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Appraisal-Based Real Estate Returns under Alternative Market Regimes

Author

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  • Carmelo Giaccotto
  • John Clapp

Abstract

In this article we use Monte Carlo simulation to study the statistical properties of real estate returns. We set up a model where transactions prices are noisy signals of true prices. We then consider a number of appraisal rules, derived from Bayesian and non-Bayesian theory, to estimate the current true price and rate of return. The class of exponential smoothing and Kalman filter rules perform well at both the disaggregate (returns on an individual property) and aggregate (returns on a real property portfolio) levels. A special case of exponential smoothing (α= 1.0) places all weight on current market data. Since this case eliminates smoothing, our results suggest that appraisers should place all weight on current data (no weight on past data) provided that they want to estimate returns rather than values. However, these results should be used with caution if sales prices are very noisy. Copyright American Real Estate and Urban Economics Association.

Suggested Citation

  • Carmelo Giaccotto & John Clapp, 1992. "Appraisal-Based Real Estate Returns under Alternative Market Regimes," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 20(1), pages 1-24.
  • Handle: RePEc:bla:reesec:v:20:y:1992:i:1:p:1-24
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    Citations

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    Cited by:

    1. Wilson, Patrick James & Okunev, John & Webb, James J, 1998. "Step Interventions and Market Integration: Tests in the U.S., U.K., and Australian Property Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 16(1), pages 91-123, January.
    2. Sampagnaro, Gabriele & Battaglia, Francesca, 2010. "Reliability and Heterogeneity of Real Estate Indexes and their Impact on the Predictability of Returns," MPRA Paper 23378, University Library of Munich, Germany.
    3. Robert Edelstein & Daniel Quan, 2006. "How Does Appraisal Smoothing Bias Real Estate Returns Measurement?," The Journal of Real Estate Finance and Economics, Springer, vol. 32(1), pages 41-60, February.
    4. Arjun Chatrath & Youguo Liang, 1998. "REITs and Inflation: A Long-Run Perspective," Journal of Real Estate Research, American Real Estate Society, vol. 16(3), pages 311-326.
    5. Gerald R. Brown & Seow-Eng Ong, 2001. "Estimating serial cross-correlation in real estate returns," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 22(7), pages 381-387.
    6. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, Elsevier.
    7. Hany Guirguis & Christos Giannikos & Randy Anderson, 2004. "The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients," The Journal of Real Estate Finance and Economics, Springer, vol. 30(1), pages 33-53, October.
    8. Jeff Fisher & David Geltner & Henry Pollakowski, 2007. "A Quarterly Transactions-based Index of Institutional Real Estate Investment Performance and Movements in Supply and Demand," The Journal of Real Estate Finance and Economics, Springer, vol. 34(1), pages 5-33, January.
    9. Shaun A. Bond & Soosung Hwang & Gianluca Marcato, 2006. "An Analysis of Commercial Real Estate Returns: Is there a Smoothing Puzzle?," Real Estate & Planning Working Papers rep-wp2006-17, Henley Business School, Reading University.
    10. Jack H. Rubens & David A. Louton & Elizabeth J. Yobaccio, 1998. "Measuring the Significance of Diversification Gains," Journal of Real Estate Research, American Real Estate Society, vol. 16(1), pages 73-86.
    11. Elizabeth Yobaccio & Jack H. Rubens & David C. Ketcham, 1995. "The Inflation-Hedging Properties of Risk Assets: The Case of REITs," Journal of Real Estate Research, American Real Estate Society, vol. 10(3), pages 279-296.

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