资源科学 ›› 2016, Vol. 38 ›› Issue (4): 738-749.doi: 10.18402/resci.2016.04.15

• 土地资源 • 上一篇    下一篇

特殊自然地物对城市住宅地价和房价的影响——以武汉市为例

杨剩富1,2(), 胡守庚1,2,3(), 徐枫1,2, 童陆亿1,2   

  1. 1. 中国地质大学(武汉)公共管理学院,武汉 430074
    2. 中国地质大学(武汉)国土资源部法律评价工程重点实验室,武汉 430074
    3. 中国科学院地理科学与资源研究所,北京 100101
  • 收稿日期:2015-08-17 修回日期:2015-11-08 出版日期:2016-04-25 发布日期:2016-04-25
  • 作者简介:

    作者简介:杨剩富,男,贵州黄平人,博士生,主要研究方向为城乡土地利用转型。E-mail:yangsf2049@126.com

  • 基金资助:
    国家自然科学基金项目(41101535);教育部人文社会科学研究基金项目(14YJCZH192);博士后基金项目(2014T70115)

Influence on urban residential land and housing prices by special natural features in Wuhan

YANG Shengfu1,2(), HU Shougeng1,2,3(), XU Feng1,2, TONG Luyi1,2   

  1. 1. School of Public Administration,China University of Geosciences,Wuhan 430074,China
    2. Key Laboratory of Legal Assessment Project,Ministry of Land and Resources,China University of Geosciences,Wuhan 430074,China
    3. Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • Received:2015-08-17 Revised:2015-11-08 Online:2016-04-25 Published:2016-04-25

摘要:

揭示微观影响因素对城市住宅地价和房价的影响作用关系,是提高城市地价和房价预测水平,有效进行土地和房地产市场宏观调控的基础工作。本文采用地理加权回归模型(GWR)定量测度与分析特殊自然地物湖、江、山体对城市住宅地价和房价的影响及其空间分异特征,并尝试根据影响因素作用的差异解释住宅地价和房价空间分布特征形成的部分原因。结果表明:①住宅地价和房价与各自然地物之间呈现空间非平稳的影响作用关系;②湖对住宅地价和房价的平均边际价值分别为0.11元/m2、0.52元/m2,江均为0.15元/m2;在区域分布上,住宅地价和房价高值区受湖和江的影响更显著,面积较大的湖泊(如东湖)因受周边商服繁华程度、供给能力等因素影响对住宅地价和房价的作用存在各向异性;城市的山体或因坡度小、分布零散及周边特殊用地结构如黄鹤楼等保护性建筑限制了区域土地的商业开发,使其对住宅地价和房价影响作用不显著;③住宅地价与房价受湖影响的作用变化趋势并未呈现空间一致性,而是切合了区域住宅地价和房价的价格走势,且变化幅度与价格高低呈正相关;住宅地价和房价受江影响的边际作用力大小差别不大,空间上表现为梯度和圈层两种不同的变化趋势。

关键词: 空间非平稳性, 住宅地价, 房价, 特殊自然地物, 江, 湖, 山体, 地理加权回归模型, 武汉市

Abstract:

Revealing the influence of micro factors on urban land price and housing price is essential to improve the predicting precision of urban land price change and house price change and exercise effective macro-control over land market and real estate market. The aim of this study was to use the Geographically Weighted Regression model to quantitatively measure the impacts of local special natural features (lakes,rivers and mountains)on urban residential land and housing prices and their spatial differences,and to explain the cause from the aspect of impact factors. We found that the spatial distributions of Moran's I,P value,R2,adjusted R2 and regression parameters of the testing models can characterize the spatially non-stationary relationship between natural features and residential land and housing prices accurately. When it comes to effect strengths,the average marginal residential land and housing price affected by lakes is 0.11 CNY/m2 and 0.52 CNY/m2,respectively,while that affected by lakes is 0.15 CNY/m2 on both residential land and housing prices. Lakes and rivers have more distinguished impacts on residential land and housing in areas with high prices according to the dynamic trends of their effects. Meanwhile,affected by the existence of surrounding commercial districts,anisotropic relationships between large lakes like East Lake and housing prices exist. Those mountains with a scattered distribution and low gradient,limited estate developments by the protected specific artificial amenities alike Yellow Crane Tower,show an insignificant influence. Rather than fitting with the spatial change trends of housing price,the spatial dynamics of residential land and housing prices are similar with that of regional trends and the change range is positively correlated with price. Rivers generate analogous impacts on both residential land and housing prices with similar marginal effects,but in two different spatial trends of gradients and spheres.

Key words: spatial non-stationarity, residential land price, housing price, special natural features, lake, river, mountain, GWR, Wuhan