Oct 4, 2023

The “Moneyball” Case for Multifamily Investing

Identifying relative value & risk through data

Blake Walker / Director, Research & Strategy

In 2003, Michael Lewis published “Moneyball”1 (later adapted into a movie of the same name), amusing its audience with a narrative on the Oakland A’s progressive, deep-rooted analytics orientation and, eventually, unexpected success in Major League Baseball. As told, the A’s adopted a quantitative approach to player evaluation and roster construction, exploiting conventional wisdom that mistakenly elevated the importance of certain player attributes, like physique, and the misunderstood value of metrics like batting average for alternative and more statistically valuable measures like on-base and slugging percentage.2

Through its progressive approach, the A’s not only became one of the winningest MLB teams (for a short period) but did so with one of the lowest payrolls. The data-dependent approach became a marvel – and, started a revolution in baseball challenging customary practices reinforced and inured over decades simply because that was the way baseball always operated. Today, most Major League teams practice some form of sabermetric research3 – the term coined to refer to the A’s numerical approach defined as “the search for objective knowledge about baseball.”4

Like the A’s, and now MLB, we believe objective research rooted in rigorous data analysis provides a better framework for developing insights. As a result, we’ve adopted a differentiated investment approach grounded in data and the examination of measurable metrics to identify relative value and risk.

Our recently expanded and proprietary ‘Heat Map Index’ (HMI) takes a fundamental approach to ascertain intrinsic or ‘true’ value across (i) multifamily (and other sectors and investment alternatives), and (ii) top US cities (MSAs), the latter of which we contend not only offer naturally distinctive opportunity sets and risks, but can, like securities, become mispriced for a variety of reasons.
In this way, the HMI produces insights on ‘when’ and ‘where’ to shift our investment behaviors toward “offense” or “defense” – and, because of its systematized evaluation of opportunity based not on forecasts per se but relative value “normalization”, insights are producible and replicable across varying economic landscapes. In other words, by identifying ‘true’ value, the HMI generates transparency where opacity otherwise exists during periods of limited transaction volume, when pricing dislocation exists, and opportunity is ripe (or risk is high). As history has proven, some of the best times for capturing outsized returns – and, importantly, avoiding risk – are under obscure conditions and in their aftermath.

Our excitement about the period ahead as we deploy the HMI and its methodology is discernable as, like the A’s, we see the benefits of a differentiated, data-intensive approach against a landscape where conventional practices generally still rule. Practices that inured over past decades during the secular decline of interest rates and cap rates, where risk-taking was effectively rewarded.

1 Shortened from “Moneyball: The Art of Winning an Unfair Game.”
2 Metric expressing “power-adjusted” batting average, where total bases via hit (i.e. single is one, double is two, etc.,) is divided by at-bats.
3 Major League Baseball
4 Bill James, baseball analyst, author and former Senior Advisor on Baseball Operations for the Boston Red Sox.