中国县域住宅价格的空间差异特征与影响机制

doi:10.11821/dlxb201608004

Abstract

Abstract:

In contemporary China, housing price has become the vital issue, which attracted considerable attention from the governments and inhabitants. However, there are relatively few studies on spatial differentiation of housing prices in China at the county level. In addition, there is no unanimous conclusion on the main factors influencing spatial differentiation of housing price. To meet this deficiency, using spatial autocorrelation and kernel density function, this study examines the spatial differentiation pattern, spatial correlation characteristics and the distribution shape of housing prices in China, and constructs the 'Pyramid of Housing Price Grade' based on the average housing prices of China's counties in 2014. Furthermore, a housing price model is established according to the demand-supply-market theory in order to explore the impact factors, and intensity differences of the influencing factors are also analyzed. The main conclusions are as follows: (1) There exists significant spatial correlation and agglomeration of housing prices in China's counties, and the differentiation patterns are featured by the administrative grade and the spatial agglomeration simultaneously; (2) The housing prices present pyramid-ranked distribution in China. This finding indicates that the higher the housing price, the less the urban population. (3) The proportion of rental households, scale of the floating population, housing affordability, housing market activity and the cost of land are five core influencing factors of housing prices in China's counties. The intensities of these influencing factors vary across administrative grade sub-markets.

Key words: housing prices, influencing factors, influencing mechanism, spatial differentiation, county unit, China

Shaojian WANG, Yang WANG, Xueqin LIN, Hong'ou ZHANG. Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties[J].Acta Geographica Sinica, 2016, 71(8): 1329-1342.

Figures/Tables 10

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影响因素	代表性指标(单位)	对住宅价格的影响路径	预期影响	指标计算过程与数据来源
F₁租房户比例	租房户数占总户数的比重(%)	住房刚性需求和购房困难程度	正向	“六普”调查的租房户数除以总户数
F₂流动人口规模	城镇流动人口数量 (人)	住房的综合性需求	正向	“六普”常住人口口径的城镇人口减去户籍人口口径非农人口
F₃住房支付能力	城镇在岗职工平均工资(元)	住房购买力和房价承受力、住房供给弹性	正向	国家统计部门统计的职工平均工资
F₄住房市场活跃度	房地产业就业人口比重(%)	房地产市场成熟程度、住房投资需求	正向	“六普”调查的房地产业从业人员数除以16岁以上各行业从业人员总数
F₅土地成本	土地平均出让价格 (万元/hm²)	住房供给	正向	国土部门发布的土地出让收益除以土地出让面积

影响因素	非标准化系数	标准误差	标准化系数	t值	显著性水平	模型的相关参数
待定系数 (α₀)	-1103.623	178.337	-	-6.188	0.000	R = 0.855
F₁租房户比例	28.680	6.657	0.065	4.308	0.000	R²= 0.732
F₂流动人口规模	0.001	0.000	0.033	2.882	0.007	调整R²= 0.731
F₃住房支付能力	0.089	0.007	0.178	12.947	0.000	F = 1502.289
F₄住房市场活跃度	1448.468	72.222	0.281	20.056	0.000	Sig.= 0.000
F₅土地成本	2.702	0.068	0.488	39.482	0.000	df = 5

	租房户比例	流动人口规模	住房支付能力	住房市场活跃度	土地成本
全国	0.3291	0.1664	0.2816	0.3214	0.3837
省会城市市区	0.9069	0.6452	0.8307	0.9244	0.9070
地级市市区	0.8790	0.0095	0.9108	0.8166	0.7141
县和县级市	0.7844	0.7382	0.2829	0.7489	0.7022

Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties

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