Real Estate Price Impact Factors Analysis Based on Spatial Econometrics

Traditional models for panel data were widely used to analyze real estate price impact factors. But traditional models for panel data may cause spatial dependence problems. So spatial and geographical factors should be taken into account. Global spatial autocorrelation Moran´s I indices are calculated and local spatial autocorrelation LISAs are figured. Then spatial regression models, including spatial lag model and spatial error model, are presented and empirical analysis is made to study real estate price impact factors in China. Traditional model, spatial lag model and spatial error model are compared and the most appropriate model is chosen. The one with the best fit is spatial error model. The result shows that the main impact factors of real estate price in China are income, construction cost, employment rate and spatial factors.