Real estate has always had a data problem. Granular operating performance - NOI, cap rates, operating expenses, capital expenditures by property type and metro - is either locked inside private ownership, expensive to access, or simply unavailable.

Publicly traded REITs have significant disclosure requirements and between their operating results and their stock price trends, they have taught us a lot about the things you’d expect, including portfolio construction strategies and the overall benefits and challenges of owning each major asset class of U.S. real estate at scale.

And while we can’t yet use REIT data to get to the individual asset performance, with an abundance of REIT reporting since the 1990s and now with the benefit of modern analytical techniques, we’re getting closer. Thanks to new research, for the first time, a methodology exists to extract those fundamentals directly from publicly available REIT filings, updated quarterly, down to the MSA level.¹

Specifically, the study’s authors developed a new methodology that estimates property values, NOI, cap rates, operating expenses and capital expenditures per square foot, all by property type and MSA, over time. They study but don’t publish similar time-series results on effective gross income, total return, and a free cashflow-based yield. They derive all that from publicly available REIT reporting and the estimates from their models can be updated quarterly as REITs publish new operating and earnings results. The researchers tested this new methodology on the entire REIT universe, tracking REIT-owned assets from 2004 to 2018, and did significant analysis of office properties in LA and apartments in Atlanta.

It sounds wonky but this is big news because real estate is opaque, thinly traded and broadly held. That means operating performance especially over meaningful time periods is hard to find unless you own an asset. Even then you may not know if you’re managing it efficiently. This type of performance data is especially inaccessible to people without CoStar or other expensive subscriptions, and even then is often flawed or incomplete. Investors need this data - for underwriting and benchmarking purposes - to make informed decisions about their assets and new deals.

This methodology is also important for another reason: REIT operating information can be mapped on to REIT stock prices to provide the public market’s current perspective on overall real estate valuation. That yields a timely, market-priced alternative to historical transaction prices or appraisals. “The stock market is … more informationally efficient than the private property market, and such stock market based indexes tend to lead private market based indexes in time, at least regarding the major turning points in the market cycle,” the study notes. We’ve written before about how hard it is to accurately value real estate without a sale, and this is one new way.

A core problem in perfecting this analysis: REITs usually do not focus on a single property type and rarely own in one geographical area, but that information is “the type of granularity necessary for commercial property performance analysis.” The heavy lifting of the methodology solves this problem to get “pure” results by property type and by MSA.

Below, for example, are various relationships derived from REIT reporting for Los Angeles office buildings alone. The multiple lines reflect tweaks in the methodology and the light blue line is the one the authors imply would be the most accurate. But the general consistency of each variation - the similarity in each colored trendline - suggests the overall methodology is robust.

Taken together, “the promise of the methodology introduced here, to provide the ability to obtain a rich new source of vital commercial property performance and operating data for industry and academic researchers, seems to be realistic.” That promise matters in an industry where operating transparency has historically been scarce and benchmarking is expensive and incomplete. It is especially compelling because enterprise value reflects stock market pricing; the framework embeds a forward-looking valuation perspective.

More broadly, this study is evidence of a larger shift: we have more data in real estate (and maybe everywhere) than we have insight. Researchers are only beginning to extract what decades of REIT reporting can reveal, and this line of thinking will almost certainly expand into richer operating metrics, free cash flow analysis, and cycle forecasting. The informational bar for being a fully informed real estate investor is rising. Our business has not traditionally been the most research-intensive asset class but advantages will accrue to those who learn how to use the data we have. The world will increasingly belong to those who know how to read the data everyone else is sitting on.

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