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Rationality and Momentum in Real Estate Investment Forecasts

Author

Listed:
  • Dimitrios Papastamos

    (Eurobank EFG Property Services S.A.)

  • Fotis Mouzakis

    (Frynon Consulting)

  • Simon Stevenson

    (School of Real Estate & Planning, Henley Business School, University of Reading)

Abstract

This study examines the rationality and momentum in forecasts for rental, capital value and total returns for the real estate investment market in the United Kingdom. In order to investigate if forecasters are affected by the general economic conditions present at the time of forecast we incorporate into the analysis Gross Domestic Product (GDP) and the Default Spread (DS). The empirical findings show high levels of momentum in the forecasts, with highly persistent forecast errors. The results also indicate that forecasters are affected by adverse conditions. This is consistent with the finding that they tend to exhibit greater forecast error when the property market is underperforming and vice-versa.

Suggested Citation

  • Dimitrios Papastamos & Fotis Mouzakis & Simon Stevenson, 2014. "Rationality and Momentum in Real Estate Investment Forecasts," Real Estate & Planning Working Papers rep-wp2014-07, Henley Business School, University of Reading.
    • Handle: RePEc:rdg:repxwp:rep-wp2014-07
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    File URL: http://centaur.reading.ac.uk/36851/1/wp0714.pdf
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    as
    1. Dovern, Jonas & Weisser, Johannes, 2011. "Accuracy, unbiasedness and efficiency of professional macroeconomic forecasts: An empirical comparison for the G7," International Journal of Forecasting, Elsevier, vol. 27(2), pages 452-465.
    2. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2013. "Forecasting US housing starts under asymmetric loss," Applied Financial Economics, Taylor & Francis Journals, vol. 23(6), pages 505-513, March.
    3. Victor Zarnowitz & Phillip Braun, 1993. "Twenty-two Years of the NBER-ASA Quarterly Economic Outlook Surveys: Aspects and Comparisons of Forecasting Performance," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 11-94, National Bureau of Economic Research, Inc.
    4. Patricia M. Dechow & Amy P. Hutton & Richard G. Sloan, 2000. "The Relation between Analysts' Forecasts of Long†Term Earnings Growth and Stock Price Performance Following Equity Offerings," Contemporary Accounting Research, John Wiley & Sons, vol. 17(1), pages 1-32, March.
    5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    6. Christian Pierdzioch & Jan-Christoph Rülke & Georg Stadtmann, 2012. "Housing Starts in Canada, Japan, and the United States: Do Forecasters Herd?," The Journal of Real Estate Finance and Economics, Springer, vol. 45(3), pages 754-773, October.
    7. Fintzen, David & Stekler, H. O., 1999. "Why did forecasters fail to predict the 1990 recession?," International Journal of Forecasting, Elsevier, vol. 15(3), pages 309-323, July.
    8. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
    9. Gultekin Isiklar & Kajal Lahiri & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross‐country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725, September.
    10. Fotis Mouzakis & David Richards, 2007. "Panel Data Modelling of Prime Office Rents: A Study of 12 Major European Markets," Journal of Property Research, Taylor & Francis Journals, vol. 24(1), pages 31-53, February.
    11. Oller, Lars-Erik & Barot, Bharat, 2000. "The accuracy of European growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 16(3), pages 293-315.
    12. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    13. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    14. Batchelor, Roy & Dua, Pami, 1991. "Blue Chip Rationality Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 23(4), pages 692-705, November.
    15. Roy Batchelor, 2007. "Forecaster Behaviour and Bias in Macroeconomic Forecasts," ifo Working Paper Series 39, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    16. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    17. Patrick Mcallister & Graeme Newell & George Matysiak, 2008. "Agreement and Accuracy in Consensus Forecasts of the UK Commercial Property Market," Journal of Property Research, Taylor & Francis Journals, vol. 25(1), pages 1-22, June.
    18. Nordhaus, William D, 1987. "Forecasting Efficiency: Concepts and Applications," The Review of Economics and Statistics, MIT Press, vol. 69(4), pages 667-674, November.
    19. Holden, K & Peel, D A, 1990. "On Testing for Unbiasedness and Efficiency of Forecasts," The Manchester School of Economic & Social Studies, University of Manchester, vol. 58(2), pages 120-127, June.
    20. James H. Stock & Mark W. Watson, 1993. "Business Cycles, Indicators, and Forecasting," NBER Books, National Bureau of Economic Research, Inc, number stoc93-1, July.
    21. Tilman Ehrbeck & Robert Waldmann, 1996. "Why Are Professional Forecasters Biased? Agency versus Behavioral Explanations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 111(1), pages 21-40.
    22. Stock, James H. & Watson, Mark W. (ed.), 1993. "Business Cycles, Indicators, and Forecasting," National Bureau of Economic Research Books, University of Chicago Press, edition 1, number 9780226774886, December.
    23. Diery Seck, 1996. "The Substitutability of Real Estate Assets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 24(1), pages 75-95, March.
    24. Harvey, Campbell R, 1991. "The World Price of Covariance Risk," Journal of Finance, American Finance Association, vol. 46(1), pages 111-157, March.
    25. Jordi Pons-Novell, 2003. "Strategic bias, herding behaviour and economic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 67-77.
    26. Kajal Lahiri & Xuguang Sheng, 2010. "Measuring forecast uncertainty by disagreement: The missing link," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 514-538.
    27. David C. Ling, 2005. "A Random Walk Down Main Street: Can Experts Predict Returns on Commercial Real Estate?," Journal of Real Estate Research, American Real Estate Society, vol. 27(2), pages 137-154.
    28. David C. Ling & Andy Naranjo, 1999. "The Integration of Commercial Real Estate Markets and Stock Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 27(3), pages 483-515, September.
    29. James H. Stock & Mark W. Watson, 1993. "A Procedure for Predicting Recessions with Leading Indicators: Econometric Issues and Recent Experience," NBER Chapters, in: Business Cycles, Indicators, and Forecasting, pages 95-156, National Bureau of Economic Research, Inc.
    30. Harrison Hong & Jeffrey D. Kubik, 2003. "Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts," Journal of Finance, American Finance Association, vol. 58(1), pages 313-351, February.
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    More about this item

    Keywords

    Property Forecasts; Forecast Errors; Momentum; Bias; Efficiency;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • L80 - Industrial Organization - - Industry Studies: Services - - - General

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