重庆市主城区住房价格影响因子的空间异质性@@@Spatial heterogeneity in factors affecting Chongqing housing prices

： Based on price data of 2 ， 449 housing projects in the main district of Chongqing in 2015 ， we used hedonic price modeling ， spatial expansion modeling and geographically weighted regression （ GWR ） modeling to simulate the spatial heterogeneity in impact factors of housing price for this polycentric mountainous city. After comparing the three models ， we found that the spatial expansion model and the GWR model performed better than the hedonic price model ， and the GWR model performed better than the spatial expansion model when considering effectiveness and accuracy. In addition ， the GWR model proved to be an effective method to explore spatial heterogeneity ， which can reflect the spatial patterns of heterogeneity visually. A few variables ， such as building age ， distance to city center ， and distance to city subcenters ， were the most important factors affecting housing prices. The variable of TPI had a significant effect on housing price. When the terrain was flatter ， the price would get higher ， too. The effect of each factor on housing price varied spatially and significantly. The spatial pattern of polycentric cities such as Chongqing was more complex than for monocentric cities. The complexity was mainly considered to be closely related to the constraints of natural barriers and strategies of polycentric urban development in Chongqing. Polycentric development broke the monopoly of the housing submarket and increased the effective supply of housing in limited spaces of Chongqing. However ， high-profile houses are still concentrated in the narrow valley floors between Zhongliang and the Tongluo Mountains where high-quality public facilities are located. Considering the lag in supply of public facilities in suburbs ， substantial funds are needed to improve the accessibility of suburban housing to better public facilities.