How a Real Estate Startup Uses Artificial Intelligence To Predict Home Listings

As the difference between human's digital and offline lives disappears, behavior becomes increasingly predictable. JL

Matt Hunckler reports in Forbes:

The platform tracks attributes and home-selling behavior of 214 million people in the U.S. in order to calculate its seller scores, which are automatically updated when any factors change. (The) platform analyzes 700 personal factors (such as demographics, income changes, purchasing behavior and life events) of every agent’s contact and cross-references them against national averages. The resulting “seller score” indicates how likely they are to sell their home.
It’s well known in the real estate business that agents get most of their work through personal relationships. When someone needs help selling their home, they’ll often turn to the agent who lives three houses down the street or did a great job selling a friend or family member’s property two years ago.
Many agents periodically put out feelers to see if anyone in their network is thinking about listing a house. The problem is that once an agent is successful enough to have hundreds or thousands of contacts, following up on every lead becomes close to impossible, and they’re bound to miss great business opportunities.
“These top agents are missing 70 to 80 deals from people they already know. They realize they just don’t have enough time, so they would love to hand it off to some form of assistant that’s going to tell them where they should direct their relational efforts,” said Mike Schneider, co-founder and CEO of First.
First is a startup from Durham, NC that’s using data science and machine learning to offer the kind of help real estate agents need. Its SaaS platform analyzes over 700 personal factors (such as demographics, income changes, purchasing behavior and life events) of every agent’s contact and cross-references them against national averages. The resulting “seller score” indicates how likely they are to sell their home, helping agents know exactly when to reach out for the best chance at landing a deal.
First has grown quickly since launching its product in early 2016, securing $2.35 million in seed funding (according to Crunchbase) and assembling a team of 26 employees. The company is now positioned to scale up and bring its unique listing-prediction platform to busy real estate agents across the country.
Reimagining CRM Software for Real Estate
First’s potential lies primarily in the way it amplifies agents’ natural strengths while compensating for their weaknesses. Speaking generally, real estate agents are great at building and maintaining personal relationships with clients, but according to Schneider, what they’re not so great at is marketing. (This is why you get so many refrigerator magnets, postcards and newsletters in the mail from agents hoping to generate leads.)
CRM software—the gold standard for deciding when and where to engage customers—is typically a poor fit for real estate agents because it requires some amount of marketing knowledge and expertise. It’s also time-consuming to use and overly complicated for most agents’ needs. First sidesteps these problems by automating all of the data collection and analysis, letting agents focus on relationship management.
The platform tracks attributes and home-selling behavior of 214 million people in the U.S. in order to calculate its seller scores, which are automatically updated when any factors change. Agents can use these scores as a guide for outreach,
theoretically winning more deals in less time than if they had reached out to their network at random.
“Instead of meeting with 20 people to talk to one who’s going to move this year, I can actually put [an agent] in front of 20 people where every fifth person is going to sell their house. I can also time it so [the agent is] connected with them six to nine months before they’re thinking about listing,” Schneider said.
Big Plans for Tech-Enabled Disruption
Following nearly two years of growth, the First platform is used today by hundreds of agents at real estate companies like Coldwell Banker, Keller Williams, RE/MAX and Century 21. The team at First intends to keep growing this number and are targeting the top-performing 20% of U.S. agents for adoption.
Schneider reported that the company’s biggest competitors are not other digital tools, but rather real estate coaches, whom agents sometimes hire for help with prioritizing leads. However, Schneider believes First’s sophisticated, analytics-based platform and $450/month price point will
give it the edge it needs to keep growing its user base.
First has set aggressive growth goals for the next two years in the hopes of expanding its share of the real estate market. If it can meet these goals and keep attracting new users to its platform, then its data-driven, streamlined software could change the way many agents find listing opportunities.
“We’re going to grow this to become the next big SaaS company in the [Research] Triangle. We’re going after the biggest chunk of real estate, and we believe we can direct the most transactions,” Schneider said.