地理学报

地理学报 ›› 2021, Vol. 76 ›› Issue (8): 1924-1938.doi: 10.11821/dlxb202108008

• 城市与人类健康 • 上一篇    下一篇

广州中心城区住宅租金差异的核心影响因素

王洋1,2(), 吴康敏1,2(), 张虹鸥1,2   

  1. 1.广东省科学院广州地理研究所,广州 510070
    2.粤港澳大湾区战略研究院,广州 510070
  • 收稿日期:2020-05-06 修回日期:2021-03-26 出版日期:2021-08-25 发布日期:2021-10-25
  • 通讯作者: 吴康敏(1991-), 男, 广东汕头人, 博士, 助理研究员, 研究方向为城市地理与创新地理。E-mail: kangmwu@163.com
  • 作者简介:王洋(1984-), 男, 黑龙江黑河人, 博士, 研究员, 硕士生导师, 研究领域为城市地理、城市与区域规划。E-mail: wyxkwy@163.com
  • 基金资助:
    国家自然科学基金项目(41871150);粤港澳大湾区战略研究院建设专项(2020GDASYL-20200201001)

The core influencing factors of housing rent difference in Guangzhou's urban district

WANG Yang1,2(), WU Kangmin1,2(), ZHANG Hong'ou1,2   

  1. 1. Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    2. Institute of Strategy Research for the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou 510070, China
  • Received:2020-05-06 Revised:2021-03-26 Published:2021-08-25 Online:2021-10-25
  • Supported by:
    National Natural Science Foundation of China(41871150);Special Project of Institute of Strategy Research for Guangdong, Hong Kong and Macao Greater Bay Area Construction(2020GDASYL-20200201001)

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摘要：

构建并阐述城市住宅的特征租金理论框架,建立包括建筑特征、便利性特征、环境特征、区位特征在内的“四分法”特征租金模型。以2020年3月广州中心城区23126套待租住宅的挂牌月租金单价为基本数据,通过分级空间统计和空间自相关分析广州中心城区住宅租金的空间差异格局与空间关联性,构建4要素12个指标的广州中心城区住宅租金影响因素指标体系,通过3种模型比选,采用空间误差模型测度住宅租金的影响因素,并筛选核心影响因素。结果表明：① 在研究城市内部住宅租金影响因素时,可采用本文构建的特征租金理论框架及其特征租金模型;② 广州中心城区中低租金水平的住宅数量最多,住宅租金呈现核心区高,外围城区低的空间分异格局,具有显著的空间集聚和空间关联特征;③ 建筑特征（建筑面积、朝向与楼层、房龄、电梯与物业）、便利性特征（地铁便利性、办公便利性、基础教育便利性）、环境特征（公园可达性、工业污染影响）和区位特征（距市中心距离）共4个方面的10个因素对广州中心城区住宅租金差异有显著影响;④ 建筑面积、房龄和距市中心距离是住宅租金的3个最关键核心影响因素,电梯与物业、办公便利性也是核心影响因素。

关键词: 住宅租金, 特征租金理论, 特征租金模型, 租金差异, 核心影响因素, 广州

Abstract:

This study constructs a theoretical framework of hedonic rent of urban housing and establishes the elements of housing characteristics suitable for rental housing. The elements include building, convenience, environment, and location characteristics. Based on the monthly data of 23126 houses available for rent in Guangzhou's urban district in March 2020, the spatial difference pattern and spatial correlation of housing rents in the district are analyzed through hierarchical spatial statistics and spatial autocorrelation. We established an index system of housing rent including 12 influencing factors. Through the comparison and selection of three models, the spatial error model is used to measure the influencing factors of housing rent, and the core influencing factors are identified. The results reveal that: (1) The theoretical framework and model of hedonic rent can be used to examine the influencing factors of urban housing rent. (2) Low- and middle-rent houses account for the largest proportion of rental housing in Guangzhou's urban district. Housing rent presents a spatial differentiation pattern that is higher in the core area and lower in the peripheral urban area and old city, with significant spatial agglomeration and correlation characteristics. (3) Four characteristics -- building (building area, orientation & floors, building age, elevator & property management), convenience (metro, office, education convenience), environment (park accessibility, industrial pollution), and location characteristics (distance from central business district (CBD)) -- including 10 factors, have a significant impact on the housing rent difference in Guangzhou's urban district. (4) Building area, building age, and distance from CBD are the three most important core influencing factors for housing rent, followed by elevator and property management and office convenience.

Key words: housing rent, hedonic rent theory, hedonic rent model, rent difference, core influencing factor, Guangzhou