北京市房价与地价的动态关系——基于结构方程模型的实证分析

doi:10.11821/dlyj201610003

摘要/Abstract

摘要：

城市房价与地价之间的关系错综复杂,不仅受多种因素的交织影响,相互之间也存在动态关系。研究房价与地价关系的传统方法,如Granger因果检验和回归分析等,无法刻画房价与地价之间多维的网络状关系,相比之下,结构方程模型能同时处理多个内生潜变量,且不受观测指标共线性的影响,为刻画地价与房价的交互作用提供了新的工具。从住房与土地市场的供需传导机制出发,推导出房价与地价的结构模型,以北京市为例,运用2003-2013年居住用地价格和2014年在售楼盘价格,与北京市GIS电子地图相匹配,提取商服中心可达性、公共交通可达性、道路可达性、商服繁华度、设施便利性等解释变量,构建地价与房价结构方程模型,分析二者之间的结构关系。

关键词: 居住地价, 房价, 结构方程, 北京

Abstract:

The intricate relationship between urban housing price and land price is influenced by mingle factors and contains a dynamic interaction between each other. Traditional approaches for this topic, such as Granger test and multiple regression, are quite limited in studying the multidimensional relationship within them. In contrast, SEM (Structural Equation Modeling) can handle multiple endogenous latent variables simultaneously and overcome the collinearity of independent variables, so it could be an effective approach to characterize the interaction between urban housing price and land price. Under this background, this paper firstly deduces a theoretical structural model of land price and housing price based on the supply and demand chain of land and housing market. Then, a GIS database is built for Beijing by utilizing the residential land transaction price records from 2003 to 2013 and housing price published on housing-sale website in 2014. Within the SEM, five types of explanatory variables are included, namely, accessibility to important commercial centers, accessibility to public transport, accessibility to highway, concentration of commercial services and concentration of facilities. After that, four models (with all parameters estimated with PLS) are built taking account of the effect of spatial heterogeneity, spatial autocorrelation and the effect of floor-ground-area ratio. At last, this paper arrives at 4 conclusions: (1) Land price in the past has significant effect on current housing price. For the case of Beijing, the estimated factor is between 0.2 and 0.4. (2) Regarding the influence of different explanatory factors, some mainly affect the land price, such as the accessibility to highway and the concentration of commercial services; some mainly affect house price, such as the accessibility to public transport and the concentration of facilities; some affect both, such as the accessibility to important commercial centers. (3) Ground land price has greater effect on housing price than floor land price. The control of floor-ground-area ratio in Beijing's urban planning reduces the impact of land market on house market, as well as the impact of land supply and demand fluctuation on housing price. (4) With the SEM, this paper concludes the relationship between housing price and land price in shape of network instead of chain, also proves SEM to be an effective approach to study the complicated relationship between land market and housing market.

Key words: residential land price, housing price, Structural Equation Model, Beijing

甘霖, 冯长春, 王乾. 北京市房价与地价的动态关系——基于结构方程模型的实证分析[J]. 地理研究, 2016, 35(10): 1831-1845.

Lin GAN, Changchun FENG, Qian WANG. Dynamic relationship between housing price and land price in Beijing: Based on Structural Equation Modeling[J]. GEOGRAPHICAL RESEARCH, 2016, 35(10): 1831-1845.

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内生潜变量	观测指标	指标描述（元/m²）	均值	进入模型
房价HP₀	HP	在售新房小区均价	35978.19	模型0、模型1
房价HP₁	HP	在售新房小区均价	35978.19	模型2、模型3
	HP-K5	周围5个距离最近的小区的加权平均房价	35741.34
	HP-K15	周围15个距离最近的小区的加权平均房价	36729.38
	HP-K30	周围30个距离最近的小区的加权平均房价	37855.20
地价FP₀	FP	小区所在地点2003-2013年楼面地价插值结果	8862.55	模型0
地价FP₁	FP03-06	小区所在地点2003-2006年楼面地价插值结果	6281.41	模型1
	FP07-10	小区所在地点2007-2010年楼面地价插值结果	8844.81
	FP11-13	小区所在地点2011-2013年楼面地价插值结果	11332.29
地价FP₂	FP	小区所在地点2003-2013年楼面地价插值结果	8862.55	模型2
	FP-K5	周围5个距离最近的地块的加权平均楼面地价	8701.60
	FP-K15	周围15个距离最近的地块的加权平均楼面地价	8830.85
	FP-K30	周围30个距离最近的地块的加权平均楼面地价	8765.58
地价GP	GP	小区所在地点2003-2013地面地价插值结果	15330.29	模型3
	GP-K5	周围5个距离最近的地块的加权平均地面地价	15107.62
	GP-K15	周围15个距离最近的地块的加权平均地面地价	15446.99
	GP-K30	周围30个距离最近的地块的加权平均地面地价	15317.68

外生潜变量	观测指标	指标描述	均值	特征价格模型回归系数
商服中心可达性Com-Center	CENf	最近商服中心作用分值	66.14	500.16^***
道路可达性 Road-Trans	HWENf	最近高速路入口作用分值	79.30	-104.39
道路可达性 Road-Trans	HWEXf	最近高速路出口作用分值	78.82	124.54
公共交通可达性 Pub-Trans	BUSf	2000 m内公交站作用分值	841.65	-0.22
	SUBf	2000 m内地铁站作用分值	89.24	-7.08
	BUSn	2000 m范围内公交站数量	23.67	-44.03
	SUBn	2000 m范围内地铁站数量	2.43	1036.83
商服繁华度 Prosperity	SHOPn	1000 m范围内超市商店数量	16.70	176.75^*
商服繁华度 Prosperity	BANKn	1000 m范围内银行数量	7.70	233.42
设施便利性 Facility	HOSn	2000 m范围内医院数量	4.21	-1102.26^**
	KIDn	1000 m范围内幼儿园数量	3.15	130.10
	SCHn	2000 m范围内中小学数量	9.90	344.31^*
	TOURn	1000 m范围内公园数量	6.68	237.96^***

潜变量	区分效度AVE				复合信度CR				α系数CCA
潜变量	模型0	模型1	模型2	模型3	模型0	模型1	模型2	模型3	模型0	模型1	模型2	模型3
HP	1.00	1.00	0.89	0.89	1.00	1.00	0.97	0.97	1.00	1.00	0.96	0.96
LP	1.00	0.91	0.96	0.97	1.00	0.97	0.99	0.99	1.00	0.95	0.99	0.99
Com-Center	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00	1.00
Road-Trans	0.98	0.98	0.98	0.98	0.99	0.99	0.99	0.99	0.98	0.98	0.98	0.98
Pub-Trans	0.61	0.61	0.61	0.62	0.85	0.86	0.86	0.86	0.82	0.82	0.82	0.82
Prosperity	0.88	0.88	0.88	0.88	0.94	0.94	0.94	0.94	0.87	0.87	0.87	0.87
Facility	0.60	0.60	0.60	0.60	0.85	0.85	0.85	0.85	0.76	0.76	0.76	0.76

影响路径	模型0			模型1		模型2			模型3
影响路径	路径系数		T值		路径系数	T值	路径系数	T值	路径系数	T值
商服中心可达性—房价	0.371^***	7.478		0.229^***	3.739	0.443^***	16.166		0.414^***	19.654
道路可达性—房价	-0.034	1.404		-0.035	1.464	-0.019	1.271		-0.019	1.546
公共交通可达性—房价	0.117^*	1.92		0.153^***	2.513	0.031^*	1.676		0.048^*	1.848
商服繁华度—房价	0.062	0.821		0.005	0.061	0.105	0.52		0.004	0.14
设施便利性—房价	0.155^*	1.741		0.12^*	1.651	0.257^***	6.556		0.112^***	3.262
商服中心可达性—地价	0.744^***	30.194		0.765^***	34.393	0.73^***	29.085		0.463^***	16.145
道路可达性—地价	0.048^*	1.655		0.027^***	3.217	0.058^**	2.079		0.033^*	1.665
公共交通可达性—地价	-0.063	1.354		-0.124	1.195	-0.066	1.524		-0.039	0.982
商服繁华度—地价	0.193^***	4.187		0.263^***	6.431	0.22^***	4.928		0.318^***	6.906
设施便利性—地价	-0.044	0.83		0.0418	0.923	-0.037	0.681		0.271^*	1.786
地价—房价	0.201^***	3.26		0.386^***	4.638	0.31^***	8.735		0.546^***	19.632