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Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices

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  • S. Wong
  • C. Yiu
  • K. Chau

Abstract

Spatial dependence is often seen as a problem in econometrics rather than in economics. This study seeks to find an economic explanation for spatially correlated real estate prices. We posit spatial dependence as a process to discover price information from neighboring property transactions. Weaker spatial dependence is expected when price information in the immediate vicinity of a subject property is abundant. In the context of apartment buildings, in addition to the more commonly known horizontal dependence, there is also spatial dependence in the vertical dimension within the same building. Based on more than 18,000 transactions of highly homogeneous apartment units in Hong Kong, we found that the trading volume of a building depresses horizontal spatial dependence, but raises vertical spatial dependence. This not only confirmed the role of trading volume in the real estate price discovery process, but also questioned the validity of constant spatial autocorrelation assumption adopted in many studies. Copyright The Author(s) 2013

Suggested Citation

  • S. Wong & C. Yiu & K. Chau, 2013. "Trading Volume-Induced Spatial Autocorrelation in Real Estate Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(4), pages 596-608, May.
    • Handle: RePEc:kap:jrefec:v:46:y:2013:i:4:p:596-608
    DOI: 10.1007/s11146-012-9375-y
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    3. Yang, Linchuan & Chau, K.W. & Wang, Xu, 2019. "Are low-end housing purchasers more willing to pay for access to basic public services? Evidence from China," Research in Transportation Economics, Elsevier, vol. 76(C).
    4. Yan Liu & Linchuan Yang & Kwong Wing Chau, 2020. "Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    5. Sidong Zhao & Kaixu Zhao & Ping Zhang, 2021. "Spatial Inequality in China’s Housing Market and the Driving Mechanism," Land, MDPI, vol. 10(8), pages 1-33, August.

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