The technology is helping real estate pros more precisely target prospects who are most likely to move in the next 12 months.
You might be able to find out who in your market is most likely to move in the next few months—even before they know.
Predictive analytics aim to provide a crystal ball into the future buying and selling habits of prospects in your area, and so far, a large percentage of predictions have been spot on. In December 2021, when the market was considered unpredictable, real estate software development company TKI identified 2,024 of 35,000 properties in Decatur, Ill., that were likely to come on the market in the next year. By the end of that timeframe, about 23%—or 463—of those homes had been listed.
“Decatur is a great example of how predictive analytics can be effective,” says TKI cofounder and CEO Tom Gamble. “We reviewed the ZIP codes in the market and reduced the size of potential listings down to a very manageable number. A brokerage or team that focused almost exclusively on this pool of prospective clients would have likely been much more successful than those who tried to be in front of all 102,000 households.”
TKI’s nSkope(link is external) platform factors in several hundred “triggers” based on shifts in the number of household members, income and neighborhood trends to reveal likely movers. The platform also assigns a “persona” and a seller profile to each property, including details like equity, income and age, providing more context about the prospect to the real estate professional.
“With a projected slowdown in 2023 sales, it is critically important to understand the lifestyle triggers that will impact listings,” Gamble says. “Using artificial intelligence to profile all homes in a community allows our platform to identify only those homes we expect to come on the market within the next six to 12 months, so you can focus on specific homes versus all homes in an area. This allows for a more targeted marketing approach, which reduces wasted time and marketing spend.”
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