Conference Agenda

Price cap policy, which has been widely adopted by local governments in China to curb the heating housing markets since 2016, has been seen as one of China's strictest housing intervention policies. This paper not only examines the effects of the price cap policy, but also investigates the underlying mechanisms from both macro and micro perspectives. Based on the data from 27 large-sized or medium-sized cities from 2015 to 2017 on the sales of newly-built houses and the listing records of second-hand houses, we apply the Difference-in-Difference method and the Hedonic Price Model to examine the causal relationship between price cap policy and local governments' newly-built housing supply behaviors. Our results suggest that the price cap policy effectively curbs the rise of house prices, and the effect is stronger in cities with higher housing price growth or lower reliance on land finance. More importantly, our mechanism analyses indicate that, on the one hand, local governments generally limit the transaction price of all newly-built houses. Still, on the other hand, they also adopt strategic behaviors to lower the city-level monthly-average transaction price by reducing the transactions of higher-priced houses while increasing the transactions of lower-priced houses. Our findings further illustrate that such strategic behaviors would exaggerate the mismatch of supply and demand. These research findings reveal that local governments would play "tricks" under administrative pressure from the central government, which enriches the understanding of the consequences of the administrative-oriented market intervention policies. The findings also provide important policy implications for the central government to determine whether and how to implement such top-down administrative policies to better realize the market intervention goal without distorting the market in the future.

We empirically investigate whether China’s anticorruption campaign directly affected government and party officials’ economic behavior, under the premise that they more likely own or afford luxury condos. Using condo sales from 2012 to 2018 in Beijing, we find that luxury condos commanded higher prices compared with ordinary condos before the anticorruption announcement in December 2012, which we call the luxury premium. However, the luxury premium decreased and became a discount after the anticorruption announcement. Furthermore, the decrease in the luxury premium was more pronounced in two districts where officials more likely reside. Using time windows before and after each of the 11 rounds of travelling inspections in our sample period after the anticorruption announcement, we also find that the luxury premium decreased after each inspection, and decreased more in the two “government districts”. We then identify subdivisions developed on land lots with unusually low land sale prices, which we assume more likely involved corruption in land sales and thus luxury condos there are more likely owned by corrupt officials. We find that the decrease in the luxury premium is more significant in these shady subdivisions. All these results are consistent with the notion that the anticorruption campaign affects officials’ demand for luxury condos.

The “Joint Property Ownership (JPO)” scheme in China is designed to provide more affordable houses, through a shared home ownership structure between local government and homebuyers. This paper documents an unexpected price change in the resale housing market induced by such a housing scheme. Using an administrative dataset of housing transactions in Beijing over 2018-2019, we estimate a triple difference model and find that housing units transacted nearby the ownership-sharing projects are associated with an average price premium of 4.4% since the sale of these projects. Our heterogeneity analyses show that such effect is stronger if the ownership-sharing project has stricter purchase restrictions, occupies more land, or if the transacted housing units are newer or nearer to the subway stations. Overall, the results suggest that the JPO scheme, which causes a housing market segmentation, has crowded out the supply of traditional housing units and may have generated adverse impact to housing affordability in a radial area.

This paper explores the changes in the information transmission effect between the housing markets of American metropolitan areas under the conditions of different economic prosperity. We apply Multivariate Markov Switching-Generalized Autoregressive Conditional Heteroscedastic-Dynamic Conditional Correlation (MMS-GARCH-DCC) model to estimate the changes in the market conditions to identify the normal and exceptional periods of overall US economic performance. This paper estimates the model for asymmetric spillover matrixes to observe whether the transmission effects of positive and negative returns between housing markets differ during normal and abnormal fluctuations. In addition to comparing the difference between the total transmission and net transmission effects between markets, this paper also uses social network analysis to observe the information transmission between these markets and find the main metropolises that can lead the market changes under different market conditions. This paper's estimated results can explain the relationship between the overall economic boom fluctuations and housing market information transmission, and verify the regional asymmetric transmission effect of the US housing market, implying that the metropolitan housing markets that dominate the rise and fall are different.