TLCengine is a company specializing in the “True Lifestyle Cost” of home buying. Beyond just mortgage, taxes, and insurance, TLCengine looks at over two dozen cost variables associated with owning a home. These factors include average utilities, commuting costs, car insurance rates, and more. Using predictive analytics, TLCengine can give buyers a highly accurate breakdown of how much house they can afford. The traditional way of looking at homeownership costs (the mortgage, taxes, and insurance mentioned earlier) only account for 50% of the costs associated with living in a home – and TLCengine accounts for this in their predictive modeling.
TLCengine was one of this year’s hackathon sponsors, and their data was instrumental in helping our teams who were looking at machine learning/artificial intelligence to help consumers and agents make better home buying decisions. This type of data is uniquely suited for helping clients make smart decisions with their money, beyond just the initial purchase of a home, and we thank them for letting our hackathon participants use their API this year, and wish them luck in launching TRUE, their new product for agents and brokers, this year!
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!
With the Hackathon fast approaching, we’re letting everyone know about our Hackathon sponsors. We’re excited that we are able to give our participants access to three of the best multiple listing services in the country – California Regional MLS, Midwest Real Estate Data, and the San Francisco Association of REALTORS® MLS. These MLSs are three of the top service providers for real estate professionals, serving over 100,000+ REALTORS® in the California and Chicagoland areas. All three also are well known in the MLS community for always looking forward to the future of real estate, creating innovative new data and analytics products for their members.
A technology-forward MLS is about more than just listing data – its about creating a seamless series of tools for its members, which includes world-class customer service, easy-to-use software tools, and innovative ideas about the future of real estate. All three of our sponsors have these qualities in spades, which is why they are considered some of the top MLS providers in the US.
Their data represents the archival housing stock of their respective areas, and is provided to our Hackathon participants in the hopes that it’ll be used to create AI solutions to some common real estate problems, like making virtual office websites ADA compliant or making better Automated Valuation Models. During the Hackathon, we’ll be tweeting about the projects as we learn about them, and we look forward to seeing what comes from this incredible wealth of data provided by our MLS partners.
With about a week to go before our iOi Summit in San Francisco, we wanted to let everyone know about another one of our data providers, Enodo. Enodo uses artificial intelligence and machine learning to analyze multifamily property information from across the country and present key investment takeaways in an easy-to-understand dashboard, cutting out the confusing tables and forms usually associated with underwriting these properties.
Enodo takes data from leading property data sources and brings it together in a digestible way to allow for property managers, investors, and other CRE professionals to make key decisions about their multifamily properties. Enodo’s software looks at about 2 million properties weekly, analyzing all that data together and using it to help users understand comparable property rents, amenities, and more.
For the iOi Hackathon, Enodo is providing our contestants with multiple tools that look at dozens of different metrics surrounding multifamily properties. These tools include Enodo’s:
These predictive analytic tools can be used with other API providers in our hackathon to create comprehensive real estate technology solutions, and we are looking forward to seeing how our participants use Enodo in their projects!
CRT Labs is hosting our first ever hackathon as part of the iOi Summit in San Francisco in late August. To find out more about this awesome event, including how you can register to be part of the hackathon, head on over to this blog post by Chad!
One real estate company is going all-in incorporating Augmented Reality and Virtual Reality into their business model. Find out about how these technologies are shaping how to do business with clients on the Fast Company website (click ahead to the second video in the playlist).