基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较

鉴于已有研究主要集中探讨住房价格的空间依赖性,较少涉及空间异质性对住房特征价格的影响,也很少尝试构建不同计量模型来比较模型间刻画住房价格影响因素空间分异的准确性,以长沙市中心城区为研究区,采用空间扩展模型和地理加权回归模型比较分析城市住房价格影响因素的空间分异,结果表明：① 空间扩展模型和地理加权回归模型都表明,长沙市中心城区的住房属性边际价格随着区位的变化而变化,揭示住房价格影响因素具有显著的空间异质性;小区环境、交通条件、教育配套、生活设施等因素对住房价格的影响强度存在明显的空间分异。② 地理加权回归模型和空间扩展模型都能对传统特征价格模型进行改进,但地理加权回归模型在解释能力和精度方面都超过空间扩展模型;对属性系数估计空间模式的分析,地理加权回归模型形成的结果比采用坐标多义扩展的空间扩展模型更为复杂和直观。

The comparison of city housing price spatial variances based on spatial expansion and geographical weighted regression models

Prior researches mostly focused on spatial dependence among house prices, ignoring the effects of spatial heterogeneity on house hedonic price; also, few comparative studies based on econometric models were conducted to obtain the accuracy of spatial variances on influencing factors of housing price. Considering the problems above and taking the center of Changsha as a research objective, this paper adopts spatial expansion model and geographic weighted regression model (GWR) to examine the spatial variances of factors that influence housing prices. The main findings are: (1) the analysis of spatial expansion model and GWR model shows that marginal prices of house attributes in the center of Changsha vary in different locations, indicating that the factors are significantly spatially heterogeneous, and some factors such as community environment, transportation conditions, educational facilities and living facilities have obvious spatial variances. (2) Spatial expansion model and GWR model can both modify traditional hedonic model; however, GWR model has stronger explanatory power and is more accurate in simulating the results than spatial expansion model; as for the analysis of attribute coefficient estimation spatial mode, the results from GWR model are more complicated and objective than those from spatial expansion model which adopts coordinate-based polysemy extension.