How to Use Big Data to Improve CRE Deal Valuation
How to Use Big Data to Improve CRE Deal Valuation
To the average consumer, “cost,” “price,” and “value” function as more or less synonymous terms. To the commercial real estate professional — and anyone who is well-schooled in economic theory — though, things are not so cut and dry.
While price is as straightforward in CRE as it is in the consumer sphere, value is far more complicated, and requires a great deal of research and calculation to determine. Indeed, CRE appraisers must not only consider concrete metrics like capitalization rate, net operating income (NOI), rental rate, and so on, they must also consider current market conditions and local social and regulatory trends.
Ultimately, the accuracy of a CRE appraisal depends upon the methodical collection and analysis of a wide variety of data points that bear upon the four standard determinants of property value.
First, one must assess demand, or the strength and magnitude of desire for asset acquisition supported by enough financial liquidity to satisfy said desire. Then, one must assess utility, or the ability of an asset to satisfy a potential buyer’s needs. Next, one must assess scarcity, or the number of comparable assets in the area, especially those that are on the market. And finally, one must assess transferability, or the ease with which an asset’s ownership rights can be shifted from seller to buyer.
Only after all of these elements have been evaluated can a CRE professional ascertain whether a transaction will amount to a good deal, that is, a deal wherein the price is equal to or less than the asset’s value.
Redefining Access to Information
It should go without saying, then, that adequately evaluating a CRE deal requires a tremendous depth and breadth of information. One must not only become an expert on the asset in question, but also pay careful attention to the goings-on in the surrounding market. Luckily, advances in data collection, organization, and analysis have enabled CRE professionals to acquire the requisite knowledge with hitherto unimaginable accuracy, efficiency and overall ease.
Consider this anecdote from Wehr Ventures founder Bob Wehrmeyer addressing how easy access to data helped him adjust a client’s acquisitions trajectory during a routine check-in call. “Based on the information I found [with a simple Google search], I started speaking in detail about what was being developed around my client, much to their surprise,” he shared. “It turns out that a competitor had picked up a piece of property nearby, catching my client completely unaware. We were then able to work on a development strategy in light of this new information, all because I was able to access it quickly and easily on the internet.”
Wehrmeyer admitted that such easy access to critical information has “changed the way [he does] business,” and he’s not alone. Big data has redefined the way many CRE insiders go about their work, not only in terms of developing a strategy but in terms of executing upon it as well.
Leveraging Data to Boost Efficiency
The first step in proper CRE deal valuation is to build a real estate pro forma, that is, a projection of cash flows for an asset stretching across the buyer’s desired holding period. In addition to aforementioned metrics like NOI and rental rates, this also involves determining an asset’s ownership costs — upkeep expenses not covered by tenants, property taxes and so on.
Previously, getting one’s hands on all of this data — both revenue-related and expense-related — was not only time-consuming but often impossible. With the emergence of data aggregation platforms, however, this entire paradigm has shifted. Leveraging both publically available municipal datasets and proprietary datasets, these platforms enable CRE professionals to survey an asset’s annual gross revenues, property taxes and total operating expenses with the click of a mouse.
Similarly, well-organized data can provide brokers and appraisers with detailed ownership histories and debt records, helping them drill beneath the recorded price of previous ownership transfers and gain a more precise understanding of whether the asset was sold either above or below its true market value. An asset’s sales history is not the only consideration — as mentioned, current social and regulatory trends play a role in determining the value of a deal — but it can reveal a host of “hidden” insights to the CRE professional shrewd enough to know what they’re looking for.
In short, while big data has not fundamentally changed the kind of information needed to properly evaluate a potential CRE deal, it has accelerated the overall evaluative process. In a certain way, the big data revolution in CRE is not unlike the invention of the automobile. Though the core of transportation — getting from Point A to Point B — was largely unchanged by its introduction, the automobile exponentially increased the rate at which people could travel down the same old roads.
Big data has proven to be a similarly remarkable boon to CRE efficiency. While it hasn’t rendered traditional evaluative practices obsolete — far from it, in fact — it has empowered every CRE professional to perform the same old tasks in a drastically more efficient and, ultimately, more effective manner.
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