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Club Convergence of House Prices: Evidence from China's Ten Key Cities

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  • Hao Meng

    (ECUST)

  • Wen-Jie Xie

    (ECUST)

  • Wei-Xing Zhou

    (ECUST)

Abstract

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than ten years. However, the structure and dynamics of the Chinese housing market are less studied. Here we perform an extensive study of the Chinese housing market by analyzing ten representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5\% of the house price growth, indicating very high systemic risk in the Chinese housing market. The ten key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest, and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices in different cities.

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  • Hao Meng & Wen-Jie Xie & Wei-Xing Zhou, 2015. "Club Convergence of House Prices: Evidence from China's Ten Key Cities," Papers 1503.05550, arXiv.org.
  • Handle: RePEc:arx:papers:1503.05550
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    References listed on IDEAS

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    Cited by:

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    2. Yun-Shi Dai & Ngoc Quang Anh Huynh & Qing-Huan Zheng & Wei-Xing Zhou, 2023. "Correlation structure analysis of the global agricultural futures market," Papers 2310.16849, arXiv.org.
    3. Cho, Younghwan & Song, Jae Wook, 2023. "Hierarchical risk parity using security selection based on peripheral assets of correlation-based minimum spanning trees," Finance Research Letters, Elsevier, vol. 53(C).
    4. Zhang, Yongjie & Cao, Xing & He, Feng & Zhang, Wei, 2017. "Network topology analysis approach on China’s QFII stock investment behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 77-88.
    5. Darrell Jiajie Tay & Chung-I Chou & Sai-Ping Li & Shang You Tee & Siew Ann Cheong, 2016. "Bubbles Are Departures from Equilibrium Housing Markets: Evidence from Singapore and Taiwan," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    6. Mateusz Tomal, 2022. "Testing for overall and cluster convergence of housing rents using robust methodology: evidence from Polish provincial capitals," Empirical Economics, Springer, vol. 62(4), pages 2023-2055, April.
    7. Wang, Yan & Wang, Yue & Li, Ming-Xia, 2019. "Regional characteristics of sports industry profitability: Evidence from China’s province level data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 946-955.
    8. Tomal Mateusz, 2019. "House Price Convergence on the Primary and Secondary Markets: Evidence from Polish Provincial Capitals," Real Estate Management and Valuation, Sciendo, vol. 27(4), pages 62-73, December.
    9. Rajesh Raj & Rath D.P., 2022. "House Price Convergence: Evidence from India [Convergence des prix des logements : le cas indien]," Working papers 893, Banque de France.
    10. Li, Yan & Jiang, Xiong-Fei & Tian, Yue & Li, Sai-Ping & Zheng, Bo, 2019. "Portfolio optimization based on network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 671-681.
    11. Nie, Chun-Xiao & Song, Fu-Tie, 2018. "Analyzing the stock market based on the structure of kNN network," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 148-159.
    12. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    13. Dai, Yun-Shi & Huynh, Ngoc Quang Anh & Zheng, Qing-Huan & Zhou, Wei-Xing, 2022. "Correlation structure analysis of the global agricultural futures market," Research in International Business and Finance, Elsevier, vol. 61(C).
    14. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.

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