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

Abstract

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

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.

Figures/Tables 8

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References 33

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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

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

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