Databases — even as elementary as Excel spreadsheets — can be treasure troves of information about commercial real estate.
Take, for instance, data that CrediFi recently pulled together about the lenders behind New York City’s most notorious slumlords. This started, of course, with New York’s published list of the “100 Worst Landlords” in New York City, an annual watchlist that puts the spotlight on New York slumlords.
Just as where there’s smoke, there’s fire – where there’s real estate, there’s financing. So we decided to take a look at the banks behind these “Worst Landlords” – and by digging deeper into the data, we came across the lenders backing those slumlords (including the number of loans per lender and the amount of each loan). A lot of money, and some familiar names show up on this lender list. A certain number of these banks we now keep on our CrediFi Watchlist. This data could be vital information for other lenders as well as developers, government officials and pretty much anyone else who’s involved in commercial real estate.
That’s just one tiny slice of information that underscores the power of data in commercial real estate lending and other aspects of the industry. And it highlights the need for increasingly sophisticated data-gathering and data-scrutinizing systems in commercial real estate.
New York-based Enertiv, a provider of “smart building” technology, says in a recent report that commercial real estate, perhaps more than any other industry, “is primed to reap the benefits” of data advancements. Case in point: Nearly one-third of commercial real estate assets around the world are managed with manual spreadsheets, according to the report.
“Commercial real estate is a $12 trillion industry,” according to a 2018 article in the Cornell Real Estate Review, “and the ability to make better data-driven decisions can save time and money for almost everyone involved.” (By the way, the article gives a shout-out to CrediFi as a data innovator in commercial real estate.)
“Not long ago, Microsoft Excel was considered cutting-edge technology,” the article points out. “As sophistication and the benefits of new data tools increase, adoption may well be required to maintain an investment edge in commercial real estate.”
The insideBIGDATA news website indicatesthat such data sophistication can give lenders, investors and others a competitive advantage in commercial real estate by making speedy assessments of property value and risk. In the case of the slumlord lenders, it might make a player in commercial real estate think twice about doing business with those lenders.
A 2018 report authored by Anne Kinsella Thompson of the MIT Center for Real Estate backs up that assessment in terms of the effect of so-called “big data” on the financing segment of commercial real estate.
“Over the past several years, research’s importance within the industry has grown. In a world where data has become ubiquitous and almost every job in the industry includes a research component, knowing how to properly examine and interpret data is a significant advantage,” the MIT report says. “However, as with everything in the real estate space, the way we research is changing due to the influence of new technological tools.”
One of those new tools is machine learning. The MIT report describes this as “a branch of artificial intelligence based on the idea that computers can learn from data, identify patterns and make decisions with minimal human intervention.”
Machine learning has led, for instance, to the proliferation of automated property valuation, the report says. Automated property valuation has given a boost to alternative lenders (also known as nontraditional or nonbank), which are quicker to adopt new technology than traditional institutions are, according to the report.
“As machine learning spreads more widely into the commercial lending sector, the use of these tools will proliferate, and thus more nonbank lending instruments will provide additional options for both large and small investors,” the report says.
Now, getting back to those slumlord lenders. Machine learning and other technology advancements promise to propel the commercial real estate industry forward — including better ways to slice and dice commercial real estate lending data and, perhaps, better ways to dissect information about slum landlords and their financial backers.