How AI Could Compete With CRE Brokerages One Day

29Feb - by aiuniverse - 0 - In AI-ONE


These are the high holy days of the commercial real estate brokerage community. So said a speaker at a CRE event two years ago that was attended by Blaine Strickland, principal of HBS Resources. The comments struck a chord because the speaker went on to say that it is unclear how long this dominance will last.

Ever since then Strickland has been thinking about what could disrupt the CRE brokerage model and has come to a few answers. Pricing transparency is one. The growing use of artificial intelligence is another.

He points to travel agents and their heyday more than two decades ago. One reason for their demise was that pricing information became more readily available to consumers and technology introduced new ways for consumers to buy tickets.

“There is a good case for comparison between yesterday’s travel agents and today’s CRE brokers,” Strickland tells Both involve the client gaining access to valuable price information and overall transparency into the system.”

But Strickland is not referring to the Zillows or CoStars of the world, at least not entirely. Valuable price information also entails an overlay of AI, necessary to accommodate the complexity of CRE. There are, for example, algorithms that can price a residential home once the square footage is plugged in, but that doesn’t work for CRE as there are too many differences among the buildings, Strickland says.

That said, there are firms that are trying to create algorithms for some types of CRE, such as single-tenant net lease properties. “A Wells Fargo building in Orlando, Fl., can compare to a similar building in Phoenix. There is a lot of commonality and an algorithm can predict what the sales price will be,” Stickland says.

There are four to five vendors that are working on this, some of which have received investment from the big brokers. These firms include Bowery Valuation, Skyline AI, GeoPhy and

Skyline AI, as one example, recently helped an investor select an apartment building in Atlanta to buy, by processing data from review sites with natural language processing. Online reviews of the asset were flagged by the system as indicating an opportunity for optimization., as another example, has mapped out grocery anchor center sales and attributed the seasonality—or time to get the best price—to that sale. For instance, Strickland says, the system could tell you when a center’s financing expired and that the lenders have to get the last $40 million out if they want their bonuses. Thus, the property’s seasonality is at the end of the year.

Not that long ago, Strickland adds, the investor would ask the broker about any grocery-anchored properties that were coming to market.

Or possibly a broker would contact a building owner to offer a broker opinion of value about, say, an apartment complex. The building owner agrees, giving the broker the necessary information to complete the analysis. The broker returns with a BOV that values the building at $2.7 million and the owner agrees to sell the building if the broker can get that price. Thus, a sale is born.

“The broker used that information as a way to create a relationship,” Strickland says. “But think about what happens if a seller and buyer already have that information.” It’s happening in the residential world already, he says, and will soon be a part of CRE.

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