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Ripple Effect of Housing Prices among Chinese Deputy Provincial Cities Based on an Alternative Approach

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  • Feixue Huang
  • Cheng Li
  • Yanxi Li

Abstract

This paper applies a two-stage procedure of non-parametric testing and business cycle dating techniques to examine the ripple effect of housing prices among Chinese cities since 2006. Empirical analysis indicates that housing prices fluctuations among nineteen Chinese cities do have ripple effect. We divide the cities into three layers: Shenzhen and Guangzhou are in the first layer; Beijing, Shanghai, Hangzhou, Nanjing, Xiamen are in the second; and other cities are in the third one. The direction is from the first layer to the third layer and the effect gets weaker and weaker. Empirical results show that: (1) Shenzhen and Guangzhou are the main regulatory targets; (2) Cities in the second layer should be paid attention to, especially unexpected fluctuations of housing prices of cities in the third layer ought to be avoided.

Suggested Citation

  • Feixue Huang & Cheng Li & Yanxi Li, 2010. "Ripple Effect of Housing Prices among Chinese Deputy Provincial Cities Based on an Alternative Approach," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 1(1), pages 19-24, November.
    • Handle: RePEc:jfr:ijba11:v:1:y:2010:i:1:p:19-24
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    References listed on IDEAS

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    1. S. Cook & C. Thomas, 2003. "An alternative approach to examining the ripple effect in UK house prices," Applied Economics Letters, Taylor & Francis Journals, vol. 10(13), pages 849-851.
    2. Birchenhall, Chris R & Osborn, Denise R & Sensier, Marianne, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
    3. repec:kap:iaecre:v:14:y:2008:i:4:p:407-421 is not listed on IDEAS
    4. Cook, Steven, 2005. "Regional house price behaviour in the UK: application of a joint testing procedure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(3), pages 611-621.
    5. Song Shi & Martin Young & Bob Hargreaves, 2009. "The ripple effect of local house price movements in New Zealand," Journal of Property Research, Taylor & Francis Journals, vol. 26(1), pages 1-24, April.
    6. Beatriz Larraz-Iribas & Jose-Luis Alfaro-Navarro, 2008. "Asymmetric Behaviour of Spanish Regional House Prices," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 14(4), pages 407-421, November.
    7. Chris Birchenhall & Denise Osborn & Marianne Sensier, 2001. "Predicting UK Business Cycle Regimes," Scottish Journal of Political Economy, Scottish Economic Society, vol. 48(2), pages 179-195, May.
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    Cited by:

    1. Gong, Yunlong & Hu, Jinxing & Boelhouwer, Peter J., 2016. "Spatial interrelations of Chinese housing markets: Spatial causality, convergence and diffusion," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 103-117.
    2. Chunping Liu & Zhirong Ou, 2022. "Revisiting the determinants of house prices in China’s megacities: Cross‐sectional heterogeneity, interdependencies and spillovers," Manchester School, University of Manchester, vol. 90(3), pages 255-277, June.
    3. Wonseok Seo & LeeYoung Kim, 2020. "Investigating the Ripple Effect through the Relationship between Housing Markets and Residential Migration in Seoul, South Korea," Sustainability, MDPI, vol. 12(3), pages 1-22, February.
    4. Mehmet Balcilar & Abebe Beyene & Rangan Gupta & Monaheng Seleteng, 2013. "‘Ripple’ Effects in South African House Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(5), pages 876-894, April.

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