An Alternative Approach To REIT Selection: Going Quant

If there’s one thing that’s plentiful on the internet today its commentary on Real Estate Investment Trusts and plenty of interested investors eager to consume it. Much of it, however, consists of largely subjective fundamental judgment. That’s fine. There are some good people doing it and I subscribe to some their services, one of which is Brad Thomas’ Forbes Real Estate Investor. What seems to be missing, though, is a primarily quantitative market-oriented point of view. My aim here is to start to fill that gap.

REITS for today


Applying The Chaikin Approach To Real Estate Investment Trusts

The Chaikin Power Gauge model is a 20-factor fundamental/technical protocol based not on statistical data mining but on the experience of Wall Street veteran Marc Chaikin in terms of how successful investors invest, which distinguishes it from the Fama-French inspired generation of academic-type formulations.

As with most models that address fundamentals, conventional financial statement items and valuation metrics are utilized. REIT investors, however, tend to work with different sets of numbers. These correlate loosely with traditional Power Gauge factors; enough so to allow one to get by using the full model. But I find that the effectiveness of the approach can be significantly enhanced by (i) eliminating from the outset the highest yields, the ones that suggest bearishness on the part of Mr. Market, and (ii) paying extra attention two of the four Power Gauge factor categories, Earnings Growth and Expert Opinion and allowing for consideration of REITs with Neutral or better overall ranks if the sub-ranks for these two categories are also neutral or better.

Table 1 shows the results of a backtest of this flexible approach; it covers the period of time during which Power Gauge was in live use. The analysis is based on T-B (Return of the Top Group minus return of the bottom Group) and Hits versus Misses (T-B being positive is a “Hit.”) Since this is a screen based analysis rather than a typically ten-“bucket” academic study, I use only two groupings; the Top consists of the group of REITs that satisfied my criteria,; the Bottom refers to all other REITs, those that failed.

Table 1

Table 1

Table 1

Picking Specific REITs

However much modeling one wants to do, it’s important today to be concerned with rent collection, the extent to which tenants are“essential.” Power Gauge’s sentiment-based factors indirectly pick up on this by tapping into collective investment-community judgments, and now, with a post-covid quarter in the books, even some of the more conventional numbers can contribute here. In any case, quant should never be a do-or-die dogmatic thing. It should be used to assist human judgment, not eliminate it.

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Disclosure: Long BRT, CUZ, DRE, GRP.U, NSA, PLYM, RESI, WSR

Disclosure: None.

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