Exploration of the regional correlation and network structure characteristics of land prices: A case study of Hebei, China

Abstract

It is important to explore the correlation characteristics of land markets among cities in order to promote coordinated developments. Based on the residential land prices in 168 counties in Hebei Province, this study used spatial econometric models and social network analysis to analyze the regional correlation effect and network structure evolution characteristics of residential land prices. The results indicated that: 1) the regional residential land price level has significant global spatial autocorrelation and local autocorrelation. High-High clusters were concentrated in cities around Beijing and Tianjin and provincial capitals, while Low-Low clusters were mostly distributed in central and southern Hebei. 2) The direct effect and spillover effect of influencing factors of residential land price were significantly different. The residents’ purchasing power, the socioeconomic level, and the land resources had significant impacts on the residential land price of the county itself, while the level of infrastructure and the policy environment had significant impacts on the residential land price of neighboring counties. 3) The degree centrality and betweenness centrality of residential land price in central counties of Hebei Province was generally high, showing a trend of agglomeration. However, the peripheral cities of Hebei Province lacked important central nodes in the network structure. From 2013 to 2020, increasing numbers of counties had shown the transmission function of “bridge,” and the balance of land price in the whole region had been constantly improved. The study found that the regional residential land price itself had spatial autocorrelation, and the spillover effect of its related factors was also the driving force that affects the transmission and diffusion of land price between counties. The change in the spatial network of county residential land price was primarily manifested in the transmission process starting from the central cities. The tightness of the spatial network was related to the number and distribution of central nodes. Hebei Province should focus on cultivating urban central nodes with development potential in marginal areas, create more land market growth poles according to local conditions, and accelerate the integration of land factor markets in Beijing, Tianjin, and Hebei to achieve healthy and balanced development of residential land prices. This study made up for the shortcomings of previous studies on land price correlations. The combination of correlation feature analysis and spatial network structure analysis was more helpful to reveal the characteristics of regional land price development, and the results could provide a reference for the formulation of urban land market regulation policies.