Analysis on the Housing Price Spatial Linkage Network of Cities in Sichuan Province Based on Gravity Model

Kehao Chen ()
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Kehao Chen: Chongqing University

A chapter in Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, 2022, pp 684-698 from Springer

Abstract: Abstract As one of the important factors affecting the development of urban, housing price and its spatial correlation are the attention of the recent academy. Based on the housing prices data, population data, GDP and geographical distance data of twenty one cities and autonomous prefectures in Sichuan Province from January 2020 to December 2020, this paper establishes the modified gravity model and constructs the spatial linkage network of urban housing prices, using the kmeans clustering algorithm to explore the spatial connection of housing prices between different urbans. The results show that: (1) from the overall network structure, the integration of housing price special linkage network in Sichuan Province is still in a low stage, and most cities are at the margin of housing price spatial linkage network; (2) from the perspective of local network structure, Chengdu is at the center of the whole housing price association network. Deyang and Meishan have certain influence on housing price changes of Chengdu in a mutual way. (3) In the result of clustering analysis, the correlation degree of urban housing prices in Sichuan Province shows a clear imbalance, and the strong correlation circle only includes Chengdu, Deyang, and Meishan. In light of the above results, this paper proposes some suggestions for a healthy housing market development according to the housing price spatial linkage network.

Keywords: Housing price; Spatial correlation; Gravity model; Kmeans algorithm (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-981-19-5256-2_54

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DOI: 10.1007/978-981-19-5256-2_54

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