The real estate business is getting shaken up at the agent level. This disruption started at the home buyer and seller level some years ago, when in 2006 Zillow first made it possible for those buyers and sellers to come up with Zestimates. Zestimates come from data provided by Zillow’s app, which uses a proprietary formula to come up with a home’s likely market value at a given time. If someone wanted to buy a house, they could get a Zestimate and take it with them to discuss matters with their realtor. An agent thus became more of a partnering consultant and less of a salesman in the process.
What was so critically important about Zillow’s new, modern way of putting once-forbidden data into the hands of everyday home buyers is that it made use of big data and machine learning. Now, real estate agents themselves are receiving enormous help in the very same way, but from their own professional perspective.
Turning big data into something accessible, affordable, and storable has brought about a growing revolution in how real estate agencies and agents are able to sift through their databases of potential home buyers to see just who is most likely at a given time to want to find a new house. Stats like the home that a particular prospective buyer is most likely to buy, and when they are most likely to buy it, allow an agent’s software to set up calls and meetings for optimal timing.
Relationships Mean Everything to Real Estate Agents
Real estate agents get nearly all of their business by way of networking and developing personal relationships. The most successful agents know their own neighborhoods and communities exceedingly well. They’re personable and outgoing. Perhaps they’re involved in the civics club, the motor club, the church, the school board, coaching kids’ sports. Over time, the best agents accumulate anywhere from hundreds to thousands of potential home buyer leads in their database.
However, agents are notorious for not being very astute at all with regards to the art and science of marketing. What this means for them is that they’re highly unlikely to get someone not already familiar with them to pick up the telephone and call their business number or stop into their office. Furthermore, they even have to be the proactive ones within their highly cultivated database of prospects, for even those individuals are going to tend to be on the “shy side” when it comes to deciding on an agent to help them buy or sell their home.
Yet, the problem here is self-explanatory: when there’s a database of many hundreds, or a thousand or more, contacts, how can an agent possibly keep up with them all? How could any agent hope to the get the timing right for the well-placed call or email received by the prospect just at the peak point of being most likely ready to buy or sell a house? The massiveness of the agent’s own painstakingly assembled database means that there are likely to be several dozens of missed opportunities to show a house and close a sale for commission each year.
Enter Data Science
A startup company out of Durham, North Carolina which has been gaining wide notoriety since its successful funding and launch in 2016 is called, simply, First. Utilising machine learning and data science analytics to monitor more than 700 personal factors, First gives what it defines as the top 20% real estate agents—those with the big databases—a kind of AI assistant to constantly look over those databases and tell them if and when they ought to be contacting a particular prospect. At the time of this writing, First has a database of approximately 214 million Americans who are, to one degree or another, potential buyers and sellers of homes.
Mike Schneider, co-founder and CEO of First, says “These top agents are missing 70 to 80 deals from people they already know. They realise 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.”
He goes on to say “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.”
First may well be the first service of its kind, but you can rest assured it won’t be the last. Proptech has gone global, with nations such as China and Hong Kong huge participants. Here in the era of Google Home and Alexa, data science and machine learning are going to continue to be expanded into the landscape of proptech, and smart agents and agencies must take heed.
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