You might already be familiar with Foursquare, which started in 2009 as social-networking-meets-Yelp-reviews. Users interacted with their environment via “check ins,” and competed against their friends to dominate their neighborhoods – and to learn more about local businesses and services. While Foursquare’s app has changed over the years, its database of points-of-interest has grown, with over 105 million documented places in their database. That’s a lot of useful location information, and we were excited that Foursquare offered our Hackathon participants access to their Places API.
Foursquare, and other geo-tagging and venue search services, prove pivotal to the home-buying experience. Buyers want to know about the neighborhood they’re moving to, and the best way to find that out is from the people who frequent the area. Knowing their opinions – and knowing how often they drop by that local coffee house you’re interested in – can help gauge opinions and sway buyers to (or away) from certain neighborhoods. Using artificial intelligence and machine learning, a great real estate application for this could be to get to know a buyer’s current taste (by analyzing their Foursquare profiles) and using that information to find the best neighborhood for them. The stress of moving long distances can easily be reduced if getting to know your new location could be based on years of check-ins to venues in your old neighborhood.
Thanks again to Foursquare for letting our Hackathon participants dive in to your Places data for our event!