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| 基于空间分位数模型的住宅价格分异的影响因素研究——以武汉市为例 | |
| 引用本文： | 卢新海,蔡大伟,曾晨.基于空间分位数模型的住宅价格分异的影响因素研究——以武汉市为例[J].地理科学进展,2021,40(2):283-292. |
| 作者姓名： | 卢新海 蔡大伟 曾晨 |
| 作者单位： | 1. 华中师范大学公共管理学院,武汉 430079 2. 华中农业大学公共管理学院,武汉 430070 3. 中国科学院地理科学与资源研究所,北京 100101 |
| 基金项目： | 国家自然科学基金项目(71673096);国家自然科学基金项目(41771563);国家自然科学基金项目(72042020);中国博士后基金特别资助项目(2019T12013) |
| 摘 要： | 住宅价格的变化是关系我国城镇化建设和社会经济高质量发展的重要问题。为探索住宅价格分位点下的影响因素,本文以我国中部国家中心城市——武汉市为例,运用空间分位数模型进行定量分析,同时将两阶段空间自回归模型结果作为对比揭示其优越性。研究表明：① 空间分位数模型不仅能考虑住宅价格的空间自相关性,而且还关注了住宅价格的条件分布特征,更为全面地描述微观因素对不同价位住宅的作用效应。②从分位点来看,高住宅价格的空间自相关性强于低住宅价格的;而且影响因素存在波动性和异质性,相比较于两阶段空间自回归的均值结果,空间分位数模型中各因素的影响程度随着分位点的变化出现上升或下降趋势,对低价、高价等不同价位的住宅影响程度存在显著差异。③ 整体而言,建筑年龄和医疗配套为负向影响因素,容积率等建筑特征、区位特征和教育配套等邻里特征变量为正向影响因素。基于研究结果,合理提高中低价位住宅区域的容积率和绿地率、加大低价住宅区的轨道交通和教育设施的投入力度等应当成为政府部门针对不同价位的住宅制定差异化政策措施的考虑方向。 |
| 关 键 词： | 住宅价格 分异 空间分位数模型 武汉市 |
| 收稿时间： | 2020-04-10 |
| 修稿时间： | 2020-07-13 |
Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City |
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| LU Xinhai,CAI Dawei,ZENG Chen.Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City[J].Progress in Geography,2021,40(2):283-292. | |
| Authors: | LU Xinhai CAI Dawei ZENG Chen |
| Institution: | 1. School of Public Administration, Central China Normal University, Wuhan 430079, China 2. College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China 3. Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China |
| Abstract: | The change of housing price is an important issue related to urbanization and high-quality social and economic development in China. In order to explore the influencing factors in terms of the quantiles of housing price, this study took Wuhan City as a case, which is the central city of central China, and used the spatial quantile regression (SQR) model for quantitative analysis. The two-stage least squares (2SLS) model result was compared with the SQR model output to reveal its superiority. The research shows that: 1) The SQR model not only can consider the spatial autocorrelation of the housing price, but also have the capability to embed the conditional distribution characteristics, which helps to better describe the driving effects of micro-factors on different housing prices. 2) In view of the quantiles, the spatial autocorrelation of the high prices is stronger than the low prices. The influencing factors show volatility and heterogeneity. Compared with the mean result of the 2SLS model, the influencing degree of each factor in the SQR model increases or decreases with the change of the quantile. There is a significant difference in the degree of influence on housing price of different levels. 3) Overall, age of the residential building and medical facilities are negative influencing factors, and building characteristics including floor area ratio, location, and neighborhood such as nearby educational facilities are positive influencing factors. Based on the results, reasonable increase of the floor area ratio and green space ratio of low- and middle-priced residential areas, and increased investment in rail transportation and educational facilities in low-priced residential areas can be taken as alternatives for the government to formulate differentiated policy measures for housing with different levels of prices. |
| Keywords: | housing price differentiation spatial quantile regression model Wuhan City |
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