Research on Real Estate Price Bubble and Turning Point——Comparative Analysis Based on Tokyo, Japan and Shanghai, China

Abstract

Abstract:

Based on the analysis of the factors affecting real estate prices from the perspective of supply and demand, this paper constructs a time-varying parameter state space model, and measures and empirically analyzes the housing bubble in Tokyo, Japan and Shanghai, China. The calculation results show that from the 1^st quarter of 2004 to the 1^st quarter of 2018, the average housing price bubble in Shanghai was 9.38%. 2009-2011 and 2015-2017 were the periods of two rounds of bubbles, with peak foams reaching 18.29% and 18.59%, respectively. Through the impulse response function, the paper analyzes the turning point of Shanghai's housing price bubble. Financial policies are short-term factors affecting the turning point of the housing price bubble. Land supply and real estate development investment are the medium-term factors affecting the turning point of the housing price bubble. The sustainability of income growth and population growth are long-term factors affecting the turning point of the housing price bubble. The bubble measurement method provides a policy reference for governments to judge the housing price bubble. The study of the turning point of housing pricing bubble can provide a concise policy framework for stabilizing housing prices, that is, the financial policy is only the symptom, the land policy and the economic and population policies are the root causes. On the premise of stabilizing finance, through comprehensive policies of land, population and economy, we can ensure that house prices will be stable and far-reaching.

Key words: housing price, bubble, turning point, state space model, impulse response

Yu Xue. Research on Real Estate Price Bubble and Turning Point——Comparative Analysis Based on Tokyo, Japan and Shanghai, China[J]. , 2019, 31(9): 58-69.

References

[1] Pollakowski H. O., Wachte S. M. The Effects of Land-Use Constraints on Housing Prices[J]. Land Economics, 1990,66(3):315-324
[2] Bernanke B. S., Gertler M. Inside the Black Box:The Credit Channel of Monetary Policy Transmission[J]. Journal of Economic Perspective, 1995,9(1):27-48
[3] Detragiache E., Vogel U., Vandenbussche J. Macroprudential Policies and Housing Prices:A New Database and Empirical Evidence for Central, Eastern and Southeastern Europe[J]. Journal of Money Credit and Banking, 2015,47(3):343-377
[4] Kuttner K. N., Shim I. Can Non-Interest Rate Policies Stabilize Housing Markets?[J]. Journal of Financial Stability, 2016,26(10):31-44
[5] 沈悦,刘洪玉.住宅价格与经济基本面:1995-2002年中国14城市的实证研究[J].经济研究, 2004,463(6):78-86
[6] 张涛,龚六堂,卜永祥.资产回报、住房按揭贷款与房地产均衡价格[J].金融研究, 2006,308(2):1-11
[7] 常飞,李秀婷,郭琨,等.货币政策对区域房地产住宅市场的差异化影响——基于国内十城市房地产住宅市场的实证研究[J].管理评论, 2013,25(10):3-9
[8] 叶欣,王婕.宏观经济环境下我国银行贷款和房价动态影响关系:基于SVAR模型的实证分析[J].管理评论, 2013,25(9):62-71
[9] 于长雷,赵红.重大网络突发事件对房地产市场的影响研究[J].管理评论, 2016,28(8):66-70
[10] 杨思群,董美.信贷政策对房价波动的影响分析[J].投资研究, 2017,36(4):90-102
[11] 唐云锋,吴琦琦.土地财政制度对房地产价格的影响因素研究[J].经济理论与经济管理, 2018,321(3):43-56
[12] Hamilton J. D., Charles H. W. The Observable Implications of Self-Fulfilling Expectations[J]. Journal of Monetary Economics, 1985,16(3):353-373
[13] 野口悠纪雄.土地经济学[M].上海:商务印书馆, 1997
[14] Abraham J. M., Hendershott P. H. Bubbles in Metropolitan Housing Markets[R]. Nber Working Papers, 1994
[15] Bourassa S. C., Martin H., Oikarinen E. Measuring House Price Bubbles[J]. Real Estate Economics, 2016,44(6):1-30
[16] André K. A., Karsten G., Frank H., et al. Bubbles and Crises:The Role of House Prices and Credit[J]. Journal of Applied Economics, 2016,31(7):1291-1311
[17] 袁志刚,樊潇彦.房地产市场理性泡沫分析[J].经济研究, 2003,448(3):35-42
[18] 史兴杰,周勇.房地产泡沫检验的Switching AR模型[J].系统工程理论与实践, 2014,34(3):676-682
[19] 吕江林.我国城市住房市场泡沫水平的度量[J].经济研究, 2010,535(6):28-41
[20] 况伟大.中国住房市场存在泡沫吗[J].世界经济, 2008,29(12):3-13
[21] 胡晓.我国房地产价格上涨背后的制度性因素——兼论房地产价格泡沫[J].中央财经大学学报, 2014,323(7):91-99
[22] 张炜.预期、货币政策与房地产泡沫——来自省际房地产市场的经验验证[J].中央财经大学学报, 2017,360(8):77-90
[23] 钟少颖,王蕊,陈锐.货币政策,市场预期与房地产价格[J].管理评论, 2016,28(5):13-22
[24] 王松涛.城市经济开放度对房地产价格的影响研究——基于中国35个大中城市面板数据模型的分析[J].南开经济研究, 2009,148(2):91-102
[25] 叶欣,王婕.宏观经济环境下我国银行贷款和房价动态影响关系:基于SVAR模型的实证分析[J].管理评论, 2013,25(9):62-71
[26] 柳冬,王雯珺,谢海滨,等.我国房地产价格影响要素分析与趋势预测[J].管理评论, 2010,22(5):3-10
[27] 张莉,年永威,皮嘉勇,等.土地政策、供地结构与房价[J].经济学报, 2017,4(3):91-117
[28] 吴璟,刘洪玉.基于灰色-马尔可夫模型的房地产周期研究[J].统计与决策, 2009,282(6):32-34
[29] 李昕,徐滇庆.房地产供求与演变趋势:澄清一种统计口径[J].改革, 2014,24(1):33-42
[30] 叶桂芳.基于国房景气指数的我国房地产市场发展趋势研究[D].暨南大学硕士学位论文, 2014