This paper examines the health consequence of rising housing prices by exploiting spatial and temporal variation in housing price appreciation linked to individual-level health data in China from 2000 to 2011. Using an instrumental variable approach, we find robust evidence that increases in housing prices significantly raise the probability of residents having chronic diseases. This negative health impact is more pronounced among individuals from lowincome families and rural to urban migrants. Exploring various possible channels, we find that marriage culture and marriage market competition exacerbates the negative health effects, particularly for males and parents with young adult sons. Our results also reveal that housing price appreciation induces negative health consequences through increased work intensity, higher mental stress, and changes in lifestyle. This paper underlines the unintended health consequences of the real estate market prosperity.

In geographically segmented credit markets, local real estate booms can divert capital away from manufacturing firms, create capital scarcity, increase local real interest rates, lower real wages, and cause underinvestment and relative decline in the industrial sector. Using exogenous variation in the administrative land supply across 172 Chinese cities, we show that the predicted variation in real estate prices does indeed cause substantially higher capital costs for manufacturing firms, reduce their bank lending, lower their capital intensity and labor productivity, weaken firms' financial performance, and reduce their TFP growth by economically significant magnitudes. This evidence highlights macroeconomic stability concerns associated with real estate booms.

Working with unique data on land values in 35 major Chinese markets and a panel of firms outside the real estate industry, we estimate standard investment equations that yield no evidence of a collateral channel effect. This is markedly different from previous work on the United States and Japan which finds economically large impacts. One reason for this appears to be that some of the most dominant firms in China are state-owned enterprises (SOEs) which are unconstrained in the sense that they do not need to rely on rising underlying property collateral values to obtain all the financing necessary to carry out their desired investment programs. However, we also find no collateral channel effect for non-SOEs when we perform our analysis on disaggregated sets of firms. Norms and regulation in the Chinese capital markets and banking sector can account for why there is no collateral channel effect operating among these firms. We caution that our results do not mean that there will be no negative fallout from a potential real estate bust on the Chinese economy. There are good reasons to believe there would be, just not through a collateral channel effect.

Volume of real estate market in some China’s large cities decreased sharply in 2010. Based on rational reconstruction of some basic economic thought and analytical narrative of the real estate price bubble ,I conclude that it should be contributed to a big gap between demand and supply, and financial crisis would break out in the coming few month in China. I make proposal that government should be reducing state-owned share.

Using quarterly data from 1998:Q1 to 2009:Q4 and monthly data from July 2005 to February 2010, this paper examines the impact of key monetary policy variables, including long- term benchmark bank loan rate, money supply growth, and mortgage credit policy indicator, on the real estate price growth dynamics in China. Empirical results consistently demonstrate that lower interest rate, faster money supply growth and loosening mortgage down payment requirement tend to accelerate the subsequent home price growth, and vice versa. These results suggest that Chinese monetary policy actions are the key driving forces behind the change of real estate price growth in China. We also show that hot money flow does not have significant impact on the change of home price growth after controlling for the money supply growth. Finally, a bullish stock market tends to accelerate subsequent home price growth.

Using quarterly data from 1998:Q1 to 2009:Q4 and monthly data from July 2005 to February 2010, this paper examines the impact of key monetary policy variables, including long-term benchmark bank loan rate, money supply growth, and mortgage credit policy indicator, on the real estate price growth dynamics in China. Empirical results consistently demonstrate that lower interest rate, faster money supply growth and loosening mortgage down payment requirement tend to accelerate the subsequent home price growth, and vice versa. These results suggest that Chinese monetary policy actions are the key driving forces behind the change of real estate price growth in China. We also show that hot money flow does not have significant impact on the change of home price growth after controlling for the money supply growth. Finally, a bullish stock market tends to accelerate subsequent home price growth.

There are several kinds of risks among bank, Real estate developer and assessment agency in mortgage loan of construction in progress. The risks were respectively analyzed theoretically by the game models based on the experience inductive of reverse selection between bank and assessment agency and moral hazard between bank and real estate developer. Conclusion can be according to the models drawn as following: long-term cooperation should be introduced between bank and assessment agency, D/V should be appropriately set by bank and hence the optimal strategy of bank should be to launch loan.

内容提要: 本文针对近年来我国房地产市场价格持续攀升但市场需求不降反升、银行信贷资金迅速向房地产业集中以及房地产市场发展与经济增长的作用等问题，以全国和各省、市、自治区的面板数据，进行了实证分析。分析结果显示，我国房地产市场供给约束问题非常严重，尤其是考虑到居民收入增长因素后，房地产市场的供求矛盾十分突出；银行房地产开发贷款和个人住房贷款强有力地支撑了房地产市场的供求；房地产市场的发展也拉动了经济的增长，但这一作用没有我们想象的大。因此，目前我国针对房地产市场的宏观调控和各项监管措施，都应该从供求两方面出发，在金融等政策上要保持连续性，并加强制度建设，更好地发挥市场机制的作用。 Abstract: Recent years, with the rising price, the housing demands also rise quickly, which is strange to the common sense of economics. In addition, the relationship between banking sector and housing industry has become more and close, and the growth of housing industry pull the economic growth dramatically. Based on the panel data of the nation and provinces, we try an empirical study on the topics above. The empirical results show that at least in the nowadays China’s market, one of the most sever problem in the real estate market is the constraints of the housing supply compared to the demand if we consider the effects of the resid ents’income growth. Bank loans are maybe the most effective factor pulling the housing industry growth, both on the supply side and the demand. To our surprise, the effect of housing consumption on the economic growth is less than we have assumed, considering the cyclical changes of the economy. . So we draw the followed policy implications in the further macroeconomic managements: we must take the measures directly to both the supply and the demand sides, keep the policies’ continuity, strengthen the institutional progress and make the market mechanism more effective.

Optimal Timing and Optimal Intensity of Real Estate Development Abstract The traditional real option approach treat firms as price taker and at the same time the firm is assumed to have monopoly power because no competition or future competitive entry is not considered in most of the real option literature. In this article we assume the real estate developer has monopoly power in a real estate submarket, given the nature of real estate market. The developer makes the timing decision as well as the intensity decision at the same time. We model the developer decision in the framework of the real option and derived the optimal timing and optimal intensity of real estate development of a certain real estate project. Our Result shows that not only the uncertainty but also the low rent sensitivity of housing demand will lead to defer of real estate development. And both the timing decision and intensity decision are sensitive to the demand factors besides the uncertainty effect.