基于半参数空间模型的房地产估值数据研究  

Study on Real Estate Valuation Data Based onSemi-parametric Spatial Model

作　　者：张琳琳 黄振生[1] ZHANG Lin-lin;HUANG Zhen-sheng(School of Science, Nanjing University of Science and Technology, Nanjing 210094, China)

机构地区：[1]南京理工大学理学院,南京210094

出　　处：《重庆工商大学学报（自然科学版）》2021年第6期114-117,共4页Journal of Chongqing Technology and Business University:Natural Science Edition

摘　　要：针对现有的房地产估值模型中不包含空间自相关性以及非线性影响因素的问题,提出了可以灵活解释变量意义的部分线性空间自回归模型来拟合房地产估值数据;对于部分线性空间自回归模型的估计问题,利用局部多项式方法与拟极大似然估计法相结合的两步估计过程得到参数部分的估计;房地产估值数据的拟合结果表明:房地产估值数据确实存在空间相关性,房屋到最近的捷运站的距离与房价呈负相关关系,而步行生活圈中便利店的数量与房价呈正相关关系,这与现实意义上的解释是相通的,另外房屋年龄与房价之间的非线性关系也被体现出来;部分线性空间自回归模型能更加客观和灵活地解释房地产估值数据的现实意义。In view of the problem that the existing real estate valuation models do not include spatial autocorrelation and nonlinear influencing factors,a partially linear spatial autoregressive model that can flexibly explain the meaning of variables is proposed to fit real estate valuation data.For the estimation of partial linear spatial autoregressive models,the two-step estimation process combining the local polynomial method and the quasi-maximum likelihood estimation method is used to obtain the estimation of the parameter part.The fitting result of real estate valuation data shows that real estate valuation data does have spatial correlation,and the distance between the house and the nearest MRT station is negatively correlated with the housing price,while there is a positive correlation between the number of convenience stores in the living circle on foot and the housing price,which is similar to the practical explanation.In addition,the nonlinear relationship between the age of house and the housing price is also reflected.Partially linear spatial autoregressive model can explain the practical significance of real estate valuation data more objectively and flexibly.

关 键 词：部分线性空间自回归模型 剖面拟极大似然估计 局部多项式估计 空间相关性 

分 类 号：O212.7[理学—概率论与数理统计]