中国房地产价格的空间统计分析

Spatial Statistical Analysis of China Real Estate Price

摘要: 本文运用包括莫兰指数、冷热点分析、核密度分析、几何中心与加权重心、标准差椭圆等在内的空间统计分析方法对2020年10月我国313个城市的房价进行了统计分析，分析发现我国城市房价具有空间自相关性，中南地区的城市房价多中心集聚，城市房价东西向水平差异较南北向更高，高房价城市聚集现象纵向较横向更频繁，城市房价空间相关性较空间差异性更突出，高值集聚多出现在我国东南部，低值集聚多出现在西部和北部地区。

Abstract: This paper mainly uses spatial statistical analysis methods, including Molan Index, Cold Hotspot Analysis, kernel density estimation, Geometric Center and Weighted Gravity, Standard Elliptic Ellipse, for statistical analysis of house prices in 313 cities in China in October 2020. Analysis found that Chinese urban house prices have space self-correlation. Most of the city house prices in mid- southern region are more centrally agglomerated. The difference between urban housing prices is higher between west and east than that of north and south. High house prices urban aggregation phenomenon is more horizontally more frequent. Spatial relevance of Chinese urban house prices is more obvious than spatial differences. The high value set is more appeared in the southeastern part of China; the low-value set is mostly in the western and northern parts.