粤港澳大湾区住房租金的空间差异与影响因素

doi:10.11821/dlyj020200272

地理研究 ›› 2020, Vol. 39 ›› Issue (9): 2081-2094.doi: 10.11821/dlyj020200272

粤港澳大湾区住房租金的空间差异与影响因素

王洋¹^,²(), 张虹鸥¹^,², 吴康敏¹^,²()

1.广州地理研究所广东省遥感与地理信息系统应用重点实验室/广东省地理空间信息技术与应用公共实验室,广州 510070
2.粤港澳大湾区战略研究院,广州 510070

收稿日期:2020-04-03 修回日期:2020-06-08 出版日期:2020-09-20 发布日期:2020-11-20
通讯作者: 吴康敏
作者简介:王洋（1984-）,男,黑龙江黑河人,博士,副研究员,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);国家自然科学基金项目(41671128);广东省科学院发展专项资金项目(2020GDASYL-20200104001);广东省科学院发展专项资金项目(2020GDASYL-20200102002);粤港澳大湾区战略研究院建设专项(2019GDASYL-0202001);国家重点研发计划(2019YFB2103101)

Spatial differentiation and influencing factors of housing rents in the Guangdong-Hong Kong-Macao Greater Bay Area

WANG Yang¹^,²(), ZHANG Hong′ou¹^,², WU Kangmin¹^,²()

1. Guangdong Key Laboratory of Remote Sensing and Geographic Information System Application/Guangdong Public Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangzhou 510070, China
2. Institute of Strategy Research for Guangdong, Hong Kong and Macao Greater Bay Area, Guangzhou 510070, China

Received:2020-04-03 Revised:2020-06-08 Online:2020-09-20 Published:2020-11-20
Contact: WU Kangmin

摘要/Abstract

摘要：

以粤港澳大湾区58个县区单元的住房平均租金为基本数据,通过“住房租金等级金字塔”构建、租金空间格局展示、空间自相关分析、跨境租金差距对比、售租比研究等方法总结粤港澳大湾区住房租金的空间差异格局与特征。从“租赁需求+城市基本面”的理论视角构建由新增人口、人均住房面积、收入水平、经济水平、产业结构、学历结构组成的租金差异影响因素模型。通过模型对比,采用空间滞后模型测度粤港澳大湾区住房租金的主要影响因素,并基于地理探测器分析其因素的影响强度差异。结果表明,粤港澳大湾区住房租金总体呈现以港澳与珠三角九市之间的境内外差异为主、以广深核心区与其他区域差异为辅的双层次差异格局。跨境租金差异程度最高,广州、深圳、珠海的“售租比”较高;收入水平、经济水平、人均住房面积和产业结构对粤港澳大湾区的住房租金差异有显著影响,其中,收入水平的影响强度最高。

关键词: 住房租金, 租房市场, 跨境差异, 宜居生活圈, 粤港澳大湾区, 珠三角

Abstract:

"Livability" is at the core of building a high-quality living circle in the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), and the excessive housing burden costs have become an important obstacle to livability. The differentiation of the rental market is an indispensable and important part of the housing market in the GHMGBA and is inseparable from the creation of a livable life circle. Based on the average housing rent of 58 counties in the GHMGBA, this study summarizes the patterns and characteristics of the spatial differences in housing rents through the construction of a “grading pyramid of housing rents”, and displays the spatial pattern of housing rents through spatial autocorrelation analysis, cross-border rent gap comparison, and price-to-rent ratio analysis. From the theoretical perspective of leasing demand and urban fundamentals, this study constructs a model of factors influencing rent differences, consisting of population growth, per capita housing area, income level, economic level, industrial structure, and education structure. Through model comparison, a spatial lag model was used to measure the main factors influencing the housing rents in the GHMGBA. Based on the geographical detector, the study further analyzed the differences in the intensity of the factors' influence. The results showed that the housing rents in the GHMGBA generally presented a two-level difference pattern. The pattern was dominated by domestic and foreign differences between Hong Kong, Macao, and nine cities in the Pearl River Delta, as well as the differences between the core areas of Guangzhou, Shenzhen, and other regions. The cross-border rent difference was the highest. Higher price-to-rent ratios were observed in Guangzhou, Shenzhen, and Zhuhai. Income level, economic level, per capita housing area, and industrial structure had a significant impact on housing rent differences in the GHMGBA. Among them, income level had the highest impact intensity. This study responds to cross-border regional differences within the country from the perspective of housing rent. Cross-border differences are not only reflected in the population′s economic, income, and institutional levels, but also in the housing rent. The key issue for the regional linkage development of the housing market in the GHMGBA and the construction of a livable and quality living area is the coordinated development across borders.

Key words: housing rent, rental market, cross-border difference, livable life circle, the Guangdong-Hong Kong-Macao Greater Bay Area, the Pearl River Delta

王洋, 张虹鸥, 吴康敏. 粤港澳大湾区住房租金的空间差异与影响因素[J]. 地理研究, 2020, 39(9): 2081-2094.

WANG Yang, ZHANG Hong′ou, WU Kangmin. Spatial differentiation and influencing factors of housing rents in the Guangdong-Hong Kong-Macao Greater Bay Area[J]. GEOGRAPHICAL RESEARCH, 2020, 39(9): 2081-2094.

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影响因素	评价指标（单位）	预期符号	数据主要时间	数据来源与数据处理方式
F₁新增人口	2016—2018年每平方公里新增常住人口数量（人/km²）	正向	2016— 2018年	2019广东统计年鉴、2019东莞统计年鉴、2019中山统计年鉴、2019中国统计年鉴、香港统计年刊（2019年版）;通过2016—2018年新增常住人口数量除以行政区面积得出
F₂人均住房面积	人均住房建筑面积（m²/人）	负向	2015年 11月	广东省2015年1%人口抽样调查、世界各国（地区）人均住房面积一览表（http://blog.sina.com.cn/s/blog_50321d940102y5rb.html）
F₃收入水平	在岗职工平均工资(元/月)	正向	2018年	2019广东统计年鉴、2019东莞统计年鉴、2019中山统计年鉴、2019中国统计年鉴;珠三角9市通过年度工资除以12个月计算得出月工资;香港、澳门为全部就业行业月收入的中位数
F₄经济水平	人均GDP（元）	正向	2018年	2019广东统计年鉴、2019东莞统计年鉴、2019中山统计年鉴、2019中国统计年鉴
F₅产业结构	第三产业增加值占GDP比例（%）	正向	2018年	2019广东统计年鉴、2019东莞统计年鉴、2019中山统计年鉴、2019中国统计年鉴
F₆学历结构	本科以上就业人口比例（%）	正向	2015年 11月	广东省2015年1%人口抽样调查、香港统计年刊（2016年版）、澳门2016统计年鉴;香港为15周岁以上人口、澳门为14周岁以上人口

模型	Adjusted R-squared	AIC	Log likelihood	Lagrange Multiplier	P of Lagrange Multiplier
普通最小二乘法回归模型（OLS）	0.8793	9.6052	2.1974	—	—
空间滞后模型（SLM）	0.8913	4.9930	5.5035	6.2836	0.0122
空间误差模型（SEM）	0.8807	9.2522	2.3739	0.1543	0.6944

指标类别（单位）	系数	标准差	z统计值	p
新增常住人口（人/km²）	0.0071	0.0250	0.2833	0.7769
人均住房建筑面积（m²/人）	-0.9065^***	0.1586	-5.7155	0.0000
在岗职工平均工资(元/月)	1.0391^***	0.2367	4.3895	0.0000
人均GDP（元）	0.2085^**	0.1023	2.0378	0.0416
第三产业增加值占GDP比例（%）	0.4815^***	0.1197	4.0224	0.0001
本科以上就业人口比例（%）	-0.1050	0.0823	-1.2768	0.2017
常数项	-7.7974^***	2.0417	-3.8191	0.0001
空间权重项	0.2740^***	0.0958	2.8592	0.0043
Adjusted R-squared：0.8913;AIC：4.9930;Log likelihood：5.5035

粤港澳大湾区住房租金的空间差异与影响因素

Spatial differentiation and influencing factors of housing rents in the Guangdong-Hong Kong-Macao Greater Bay Area

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