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A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities

Author

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  • Le Ma
  • Chunlu Liu

Abstract

Purpose - – A panel error correction model has been developed to investigate the spatial correlation patterns among house prices. This paper aims to identify a dominant housing market in the ripple down process. Design/methodology/approach - – Seemingly unrelated regression estimators are adapted to deal with the contemporary correlations and heterogeneity across cities. Impulse response functions are subsequently implemented to simulate the spatial correlation patterns. The newly developed approach is then applied to the Australian capital city house price indices. Findings - – The results suggest that Melbourne should be recognised as the dominant housing market. Four levels were classified within the Australian house price interconnections, namely: Melbourne; Adelaide, Canberra, Perth and Sydney; Brisbane and Hobart; and Darwin. Originality/value - – This research develops a panel regression framework in addressing the spatial correlation patterns of house prices across cities. The ripple-down process of house price dynamics across cities was explored by capturing both the contemporary correlations and heterogeneity, and by identifying the dominant housing market.

Suggested Citation

  • Le Ma & Chunlu Liu, 2013. "A panel error correction approach to explore spatial correlation patterns of the dominant housing market in Australian capital cities," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 6(4), pages 405-421, September.
    • Handle: RePEc:eme:ijhmap:v:6:y:2013:i:4:p:405-421
    DOI: 10.1108/IJHMA-03-2013-0021
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    Citations

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    Cited by:

    1. JingJing (Justine) Wang & John S. Croucher, 2021. "Information linkages among National, NSW, VIC, and QLD real estate markets in Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 3207-3234, June.
    2. Vijay Kumar Vishwakarma, 2021. "Long-run drivers and integration in interprovincial Canadian housing price relations," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 16(1), pages 22-40, November.
    3. Valadkhani, Abbas & Costello, Greg & Ratti, Ronald, 2016. "House price cycles in Australia’s four largest capital cities," Economic Analysis and Policy, Elsevier, vol. 52(C), pages 11-22.
    4. Feng Lan & Chengcai Jiao & Guoying Deng & Huili Da, 2021. "Urban agglomeration, housing price, and space–time spillover effect—Empirical evidences based on data from hundreds of cities in China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(4), pages 898-919, June.

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