Abstract
This paper investigates whether fund managers investing in the direct real estate market can systematically and persistently deliver superior risk-adjusted returns. The research that has been published has typically focused on the performance of managers trading public real estate securities. Our study draws on a unique data set of commercial real estate funds collated by the Investment Property Databank (IPD) in the United Kingdom, covering up to 280 funds over the period 1981 to 2006. The widespread finding is that very few managers appear to be able to generate excess risk-adjusted returns. Furthermore, there is little evidence of performance persistence in either fund returns or risk-adjusted fund returns.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
Grossman and Stiglitz (1980) argue that if information is costly, the usual assumption of the efficient markets hypothesis, that prices reflect all available information, may not hold. It is only worthwhile for investors to acquire information if they obtain a return from doing so.
The appraisal-smoothing problem in real estate has been extensively discussed in the literature, see Bond and Hwang (2007) for a summary of the literature. However, given the long-term nature of real estate investors and the sample horizons used in this study, such short term persistence effects may not be relevant to the analysis.
That is, the best forecasters may keep their forecasts private, and owners and managers may have other objectives in submitting their forecasts, for instance, to make their region look more attractive to investors.
It would have been preferable to use higher-frequency data. However, in the UK such data is much less extensive than annual data, relating to a small number of funds on a monthly basis (around 70 compared to over 200 annually) and to a short period (2001 onwards) on a quarterly basis. There are also indications that higher-frequency data would not have enhanced the analysis.
See Chan et al. (2006) for a detailed survey of risk-adjusted benchmarking of fund manager performance.
The risk-free rate for each year is determined by the quarterly average three-month Treasury bill rate.
As explained in “The Data”, the IPD indices are derived from details voluntarily submitted to the IPD by funds for benchmarking and performance measurement and attribution.
This is inferred by comparing the performances of the evaluation and ranking periods of the same period (e.g. the set of funds in the evaluation period 1992–1997 with those of the ranking period 1992–1997)—the latter includes the funds created during the previous ranking period.
If there was no persistence, the probabilities of remaining above or below median is 25%. It does not equal 50% in this calculation as expired funds are included in the analysis.
The lagged value of alpha is only significant (at the 10% level) in one of the four periods considered for analysis; lagged performance was significant at the 10% level in two out of the four 5-year periods considered.
References
Bond, S. A., Hwang, S., Mitchell, P., & Satchell, S. E. (2007). Will Private Equity and Hedge Funds Replace Real Estate in Mixed-Asset Portfolios?. Journal of Portfolio Management, Special Real Estate Issue, 74–84.
Bond, S. A., & Hwang, S. (2007). Smoothing, nonsynchronous appraisal and cross-sectional aggregation in real estate price indices. Real Estate Economics, 35, 349–382.
Brounen, D., Eichholtz, P., & Ling, D. C. (2007). Trading intensity and real estate performance. Journal of Real Estate Finance and Economics, 35, 449–474.
Brown, S., & Goetzmann, W. N. (1995). Performance persistence. Journal of Finance, 50, 679–698.
Busse, J. A., & Irvine, P. J. (2006). Bayesian alphas and mutual fund persistence. Journal of finance, 61, 2251–2288.
Carhart, M. M. (1997). On persistence in mutual fund performance. Journal of Finance, 52, 57–82.
Carhart, M. M., Carpenter, J. N., Lynch, A. W., & Musto, D. K. (2002). Mutual fund survivorship. Journal of Finance, 15, 1439–1463.
Chan, K. C., Dimmock, S. G., & Lakonishok, J. (2006). Benchmarking Money Manager Performance: Issues and Evidence, NBER Working Paper 12461.
Chiang, K. C. H., Kozhevnikov, K., Lee, M.-L., & Wisen, C. H. (2008). Further evidence on the performance of real estate funds: the case of real estate mutual funds. Real Estate Economics, 36, 47–62.
Chun, G. H., Sa-Aadu, J., & Shilling, J. D. (2004). The role of real estate in an institutional investor’s portfolio revisited. Journal of Real Estate Finance and Economics, 29, 295–320.
Devaney, S. P., Lee, S. L., & Young, M. S. (2007). Serial persistence in individual real estate returns in the UK. Working Paper, University of Reading.
Eichholtz, P., Kok, N., & Margaritova, A. (2009). Investment Style and Performance in The Global Real Estate Mutual Fund Market, paper presented at the RERI annual meeting, April 2009.
Gallo, J. G., Lockwood, L. J., & Rutherford, R. C. (2000). Asset allocation and the performance of real estate mutual funds. Real Estate Economics, 28, 165–184.
Goetzmann, W. N., & Ibbotson, R. G. (1994). Do winners repeat? Patterns in mutual fund return behavior. Journal of Portfolio Management, 20, 9–18.
Grinblatt, M., & Titman, S. (1989). Mutual fund performance: an analysis of quarterly portfolio holdings. Journal of Business, 62, 393–416.
Grossman, S. J., & Stiglitz, J. E. (1980). On the impossibility of informationally efficient markets. The American Economic Review, 70, 393–408.
Hahn, T. C., Geltner, D., & Gerardo-Lietz, N. (2005). Real estate opportunity funds. Journal of Portfolio Management, Special Real Estate Issue, 143–153.
Hartzell, J. C., Muhlhofer, T., & Titman, S. (2009). Alternative benchmarks for evaluating REIT mutual fund performance. Real Estate Economics.
Hendricks, D., Patel, J., & Zeckhauser, R. (1993). Hot hands in mutual funds: short-run persistence of relative performance 1974–1988. Journal of Finance, 48, 93–130.
Investment Property Databank. (2007). The IPD Index Guide, Edition 2.
Jensen, M. C. (1968). The performance of mutual funds in the period 1945–64. Journal of Finance, 23, 389–416.
Kallberg, J., Liu, C. L., & Trzcinka, C. (2000). The value added from investment managers: an examination of REIT funds. Journal of Financial and Quantitative Analysis, 35, 387–408.
Key, T., & Marcato, G. (2005). Direct investment in real estate: momentum profits and their robustness to trading costs. Journal of Portfolio Management. Special Real Estate Issue, 55–69.
Kosowski, R., Naik, N. Y., & Teo, M. (2007). Do hedge funds deliver alpha? A Bayesian and bootstrap analysis. Journal of Financial Economics, 84, 229–264.
Kosowski, R., Timmermann, A., Wermers, R., & White, H. (2006). Can mutual fund “Stars” really pick stocks? New evidence from a bootstrap analysis. Journal of Finance, 61, 2551–2595.
Lee, S. L., & Ward, C. W. R. (2000). Persistence of UK real estate returns: A Markov Chain analysis, Working Paper, Department of Land Management, University of Reading.
Lee, S. (2003). The Persistence of Real Estate Fund Performance paper presented at the 19th Annual American Real Estate Society Meeting, April 2003.
Lin, C. Y., & Yung, K. (2004). Real estate mutual funds: performance and persistence. Journal of Real Estate Research, 26, 69–93.
Ling, D. C. (2005). A random walk down main street: can experts predict returns on commercial real estate. Journal of Real Estate Research, 27, 137–154.
Malkeil, B. G., & Saha, A. (2005). Hedge funds: risk and return. Financial Analysts Journal, 61, 80–88.
O’Neal, E., & Page, D. (2000). Real estate mutual funds: abnormal performance and fund characteristics. Journal of Real Estate Portfolio Management, 6, 239–47.
Sharpe, W. F. (1992). Asset allocation, management style and performance measurement. Journal of Portfolio Management, 7–19.
Acknowledgements
The research was funded and commissioned under the auspices of the Investment Property Forum’s Research Programme (2008–2009). The authors acknowledge the cooperation of the Investment Property Databank, in particular Malcolm Frodsham and Roberto Diaz. The authors are also thankful for helpful comments received from Katrina Bond, Kevin Chiang, Piet Eichholtz, John Glascock, Charles Ward, and an anonymous referee, as well as participants at the European Real Estate Society Annual Conference 2008, the Maastricht-MIT Real Estate Symposium 2008, and the American Real Estate and Urban Economics Association Annual Meeting 2009.
Author information
Authors and Affiliations
Corresponding author
Appendix Potential Survivorship Bias in the Dataset
Appendix Potential Survivorship Bias in the Dataset
This appendix provides additional information about the impact of the exclusion of funds without a full history in the ranking period. Table 1 provides a breakdown of the data set to show the number of funds omitted from the ranking period for each of the five and ten-year intervals used in this study because they do not survive for the complete time of the ranking period (e.g. in the 1987–1991 ranking period five funds were omitted for this reason). In addition, new funds entering the sample are not included in the analysis if they are not in existence at the start of the ranking period; however, if the funds survive for the full length of the evaluation period they will be included in the subsequent window of analysis.
To explore the impact such exclusions may have on this study we first examine the average returns of the funds excluded to see if they differ from the funds remaining in the sample. For each set of ranking and evaluation periods Table 10 shows the average of the funds excluded to assess if there is a systematic bias associated with the omissions. The first three columns focus on the five-year ranking periods (1982–1986, 1987–1991, 1992–1996, 1997–2001), with column one displaying the (unweighted) annual averages for the funds included in the analysis. The second column displays the annual averages for those funds existing in the first year of the ranking period but subsequently excluded because they did not survive for the complete five years of the ranking period (the number of funds involved is shown in Table 1). Inspection of the columns shows that the excluded funds do have a lower average return than the funds remaining in the analysis. Column three shows the corrected annual average return for all funds including the previously omitted funds. Generally the impact of excluding funds without a full history in the ranking period is minor, as the number of non-surviving funds was small relative to the overall number of funds during the first part of the sample. The greatest impact is seen to occur in the late 1990s period (the 1997–2001 horizon). Note that funds that have a full history for the ranking period but subsequently expire during the evaluation period have been included in all analyses reported in this paper.
The final three columns of Table 10 assess the impact of the decision to exclude funds that begin after the first year of the ranking period and have a full history for the subsequent evaluation period (for example, a fund that commences in 1999 and remains in operation until 2006 will be excluded from the 1997–2001 ranking period). Inspection of columns four and five shows that excluding the newer funds from the analysis may bias downward the average performance in the evaluation period (as the newer funds have a higher average performance). Column six shows the corrected fund averages during the ranking period.
The slight difference in fund average performance between columns three and six reflects the fact that a small number of funds may have existed for a short period of time outside of the key starting dates for the five-year windows (e.g. a fund that starts in 1998 and lasts only two years).
Therefore, even though the economic impact of the omissions appears to be small, there is clearly the potential for bias in the tests of persistence. In particular, the omission of funds that performed below average in the ranking period, and those that performance above average in the evaluation period, may bias tests of persistence downward. To evaluate this potential bias, we focus on the five-year period that is likely to be most affected by the bias (ranking period 1992–1996 and evaluation period 1997–2001). In calculating the chi-square test and cross-product ratio test, the chi-square test showed most impact (rising from 7.19 to 15). However, neither the revised chi-square test nor the cross-product ratio test (which showed little change), altered the conclusions drawn from the main part of the study.
Rights and permissions
About this article
Cite this article
Bond, S.A., Mitchell, P. Alpha and Persistence in Real Estate Fund Performance. J Real Estate Finan Econ 41, 53–79 (2010). https://doi.org/10.1007/s11146-009-9230-y
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11146-009-9230-y