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Real Estate Fund Flows and the Flow-Performance Relationship


Convexity in the flow-performance relationship of traditional asset class mutual funds is widely documented, however it cannot be assumed to hold for alternative asset classes. This paper addresses this shortcoming in the literature by examining the flow-performance relationship for real estate funds, specifically open-end, direct-property funds. This investment vehicle is designed to provide the risk-return benefits of private market real estate and is available to retail investors in many countries across the globe. An understanding of fund flow dynamics associated with this investment vehicle is of particular interest due to the liquidity risk associated with holding an inherently illiquid asset in an open-end structure. Our analysis draws on the theoretical foundations provided in the literature on mutual fund flows, performance chasing, liquidity risk, participation costs and dynamics across market cycles. We focus on German real estate funds from 1990 to 2010 as this is the largest market globally and there is a high level of confidence in the data. The results show that real estate fund investors chase past performance at the aggregate level and the relationship between flows and relative performance is asymmetric (i.e., convex) at the individual fund level. Fund-level liquidity risk tends to weaken convexity, while sensitivity increases with higher participation costs. We find the flow-performance relationship varies across time, though our interpretation is asset and investment vehicle specific. The implications are applicable to investors and fund managers of open-end, direct-property funds and, more broadly, other alternative asset funds where the underlying asset may not be liquid.

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  1. A global overview of countries with relevant markets for open-end, direct-property funds is provided in Downs et al. (2015).

  2. We are grateful to an anonymous referee for many useful suggestions in this section.

  3. Our measure of fund flows is based on actual buying and selling decisions of investors, whereas most studies covering the US or UK market approximate net flows by the following formula: Flow(t) =(Fund size(t)-Fund size(t-1)*(1+Fund return(t)))/Fund size(t-1). This approximation formula assumes that all flows occur at the end of the period. Furthermore, dividend payments are treated as outflows, although they do not reflect investor decisions. In contrast, our flow data treats dividend reinvestments as an inflow as investors might be more willing to reinvest their dividends into the fund if they are satisfied with the performance.


  5. Legally, semi-institutional funds are retail funds. The similarity stops there. The minimum investment for semi-institutional funds starts at half a million Euros. We identify 13 semi-institutional fund openings in our sample.

  6. Note that fund level returns and their standard deviations refer to monthly measures of the total return over the previous twelve months, while all other variables refer to monthly data.

  7. Real estate specific fees for asset and property management are fund expenses and not paid directly by the investor. The relatively low fee for the real estate funds in our sample is likely due to scale economies and the fact that real estate funds are much larger than other asset funds. See Downs et al. (2015) for additional details on German open-end, direct-property funds.

  8. At the request of an anonymous referee, we conduct robustness tests by ranking funds based on risk-adjusted performance using one-factor and four-factor alphas. Overall, our results are stable to using these alternative ranking procedures. The results are available from the authors upon request.

  9. In untabulated results, we obtain consistent results using a more conservative approach for the performance decomposition.

  10. Our results are robust to using time-period fixed effects and standard errors clustered by funds, as well as time- and fund-fixed effects. We thank an anonymous referee for suggesting the latter approach as the superior means to address the methodological issues discussed in Olivier and Tay (2008).

  11. Our measure for fund liquidity is based on the cash reserves or “liquid assets” of the funds. Alternative liquidity measures might consider the debt capacity of real estate funds as another dimension of liquidity. Real estate funds are allowed to use leverage of up to 50 % of asset value. For example, a fund with a leverage ratio of 30 % might raise an additional 20 % of cash by borrowing against its properties until the 50 % limit is reached. A law introduced in 2007 restricts the amount of leverage that a fund may use in order to finance redemptions to 10 % of a fund’s size. In untabulated results we use a measure for fund liquidity that accounts for debt capacity. We find our results are robust to this alternative measure for fund liquidity. Additionally, when we include fund leverage as a control variable and the results do not change.

  12. Our results are robust to using alternative definitions of the financial crisis period, for example 2008 to the end of 2009.

  13. At the request of an anonymous referee, we consider the potential for tournament behavior of the type studied by Brown et al. (1996) among managers of open-end, direct-property funds. In such a setting, managers with lower relative performance midway through the year increase portfolio risk with the intention of catching peer performance by the end of the year and, consequently, attracting performance-chasing fund flows in the next period. Brown et al. (1996) hypothesize that the convexity of the flow-performance relationship incentivizes this behavior as investor response to past performance is asymmetric, hence a winning strategy benefits much more than a losing strategy suffers. Olivier and Tay (2008) consider how this behavior changes over the economic cycle when convexity is time-varying.

    While we believe this line of inquiry to be interesting and, potentially, the subject of future research, we maintain that it is beyond the scope of this paper. Perhaps more importantly, it seems unlikely that real estate fund managers could affect performance mid-period by altering risk, especially given the transaction times and liquidity of the underlying asset markets.

    At the same time, it is plausible that real estate fund managers might attempt to improve (net) performance by reducing fund fees. In untabulated results we find that fee changes are positively correlated with fund liquidity and performance, i.e., fees tend to decrease following poor performance and low fund liquidity. Although we find these results intuitive, we are not in a position to argue for causation in the current study. Nonetheless, we appreciate the referee drawing our attention to this potentially important issue.

  14. See Esrig et al. (2013) for a discussion of how defined contribution retirement planning may benefit from open-end, direct-property funds. Also, see as an example of the globalization of this real estate investment vehicle.


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We received excellent feedback from several discussants, including Aleksandar Andonov (2014 MNM Conference), Piet Eichholtz (2015 AREUEA-ASSA), and Michael White (2014 AREUEA-International) as well as helpful comments by participants at those presentations. We appreciate the comments and suggestions of an anonymous referee and the special issue editor S.E. Ong. All authors are grateful for generous support provided by the International Real Estate Business School (IREBS) Foundation. Downs gratefully acknowledges support by The Kornblau Institute at Virginia Commonwealth University. The views expressed in this article are those of the authors only and do not necessarily reflect the views of the European Central Bank.

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Correspondence to René-Ojas Woltering.

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Downs, D.H., Sebastian, S., Weistroffer, C. et al. Real Estate Fund Flows and the Flow-Performance Relationship. J Real Estate Finan Econ 52, 347–382 (2016).

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  • Open-end real estate funds
  • Fund flows
  • Flow-performance relationship
  • Liquidity risk

JEL Classification

  • G11
  • G14
  • G24