A GWR-Based Study on Spatial Pattern and Structural Determinants of Shanghai's Housing Price
利用2010年上海1014个住宅小区数据，通过地理加权回归模型揭示房价空间分异及不同因素的影响程度，发现GWR模型优于传统OLS模型。
Based on average housing price of 1014 residential quarters within the Outer Ring of Shanghai in December 2010, this paper establishes a geographically weighted regression model,and compares with least square method based on overall situation.It reveals the spatial differentiation of Shanghai housing price and impacts of different factors.According to the study.the effects of unit change in housing price influencing factor are ranked from high to low in order of building completion year,CBD,greening rate,distance to parks,distance to metro stations,distance to schools,and distance to supermarkets.In the meantime,GWR model provides better results than the traditional OLS model in goodness of fit and parameter estimation when spatial dependency is present in urban housing data,which help to reveal the complicated relationship between housing price and determinants over space.Moreover,the visualization tools allow to map the effects of model coefficients across urban landscape in detail,which traditional OLS methods are not on par with.