Effectiveness of Macro-regulation Policies on Housing Prices: A Spatial Quantile Regression Approach

Abstract

The central government of China intensively implemented a series of housing macro-regulation policies to cool down overheated housing market in 2011–2013. This research uses spatial quantile regression to try to answer the question “How effective are the policies?” Results indicate that house-purchasing restrictions are effective to kerb the speculative demand for houses but are difficult to cool down housing prices especially for the cities with higher housing prices, the other policies could effectively decrease housing prices; however, the degree of effectiveness changes across cities with different level of housing prices. This suggests that China’s government may choose market-classified housing policies in future and heavily increase reserve requirement ratios and interest rates while house-purchasing restrictions are practiced. In addition, we find the positive spatial dependence of housing prices is stronger among cities with higher housing prices, which may more easily weaken the effects of housing policies for these cities when housing prices rise.Highlights• We model effects of macro-regulation policies on housing prices by spatial quantile regression in China’s housing market.• We examine heterogeneous effectiveness of the policies across cities with different level of housing prices.• House-purchasing restrictions are effective to kerb the speculative demand for houses but are difficult to cool down housing prices effectively especially for the cities with higher housing prices.• The other macro-regulation policies effectively regulate housing prices; however, the degree of effectiveness changes across cities.• Positive spatial dependence of housing prices is stronger among cities with higher housing prices.