Housing market spillovers through the lens of transaction volume: A new spillover index approach

Proposing and applying a new spillover index approach based on data-determined structural vector autoregression to measure connectedness, we examine the daily housing market informa-tion transmission via transaction volume among Chinese city-level housing markets from 2009 to 2018. We document substantial information transmission on Chinese housing markets even within one day and find that the role a city-level housing market may play in the information transmission network resembles a pattern observed on other financial markets, which can be generally classified into three distinctive groups: prime senders, exchange centers, and prime receivers. City hierarchy and some fundamental economic factors, such as GDP per capita and average wage, appear to be significant determinants of such a pattern. The findings extend the existing voluminous literature solely based on housing prices or price volatility spillovers and shed new light on the China's government intervention strategy on the housing market.

Automated summary

In this study, a new spillover index approach based on data-determined structural vector autoregression was proposed and applied to measure connectedness among Chinese city-level housing markets. The daily information transmission via transaction volume was examined, revealing substantial information transmission even within one day. Cities were classified into three distinct groups in terms of their role in the information transmission network, with city hierarchy and fundamental economic factors such as GDP per capita and average wage playing significant roles in determining the pattern. These findings contribute to the existing literature on housing market spillovers and provide insights into China's government intervention strategy on the housing market.