基于核密度估计的南京二手房活跃度特征及影响机制研究

随着越来越多住房可通过网络查询和交易,为住房空间研究提供一个全新数据来源。本文通过对南京搜房网二手房数据挖掘,采用核密度估计空间分析方法,研究南京二手房活跃度的空间特征,重点探讨不同二手房活跃度影响因素,并通过河西、大光路和夫子庙3个案例,实证二手房活跃度的影响机制。研究发现,南京二手房活跃度空间呈现“一心三组团”的结构。二手房活跃度高的地区主要集中在南京市鼓楼区和建邺区;东部玄武高教区、南部江宁二手房活跃度较低,住房空间较为固化,居住空间发展较为成熟。影响二手房活跃度空间差异主要有住房内因和住房外因2个方面。通过多元回归模型分析表明,住房内因的价格、住房层数、绿化率和装修程度和外因的学区、地铁和区域变量显著影响二手房活跃度。居住层数、物业管理费、生活配套设施在活跃度高低2个层面 显著。

Research on the Characteristics and Influence Mechanism of Second-hand Housing Activeness in Nanjing: Using Kernel Density Estimation

Nowadays, issues regarding to housing affairs can be easily dealt through online queries and transactions, which provide a brand new data source for researches about residential space. This article mainly focuses on the causes of variations in activeness degree for different second-hand housing areas in Nanjing, through web data mining and a series of kernel density estimations. Based on three typical case studies of the Hexi district, Daguang district and Confucius Temple area, this article summarizes the underlying influential mechanism for the second-hand housing activeness in Nanjing. Research result has shown that the second-hand housing in Nanjing has a structure of “one core with three blocks”, in which the Gulou and Jianye districts are considered to be the core. Meanwhile, the sales volume in districts with highly educated inhabitants, such as Xuanwu district which lies in the eastern part of Nanjing and Jiangning district which lies in the southern part, have a low degree of activeness due to the solidified and sophisticated residential space development. There are two main factors contributing to the differentiation of activeness in second-hand housing, which are summarized as the internal factor and the external factor. The multivariate regression analysis model suggests that the internal factor is consists of the housing price, the number of building stories, the percentage of greenery coverage, and the house decoration condition. The external factor is considered to be consist of the accessibility and quality of adjacent subway stations, schools and other supporting facilities and services.