“Ultimately, what drives performance may not be what drives capital." That's the conclusion of a new paper on real estate mutual funds, and it should unsettle anyone who assumes capital allocation is a rational sorting mechanism.

Institutional and retail investors both aspire to own real estate, seeking consistent returns that, at least to an extent, are decoupled from the performance of other asset types. One would think that investors would pick from a set of opportunities - direct investment, small private funds, institutional vehicles or public mutual funds and REITs - and optimize for performance. But what actually happens is much more complicated, according to a new study.¹

The paper focused on real estate mutual funds, which own baskets of REIT stocks and have total assets under management of between $100 and $150 billion. Although not a large portion of the nearly $4 trillion of investable commercial real estate in the U.S., real estate mutual funds are revealing, as fund flows and comparisons between strategies are easy to ascertain, and these vehicles mark-to-market every day. And investor preferences are further revealed as these funds offer institutional and retail classes of shares.

The findings: Unadjusted returns are the “dominant driver of fund flows” for retail-oriented mutual funds. That means retail investors make real estate investing decisions based on 12-month, raw returns, without much consideration for risk. Institutionally focused funds see flows based on risk-adjusted returns, suggesting a higher level of sophistication.

The researchers note that the observed lack of sophistication among retail investors echoes findings in other papers. One study found that retail investors in private, unlisted REITs “make suboptimal decisions influenced by poor financial advice.” Not that institutional investors are a paragon of optimal decision making: the Haystack has written before about the suboptimal returns institutional investors often see in private real estate investing and their challenges in identifying the best managers. It may be that institutional investors show more discipline here because mutual funds make risk-adjusted sorting considerably easier.

Mutual fund performance reflects these different investor bases. As raw returns increase, fund flows to retail-focused mutual funds increase, but an increase in risk-adjusted returns has no discernible impact on investment flows into these funds. The opposite is true for institutionally focused mutual funds: improvements in risk-adjusted returns drive new investment, an improvement in simple returns has no relationship to growth in AUM.

It’s not helpful that multiple mutual fund benchmarks exist, each measuring “average” performance differently. Real estate fund managers have some flexibility to choose the benchmarks to which they are compared. This problem is especially acute because one needs a special set of criteria to benchmark real estate mutual funds, as distinct from general market funds. “Notably, the choice of benchmark model can substantially affect conclusions regarding whether [real estate mutual fund] managers generate alpha or even track the market effectively,” the paper notes.

Using appropriate, real-estate-fund-specific benchmarks, you can see evidence of the different ways retail and institutional clients invest. Net alphas - risk-adjusted returns after fees are paid to the manager - for retail-focused real estate mutual funds are negative on average during the study period from 1995 to 2022. Net alphas for institutional funds are slightly positive. Interestingly, gross alphas - risk-adjusted returns before fees - are positive for both retail- and institutionally oriented mutual funds, suggesting fund managers in general do perform better than a passive real estate mutual fund, even if some of them receive all that upside as fees.

And while simple raw returns motivate retail investors and risk-adjusted returns motivate institutional actors, there’s room for improvement on all fronts. That’s because institutional investors are responding primarily to basic “CAPM-based alphas,” which is a risk-adjusted return measure based on the classic Capital Asset Pricing Model, a 1960’s-era tool that was one of the first models to factor investment risk into performance metrics. The CAPM models have evolved, and now we have much better measures of risk-adjusted returns that “most accurately explain historical fund performance.” But the most sophisticated performance metrics “are largely ignored by both retail and institutional investors.”

As usual, outcomes from these types of principal/agent relationships are a function of incentives. In this case, real estate mutual funds earn fees based on total assets under management. As such, retail-oriented fund managers, consciously or otherwise, chase performance metrics that will attract capital, and in this case that’s “maximizing short-term raw returns.” The risks they take in service of that goal are not heavily scrutinized. Mutual fund managers that chase institutional capital know to maximize risk-adjusted returns.

The disparities in outcomes are in some ways surprising since real estate is a discipline of measurement: cash flows, comparables, benchmarks, and now increasingly real-time pricing. You would think it would be easier for capital to find the places it’s treated best, especially in transparent vehicles like REITs and mutual funds. But this paper adds to a growing body of research suggesting that even when real estate data is available, both private and public investors don’t consistently use it in the ways you might expect. Decisions cluster around simpler signals and incentives that don’t line up with optimal long-term outcomes. We still have a lot of room for improvement.

Who: Dataplor

What: One of the leaders in location intelligence.

The Sparkle: We’re still thinking about the value of various GIS applications - those that pair geographic information with data - and Dataplor has extensive data on foot-traffic in urban locations, even on a retailer-by-retailer basis. They track openings, closings and trends in foot traffic at a granular level. There’s value in knowing that point-related data, but also value in knowing trends at the city block-level and regionally. For a fun case study, here’s how an EV charger company is thinking about future locations using Dataplor information.

The smoothest ride on water is on a prototype Glider SS18 craft from a British boatmaker that features two buoyant, aluminum hulls.

Instead of riding on the surface, the two hulls cut right through waves, which is why the cabin needs to be elevated by six feet. The result is a “magic carpet ride” of smooth sailing, but because water drag is dramatically reduced the boat can cruise at 45 miles per hour.

Link: https://www.mby.com/specials/worlds-coolest-boats-glider-ss18-a-powercat-on-stilts-134807

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