For most consumers, the home buying process includes a very specific online search. People specify their neighborhood requirements, the number of bedrooms or bathrooms, backyard size, and more — yet still, the search results in a staggering amount of homes. It's way more than anyone can reasonably look at.

That's where Martin Kay and Entera Technology, the company he founded and is CEO of, come in. Kay, a 20-year veteran of the tech sector, who's bought multiple homes as rental properties, realized the way to solve the problem of that kind of search engine overload was through machine learning. He now works with some of the largest home-buying companies in the world, helping them find properties that match the specifications they have to attract the clients they want.

"All residential real estate is a consumer product," he says. "Ultimately, the people who are going to live in that home care most about, is it a nice home with a big backyard neat good schools, is it safe? The [home buying] companies are trying to figure out what do the end consumers really care about so we can give them exactly what they need?"

To do so, Entera collects data — lots and lots of it. Kay and his team have taught their software programs what a chef's kitchen is, for example. They did so by compiling tens of thousands of photos of kitchens and telling the software, "This is a kitchen." Then, they taught it to recognize what makes a chef's kitchen — a larger size, more than one sink, high-end appliances. They used the same techniques in identifying things like millennial-friendly neighborhoods or neighborhoods that were up-and-coming on the real estate scene. They draw from listings available with the Houston Association of Realtors and beyond, a vast array of tens of thousands of homes.

Officially launched in 2017, Entera blends its data collection and analysis with on-the-ground service. After Entera's proprietary software collects what it thinks home-buying companies want, members of Entera's service team go out to look at the homes.

"We're a little bit like Netflix," he says. "They go out and get content from everyone, and they begin to watch your behavior. So, Netflix has 2,000 profiles and you probably fit five or six of those. We have almost 100 profiles and what we do is say, we're going to understand what you want, watch your behavior and instead of giving you 40,000 properties on a big map, we actually match you based on your preferences, to the five or six houses that are best for you."

While Entera has been working with larger home-buying companies — like firms that buy tens of thousands of homes every year — Kay says they have begun working with smaller entities, and he figures within the next few years, Entera will be using the same data collection and machine learning to work with individual home buyers.

Based in Houston, Entera has operations in New York and San Francisco as well. The company has 17 full-time employees, along with approximately 100 contractors in its markets. And while Kay understand a human touch is needed in business, he loves that he can use a data model to present unbiased opinions to his clients.

"[Real estate] actually affects people's lives meaningfully," Kay says. "Real estate data — where you live, what your neighborhood is, how you make that choice — …this data matters to people in a way they can tangibly touch and understand and feel. We can help people make what are big, complex choices that are often highly ambiguous. I love it because it matters. You can measure how it matters immediately."

Data-driven tech

  

Courtesy of Entera

Entera focuses on collecting data and analysis and pairs it with on-the-ground service. After Entera's proprietary software collects what it thinks home-buying companies want, members of Entera's service team go out to look at the homes.