Quantile Regression Method of House Price Index for Newly Building Condominiums in China

The house price index plays a very important role in the real estate economy due to it is an indicator of real estate price changing. Aiming at the compilation model of China's new ordinary residential housing price index based on the matching set of upright adjacent floors, the parameter estimation method is improved, and the ordinary least square regression (OLS) is replaced by quantile regression to eliminate the extreme sensitivity of least square regression to outliers. This paper puts forward the quantitative index to evaluate the advantages and disadvantages of different methods to compile house price index, and makes an empirical analysis of the above ideas based on the loan data of commercial banks. The conclusions we got are more consistent with the actual results, more stable, and in line with the requirements of the third generation of housing price index compilation methods.