地理学报 ›› 2017, Vol. 72 ›› Issue (6): 1049-1062.doi: 10.11821/dlxb201706008

• 城市研究 • 上一篇    下一篇

北京市居住用地出让价格的空间格局及影响因素

崔娜娜1,2(), 冯长春1,2(), 宋煜3   

  1. 1. 北京大学城市与环境学院,北京 100871
    2. 国土资源部国土规划与开发重点实验室,北京 100871
    3. 北京大学经济学院 北京 100871
  • 收稿日期:2016-10-09 修回日期:2017-03-07 出版日期:2017-06-25 发布日期:2017-07-13
  • 作者简介:

    作者简介:崔娜娜(1990-), 女, 河南周口人, 博士生, 主要研究方向为城市与区域规划、土地与房地产经济。E-mail: cuinana@pku.edu.cn

  • 基金资助:
    京津冀土地优化利用管控技术方法研究(201511010-3A)

Spatial pattern of residential land parcels and determinants of residential land price in Beijing since 2004

Nana CUI1,2(), Changchun FENG1,2(), Yu SONG3   

  1. 1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    2. Key Laboratory of Territorial Planning and Development, Ministry of Land and Resources, Beijing 100871, China
    3. School of Economics, Peking University, Beijing 100871, China
  • Received:2016-10-09 Revised:2017-03-07 Online:2017-06-25 Published:2017-07-13
  • Supported by:
    Methods of the Land Use Optimization and Control Study for Beijing-Tianjin-Hebei Region, No.201511010-3A

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