沈阳市住房价格空间分异格局及其影响因素研究

徐丹萌; 李欣; 张苏文

doi:10.13959/j.issn.1003-2398.2021.06.014

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人文地理 ›› 2021, Vol. 36 ›› Issue (6) : 125-134. DOI: 10.13959/j.issn.1003-2398.2021.06.014

经济

沈阳市住房价格空间分异格局及其影响因素研究

徐丹萌¹, 李欣¹, 张苏文²

作者信息 +

SPATIAL DIFFERENTIATION PATTERNS AND INFLUENCING FACTORS ANALYSIS OF HOUSING PRICES IN SHENYANG

XU Dan-meng¹, LI Xin¹, ZHANG Su-wen²

Author information +

文章历史 +

摘要

本文以我国典型的老工业城市沈阳为例来分析其住房价格空间分异特征与影响机理。通过大数据方法获取该市1450个住宅小区的房价及特征数据，利用Kriging空间插值法模拟其房价空间分布格局，并从社区、公共配套设施和交通出行等方面构建地理加权回归模型，探究各因子对房价空间分布的影响机理。结果表明：①沈阳市住房价格呈现出多中心的空间结构，且长白区域已成为新的价格峰值区。②特征因素对住房价格的影响具有显著的空间异质性，其中，公共配套设施和地铁站对房价表现出较高的影响力，并对住房价格的作用程度呈现明显空间差异性。③受“强政府、弱市场”等的长期影响，政府调控下的城市资源分配不均衡成为沈阳等老工业城市住房价格空间分异的根本原因。

Abstract

Affordable housing plays a significant role for the wellbeing of people all over the world. However, against the background of housing commodification and market reforms since 1978 in China, housing price in many cities especially mega cities such as Beijing, Shanghai, Shenzhen and Guanghzou in China has undergone rapidly increasing. The fact negatively affects housing accessibility of many residents and leads to socio-spatial polarization of many cities. Driven by this concern, this research explores the spatial distribution pattern of housing prices and the influencing factors of Shenyang, a typical old industrial city in China. Based on POI data and the Kriging method, we firstly simulated the spatial distribution pattern of housing prices in Shenyang. Then, 11 independent variables were selected (consisting of community characteristics, public facilities and public transportations) to investigate mechanisms underlying the spatial differential pattern of housing prices of Shenyang, based on the Geographically Weighted Regression model (GWR). The results are as following. First, the housing price of different communities in Shenyang spatially forms a multi-center structure. Changbai region has replaced Shenhe and Heping districts as the new peak price area. Second, the independent variables show significant spatial heterogeneity. Variables related to community characteristics, such as ratio of green space, parking lot ratio and neighbourhoods management fees, have significant positive effects on housing price in general. Third, we found that urban housing market development of old industrial cities such as Shenyang has long been featured by the "strong government, weak market" development strategies.

导出引用

徐丹萌, 李欣, 张苏文. 沈阳市住房价格空间分异格局及其影响因素研究[J]. 人文地理. 2021, 36(6): 125-134 https://doi.org/10.13959/j.issn.1003-2398.2021.06.014

XU Dan-meng, LI Xin, ZHANG Su-wen. SPATIAL DIFFERENTIATION PATTERNS AND INFLUENCING FACTORS ANALYSIS OF HOUSING PRICES IN SHENYANG[J]. HUMAN GEOGRAPHY. 2021, 36(6): 125-134 https://doi.org/10.13959/j.issn.1003-2398.2021.06.014

中图分类号： C912.81

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