文献类型：期刊文献

英文题名：Analysis on the Housing Price Relationship Network of Large and Medium-Sized Cities in China Based on Gravity Model

作者：Wu, Guancen[1];Li, Jing[1];Chong, Dan[1];Niu, Xing[2]

机构：[1]Shanghai Univ, Sch Management, Shanghai 200444, Peoples R China;[2]East China Univ Sci & Technol, Sch Social & Publ Adm, Shanghai 200237, Peoples R China

年份：2021

卷号：13

期号：7

外文期刊名：SUSTAINABILITY

收录：;WOS:【SSCI(收录号:WOS:000638925900001)，SCI-EXPANDED(收录号:WOS:000638925900001)】；

基金：This work was supported by the Shanghai Planning Office of Philosophy and Social Sciences, the National Planning Office of Philosophy and Social Sciences, and Department of Social Sciences of Ministry of Education under grant numbers [2019BCK002], [16ZDA083] and [20YJC630108].

语种：英文

外文关键词：housing price; spatial linkage; social network analysis

摘要：The relationship among cities is getting closer, so are housing prices. Based on the sale price of stocking houses in thirty-five large and medium-sized cities in China from 2010 to 2021, this study established the modified gravity model and used the method of social network analysis to explore the spatial linkage of urban housing prices. The results show that: (1) from the overall network structure, the integration degree of housing price network in China is still at a low stage, and the influence of housing price is polarized; (2) from the individual network structure, Beijing, Shanghai, Shenzhen, Nanjing, Hangzhou, and Hefei have a higher degree of centrality. Chengdu, Xining, Kunming, Urumqi, and Lanzhou stay in an isolation position every year; (3) from the results of cohesive subgroup analysis, different cities play different roles in the block each year and have different influences on other cities. (4) Emergencies, such as outbreaks of COVID-19, also have an impact on the housing price network. Structural divergence among urban housing prices has become more pronounced, and the diversity of house price network has been somewhat reduced. Based on the above findings, this paper puts forward some recommendations for the healthy development of housing market from the perspective of housing price network.