基于地理加权回归的上海市房价空间分异及其影响因子研究  

A GWR-Based Study on Spatial Pattern and Structural Determinants of Shanghai's Housing Price

作　　者：汤庆园[1] 徐伟[1,2] 艾福利[3] 

机构地区：[1]华东师范大学中国现代城市研究中心,中国上海200062 [2]莱斯布里奇大学地理系 [3]北京师范大学减灾与应急管理研究院,中国北京100875

出　　处：《经济地理》2012年第2期52-58,共7页Economic Geography

基　　金：教育部人文社会科学重点研究基地重大项目(11JJDZH006);香港研究基金会项目(747509H);加拿大Lethbridge大学研究基金项目(Grant#13253)

摘　　要：利用上海市外环以内2010年12月1014个小区的平均房价数据,通过构建地理加权回归模型,并与基于全局最小二乘法(OLS)进行比较,揭示上海小区房价的空间分异和不同影响因子的影响。研究发现,每增加或减少一个单位各影响因子对房价的影响大小依次为:建成时间,到CBD距离,绿化率,到公园距离,距地铁站距离,距超市距离和距学校距离。同时,地理加权回归分解成局部参数估计优于OLS提供的全局参数估计,它可以深刻的揭示出房价和空间影响因子之间复杂的关系,而且可视化的工具可以用地图的形式更详细的呈现出城市房价的整体景观,这些都是传统OLS无法比拟的。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 fxom 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.

关 键 词：房价 空间分异 地理加权回归 上海 

分 类 号：F293.3[经济管理—国民经济]