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Regional Space–time Differences and Dynamic Evolution Law of Real Estate Financial Risk in China

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  • Xia Beibei

    (School of Engineering Economics, Henan Finance University, Zhengzhou, 450000, China)

Abstract

This study develops a framework to evaluate real estate financial risk from three perspectives: internal risk factors, external risk factors, and interconnected risk factors, examining regional space–time differences and dynamic evolution laws across 30 provinces in China. The research finds that (1) interconnected risk factors are key contributors to real estate financial risk. (2) Significant regional differences in real estate financial risks exist across China. The western and northeastern regions are hotspots for risk, while the eastern region exhibits the most pronounced market stratification and polarization. (3) There is significant spatial autocorrelation in China’s real estate financial risks, with most provinces showing high–high (HH) and low–low (LL) clustering. HH clusters are primarily located in the western and northeastern regions, while LL clusters are more prevalent in the central and eastern regions. (4) The distribution of real estate financial risks follows a single-peak evolutionary pattern, characterized by the dynamic transition of weakening LL clusters and strengthening HH clusters. (5) The dynamic evolution of China’s real estate financial risks exhibits strong “spatial stickiness” and “positive reinforcement.” Over time, the probability of regions transitioning to higher-risk types increases, demonstrating a trend toward escalating risk levels.

Suggested Citation

  • Xia Beibei, 2025. "Regional Space–time Differences and Dynamic Evolution Law of Real Estate Financial Risk in China," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 19(1), pages 1-16.
    • Handle: RePEc:bpj:econoa:v:19:y:2025:i:1:p:16:n:1001
    DOI: 10.1515/econ-2025-0134
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    1. Fengyun Liu & Honghao Ren & Chuanzhe Liu & Dejun Tan, 2022. "Formation of Financial Real Estate Risks and Spatial Interactions: Evidence from 35 Cities in China," JRFM, MDPI, vol. 15(12), pages 1-21, December.
    2. Zhou, Wenwen & Chen, Mengyao & Yang, Zaoli & Song, Xiaobo, 2021. "Real estate risk measurement and early warning based on PSO-SVM," Socio-Economic Planning Sciences, Elsevier, vol. 77(C).
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    4. Fry, J. M., 2009. "Bubbles and contagion in English house prices," MPRA Paper 17687, University Library of Munich, Germany.
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