Spatiotemporal change and influencing factors of resource-based cities&#39; housing prices in China

Dong-sheng ZHAN; Qian-qian WU; Jian-hui YU; Wen-zhong ZHANG; Juan-feng ZHANG

doi:10.31497/zrzyxb.20201206

Journals >Journal of Natural Resources >Volume 35 >Issue 12 >Page 2888 > Article

Journal of Natural Resources
Vol. 35, Issue 12, 2888 (2020)

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

Dong-sheng ZHAN¹, Qian-qian WU¹, Jian-hui YU^{2、3、4、*}, Wen-zhong ZHANG^2、3、4, and Juan-feng ZHANG¹

Author Affiliations

¹School of Management, Zhejiang University of Technology, Hangzhou 310023, China

²Institute of Geographic Science and Natural Resources Research, CAS, Beijing 100101, China

³Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China

⁴College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

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DOI: 10.31497/zrzyxb.20201206 Cite this Article

Dong-sheng ZHAN, Qian-qian WU, Jian-hui YU, Wen-zhong ZHANG, Juan-feng ZHANG. Spatiotemporal change and influencing factors of resource-based cities' housing prices in China[J]. Journal of Natural Resources, 2020, 35(12): 2888 Copy Citation Text

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Fig. 1. Temporal change of resource-based cities' housing prices in China during 2011-2018

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Fig. 2. Spatial patterns of resource-based cities’ housing prices in China during 2011-2018

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Fig. 3. Hot and cold spots spatial patterns of resource-based cities' housing prices in China during 2011-2018

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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

Table 1. Descriptive characteristics of different types of resource-based cities

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

Table 2. Globe Moran's I of resource-based cities' housing prices and its change in China

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	-

Table 3. Explanatory variables and their expected direction of resource-based cities' housing prices in China

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

Table 4. Parameter estimate result of Spatial Durbin Model

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

Table 5. Direct and indirect effect estimates of explanatory variables

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