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 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.”
Real estate pros are increasingly turning to predictive analytics tools—the technology is being baked into CRMs—to better target marketing efforts. “No business wants to waste a penny marketing to someone who will never utilize their product or service,” Gamble says. “The waste of time and money in the real estate industry is incredible. Think about the seemingly billions of leads that are distributed annually that close at 1% to 2% rates. The lead gen game is largely broken. Agents rightfully don’t want to chase them for limited results.”
Real estate practitioners also face steep competition for listings in a low inventory environment. Predictive analytics can be used to get a “head start in marketing to the right potential sellers” and get in front of them early, Gamble says. “Predictive analytics uses modern technology to shrink the pool of prospective clients and allow agents to target them properly.”
Most Likely Movers in the Next 12 Months
Empty nesters and retirees are likely to have a significant impact on the housing market over the next 12 months, according to new findings from TKI’s nSkope Predictive Analytics Report. TKI culled 300-plus demographic and lifestyle data points to uncover who is the likeliest to move and where.
TKI predicts that 5.7 million homes nationwide have a high probability of coming on the market over the next 12 months, and homes belonging to older Americans make up a big chunk of that anticipated inventory. Here’s who the study predicts has the greatest likelihood of moving:
- Empty nesters between the ages of 45 to 64 with no children living at home: expected to make up 10.2% of listings
- Single adults (ages 35 to 64): 8.7%
- Retirees (ages 65 and older): 7.5%
- Older families with at least one child 18 to 24 years old living at home: 6.3%