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

Abstract

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.

Analysis of the Spatiotemporal Heterogeneity of Housing Prices’ Association in China: An Urban Agglomeration Perspective

“…First, compared with the research on the national geography area [13,33,34], regional housing prices were gradually aggregating in the urban agglomerations. This may be due to the fact that the intensity and speed of information, capital and population flows within urban agglomerations have exceeded that of cross-urban agglomeration units, causing a concentration of regional housing prices.…”

“…Therefore, the gravity model can be used to quantitatively study the spatial interaction strength of housing prices' association between cities. Based on the modifications of the gravity model in previous studies [1,13], this paper constructs a modified gravity model to measure the strength of urban housing prices' association by considering geographical and economic distance between cities, and to be extended by the social network analysis method.…”

“…Drawing on social network analysis, Wang et al identified that the interaction of housing prices among cities is in the form of a network structure in the Beijing-Tianjin-Hebei region in 2013 [32]. Wu et al and Fang and Pei used the SNA method to investigate the structural characteristics of spatial correlation networks of housing prices of thirteen cities in China [13,33]. Additionally, Chen et al and Quan et al empirically examined the factors influencing the network structure of urban housing prices' associations by using the SNA method and QAP aggression [12,34].…”

“…According to these findings, the linkage relationship between housing prices in cities no longer presents a simple geographical "close neighbor" effect, but rather a very complex multithreaded structure, namely a network [12]. Accordingly, housing price correlation between cities can also be considered as a complex network [13]. China has dozens of urban agglomerations of different development scales, and complicated housing price relationships occur among different regions.…”

Network Analysis of Housing Price Comovements of a Hundred Chinese Cities

“…They also find that house prices in London would have the strongest influence when regional housing markets are less connected. Wu et al (2021) use sale prices of stocking houses from 35 large- and medium-sized cities in China for 2010–2021 to investigate the spatial linkage through a modified gravity model and network analysis. They find that the integration degree of the housing price network is relatively low and housing price influences are polarised.…”