The Impact Of Macroeconomic Factors On Housing Prices Based On Dynamic Panel Quantile Regression

The real estate industry has developed rapidly and has become an important part of the national economy in China.The rapid growth of housing prices has also become the focus of the whole society.High housing prices not only affects the daily life of the residents,but also endangers the stable development of the economy.Although the government has issued a number of regulatory policies,it has only suppressed the rise in house prices in the short term.Once the regulatory policies are cancelled,housing prices will immediately rebound and rise rapidly again.In order to effectively control housing prices,the research of the impact of macroeconomic factors on housing prices is still a very important issue.Most of the previous literatures focus on the research of macroeconomic factors on average housing prices.Such research is deviated from reality.Each city's macro-environment is different,the driving mechanism on housing prices also is different.The government's control policies are implemented in high housing prices cities.Therefore,this paper uses dynamic panel quantile regression to investigate the impact of macroeconomic factors on different housing prices.The research content is more abundant and reasonable,and it can also provide more reasonable suggestions for the government to formulate regulatory policies.This paper is based on the panel data of 35 major cities from 2002 to 2016,the variables in the model include housing prices,income,economic openness,interest rates and population.After the cross-sectional dependence test,unit root test and cointegration test,we employ the system GMM method and dynamic panel quantile regression to estimate the parameters.The results obtained by the system GMM method are consistent with the previous literatures.The results obtained by the dynamic panel quantile regression are more abundant than the mean analysis,and we have obtained some innovative conclusions: The impact of income is positive and significant across quantiles.But the impact becomes greater in high quantile,which indicates that income has a greater impact in the high housing prices cities.For the economic openness,there is no significant effects in the high housing prices cities,which indicates that there is no the Balassa-Samuelson effect.The low and medium housing prices cities exist the Balassa-Samuelson effect.Interest rates have positive and significant effects in the low housing prices cities,and there is no effect in thehigh housing prices cities.The coefficients of interest rates at various quantiles are smaller.The population has a significant positive effect across quantiles,indicating that population is an important factor in promoting housing prices.Based on the results of the empirical analysis,we provide some important policy implications.