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SAN FRANCISCO — Wouldn’t it be nice to know if someone wanted to sell a home before he or she did? Real estate agencies might never be able to truly predict the future, but thanks to advances in predictive analytics and advanced data, they’re getting pretty close.
Accoridng to Cary Sylvester, vice president of industry development at Keller Williams Realty International in Austin, the biggest challenge is taking all of that data and figuring out how to effectively use it.
“Insight is the difference between having a lot of data and doing something with it,” she said during a panel discussion at Inman Connect San Francisco. “Don’t create a big pool of data and just leave it there. Constantly update it to make that data better, smarter and more useful.”
Mike Schneider, CEO of First.io, a predictive intelligence and automation service for real estate, believes that there’s pools of data right in front of you on Facebook and Twitter — you just need to collect and decipher it.
Schneider will help agents find data on potential clients via social media. Did they get a new job? Are they expecting a child and growing family? Did they just renovate their home? Accoridng to him, any sort of life event that could bring about the need or want for a new home should be monitored for leads.
Not that it’s easy. “Personalization actually works, but its hard to scale,” he said.
Part of personalization and learning about potential clients is knowing how and when to use it, according to Sylvester.
“Customers leave clues everywhere,” she said. “But knowing how to reach those people with the right message, in the right way, at the right time is important.”
The right way includes tailoring data that is already available to specific homebuyers, accoridng to Krishna Malyala, co-founder of predictive data firm TLCEngine.
Malyala, who is also an agent, says he’ll create lists of bars, restaurants and other surroundings tailored for each client. And it can go deeper than just hangout spots: Malyala also creates lists of doctors, hospitals and other important services homebuyers don’t always think about when buying home.
Malyala also believes that predictive analytics can help a buyer budget the living costs of a potential home by using algorithms to predict the costs of utilities and other monthly costs that come with homeownership.
“You don’t even need to use all these tools,” he said. “It’s just a matter of looking into their sphere and tailoring it to them.”
So what is the next necessary step for predictive analytics to take in order to further serve the real estate industry? Work together, according to the panel.
“The biggest limitation right now is that everything is fragmented,” Sylvester said, explaining that services to look at consumer predictions, website traffic and marketing data are all separate.
