中国资源型城市房价时空变化与影响因素分析

doi:10.31497/zrzyxb.20201206

自然资源学报 ›› 2020, Vol. 35 ›› Issue (12): 2888-2900.doi: 10.31497/zrzyxb.20201206

中国资源型城市房价时空变化与影响因素分析

湛东升¹(), 吴倩倩¹, 余建辉²^,³^,⁴(), 张文忠²^,³^,⁴, 张娟锋¹

1.浙江工业大学管理学院,杭州 310023
2.中国科学院地理科学与资源研究所,北京 100101
3.中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
4.中国科学院大学资源与环境学院,北京 100049

收稿日期:2019-09-06 修回日期:2019-11-11 出版日期:2020-12-28 发布日期:2021-02-28
通讯作者: 余建辉 E-mail:zhands@126.com;yujh@igsnrr.ac.cn
作者简介:湛东升（1987- ）,男,安徽寿县人,博士,副教授,硕士生导师,主要从事宜居城市与区域发展研究。E-mail: zhands@126.com
基金资助:
教育部哲学社会科学研究重大课题攻关项目(18JZD033);国家自然科学基金项目(41671166);国家自然科学基金项目(42001120)

Spatiotemporal change and influencing factors of resource-based cities' housing prices in China

ZHAN Dong-sheng¹(), WU Qian-qian¹, YU Jian-hui²^,³^,⁴(), ZHANG Wen-zhong²^,³^,⁴, ZHANG Juan-feng¹

1. School of Management, Zhejiang University of Technology, Hangzhou 310023, China
2. Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China
3. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
4. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2019-09-06 Revised:2019-11-11 Online:2020-12-28 Published:2021-02-28
Contact: Jian-hui YU E-mail:zhands@126.com;yujh@igsnrr.ac.cn

摘要/Abstract

摘要：

基于禧泰数据库的全国城市二手房价监测数据,利用描述统计和GIS空间分析方法详细分析了2011—2018年126个中国资源型城市房价时空变化特征,并采用空间杜宾模型进一步揭示了其影响因素。研究结果表明：（1）2011年和2018年中国资源型城市平均房价分别为4105元/m²和5675元/m²,再生型城市、成熟型城市、成长型城市和衰退型城市的平均房价依次递减;（2）2011—2018年中国资源型城市平均房价呈现出波动上升的态势,房价增长率为38.2%,远低于全国城市平均房价增长幅度55.3%,且不同类型资源型城市房价的增长幅度有所差异,以成熟型和再生型城市的房价增幅相对较大;（3）中国资源型城市房价和变化存在显著的空间集聚特征,房价热点区主要集中在东部地区和中部地区城市,房价冷点区主要以东北地区和西部地区城市为主;（4）空间杜宾模型显示,人均GDP、人均住房开发投资、多样化指数、专业化指数和工业废水排放强度是影响中国资源型城市房价空间差异的主要因素。

关键词: 资源型城市, 房价, 时空变化, 影响因素, 空间杜宾模型, 中国

Abstract:

Based on national second-hand housing price monitoring data from CityRE database, spatiotemporal change characteristics of 126 resource-based cities' housing prices in China during 2011 to 2018 are analyzed in detail using descriptive statistics and GIS spatial analysis method, and its influencing factors are further revealed by Spatial Durbin Model. The results show that: (1) The average housing prices of resource-based cities in 2011 and 2018 are 4105 and 5675 yuan per square metre respectively, and average housing prices of regenerative cities, mature cities, growing cities and declining cities decrease in turn. (2) Average housing prices of resource-based cities in China fluctuated upward from 2011 to 2018 with a growth rate of 38.2%, which is lower than that of the national average housing prices. In addition, the growth rate of housing price varies across different types of resource-based cities, while mature and regenerative cities have relatively large values. (3) There are significant spatial agglomeration characteristics of housing prices and the price change in resource-based cities. Hot spots of housing prices are mainly concentrated in the eastern and central regions, while cold spots of housing prices are mainly distributed in the northeastern and western regions. (4) Spatial Durbin Model suggests that per capita GDP, per capita investment in housing development, diversity index, specialization index and industrial wastewater discharge intensity are the main factors affecting housing prices' spatial differentiation of resource-based cities in China.

Key words: resource-based cities, housing prices, spatiotemporal change, influencing factor, Spatial Durbin Model, China

湛东升, 吴倩倩, 余建辉, 张文忠, 张娟锋. 中国资源型城市房价时空变化与影响因素分析[J]. 自然资源学报, 2020, 35(12): 2888-2900.

ZHAN Dong-sheng, WU Qian-qian, YU Jian-hui, ZHANG Wen-zhong, ZHANG Juan-feng. Spatiotemporal change and influencing factors of resource-based cities' housing prices in China[J]. JOURNAL OF NATURAL RESOURCES, 2020, 35(12): 2888-2900.

图/表 8

表1

图1

图2

表2

图3

表3

表4

表5

参考文献 33

[1]	余建辉, 张文忠, 王岱. 中国资源枯竭城市的转型效果评价. 自然资源学报, 2011,26(1):11-21.
	[ YU J H, ZHANG W Z, WANG D. Evaluation of the China's resource-exhausted cities' transformation effect. Journal of Natural Resources, 2011,26(1):1-11.]
[2]	余建辉, 张文忠, 王岱, 等. 资源枯竭城市转型成效测度研究. 资源科学, 2013,35(9):1812-1820.
	[ YU J H, ZHANG W Z, WANG D, et al. The effect of resource-exhausted city transformation. Resources Science, 2013,35(9):1812-1820.]
[3]	汤庆园, 徐伟, 艾福利. 基于地理加权回归的上海市房价空间分异及其影响因子研究. 经济地理, 2012,32(2):52-58.
	[ TANG Q Y, XU W, AI F L. A GWR-based study on spatial pattern and structural determinants of Shanghai's housing price. Economic Geography, 2012,32(2):52-58.]
[4]	宋伟轩, 陈艳如, 孙洁, 等. 长三角一体化区域城市房价空间分异的模式与效应. 地理学报, 2020,75(10):2109-2125.
	[ SONG W X, CHEN Y R, SUN J, et al. Spatial differentiation of urban housing prices in integrated region of Yangtze River Delta. Acta Geographica Sinica, 2020,75(10):2109-2125.]
[5]	KONG F, YIN H, NAKAGOSHI N. Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan city, China. Landscape & Urban Planning, 2007,79(3):240-252.
[6]	WANG Y, WANG S, LI G, et al. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 2017,79:26-36.
[7]	YUAN F, WU J, WEI Y D, et al. Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China. Land Use Policy, 2018,75:225-236.
[8]	ANSELIN L, GALLO J L. Interpolation of air quality measures in hedonic house price models: Spatial aspects. Spatial Economic Analysis, 2006,1(1):31-52.
[9]	王良健, 颜蕾, 李中华, 等. 土地供应计划对房价的传导机制研究. 自然资源学报, 2015,30(11):1823-1833.
	[ WANG L J, YAN L, LI Z H, et al. The transmission mechanism of land supply planning on housing prices. Journal of Natural Resources, 2015,30(11):1823-1833.]
[10]	李峰清, 赵民. 关于多中心大城市住房发展的空间绩效: 对重庆市的研究与延伸讨论. 城市规划学刊, 2011, (3):8-19.
	[ LI F Q, ZHAO M. A discussion on housing development in multi-center cities: Phenomenon & planning implication of Chongqing. Urban Planning Forum, 2011, (3):8-19.]
[11]	TIAN G, WEI Y D, LI H. Effects of accessibility and environmental health risk on housing prices: A case of Salt Lake county, Utah. Applied Geography, 2017,89:12-21.
[12]	BAFFOE-BONNIE J. The dynamic impact of macroeconomic aggregates on housing prices and stock of houses: A national and regional analysis. The Journal of Real Estate Finance and Economics, 1998,17(2):179-197.
[13]	WEN H, TAO Y. Polycentric urban structure and housing price in the transitional China: Evidence from Hangzhou. Habitat International, 2015,46:138-146.
[14]	SALVATI L, CIOMMI T M, SERRA P. Exploring the spatial structure of housing prices under economic expansion and stagnation: The role of socio-demographic factors in metropolitan Rome, Italy. Land Use Policy, 2019,81:143-152.
[15]	TSUI K, TAN D, CHOW C, et al. Does tourism drive house prices in the OECD economies? Evidence from augmented mean group estimator. Tourism Management, 2019,74(10):392-395.
[16]	GARZAA N, OVALLEB M. Tourism and housing prices in Santa Marta, Colombia: Spatial determinants and interactions. Habitat International, 2019,87(5):36-43.
[17]	HUANG Z, CHEN R, XU D, et al. Spatial and hedonic analysis of housing prices in Shanghai. Habitat International, 2017,67(9):69-78.
[18]	CORDARA R, CHIARAZZO V, OTTOMANELLI M, et al. The impact of undesirable externalities on residential property values: Spatial regressive models and an empirical study. Transport Policy, 2019,80:177-187.
[19]	MUSSA A, NWAOGU U G, POZO S. Immigration and housing: A spatial econometric analysis. Journal of Housing Economics, 2017,35:13-25.
[20]	王劲峰, 廖一兰, 刘鑫. 空间数据分析教程. 北京: 科学出版社, 2010.
	[ WANG J F, LIAO Y L, LIU X. Spatial Data Analysis Tutorial, Beijing: Science Press, 2010.]
[21]	赵雪雁, 陈欢欢, 马艳艳, 等. 2000—2015年中国农村能源贫困的时空变化与影响因素. 地理研究, 2018,37(6):1115-1126.
	[ ZHAO X Y, CHEN H H, MA Y Y, et al. Spatio-temporal variation and its influencing factors of rural energy poverty in China from 2000 to 2015. Geographical Research, 2018,37(6):1115-1126.]
[22]	余建辉, 李佳洺, 张文忠. 中国资源型城市识别与综合类型划分. 地理学报, 2018,73(4):677-687.
	[ YU J H, LI J M, ZHANG W Z. Identification and classification of resource-based cities in China. Acta Geographica Sinica, 2018,73(4):677-687.]
[23]	王洋, 王德利, 王少剑. 中国城市住宅价格的空间分异格局及影响因素. 地理科学, 2013,33(10):1157-1165.
	[ WANG Y, WANG D L, WANG S J. Spatial differentiation patterns and impact factors of housing prices of China's cities. Scientia Geographica Sinica, 2013,33(10):1157-1165.]
[24]	王少剑, 王洋, 蔺雪芹, 等. 中国县域住宅价格的空间差异特征与影响机制. 地理学报, 2016,71(8):1329-1342.
	[ WANG S J, WANG Y, LIN X Q, et al. Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties. Acta Geographica Sinica, 2016,71(8):1329-1342.]
[25]	刘嘉毅, 陶婷芳, 夏鑫. 产业结构变迁与住宅价格关系实证研究: 来自中国内地的经验分析. 财经研究, 2014,40(3):73-84.
	[ LIU J Y, TAO T F, XIA X. Empirical study of the relationship between industrial structure change and housing prices: Evidence from the Chinese mainland. Journal of Finance and Economics, 2014,40(3):73-84.]
[26]	夏凯丽, 田曦, 应瑞瑶. 产业集聚对房价上涨影响机制的经验分析. 统计与决策, 2017, (11):128-132.
	[ XIA K L, TIAN X, YING R Y. An empirical analysis of the influence mechanism of industrial agglomeration on the rise of house prices. Statistics & Decision, 2017, (11):128-132.]
[27]	徐辉, 杨烨. 人口和产业集聚对环境污染的影响: 以中国的100个城市为例. 城市问题, 2017, (1):53-60.
	[ XU H, YANG Y. The impact of population and industrial agglomeration on environmental pollution: A case study of 100 cities in China. Urban Problems, 2017, (1):53-60.]
[28]	韩艳红, 尹上岗, 李在军. 长三角县域房价空间分异格局及其影响因素分析. 人文地理, 2018,33(6):87-95.
	[ HAN Y H, YIN S G, LI Z J. Spatial differentiation and influence factors analysis of county region housing prices in the Yangtze River Delta. Human Geography, 2018,33(6):87-95.]
[29]	戴其文, 姚意旗, 张晓奇, 等. 环境对城市房价影响研究述评与展望. 资源科学, 2019,41(4):627-642.
	[ DAI Q W, YAO Y Q, ZHANG X Q, et al. Review and prospect of research on the influence of the environment on spatial disparity of urban housing prices. Resources Science, 2019,41(4):627-642.]
[30]	CHEN S Y, JIN H. Pricing for the clean air: Evidence from Chinese housing market. Journal of Cleaner Production, 2019,26(1):297-306.
[31]	LESAGE J P, PACE R K. Spatial econometric modeling of origin-destination flows. Journal of Regional Science, 2008,48(5):941-967.
[32]	董纪昌, 张欣, 周克成, 等. 基于空间计量的我国房地产价格集聚与差异研究. 系统工程理论与实践, 2016,36(4):875-883.
	[ DONG J C, ZHANG X, ZHOU K C, et al. The research on real estate price agglomeration and heterogeneity in China based on spatial econometrics. System Engineering-Theory & Practice, 2016,36(4):875-883.]
[33]	赵林, 韩增林, 马慧强. 东北地区城市人居环境质量时空变化分析. 地域研究与开发, 2013,32(2):73-78.
	[ ZHAO L, HAN Z L, MA H Q. Analysis on the temporal and spatial variation about living environment of the cities on the Northeast region. Areal Research and Development, 2013,32(2):73-78.]

类型	资源开发阶段	资源保障能力	社会经济发展
成长型城市	上升阶段	潜力大	后劲足
成熟型城市	稳定阶段	强	水平较高
衰退型城市	趋于枯竭	弱	滞后
再生型城市	基本摆脱资源依赖	弱	步入良性发展轨道

变量	Moran's I	Z值	P值
2011年	0.085	3.2305	0.001
2012年	0.114	4.2119	0.000
2013年	0.119	4.3074	0.000
2014年	0.133	4.7191	0.000
2015年	0.165	5.8498	0.000
2016年	0.206	7.3935	0.000
2017年	0.246	9.1191	0.000
2018年	0.273	10.0863	0.000
2011—2018年变化值	0.163852	5.9950	0.000

维度	解释变量	变量代码	预期影响
供给需求	人口密度/(人/km²)	PD	+
	人均住房开发投资/元	PHI	-
经济发展	人均GDP/元	PGDP	+
	第三产业比例/%	TIR	+
产业集聚	采矿业专业化	SI	-
	多样化	DI	+
人居环境	每万人普通小学/个	PRI	+
	每万人医院数/个	HOS	+
	每万人拥有公共汽车/辆	BUS	+
	人均公园绿地面积/km²	GA	+
	建成区绿化率/%	GR	+
环境污染	单位GDP工业废水排放量/(t/万元)	IW	-
	单位GDP工业二氧化硫排放量/(t/亿元)	SO₂	-
	单位GDP工业烟(粉)尘排放量/(t/亿元)	IS	-

解释变量	Main	t	WX	t
PD	-0.0587	-1.26	-0.2430	-1.13
PHI	0.0380^**	2.25	0.0882^*	1.92
PGDP	0.0570^**	2.26	0.0660	0.43
TIR	0.0004	0.26	-0.0071	-0.79
SI	-0.0081^**	-2.40	0.0613^**	2.05
DI	-0.0239^*	-1.80	-0.1420^*	-1.67
PRI	0.0059	1.38	0.0278	1.50
HOS	0.0026	1.26	-0.0219	-1.01
BUS	0.0006	0.60	-0.0234^**	-2.13
GA	0.0019	0.34	-0.0028	-0.15
GR	0.0001	-0.20	0.0067^**	2.36
IW	-0.0018^**	-2.03	0.0007	0.07
SO₂	0.0000	-0.17	0.0008	1.57
IS	0.0000	0.81	0.0000	-0.22
ρ (W×HP)	0.498^***		Likelihood L	1080.9237

解释变量	直接效应		间接效应		总效应
解释变量	系数	P值	系数	P值	系数	P值
PD	-0.0595	0.175	-0.5440	0.276	-0.6035	0.225
PHI	0.0413^**	0.014	0.2270^*	0.091	0.2683^**	0.042
PGDP	0.0583^**	0.016	0.1830	0.596	0.2413	0.487
TIR	0.0003	0.87	-0.0152	0.469	-0.0149	0.481
SI	-0.0068^**	0.050	0.1160	0.134	0.1092	0.161
DI	-0.0276^**	0.031	-0.3170	0.115	-0.3446^*	0.089
PRI	0.0066	0.141	0.0640	0.167	0.0706	0.135
HOS	0.0022	0.321	-0.0410	0.419	-0.0388	0.451
BUS	0.0002	0.853	-0.0490^*	0.064	-0.0488^*	0.071
GA	0.0022	0.693	-0.0071	0.848	-0.0049	0.893
GR	0.0001	0.925	0.0139^**	0.031	0.0140^**	0.036
IW	-0.0018^*	0.081	0.0003	0.99	-0.0015	0.925
SO₂	0.0000	0.932	0.0016	0.14	0.0016	0.148
IS	0.0000	0.458	0.0000	0.859	0.0000	0.889

中国资源型城市房价时空变化与影响因素分析

Spatiotemporal change and influencing factors of resource-based cities' housing prices in China

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 33

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	段存儒, 曾贤刚. 中国资源型城市转型对劳动力需求的影响[J]. 自然资源学报, 2021, 36(3): 606-617.
[2]	李德山, 赵颖文, 李琳瑛. 煤炭资源型城市环境效率及其环境生产率变动分析──基于山西省11个地级市面板数据[J]. 自然资源学报, 2021, 36(3): 618-633.
[3]	冀正欣, 王秀丽, 李玲, 关小克, 蔚霖, 许月卿. 南阳盆地区耕地利用效率演变及其影响因素[J]. 自然资源学报, 2021, 36(3): 688-701.
[4]	杨钊, 刘永婷, 秦金芳, 刘斌, 王盼盼, 刘世杰, 徐致云. 长三角游乐型主题公园客流时空分布特征及其影响因素分析——以上海欢乐谷、常州恐龙园、芜湖方特为例[J]. 自然资源学报, 2021, 36(3): 722-736.
[5]	李在军, 胡美娟, 张爱平, 周年兴. 工业生态效率对PM_2.5污染的影响及溢出效应[J]. 自然资源学报, 2021, 36(3): 737-751.
[6]	马恩朴, 蔡建明, 林静, 廖柳文, 郭华, 韩燕. 近30年中国农业源氮磷排放的格局特征与水环境影响[J]. 自然资源学报, 2021, 36(3): 752-770.
[7]	王长松, 段蕴歆, 张然. 历史时期黄河流域城市空间格局演变与影响因素[J]. 自然资源学报, 2021, 36(1): 69-86.
[8]	赵雪雁, 杜昱璇, 李花, 王伟军. 黄河中游城镇化与生态系统服务耦合关系的时空变化[J]. 自然资源学报, 2021, 36(1): 131-147.
[9]	刘晶晶, 王静, 戴建旺, 翟天林, 李泽慧. 黄河流域县域尺度生态系统服务供给和需求核算及时空变异[J]. 自然资源学报, 2021, 36(1): 148-161.
[10]	阎晓, 涂建军. 黄河流域资源型城市生态效率时空演变及驱动因素[J]. 自然资源学报, 2021, 36(1): 223-239.
[11]	余斌, 李营营, 朱媛媛, 卓蓉蓉, 曾菊新. 中国中部农区乡村重构特征及其地域模式——以江汉平原为例[J]. 自然资源学报, 2020, 35(9): 2063-2078.
[12]	何培培, 张俊飚, 何可, 陈柱康. 农业生产何以存在低碳效率幻觉?——来自1997—2016年31个省份面板数据的证据[J]. 自然资源学报, 2020, 35(9): 2205-2217.
[13]	成升魁, 沈镭, 封志明, 钟帅. 中国自然资源研究的发展历程及展望[J]. 自然资源学报, 2020, 35(8): 1757-1772.
[14]	沈镭, 钟帅, 胡纾寒. 新时代中国自然资源研究的机遇与挑战[J]. 自然资源学报, 2020, 35(8): 1773-1788.
[15]	张圆刚, 余向洋. 生活空间重构旅游者的乡村游憩影响因素与路径——一个模糊集的定性比较分析[J]. 自然资源学报, 2020, 35(7): 1633-1646.