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Linear and Nonlinear Predictability of International Securitized Real Estate Returns: A Reality Check

Author

Listed:
  • Juan Cabrera

    (Ramapo College of New Jersey)

  • Tao Wang

    (Queens College and the Graduate Center, The City University of New York)

  • Jian Yang

    (University of Colorado Denver)

Abstract

This paper examines short-horizon return predictability of ten largest international securitized real estate markets, with special attention paid to exploring possible nonlinearity-in-mean as well as nonlinearity-in-variance predictability. Although international securitized real estate returns are generally not predictable based on commonly used statistical criteria, there is much evidence for the predictability based on economic criteria (i.e., direction of price changes and trading rule profitability), which is more often due to nonlinearity-in-mean. The forecast combinations for various models appear to improve the forecasting performance, while the allowance of data-snooping bias using White’s Reality Check substantially mitigates spurious out-of-sample forecasting performance and weakens otherwise overwhelmingly strong predictability. Overall, there is robust evidence for the predictability in many international securitized real estate markets.

Suggested Citation

  • Juan Cabrera & Tao Wang & Jian Yang, 2011. "Linear and Nonlinear Predictability of International Securitized Real Estate Returns: A Reality Check," Journal of Real Estate Research, American Real Estate Society, vol. 33(4), pages 565-594.
    • Handle: RePEc:jre:issued:v:33:n:4:2011:p:565-594
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    Cited by:

    1. repec:ipg:wpaper:2014-585 is not listed on IDEAS
    2. Zhou, Jian, 2013. "Conditional market beta for REITs: A comparison of modeling techniques," Economic Modelling, Elsevier, vol. 30(C), pages 196-204.
    3. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    4. Alqaralleh, Huthaifa & Canepa, Alessandra, 2020. "Housing market cycles in large urban areas," Economic Modelling, Elsevier, vol. 92(C), pages 257-267.
    5. Alessandra Canepa & Emilio Zanetti Chini & Huthaifa Alqaralleh, 2020. "Global Cities and Local Housing Market Cycles," The Journal of Real Estate Finance and Economics, Springer, vol. 61(4), pages 671-697, November.
    6. Huthaifa Alqaralleh & Ahmad Al-Majali & Abeer Alsarayrh, 2021. "Analyzing the Dynamics Between Macroeconomic Variables and the Stock Indexes of Emerging Markets, Using Non-linear Methods," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(3), pages 193-204, May.
    7. Glennon, Dennis & Kiefer, Hua & Mayock, Tom, 2018. "Measurement error in residential property valuation: An application of forecast combination," Journal of Housing Economics, Elsevier, vol. 41(C), pages 1-29.
    8. Alessandra Canepa & Emilio Zanetti Chini & Huthaifa Alqaralleh, 2022. "Global Cities and Local Challenges: Booms and Busts in the London Real Estate Market," The Journal of Real Estate Finance and Economics, Springer, vol. 64(1), pages 1-29, January.
    9. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    10. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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