| 南昌市二手房价格影响因子的空间异质性 |
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| 引用本文： | 王妮,吴波,张晓辉.南昌市二手房价格影响因子的空间异质性[J].测绘与空间地理信息,2021,44(1):20-24. |
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| 作者姓名： | 王妮 吴波 张晓辉 |
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| 作者单位： | 江西师范大学 地理与环境学院,江西 南昌330022;江西师范大学 地理与环境学院,江西 南昌330022;江西师范大学 地理与环境学院,江西 南昌330022 |
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| 基金项目： | 国家自然科学基金项目——基于指数分布族的广义时空地理加权回归模型研究 |
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| 摘 要： | 以安居客网站爬取的2018年10月894个南昌市住宅小区二手房价格为研究对象,利用地理加权回归模型探讨了建筑特征、邻里特征、区位特征等方面各影响因子对住宅价格的作用差异。研究结果表明:1)地理加权回归(GWR)模型的拟合结果优于OLS模型,将回归系数结果空间可视化发现南昌市二手房价格影响因子具有空间异质性。2)不同因子对价格影响程度不同,其中对南昌市二手房价格影响较大的因子是房龄、绿化率以及与CBD的距离。3)同一因子对住房价格的影响在不同空间也具有差异性。其中主要是绿化率、容积率、重点学校、购物中心及地铁对新开发区的二手房价格影响比较大,对老城区影响较小;商务中心区和三甲医院对南昌县二手房价的影响最大;而房龄和旅游景点对老城区影响比较大。
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| 关 键 词： | 地理加权回归模型 二手房价格 空间异质性 南昌 |
Spatial Heterogeneity of Affecting Factors of Second-hand Housing Prices in Nanchang |
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| WANG Ni,WU Bo,ZHANG Xiaohui.Spatial Heterogeneity of Affecting Factors of Second-hand Housing Prices in Nanchang[J].Geomatics & Spatial Information Technology,2021,44(1):20-24. |
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| Authors: | WANG Ni WU Bo ZHANG Xiaohui |
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| Institution: | (School of Geography and Environment,Jiangxi Normal University,Nanchang 330022,China) |
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| Abstract: | Taking the price of second-hand housing in 894 residential districts in Nanchang crawled on the Anjuke website in October 2018 as the research object,the geographically weighted regression model was used to explore the differences in the effects of various influencing factors on building prices,such as architectural characteristics,neighborhood characteristics,and location characteristics.The results show that:(1)The fitting results of the geographically weighted regression(GWR)model are better than the OLS model.The spatial visualization of the regression coefficient results reveals that the spatially influential factors of second-hand housing prices in Nanchang have spatial heterogeneity.(2)Different factors have different degrees of impact on prices.Among them,the factors that have a greater impact on the price of second-hand housing in Nanchang are house age,greening rate,and distance from the CBD.(3)The impact of the same factor on housing prices is also different in different spaces.Among them,the main greening rate,floor area ratio,key schools,shopping centers and subways have a greater impact on second-hand housing prices in the new development zone,and have a smaller impact in the old urban area.The central business district and the grade-A tertiary hospitals have the largest impact on second-hand housing prices in Nanchang city and house age and tourist attractions have a greater impact in the old urban area. |
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| Keywords: | geographically weighted regression model second-hand housing prices spatial heterogeneity Nanchang |
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