我国商品住宅房价格影响因素的空间统计分析  

The commodity residential housing price impact factors analysis based on spatial statistics

作　　者：阮淑萍[1] 汪燕春 金升平[3] RUAN Shu-ping;WANG Yan-chun;JIN Sheng-ping(Wuhan Institute of Shipbuilding Technology,Wuhan 430050,China;Huangshi Second School,Huangshi 435003,China;Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉船舶职业技术学院,湖北武汉430050 [2]黄石市第二中学,湖北黄石435003 [3]武汉理工大学理学院,湖北武汉430070

出　　处：《湖北师范大学学报（自然科学版）》2018年第4期14-20,共7页Journal of Hubei Normal University：Natural Science

基　　金：国家自然科学基金项目(51479156);湖北省统计科研计划项目(HB132-10)

摘　　要：运用空间统计分析方法对我国31个省市2004～2013年的商品住宅房的价格进行研究,发现房价存在明显的空间异质性,经济发达的东部地区房价明显高于经济比较落后、房地产开发起步晚的中、西部地区。商品住宅房及其影响因素的空间相关性检验表明,中国省域房价及其影响因素存在明显的空间正相关性。考虑到房价及其影响因素的空间相关性和空间异质性,通过运用空间滞后模型,找出各个因素对于房价的影响。结果表明:在影响房价的因素中房价人均GDP、平均土地价格对房价的影响比较大,且与房价呈正相关;房屋竣工面积和房价呈负相关,对房价影响不大。空间滞后模型和多元回归模型结果比较可知,在加入空间相关性之后,人均GDP、平均土地价格对房价的影响有所减弱。The paper research commodity residential housing price disparity on 31 provinces in 2004-2013 based on spatial statistical analysis models.The result shows that the prices are obvious spatial heterogeneity and prices in economically developed eastern are significantly higher than that in the relatively backward western region.The spatial correlation test shows that there are significantly positive spatial autocorrelation.Taking this spatial heterogeneity and spatial autocorrelation into consideration,the paper use the spatial lag model to find out the effects of various factors on housing prices.The results show that the per capita GDP and the average of land price have a great impact on the house price and they are positive correlated.On the contrary,the completed areas are negative correlation with house price.Compared spatial lag model with multiple regression model,we found that the spatial correlation weaken the impact of per capital GDP and average land on house price.

关 键 词：商品住宅房价 影响因素 空间相关性 空间滞后模型 

分 类 号：O163[理学—数学]