城市研究

北京城市住宅土地市场空间异质性模拟与预测

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  • 1. 中国科学院可持续发展分析与模拟重点实验室, 北京 100101;
    2. 中国科学院地理科学与资源研究所, 北京 100101;
    3. 中国科学院研究生院, 北京 100049;
    4. 英国伦敦政治经济学院, 伦敦 WC2A 2AE
董冠鹏(1985-), 男, 河南许昌人, 硕士研究生, 主要从事区域与城市发展研究。E-mail: donggp.08s@igsnrr.ac.cn

收稿日期: 2010-10-11

  修回日期: 2011-02-15

  网络出版日期: 2011-06-20

基金资助

国家自然科学基金项目(40971077)

Spatial Heterogeneity in Determinants of Residential Land Price: Simulation and Prediction

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  • 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China;
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China;
    4. London School of Economics and Political Science, London WC2A 2AE, United Kingdom

Received date: 2010-10-11

  Revised date: 2011-02-15

  Online published: 2011-06-20

Supported by

National Nature Science Foundation of China, No.40971077

摘要

土地制度转型和空间重构背景下,价格信号在土地区位配置及空间结构塑造方面发挥出有效性。基于北京市2004-2009 年居住用地出让地块微观数据,利用空间扩展模型、地理加权回归模型和特征价格模型对居住用地价格影响因素及其空间异质性进行了有效检验和预测。模型结果表明:① 居住用地价格影响因素存在着显著的空间异质性,重点小学、轨道交通和公园等设施便利性因素在不同区域对地价的作用强度存在明显差异。② 相比于特征价格模型和空间扩展模型,GWR模型能够有效刻画土地市场空间异质性的离散性、突变性和跳跃性,因而其对居住用地影响因素的空间异质性刻画和居住用地价格的预测最为准确。③ 居住用地价格影响因素的空间异质性表明居住用地子市场存在的可能性,利用GWR模型对地价影响因素的估计可以为土地子市场的划分提供方法借鉴。

本文引用格式

董冠鹏, 张文忠, 武文杰, 郭腾云 . 北京城市住宅土地市场空间异质性模拟与预测[J]. 地理学报, 2011 , 66(6) : 750 -760 . DOI: 10.11821/xb201106004

Abstract

Hedonic land price models typically impose a spatial homogeneous price structure on land characteristics throughout the entire land market. However, there are increasing theoretical and empirical evidences that the marginal values of many crucial attributes of land parcels vary across space. Theoretically, localized and inelastic land supply results in spatial mismatch between demand and supply of land with certain attributes, which causes the spatial heterogeneous effects of these attributes. In this paper, we establish a series of models to evaluate the determinants of residential land price and the spatial heterogeneity of the determinants. First, we use hedonic models to diagose the determinants of residential land price. Second, we use the spatial expansion models and geographically weighted regression model (GWR) to depict the spatial instability in the impacts of land attributes. Third, we compare the prediction accuracy of the two models by predicting the land price of 10% random selected land parcels. We take Beijing as a case study and use the information of auctioned residential land parcels during 2004-2009 and GIS data of Beijing's public facilities. Based on the analysis, several conclusions are drawn as follows. 1) Spatial dependence of local residential land price and the spillover effect of local commercial land exert great effects on the residential land price, while the impact of the distance on CBD is insignificant, which indicates that the residential land market is probably local rather than global. 2) Among several public facilities, in terms of Nearest-Distance Accessibility Criteria, only the local prime elementary school, park and rail transit accessibility have a significant effect on the residential land price. 3) There is an obvious spatial pattern in the impacts of the land attributes on the land price, which is an evident signal of existence of land submarkets. 4) As the spatial expansion model imposes a fixed and definite function of spatial coordinates on the spatial heterogeneity in the marginal effect of land parcel attributes, it does not perform as well as GWR models in depicting spatial variation and prediction accuracy. GWR models perform best in explaining the land price variation, depicting spatial heterogeneity and prediction accuracy comparing to hedonic models and spatial expansion models. Also, GWR models provide a useful framework for delineating residential land submarket boundaries.

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