城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例

doi:10.18306/dlkxjz.2018.10.007

地理科学进展 ›› 2018, Vol. 37 ›› Issue (10): 1371-1380.DOI: 10.18306/dlkxjz.2018.10.007

城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例

瞿诗进^1,²(), 胡守庚^1,^2,^*(), 李全峰³, 杨剩富^1,²

1. 中国地质大学(武汉)公共管理学院,武汉 430074
2. 国土资源部法律评价工程重点实验室,武汉 430074
3. 东北农业大学资源与环境学院,哈尔滨 150030

收稿日期:2018-01-06 修回日期:2018-07-26 出版日期:2018-10-28 发布日期:2018-10-28
通讯作者: 胡守庚
作者简介:
作者简介：瞿诗进(1990-),男,湖北荆州人,博士生,从事城市土地利用转型及其优化配置等研究,E-mail: qusj903@126.com。
基金资助:
国家自然科学基金项目(41101535,41671518);国土资源部公益性行业科研专项(201511004);National Natural Science Foundation of China, No.41101535, No.41671518;Commonwealth Project of the Ministry of Land and Resources, No.201511004

Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity: A case study of Wuhan City

Shijin QU^1,²(), Shougeng HU^1,^2,^*(), Quanfeng LI³, Shengfu YANG^1,²

1. School of Public Administration, China University of Geosciences, Wuhan 430074, China
2. Key Laboratory of Legal Assessment Project, Ministry of Land and Resources, Wuhan 430074, China
3. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

Received:2018-01-06 Revised:2018-07-26 Online:2018-10-28 Published:2018-10-28
Contact: Shougeng HU

摘要/Abstract

摘要：

定量测度微观因素对住宅地价影响程度的时空分异特征是有效揭示住宅地价形成机制的重要条件。本文基于武汉市2007-2015年公开出让的住宅地价样点数据,采用广义可行最小二乘法(FGLS)修正的特征价格模型(Hedonic),测度微观影响因素对住宅地价影响程度的时空演变规律。结果显示,各要素对住宅地价影响程度的时序变化特征复杂,空间区域差异明显,具体而言：①2007-2015年间,武汉市住宅地价因素的影响程度从高到低依次为：容积率>医院>城市中心>城市副中心>地铁>公园。②在时序上,城市中心对住宅地价的影响程度呈增强趋势;城市副中心对住宅地价影响整体有所减弱;医院对住宅地价的作用程度呈指数式上涨;地铁对住宅地价增值作用主要发生在开通后;公园的影响持续下降;容积率的影响则在时序上先减后增,与住宅用地出让面积呈反向变动关系。③在空间上,在二环内中心区域,公园、容积率等对住宅地价影响程度较高;而在二环以外,与生活便利性相关的地铁、医院对住宅地价的作用更强;城市中心和副中心功能对住宅地价作用在空间上的互补效应明显。基于研究结果,强化景观用地等的宏观配置和保护力度,审慎确定容积率等是当前城市土地管理值得关注的问题。

关键词: 住宅用地, 影响因素, 时空分异, 特征价格模型, 武汉市

Abstract:

Measuring the spatiotemporal differences of the impacts of main driving factors on residential land price quantitatively is a basic work to understanding the dynamic patterns and formation mechanism of land price, which is also helpful for the optimal allocation of urban land. The hedonic model improved by feasible generalized least squares (FGLS) was employed in this study. The results show that: the impacts of key factors on Wuhan City's residential land price between 2007-2015, ranked from high to low, were floor area ratio, hospitals, central business district (CBD), sub-CBDs, the metro, and parks. In the time series, the impact of CBD showed an upward trend; the impact of sub-CBDs showed a downward trend; the influence of hospitals increased exponentially and the impact of metro stations increased logarithmically; and the effect of parks decreased continuously. The impact of floor area ratio first decreased then increased. Among the different regions, within the second ring road parks and floor area ratio have greater regression coefficients, but factors including metro stations and hospitals had more obvious influences on residential land price in the areas outside the second ring road. The impacts of CBD and sub-CBDs on residential land price were obvious complementary in space. Based on the results of this study, current urban land management should pay attention to the protection and allocation of natural landscape and the determination of residential land parcel's floor area ratio.

Key words: residential land, influencing factors, spatiotemporal differentiation, hedonic model, Wuhan City

瞿诗进, 胡守庚, 李全峰, 杨剩富. 城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例[J]. 地理科学进展, 2018, 37(10): 1371-1380.

Shijin QU, Shougeng HU, Quanfeng LI, Shengfu YANG. Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity: A case study of Wuhan City[J]. PROGRESS IN GEOGRAPHY, 2018, 37(10): 1371-1380.

图/表 8

图1 研究区与地价样点空间分布示意图注：S1为武汉广场商圈;S2为江汉路商圈;S3为王家湾商圈;S4为钟家村商圈;S5为徐东大街商圈;S6为中南路商圈;S7为光谷广场商圈

Fig.1 Location of the study area and sample residential parcels

图2 武汉市住宅地价空间插值分布图

Fig.2 Spatial distribution of residential land price generated by the ordinary kriging interpolation method for Wuhan City

表1 2007-2015武汉市整体回归结果

Tab.1 Results of regression of the 2007-2015 model for Wuhan City

变量	CBD	SUBCBD	HOSP	METRO	PARK	FAR	Y2008	Y2009	Y2010	Y2011	Y2012	Y2013	Y2014	Y2015
系数	-0.16	-0.11	-0.16	0.17	0.14	0.73	-0.40	-0.20	-0.15	-0.07	-0.31	-0.24	-0.08	0.07
t值	-3.92	-4.10	-5.85	3.00	2.76	11.37	-5.31	-2.70	-2.12	-0.91	-4.87	-2.39	-1.06	0.72
P值	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.03	0.37	0.00	0.02	0.29	0.47
模型参数		样本数=427;R²=0.56;修正R²=0.54;F=36.91,Sig.=0.00

图3 城市中心与城市副中心对住宅地价影响程度的时序变化趋势①(①图中系数为0表示变量未通过模型10%显著性水平检验,图4-6同理。)

Fig.3 Trends of impact of the central business district (CBD) and sub-districts (SUBCBD) on residential land price

图4 医院与地铁对住宅地价影响程度的时序变化趋势

Fig.4 Trends of impact of hospitals (HOSP) and subways (METRO) on residential land price

图5 公园景观对住宅地价影响程度和住宅地价指数的时序变化趋势

Fig.5 Trends of land price index and impact of parks (PARK) on residential land price

图6 容积率对住宅地价影响程度和住宅用地出让面积的时序变化趋势

Fig.6 Trends of transaction area (AREA) and impact of floor area ratio (FAR) on residential land price

表2 二环内外区域回归结果差异表

Tab.2 Regression results of different regional models

区域	回归参数	区位因素		邻里因素			其他因素	R²
区域	回归参数	CBD	SUBCBD	HOSP	METRO	PARK	FAR	R²
二环内	系数	-0.23	-0.05	0.00	0.14	0.27	0.85	0.54
	P值	0.00	0.40	0.93	0.06	0.00	0.00	0.54
二环外	系数	-0.02	-0.09	-0.22	0.16	-0.05	0.55	0.48
	P值	0.66	0.02	0.00	0.07	0.41	0.00	0.48

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城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例

Quantitative evaluation of the impacts of driving factors on urban residential land price and analysis of their spatio-temporal heterogeneity: A case study of Wuhan City

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