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The Predictability of Real Estate Excess Returns: An Out-of-Sample Economic Value Analysis

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Abstract

We examine the predictability of private and public real estate returns using recursive, out-of-sample, linear and Markov switching models, employing a rich set of predictor variables. We find considerable improved predictive power compared to simple regression models, especially at the intermediate horizon. Next, we test whether such improved forecasting accuracy translates into a positive risk-adjusted out-of-sample performance by performing a recursive mean-variance portfolio allocation analysis. We observe significant improvements in realized Sharpe ratios and mean-variance utility scores, especially when employing Markov switching models and exploiting predictability at intermediate horizons. Furthermore, our results are robust to the inclusion of transaction costs.

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Notes

  1. More specifically, for example, in the case of public real estate and H = 1, we exploit all the information available at the end of December 2004 to forecast the excess return as of the end of January 2005; in the case of H = 6, we exploit the information available at the end of July 2004 to predict the cumulative, 6-month excess return over the period August 2004 – January 2005; in the case of H = 60, we use the information available in January 2000 to forecast the 60-month excess return over the period February 2000 – January 2005. A similar logic is applied to private real estate, but with quarterly frequency.

  2. At least in-sample, the fit of the model would benefit from setting K > 2. In this sense, our results may be interpreted as providing a lower bound to the empirical results, one could obtain. However, it is unclear whether increasing the number of regimes may improve the OOS predictive accuracy. To balance between empirical fit and estimation burden, especially in the case of private real estate, for which we have shorter available time series, we focus on the case of K = 2.

  3. Following Campbell and Thompson (2008), Goyal and Welch (2008) and Rapach et al. (2010), we constrain the weights of the risky assets to lie between 0% and 150%, so that wi = 0 if wi < 0,and wi = 150% if wi > 1.5.

  4. While Ling et al. (2000) also experiment with higher transaction costs, their sample is based a period prior to 2000 when transaction costs were arguably higher than during our 2005–2018 sample period. Therefore, adopting their “low cost” configuration is sensible for the purposes of our analysis.

  5. The Index of Consumer Sentiment is available at http://www.sca.isr.umich.edu/charts.html.

  6. However, to avoid losing an excessive number of observations, in the case of NCREIFMOM, we fill the initially missing values with momentum estimates based on REITs data, available since 1972.

  7. We do not report all outputs of these estimation problems for brevity. The outputs are available from the authors upon request. Such tables and plots consist of in-sample outputs and are only indirectly related to the forecasting performance of the different models and their power to generate economic value. For instance, MS models (differently from linear predictive regressions) tend to imply increasing R-squares that exploit their non-linear flexible features, but this is known to not always imply a satisfactory forecasting performance.

  8. In particular, Figs. 1 and 2 refer to the MSIH model.

  9. The graphs derived from the remaining models carry similar qualitative features and are available from the authors upon request.

  10. This is not a necessary condition, as the fact that maximum likelihood (ML) estimates of the MS slope coefficients ought to straddle OLS estimates is neither an implication of the problem, nor it has been imposed.

  11. All models seem to systematically under-predict the actual values of the series. Note that this is possible, although grossly sub-optimal in OOS tests, even though by construction the in-sample residuals are forced to have zero mean.

  12. The case of H = 60 months sits in between, in the sense that while the MS models struggle to guarantee positive \( {R}_{OOS}^2 \) (even though a few of them lead to \( {R}_{OOS}^2 \) in excess of 0.5), RMSFE tends to favor both MSI and MSIH over linear models.

  13. In fact, on average for H = 3 and 6 months, MSI tends to generally outperform MSIH, with occasional exceptions. Because MSIH implies more delicate estimation issues vs. MSI, in the text we focus on MSI.

  14. In Tables 4 and 5, the results for the realized recursive mean returns generally mimic the results reported for the realized Sharpe ratios and utility gains. As there is no evidence of predictive power at H = 60, both the linear and the MS models (especially the MSI model) fail to generate economic value when compared to the sample mean benchmark. We caution the reader that using an H = 60 horizon implies loss of data to perform the back-testing using the last portion of the sample and this implicitly limits the assessment of the economic value over the period of 2005–2013, giving a considerable weight to the realized returns during the GFC.

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Acknowledgements

We gratefully acknowledge Wayne Archer, David C. Ling, Gianluca Marcato and Andy Naranjo for valuable comments and suggestions. We also thank the participants of the USF-UF-UCF Critical Issues in Real Estate Symposium, 2019, for helpful discussion.

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Authors and Affiliations

  1. Bocconi University, Via Guglielmo Röntgen 1, 20136, Milan, Italy

    Massimo Guidolin

  2. Baffi-CAREFIN Center, Milan, Italy

    Massimo Guidolin

  3. IGIER, Milan, Italy

    Massimo Guidolin

  4. Bicocca University, Piazza dell’Ateneo Nuovo, 1, 20126, Milan, Italy

    Manuela Pedio

  5. Baffi-CAREFIN Center, Milan, Italy

    Manuela Pedio

  6. Whitman School of Management, Syracuse University, 721 University Ave, Syracuse, NY, 13244, USA

    Milena T. Petrova

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  1. Massimo Guidolin
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Correspondence to Milena T. Petrova.

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Appendix

Appendix

Table A1 Private real estate – realized OOS performance over sub-samples
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Table A2 Public real estate – realized OOS performances over sub-samples
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Table A3 Private real estate – mean-variance realized OOS performance (γ = 2)
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Table A4 Private real estate – mean-variance realized OOS performance (γ = 10)
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Table A5 Public real estate – mean-variance realized OOS performance (γ = 2)
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Table A6 Public real estate – mean-variance realized OOS performance (γ = 10)
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Guidolin, M., Pedio, M. & Petrova, M.T. The Predictability of Real Estate Excess Returns: An Out-of-Sample Economic Value Analysis. J Real Estate Finan Econ 67, 108–149 (2023). https://doi.org/10.1007/s11146-020-09769-2

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