This paper puts forward a method of dealing housing price differences firstly, studying the spatial effects of housing price and reasons in 35 cities by the common correlated effects estimator. Housing price exists obvious spatial effects, which is illustrated by the strong convergences and cross dependences in different cities. Meanwhile, housing price in different city has certain variances and significant lead-lag relationships. Average price, average income and average loan have evident impact on housing price. With common factors, housing price in different cities has strong cross dependence and convergence. Housing price in more developed cities grows higher and faster, and run more dependently. But housing price in less developed cities is more vulnerable to external influence. Per capita disposable income and loan in higher-price cities drive more than in lower-price cities to the housing price. So is the volatility. In the short run, loan mainly drives housing price fluctuations, while in the long run, per capita disposable income plays a bigger role than loan to the housing price.
Application of Statistics and Management
housing price fluctuations, spatial effects, CCE estimate, ML estimation