This discussion focuses on Big Data in the real estate industry.
Sramana Mitra: Let’s start by introducing our audience to yourself and Ten-X.
Sheridan Hitchens: I’ve been in the industry for about 20 years. I started off my career with Procter & Gamble. I was in strategy consulting for a long while. For the last 12 years, I’ve been in the Internet space, primarily in leading product and data groups.
For the last five years, I’ve been heading up our data products group at Ten-X, formerly called auction.com. That involves what you would describe as traditional business intelligence including dashboard and reporting, data engineering, and data science. That’s a little bit about me. Ten-X is the leading real estate marketplace in the United States where people can buy and sell real estate through our platform.
These properties can be anything from a foreclosure in Detroit to an office building in Manhattan. We actually hold the Guinness World Record for the largest online transaction ever which is $96 million for an office building in Southern California. We’re funded by a few investors including CapitalG, which is one of Google’s capital arms.
Sramana Mitra: We have covered auction.com in the entrepreneur journeys series. We know the company. Let’s focus on the Big Data angle. Tell us what you do with data.
Sheridan Hitchens: Backing up one level, when you think about what a marketplace is, it’s essentially an exchange of data or information. We don’t own, buy, or sell any of the properties ourselves. We provide a platform so people can do that. The way we do that is by providing data on both the buyer and seller side.
In many ways, data is actually core to our product. That’s something that we see emphasized on a daily basis. One key differentiator for us is our data and what we specifically do with our data. We make sure that our buyers and sellers have the right access to data to make sure that our buyers can find the right asset.
Sramana Mitra: Can we double-click down on this and take us through some use cases?
Sheridan Hitchens: One of the things that we do here in our data science group is, we provide a recommendation engine. We have a lot of different assets on our platform. Making sure that we help our buyers buy the right assets is important for us.
We’re not like Amazon in the sense that, “If you bought an iPad, we know you might like to buy an iPad cover. We have close to an infinite inventory of that.” We sell our assets once typically. We use a recommendation engine to look at the patterns of behavior of what that individual user has done and work out what would be the most appropriate asset set to deliver to that user.