An article in The Wall Street Journal describes how institutional investors are using computer algorithms to purchase large numbers of rental properties. Specifically, it explains how one data scientist, Martin Kay, used the strategy to create Entera Technology.
The article reports that Mr. Kay began buying rental properties in Texas in 2010, using machine learning to cull out listings that might attract appealing tenants: “For Mr. Kay and like-minded investors, that typically meant families seeking suburban lifestyles.” Once he started scooping up properties, the article says, rivals began to take notice, and some asked for help, which led to the creation of Entera. Other companies emerged as well (including Progress Residential and Amherst Residential).
“Like a dating app,” the article explains, “Entera starts by asking clients what they want. Besides screening for easily quantifiable characteristics like age, number of rooms, square footage, school district, property taxes and flood-zone status, it also attempts to measure qualitative aspects and uses algorithms to predict future value.” The program then scans listing photos and property descriptions for key words. “Several factors go into predicting financial returns and future value,” the article explains, “including proximity to a Starbucks, yoga studio or tattoo parlor—and whether a tattoo parlor signals a neighborhood on the upswing.”
Entera’s software catalogs about 850 home characteristics and thousands of other data points with respect to neighborhoods, then suggests an offer price based on sales activity nearby. Once an investor chooses a property, the company dispatches a representative to confirm its condition before completing the sale.
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