Machine Learning Is Disrupting the Real Estate Market
A group of companies are looking to leverage big data and machine learning to disrupt the process of buying and selling real estate. One company using big data to stream real estate transactions is Landis. The company has developed a platform that matches institutional buyers (primarily in New York City, N.Y.) with residential property owners (primarily in the Midwest).
Cyril Berdugo, a Landis’ co-founder, was previously head of acquisitions for Crown Acquisitions, a large institutional real estate investor in New York. Landis utilizes machine learning to analyze property data in order to determine which owners might be flippers, which are typical owners, and who might be long-term buyers.
“We can spot from the data patterns when sellers might be selling property, when flippers might be selling property and we can actually pre-empt them doing that,” Berdugo tells Datanami. “We can see it far enough in advance that we can give them a call and say, hey we think you might be selling those properties two months from now.”
Landis’ machine learning algorithms can automatically identify the features that institutional buyers prefer the most on the buying side of the real estate transactions. The company charges a 2.5 percent commission to sellers and a 1.5 commission to buyers. The outcome is that it cuts the costs of completing a real estate transaction by eliminating the need for agents, while speeding up the transaction.
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