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Predictability in Equilibrium: The Price Dynamics of Real Estate Investment Trusts

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  • Dennis R. Capozza
  • Ryan D. Israelsen

Abstract

This research hypothesizes that, in markets where information costs, transaction costs and the economic impact of information can vary widely, we should expect predictability to vary systematically. We test this hypothesis with data on equity real estate investment trusts (REITs) from 1985 to 1992. We document that levels of predictability vary with firm characteristics like leverage, size and focus. Momentum is stronger for larger, more levered REITs. Reversion is faster for focused, levered REITs. The results are consistent with the hypothesis that, in equilibrium, securities, where information is either less costly to acquire or has less impact on fundamental value, should exhibit less predictability.

Suggested Citation

  • Dennis R. Capozza & Ryan D. Israelsen, 2007. "Predictability in Equilibrium: The Price Dynamics of Real Estate Investment Trusts," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(4), pages 541-567, December.
    • Handle: RePEc:bla:reesec:v:35:y:2007:i:4:p:541-567
    DOI: 10.1111/j.1540-6229.2007.00200.x
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    File URL: https://doi.org/10.1111/j.1540-6229.2007.00200.x
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    References listed on IDEAS

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    1. Huddart, Steven & Ke, Bin & Shi, Charles, 2007. "Jeopardy, non-public information, and insider trading around SEC 10-K and 10-Q filings," Journal of Accounting and Economics, Elsevier, vol. 43(1), pages 3-36, March.
    2. John L. Glascock & Szu-Yin Hung, 2005. "Momentum Profitability and Market Trend: Evidence from REITs," ERES eres2005_184, European Real Estate Society (ERES).
    3. Kim, J.W. & Leatham, D.J. & Bessler, D.A., 2007. "REITs' dynamics under structural change with unknown break points," Journal of Housing Economics, Elsevier, vol. 16(1), pages 37-58, March.
    4. Brunnermeier, Markus K., 2001. "Asset Pricing under Asymmetric Information: Bubbles, Crashes, Technical Analysis, and Herding," OUP Catalogue, Oxford University Press, number 9780198296980.
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    Cited by:

    1. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    2. Kouwenberg, Roy & Zwinkels, Remco, 2014. "Forecasting the US housing market," International Journal of Forecasting, Elsevier, vol. 30(3), pages 415-425.
    3. Katlego Kola & Tumellano Sebehela, 2021. "Market The (De)merits of using Integral Transforms in Predicting Structural Break Points," International Real Estate Review, Global Social Science Institute, vol. 24(3), pages 405-467.
    4. Yuval Arbel & Danny Ben-Shahar & Eyal Sulganik, 2009. "Mean Reversion and Momentum: Another Look at the Price-Volume Correlation in the Real Estate Market," The Journal of Real Estate Finance and Economics, Springer, vol. 39(3), pages 316-335, October.
    5. Paul M Anglin & Yanmin Gao, 2011. "Integrating Illiquid Assets into the Portfolio Decision Process," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 39(2), pages 277-311, June.

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