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Active Management in Real Estate Mutual Funds

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Abstract

This paper examines active management in real estate mutual funds (REMFs). The REMF industry has expanded as the underlying REIT industry has developed over time, but the number of REMFs experienced a sharp decline following the global financial crisis. The likelihood of termination is greater for smaller funds and funds with higher expense ratios. Using various measures of active management (Fund R2, Active Share, Property-Type Concentration Index, and Return Gap), we observe that real estate fund managers have become less active over time. In contrast to the findings for more broadly diversified equity funds, these activeness measures do not explain the future performance of REMFs. To the extent that geographic diversification measures activeness, we find no evidence that the performance of REMFs holding geographically diversified portfolios differs from the performance of REMFs with concentrated portfolios. Overall, our findings shed light on the uniqueness of REMFs relative to diversified equity mutual funds.

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Notes

  1. See, for example, Chan et al. (1998) and Young (2000).

  2. Our reading of selected REMF prospectuses suggests that fund portfolios are mostly focused on REITs. In addition, Gallo et al. (2000) state that “real estate funds are allowed to invest 35% of their assets outside of real estate.” We recognize that a portion of a fund’s portfolio can be allocated to non-REIT real estate investments, such as real estate operating companies, homebuilders, or directly in properties, and these non-REIT investments can contribute to REMF activeness and fund performance. However, since the vast majority of the REMFs’ holdings are in REITs, the overall fund activeness and its relation to performance should be substantially reflected in a fund’s asset allocation among these securities.

  3. Whether the performance of mutual funds is driven by luck or skill has been an ongoing debate for decades. Earlier studies (e.g., Jensen 1968; Malkiel 1995; Gruber 1996; Carhart 1997; Fama and French 2010;) suggest that mutual fund performance is likely to be attributed to chance. Pástor et al. (2015) find that active managers have become more skilled over time, but this skill does not enhance fund performance, due to the increased size of the active fund industry.

  4. These data are from the Investment Company Institute 2017 Fact Book.

  5. http://money.usnews.com/money/personal-finance/mutual-funds/articles/2013/06/10/are-there-too-many-mutual-funds

  6. See, for example, Gallo et al. (2000) and Hartzell et al. (2010).

  7. For robustness, we also construct the logistic transformation of R2, as in Amihud and Goyenko (2013). This is performed to improve the qualities of the R2-distribution. Our results are similar when using this version of the activeness measure.

  8. As an alternative, when we treat the National Association of Real Estate Investment Trusts (NAREIT) US Real Estate Index or the Ziman value-weighted REIT index as the market benchmark, R-squared values from the rolling regressions are high, and do not exhibit much variation. However, our main results remain qualitatively consistent.

  9. For robustness, we used the National Association of Real Estate Investment Trusts (NAREITs) index as a benchmark, and the results were similar.

  10. Other studies also apply this method of name screening. See, for example, Kacperczyk et al. (2008) and Amihud and Goyenko (2013).

  11. We use these 23 index funds later for comparative purposes.

  12. Kaushik and Pennathur (2013) note that 90% of REITs are equity REITs, and real estate mutual funds invest primarily in equity REITs.

  13. When we apply these filters to the sample of REMFs, we obtain 124 funds. This is an 18% reduction from 151 funds in our sample. Since our results are not affected by these filter applications, we report the baseline findings for 151 funds.

  14. Since Fund (1-R2) is estimated using 36 monthly observations, we also use non-overlapping periods by measuring the independent variables as of the end of the quarter before the beginning of the 36-month window of the Fund (1-R2) estimation. We obtain similar results, as activeness measures tend to be persistent over time.

  15. We control for fund characteristics, such as fund size, expense ratio, turnover, and age, which are typically used in alpha-predictive regressions in the mutual fund literature. See, for example, Cremers and Petajisto (2009), Amihud and Goyenko (2013), Kacperczyk et al. (2005, 2008).

  16. We find no significant activeness-performance relation for REMFs when alpha is measured over a longer period of one year.

  17. We explore whether our results apply to funds that trade more actively than others. In each quarter, we classify all REMFs into two groups, below and above the median, based on their portfolio turnover, as reported in CRSP. We find no relation between activeness and performance across both groups of this sort.

  18. The results remain qualitatively consistent when the other three alternative alpha measures are used.

  19. Due to data limitations, our sample ends in 2015. We were able to partially update our analysis by extending two activeness measures, Fund (1-R2) and Return Gap, and two fund performance measures, αCAPM and α4-factor, through December 2018. We repeat the analyses in Table 5, and find similar results. This partially updated analysis is available upon request.

  20. Other mutual fund studies also show that coefficients on some control variables are sensitive to the method of measuring fund alpha. See, for example, Table 2 in Kacperczyk and Seru (2007). The coefficient on fund size is positive and statistically significant when the dependent variable is the three-factor alpha, and significantly negative when the dependent variable is the four-factor alpha. The coefficient on fund turnover also switches sign.

  21. We obtain the CCRSI value-weighted index data from https://www.costargroup.com/costar-news/ccrsi.

  22. Consistent with Kallberg et al. 2000, we find that the NAREIT and CCRSI indices are uncorrelated. The correlation coefficient in our sample is −0.001 (p value = 0.98).

  23. The Western region includes states AK, CA, CO, HI, ID, MT, NV, OR, UT, WA, and WY; the Southern region includes AL, AZ, AR, DE, DC, FL, GA, KY, LA, MD, MS, NM, NC, OK, SC, TN, TX, VA, and WV; the Northeastern region includes CT, ME, MA, NH, NJ, NY, PA, RI, VT; the Midwest includes IL, IN, IA, KS, MI, MN, MO, NE, ND, OH, SD, and WI.

  24. Gyourko and Nelling (1996) use this approach to measure diversification in REITs.

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Acknowledgements

We thank McKay Price, Minjoon Lee, Jerchern Lin, Leng Ling, Crocker Liu, as well as participants at the Financial Management Association 2017 Annual Meeting, the Southern Finance Association 2017 Annual Meeting, the Eastern Finance Association 2018 Annual Meeting, and the American Real Estate and Urban Economics Association 2018 National Conference for their suggestions. This study was supported by the Michael J. Morris Grant for Scholarly Research from Saint Joseph’s University.

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  1. Department of Finance, Haub School of Business, Saint Joseph’s University, 5600 City Avenue, Mandeville 239, Philadelphia, PA, 19131, USA

    Viktoriya Lantushenko

  2. Department of Finance, Drexel University, 3220 Market Street, 11th Floor LeBow Hall, Philadelphia, PA, 19104, USA

    Edward Nelling

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  1. Viktoriya Lantushenko
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Correspondence to Viktoriya Lantushenko.

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Lantushenko, V., Nelling, E. Active Management in Real Estate Mutual Funds. J Real Estate Finan Econ 61, 247–274 (2020). https://doi.org/10.1007/s11146-019-09722-y

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