北京市居住用地出让价格的空间格局及影响因素

doi:10.11821/dlxb201706008

摘要/Abstract

摘要：

以2004-2015年北京市六环内居住用地的交易样本为基本数据,借助ArcGIS、GS+、Surfer和Geoda软件,采用空间趋势面分析、最近邻指数（NNI）、探索性空间分析（ESDA）探讨居住用地出让和居住地价的空间格局特征,并对比OLS和空间回归模型（SLM和SEM）进一步探讨居住地价的影响因素。结果表明：① 北京市居住用地出让数量和出让面积不均衡,五六环间出让的居住用地最多。② 出让地块沿主要道路（如京石高速、京开高速、京沪高速及京藏高速）、地铁线（如地铁1号线、5号线、6号线、15号线、房山线、大兴线及亦庄线）呈轴线扩散,其中远郊区域这一特征更加明显。③ 居住地价从中心向外围总体表现“倒U型”趋势,且呈多中心圈层递减结构。④ 呈集聚分布模式,存在空间自相关,低值集聚区和高值集聚区明显。⑤ 模型对比方面,SLM>SEM>OLS,说明北京市居住地价存在实质性的空间依赖,而非干扰性的空间依赖。周边居住地块的价格、公交站、地铁站、重点小学、占地面积、容积率和出让方式对居住地价有显著影响。

关键词: 居住用地价格, 空间格局, 探索性空间分析, 空间滞后模型, 空间误差模型, 北京市

Abstract:

In this paper, we take Beijing as a case study and employ the residential leasing parcel data from 2004 to 2015 within the Sixth Ring Road of Beijing metropolitan area. Also, we use the GIS data of Beijing's public facilities, such as bus stations, railway stations, park, hospital, primary school and so on. With the help of ArcGIS, GS+, Surfer and Geoda Software, we explore the spatial pattern of residential land parcels, residential land price and determinants of residential land price in Beijing. In the first place, we use the methods of Spatial Trend Analysis, Nearest Neighbor Index (NNI), Exploratory Spatial Data Analysis (ESDA) to explore the spatial pattern of residential land parcels and their price in Beijing. In the second place, we compare the spatial econometric models (SLM and SEM) with traditional OLS model to further explore the determinants of residential land price in Beijing. Based on the analysis, the main conclusions are drawn as follows. (1) The number of residential land leasing parcels is not balanced among years and ring roads. The residential land leasing parcels in the last 20 years are mainly concentrated between the fifth and the sixth ring roads in Beijing. (2) Residential land parcels are generally distributed along the main roads (such as Beijing-Shijiazhuang Expressway, Beijing-Kaifeng Expressway, Beijing-Shanghai Expressway and Beijing-Tibet Expressway) and the subway lines (such as Line 1, Line 5, Line 6, Line 15, Fangshan Line, Daxing Line and Yizhuang Line), which is more obvious in outer suburban areas. (3) Generally, there exists an inverted U-shaped curve trend, indicating that residential land price declines gradually from the city center to the city fringes as a whole, and spatial pattern of residential land price has turned from mono-centric structure to poly-centric structure. (4) Residential land price demonstrates a spatial cluster distribution pattern. There exists obvious spatial autocorrelation in residential land price and it is easy to distinguish "cold spots" from "hot spots". (5) In the model selection, we compare the spatial econometric model (SLM and SEM) with the traditional OLS model. The result shows that SLM is the best, followed by SEM, indicating that there indeed exist spatial spillover effects and spatial dependence in residential land price rather than error dependence. The residential land price is mainly affected by the surrounding residential land price, distance to bus station, distance to subway station, distance to key primary school, area of land parcel, FAR and the type of land leasing. However, in this paper, one drawback is that we fail to take macroeconomic policy factors into consideration, which may play a key role in the formation of residential land price. Also, we have not considered the subway's impact in different periods such as planning period, construction period and operation period on residential land price, which needs to be further studied.

Key words: residential land price, spatial pattern, exploratory spatial data analysis, spatial lag model, spatial error model, Beijing

崔娜娜, 冯长春, 宋煜. 北京市居住用地出让价格的空间格局及影响因素[J]. 地理学报, 2017, 72(6): 1049-1062.

Nana CUI, Changchun FENG, Yu SONG. Spatial pattern of residential land parcels and determinants of residential land price in Beijing since 2004[J]. Acta Geographica Sinica, 2017, 72(6): 1049-1062.

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解释变量	变量描述	数据来源	均值	标准差
居住地块单价(land_price)	每平方米出让居住地块的成交价格(元/m²)	样本交易数据	25458	19145
天安门(Tiananmen)	地块到天安门距离(m)	实测距离	18242	6459
公交站(Bus)	地块到最近公交站的距离(m)	实测距离	388	261
地铁站(Subway)	地块到出让年份开通运营的最近地铁站距离(m)	实测距离	5112	5450
公园(Park)	地块到最近公园的距离(m)	实测距离	1860	1380
三甲医院(Hospital)	地块到最近三甲医院的距离(m)	实测距离	6573	4732
重点小学(Pschool)	地块到最近重点小学的距离(m)	实测距离	10103	6284
地块面积(Area)	居住地块面积(万m²)	样本交易数据	8.47	6.7
地块面积的平方(Area²）	居住地块面积的平方(万m²×万m²)	样本交易数据	116.42	204.76
容积率(FAR）	地块出让的容积率(%)	样本交易数据	2.27	0.75
出让方式(Leasing)	挂牌=1,招标=0	样本交易数据	0.68	0.47

变量	系数	标准差	t统计量	P值
Intercept	12.99	0.887	14.637	0.000
Ln Tiananmen	-0.28^***	0.119	-2.302	0.022
Ln Bus	0.10^**	0.042	2.329	0.020
Ln Subway	-0.12^***	0.027	-4.389	0.000
Ln Park	0.05	0.044	1.138	0.256
Ln Hospital	0.04	0.045	0.880	0.379
Ln Pschool	-0.20^***	0.058	-3.449	0.001
Area	0.03^**	0.012	2.231	0.026
Area²	-0.001^**	0.000	-2.602	0.010
FAR	0.32^***	0.042	7.569	0.000
Leasing	0.32^***	0.064	5.064	0.000
R²	0.432
调整R²	0.418
LogL	-360.4
AIC	742.8
SC	787.3

检验统计量	统计量	P值
Breusch-Pagan	10.386	0.407
Koenker-Bassett	14.261	0.161
Moran's I(error)	4.856	0.000
LMLAG	15.854	0.000
R-LMLAG	4.624	0.032
LMERR	11.251	0.001
R-LMERR	0.0207	0.886

变量	空间滞后模型(SLM)				空间误差模型(SEM)
变量	系数		标准差	P值	系数	标准差	P值
ρ/λ	0.441^***	0.1	0.000		0.511^***	0.107	0.000
Intercept	6.382	1.687	0.000		12.712	1.248	0.000
Ln Tiananmen	-0.097	0.12	0.420		-0.243	0.169	0.420
Ln Bus	0.087^**	0.04	0.031		0.085^**	0.04	0.031
Ln Subway	-0.121^***	0.027	0.000		-0.144^***	0.028	0.000
Ln Park	0.046	0.043	0.282		0.039	0.046	0.282
Ln Hospital	0.024	0.043	0.575		0.018	0.057	0.575
Ln Pschool	-0.121^**	0.059	0.040		-0.146^**	0.074	0.040
Area	0.025^**	0.012	0.035		0.021^*	0.011	0.035
Area²	-0.001^**	0.000	0.013		-0.001^**	0.000	0.013
FAR	0.328^***	0.041	0.000		0.334^***	0.041	0.000
Leasing	0.336^***	0.062	0.000		0.307^***	0.061	0.000
R²	0.458				0.461
LogL	-352.48				-352.09
AIC	728.96				726.19
SC	777.5				770.68