield,equivalent to the dividend-price ratio in the stock market,is of particular importance in assessing the housing market because it reveals an agent's expectations of future returns and rental growth in the housing market. Moreover,given the rapid rise in rental prices in major cities over the past few years,the rental yield has triggered a fresh wave of attention and public discussion regarding the forces driving the fluctuation in rental yield.From the perspective of asset pricing,researchers usually use the dynamic Gordon growth model,which is originally developed by Campbell and Shiller to decompose the stock market dividend yield in order to relate rental yield to future rental growth and housing returns. However,in the literature of real estate economics,a large body of work on housing market fluctuations applies the user cost of housing model. This model is in fact a no-arbitrage condition in which the marginal benefits( rental price) equal the marginal cost of housing including the cost of capital,the potential capital gain or loss,and the risk premium of owning relative to renting a house.Therefore,we incorporate the user cost of housing model into the standard Campbell-Shiller present-value model of rental yield to decompose the rental yield into three components: the expected cost of capital,which measures the cost of capital of buying a house; the expected risk premium of owing versus renting,which gauges the premium of house tenure as a hedge against the risk of renting a house; and the expected future rent growth,which captures the value of housing service flows in the future. Based on our theoretical model,we exploit a unique matched dataset of the sale prices and rents for the four Chinese first-tier cities compiled by the DTZ Company,the base-year rental yield from CICC,and interest rates from the PBC's website. We then construct empirical proxies for the relevant expectations in our present-value model using the vector autoregressive method. We apply the variance decomposition approach to examine quantitatively how much of the variation in rental yield comes from the three components mentioned above. Our results show that the cost of capital plays the most vital role in all four major cities,followed by changes in the risk of owning versus renting.While innovation in rental growth also plays a part in the fluctuation of rental yield,it is not as significant as the cost of capital. Recently in particular,the risk premium of renting relative to owning a house in most first-tier cities seems to be rising.Our contribution is three-fold. First,to our knowledge,no prior study has applied the dynamic Gordon growth model in China's four major cities to examine the driving forces in the housing market by exploiting a unique sample of matched transaction data on housing and rental prices. This exercise extends the research perspective and framework of the existing literature. Second,and more importantly,in contrast to the common practice of decomposing return into the risk-free rate and risk premium in the stock market,and the static relation between rental yield and housing user cost in the real estate economics literature,this study combines the standard present-value model with the classical user cost of housing model to build a dynamic user cost model to identify the forces driving the fluctuations in rental yield in China's housing market. Third,our approach provides a new model-based measure of the risk premium of owning versus renting,which is not directly observable,and gauges its impact on housing market fluctuations.
Housing prices in China's major cities have been surging since 2003. Given that this may be one of the main macro risks to the real economy in China,great effort has gone into establishing the economic forces driving the large swings in China's housing market. Asset pricing theory typically tries to answer this question by relating an asset's price to its future cash flow uncertainty,usually in the form of a present-value statement.Among the various measures of valuation,the rental yield,equivalent to the dividend-price ratio in the stock market,is of particular importance in assessing the housing market because it reveals an agent's expectations of future returns and rental growth in the housing market. Moreover,given the rapid rise in rental prices in major cities over the past few years,the rental yield has triggered a fresh wave of attention and public discussion regarding the forces driving the fluctuation in rental yield.From the perspective of asset pricing,researchers usually use the dynamic Gordon growth model,which is originally developed by Campbell and Shiller to decompose the stock market dividend yield in order to relate rental yield to future rental growth and housing returns. However,in the literature of real estate economics,a large body of work on housing market fluctuations applies the user cost of housing model. This model is in fact a no-arbitrage condition in which the marginal benefits( rental price) equal the marginal cost of housing including the cost of capital,the potential capital gain or loss,and the risk premium of owning relative to renting a house.Therefore,we incorporate the user cost of housing model into the standard Campbell-Shiller present-value model of rental yield to decompose the rental yield into three components: the expected cost of capital,which measures the cost of capital of buying a house; the expected risk premium of owing versus renting,which gauges the premium of house tenure as a hedge against the risk of renting a house; and the expected future rent growth,which captures the value of housing service flows in the future. Based on our theoretical model,we exploit a unique matched dataset of the sale prices and rents for the four Chinese first-tier cities compiled by the DTZ Company,the base-year rental yield from CICC,and interest rates from the PBC's website. We then construct empirical proxies for the relevant expectations in our present-value model using the vector autoregressive method. We apply the variance decomposition approach to examine quantitatively how much of the variation in rental yield comes from the three components mentioned above. Our results show that the cost of capital plays the most vital role in all four major cities,followed by changes in the risk of owning versus renting.While innovation in rental growth also plays a part in the fluctuation of rental yield,it is not as significant as the cost of capital. Recently in particular,the risk premium of renting relative to owning a house in most first-tier cities seems to be rising.Our contribution is three-fold. First,to our knowledge,no prior study has applied the dynamic Gordon growth model in China's four major cities to examine the driving forces in the housing market by exploiting a unique sample of matched transaction data on housing and rental prices. This exercise extends the research perspective and framework of the existing literature. Second,and more importantly,in contrast to the common practice of decomposing return into the risk-free rate and risk premium in the stock market,and the static relation between rental yield and housing user cost in the real estate economics literature,this study combines the standard present-value model with the classical user cost of housing model to build a dynamic user cost model to identify the forces driving the fluctuations in rental yield in China's housing market. Third,our approach provides a new model-based measure of the risk premium of owning versus renting,which is not directly observable,and gauges its impact on housing market fluctuations.
引文
[1]陈斌开、徐帆和谭力,2012,《人口结构转变与中国住房需求:1999-2005年---基于人口普查数据的微观实证研究》,《金融研究》第1期,第129~140页。
[2]陈斌开和张川川,2016,《人力资本和中国城市住房价格》,《中国社会科学》第5期,第43~64+205页。
[3]陈建、陈英楠和刘仁和,2009,《所有权成本、投资者预期与住宅价格波动:关于国内四大城市住宅市场的经验研究》,《世界经济》第10期,第14~24页。
[4]陈英楠和刘仁和,2013,《通货膨胀与租金收益率变动:理性还是非理性?》,《金融研究》第7期,第61~73页。
[5]蒋贤锋、王贺和史永东,2008,《我国金融市场中基准利率的选择》,《金融研究》第10期,第22~36页。
[6]况伟大,2010,《预期、投机与中国城市房价波动》,《经济研究》第9期,第67~78页。
[7]陆铭、欧海军和陈斌开,2014,《理性还是泡沫:对城市化、移民和房价的经验研究》,《世界经济》第1期,第30~54页。
[8]沈悦和刘洪玉,2004,《住宅价格与经济基本面:1995-2002年中国14城市的实证研究》,《经济研究》第6期,第78~86页。
[9]肖立晟和陈思翀,2013,《中国国际投资头寸表失衡与金融调整渠道》,《经济研究》第7期,第20~34页。
[10]张川川、贾坤和杨汝岱,2016,《“鬼城”下的蜗居:收入不平等与房地产泡沫》,《世界经济》第2期,第120~141页。
[11]中国人民银行房地产金融分析小组,《2004中国房地产金融报告》,2005年。http://www.pbc.gov.cn/history_file/files/att_15025_1.pdf
[12]周晖和王擎,2009,《货币政策与资产价格波动:理论模型与中国的经验分析》,《经济研究》第10期,第61~74页。
[13]邹至庄和牛霖琳,2010,《中国城镇居民住房的需求与供给》,《金融研究》第1期,第1~11页。
[14]Binsbergen,J.H.and R.S.J.Koijen.2010.“Predictive Regressions:A Present-Value Approach”,Journal of Finance,65(4):1439-1471.
[15]Campbell,J.Y.and R.J.Shiller.1988.“The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors”,Review of Financial Studies,1(3):195-228.
[16]Campbell,J.Y.,S.Giglio,and C.Polk.2013.“Hard Times”,Review of Asset Pricing Studies,3(1):95-132.
[17]Campbell,S.D.,M.A.Davis,J.Gallin,and R.F.Martin.2009.“What Moves Housing Markets:A Variance Decomposition of the Rent-price Ratio”,Journal of Urban Economics,66(2):90-102.
[18]Cochrane,J..2011.“Presidential Address:Discount Rates”,Journal of Finance,66(4):1047-1108.
[19]Gordon,M.,1962,The Investment,Financing,and Valuation of the Corporation,Irwin,Homewood,IL.
[20]Han,L.2013.“Understanding the Puzzling Risk-Return Relationship for Housing”,Review of Financial Studies,26(4):877-928.
[21]Hendershott,P.and J.Slemrod.1983.“Taxes and the User Cost of Capital for Owner-Occupied Housing”,Journal of the American Real Estate and Urban Economics Association,10(4):375-393.
[22]Hiebert,P.and M.Sydow.2011.“What Drives Returns to Euro Area Housing?Evidence from a Dynamic Dividenddiscount Model”,Journal of Urban Economics,70(2):88-98.
[23]Himmelberg,C.,C.Mayer,and T.Sinai.2005.“Assessing High House Prices:Bubbles,Fundamentals,and Misperceptions”,Journal of Economic Perspectives,19(4):67-92.
[24]Hott,C.and P.Monnin.2008.“Fundamental Real Estate Prices:An Empirical Estimation with International Data”,Journal of Real Estate Finance and Economics,36(4):427-450.
[25]Mc Carthy,J.and R.W.Peach.2004.“Are Home Prices the Next Bubble?”,FRBNY Economic Policy Review,11∶1-17.
[26]Mc Carthy,J.and R.W.Peach.2005.“Is there a Bubble in the Housing Market Now?”,NFI Policy Brief No.2005-PB-01.
[27]Mishkin,F.S..2007.“Housing and the Monetary Transmission Mechanism”,NBER Working Paper,No.13518.
[28]Plazzi A.,W.Torous,and R.Valkanov.2010.“Expected Returns and Expected Growth in Rents of Commercial Real Estate”,Review of Financial Studies,23(9):3469-3519.
[29]Poterba J.1984.“Tax Subsidies to Owner Occupied Housing:An Asset Market Approach”,Quarterly Journal of Economics,99(4):729-752.
[30]Sinai,T.and N.S.Souleles.2005.“Owner-occupied Housing as a Hedge against Rent Risk”,Quarterly Journal of Economics,120(2):763-789.
1数据来源与说明详见本文第三部分。
2陈建等(2009)对住房使用成本模型相关研究进行了综述,本文在此不再赘述。
1虽然从2011年1月28日起,上海开始试点对个人住房保有环节征收房产税,也就是世界各国与地区对保有住房征收的财产税或物业税(Property Tax),但对本文研究的影响可以忽略不计。
1当然E Rt+()1≠exp E rt+(())1,因为E[f()]x≠f[E(x)]。但是,我们可以近似得到log EtRt+(())1≈Etrt+()1。例如,假设住房回报率R服从对数正态分布,那么则有E(R)=exp E(r)+σ2()r/(2)。如果r-E(r)较小,那么类似泰勒级数展开的一阶近似,作为二阶以上高次项的σ2()r/2=E(r-E())r[2]/2会足够小到可以被忽略不计,所以等式E(R)≈exp(E())r近似成立。
1参见中国人民银行房地产金融分析小组(2005)。
1例如,Campbell and Shiller(1988)关于股票市场的开创性研究,以及Campbell et al.(2009)与Hiebert and Sydow(2011)等在住房市场上的应用。国内利用Campbell-Shiller现值模型的相关研究也遵循了同样的处理方法,例如,陈英楠和刘仁和(2013),肖立晟和陈思翀(2013)。
[2]陈斌开和张川川,2016,《人力资本和中国城市住房价格》,《中国社会科学》第5期,第43~64+205页。
[3]陈建、陈英楠和刘仁和,2009,《所有权成本、投资者预期与住宅价格波动:关于国内四大城市住宅市场的经验研究》,《世界经济》第10期,第14~24页。
[4]陈英楠和刘仁和,2013,《通货膨胀与租金收益率变动:理性还是非理性?》,《金融研究》第7期,第61~73页。
[5]蒋贤锋、王贺和史永东,2008,《我国金融市场中基准利率的选择》,《金融研究》第10期,第22~36页。
[6]况伟大,2010,《预期、投机与中国城市房价波动》,《经济研究》第9期,第67~78页。
[7]陆铭、欧海军和陈斌开,2014,《理性还是泡沫:对城市化、移民和房价的经验研究》,《世界经济》第1期,第30~54页。
[8]沈悦和刘洪玉,2004,《住宅价格与经济基本面:1995-2002年中国14城市的实证研究》,《经济研究》第6期,第78~86页。
[9]肖立晟和陈思翀,2013,《中国国际投资头寸表失衡与金融调整渠道》,《经济研究》第7期,第20~34页。
[10]张川川、贾坤和杨汝岱,2016,《“鬼城”下的蜗居:收入不平等与房地产泡沫》,《世界经济》第2期,第120~141页。
[11]中国人民银行房地产金融分析小组,《2004中国房地产金融报告》,2005年。http://www.pbc.gov.cn/history_file/files/att_15025_1.pdf
[12]周晖和王擎,2009,《货币政策与资产价格波动:理论模型与中国的经验分析》,《经济研究》第10期,第61~74页。
[13]邹至庄和牛霖琳,2010,《中国城镇居民住房的需求与供给》,《金融研究》第1期,第1~11页。
[14]Binsbergen,J.H.and R.S.J.Koijen.2010.“Predictive Regressions:A Present-Value Approach”,Journal of Finance,65(4):1439-1471.
[15]Campbell,J.Y.and R.J.Shiller.1988.“The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors”,Review of Financial Studies,1(3):195-228.
[16]Campbell,J.Y.,S.Giglio,and C.Polk.2013.“Hard Times”,Review of Asset Pricing Studies,3(1):95-132.
[17]Campbell,S.D.,M.A.Davis,J.Gallin,and R.F.Martin.2009.“What Moves Housing Markets:A Variance Decomposition of the Rent-price Ratio”,Journal of Urban Economics,66(2):90-102.
[18]Cochrane,J..2011.“Presidential Address:Discount Rates”,Journal of Finance,66(4):1047-1108.
[19]Gordon,M.,1962,The Investment,Financing,and Valuation of the Corporation,Irwin,Homewood,IL.
[20]Han,L.2013.“Understanding the Puzzling Risk-Return Relationship for Housing”,Review of Financial Studies,26(4):877-928.
[21]Hendershott,P.and J.Slemrod.1983.“Taxes and the User Cost of Capital for Owner-Occupied Housing”,Journal of the American Real Estate and Urban Economics Association,10(4):375-393.
[22]Hiebert,P.and M.Sydow.2011.“What Drives Returns to Euro Area Housing?Evidence from a Dynamic Dividenddiscount Model”,Journal of Urban Economics,70(2):88-98.
[23]Himmelberg,C.,C.Mayer,and T.Sinai.2005.“Assessing High House Prices:Bubbles,Fundamentals,and Misperceptions”,Journal of Economic Perspectives,19(4):67-92.
[24]Hott,C.and P.Monnin.2008.“Fundamental Real Estate Prices:An Empirical Estimation with International Data”,Journal of Real Estate Finance and Economics,36(4):427-450.
[25]Mc Carthy,J.and R.W.Peach.2004.“Are Home Prices the Next Bubble?”,FRBNY Economic Policy Review,11∶1-17.
[26]Mc Carthy,J.and R.W.Peach.2005.“Is there a Bubble in the Housing Market Now?”,NFI Policy Brief No.2005-PB-01.
[27]Mishkin,F.S..2007.“Housing and the Monetary Transmission Mechanism”,NBER Working Paper,No.13518.
[28]Plazzi A.,W.Torous,and R.Valkanov.2010.“Expected Returns and Expected Growth in Rents of Commercial Real Estate”,Review of Financial Studies,23(9):3469-3519.
[29]Poterba J.1984.“Tax Subsidies to Owner Occupied Housing:An Asset Market Approach”,Quarterly Journal of Economics,99(4):729-752.
[30]Sinai,T.and N.S.Souleles.2005.“Owner-occupied Housing as a Hedge against Rent Risk”,Quarterly Journal of Economics,120(2):763-789.
1数据来源与说明详见本文第三部分。
2陈建等(2009)对住房使用成本模型相关研究进行了综述,本文在此不再赘述。
1虽然从2011年1月28日起,上海开始试点对个人住房保有环节征收房产税,也就是世界各国与地区对保有住房征收的财产税或物业税(Property Tax),但对本文研究的影响可以忽略不计。
1当然E Rt+()1≠exp E rt+(())1,因为E[f()]x≠f[E(x)]。但是,我们可以近似得到log EtRt+(())1≈Etrt+()1。例如,假设住房回报率R服从对数正态分布,那么则有E(R)=exp E(r)+σ2()r/(2)。如果r-E(r)较小,那么类似泰勒级数展开的一阶近似,作为二阶以上高次项的σ2()r/2=E(r-E())r[2]/2会足够小到可以被忽略不计,所以等式E(R)≈exp(E())r近似成立。
1参见中国人民银行房地产金融分析小组(2005)。
1例如,Campbell and Shiller(1988)关于股票市场的开创性研究,以及Campbell et al.(2009)与Hiebert and Sydow(2011)等在住房市场上的应用。国内利用Campbell-Shiller现值模型的相关研究也遵循了同样的处理方法,例如,陈英楠和刘仁和(2013),肖立晟和陈思翀(2013)。
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