- We’ve seen a lot of science fiction revolving around using personal data to “pay” for goods and services, but one campus coffeeshop is making it science fact. Shiru Cafe on Brown University’s campus uses student’s personal information (including name, age, and college major) as currency, allowing anyone with a university ID to exchange caffeine for the ability for corporate partners to advertise to the customers through not only visual displays, but from the baristas themselves. The information given to advertisers, according to the café owners, does not include any personally identifying factors, but rather comprises of an aggregation of their entire clientele.
- California becomes the first state to sign a cybersecurity law specifically focused on smart home devices. Starting on January 1st, 2020, smart home device manufacturers must equip their devices with reasonable security measures to protect consumers from unauthorized modification, access, and information disclosure. “If it can be accessed outside a local area network with a password, it needs to either come with a unique password for each device, or force users to set their own password the first time they connect,” which would slow down the ability of hackers to use default usernames/passwords to gain access to devices remotely.
- A new start-up is looking to revolutionize how we buy homes. Instead of selling to the highest bidder, Bungalo sells their flipped homes to the first bidder that is pre-qualified for a mortgage at the listing price. The service has launched in the Dallas-Forth Worth and Tampa areas, and is sure to be a company to watch in our industry.
- Forbes takes a look at how Blockchain and the Internet of Things are shaping the future of real estate. They’ve noticed the same trend we have (and that Joe and I just recorded a webinar about) – technology in the real estate vertical revolves around making life easier, including the speed at which deals move and the efficiency of living in a smart home.
- Hydro-and-aquaponics systems are becoming increasingly common features in high-end restaurants in New York City. But they’re also popping up in unexpected places, including the cafeteria of a Manhattan high school. “As part of a nonprofit program called Teens for Food Justice, a handful of schools in Brooklyn, the Bronx, and Manhattan have turned spare classrooms, unused science labs, and, in one case, an empty closet into urban hydroponic farms, an experiment in self-sufficiency, science education, and food equity.” I’m excited to see where projects like this go.
Bonus: Last week, we told you about Amazon’s newest investment into residential real estate with their investment into prefab-home builder Plant Prefab. This week, Yahoo! Finance did a great little rundown on how Amazon has been reaching into the residential real estate space, which includes their “Hire a REALTOR®” campaign and more.
Last up for our hackathon sponsorships is API sponsor Solaria Labs, creators of ShineAPI. ShineAPI is the developer portal of Solaria Labs, the innovation lab for Liberty Mutual Insurance. ShineAPI’s first and primary product is Total Home Score, a product which combines Liberty Mutual data and open source data to deliver address level insights on daily living in a home. Total Home Score, like fellow sponsor TLCengine’s True Lifestyle Cost, allows homebuyers to find out more than just mortgage and tax information about properties they are interested in.
The Total Home Score documentation from the ShineAPI website sums up their product best, and I’ve quoted it below. A Total Home Score takes into account noise levels, road congestion and safety, and neighborhood amenities, giving you a complete view of a property with what little time you can spend at each place.
Total Home Score estimates five important livability dimensions:
- Quiet Score: The extent to which a home will be quiet and peaceful, taking into account busy roads, public transit, and train/subway routes. A higher score is quieter.
- Road Safety Score: The overall feeling of safety for roads surrounding a home, based on auto telematics data measuring speeding and aggressive driving. A higher score is safer.
- Errand Score: A measure of a home’s proximity to common errand locations like grocery stores, gas stations, dry cleaners and more. A higher score indicates greater convenience.
- Entertainment Score: A measure of a home’s proximity to common entertainment venues like restaurants/bars, movie theaters, recreational sports facilities, and more. A higher score indicates greater convenience.
- Traffic Score: The extent to which a home is affected by traffic congestion on nearby roads during rush hour. A higher score indicates less congestion.
Total Home Score also returns up to three explanatory factors that had the greatest impact on “why” each score was less than 100.
Total Home Score data is currently available for locations in the greater Chicago metro area and the entirety of Massachusetts, with new geographical locations being added on an ongoing basis.
Total Home Score is powered by Liberty Mutual’s enterprise data on auto telematics and map data from OpenStreetMap contributors.
As the amount of time buyers spend in a home shortens – and considering that AR/VR technologies are making it easier than ever for long distance buyers to view homes hundreds of miles away – knowing these factors about a home will give a clearer view of each property, and help Realtors® find the right home for their clients. We’re excited to see this type of data become commonplace in MLS listings, and thank them for making their API available for our hackathon this year!
Realtors Property Resource® (RPR®) is 100% owned and operated subsidiary of the National Association of REALTORS® and is offered as a member benefit of NAR. RPR® actively collects listing data from 96% of the MLS’s in the United States and marries that with public record data demographic data and neighborhood data to provide analysis and reporting tools to help REALTORS® service consumers in the buying and selling of their homes. RPR® is an invaluable resource for REALTORS®, and we were excited that they were able to provide our hackathon participants with a special hackathon-only version of their Sandbox environment for developers.
Our friends at RPR® had a bunch of awesome ideas about how their data, combined with artificial intelligence and machine learning, could be used to improve how our members buy and sell homes – the ideas are so good, I’m going to let RPR® give their ideas directly instead of summarizing it from the emails we had back and forth during the hackathon process.
Neighborhood Turnover Rate: For example comparing count of actual SFR properties in a geo location zip census tracts etc and the number of properties closed in a month (run multiple counts month by month for the last year). Also compared the average/median sale prices over time to track trends.
– This could help Realtors and Brokerages identify neighborhoods in which they can focus their marketing efforts for a higher ROI.
– This can also help Brokerages measure possible revenue based on sales volume (sales price volume).
Correlation between distressed properties and the last sale date or sale price: For example search for SFR properties with a distressed date (YTD) in the city of San Francisco. Compare the distressed date to the sale date track a pattern and also analyze the sold price.
– This could help Realtors locate areas and price points that are about to be impacted by foreclosures.
– This could also provide Lending and Government institutions with a timeline of when the purchases occurred so they can be linked to changes in the job market interest rates lending options at the time or lack of market price bubble etc…
A couple of our teams used RPR® data in their final projects, using the data from fifteen million public records across several states in order to analyze trends in public records and property listing data. You’ll notice a theme of several of our sponsors is to collect data from multiple places and create a more streamlined system for displaying it; artificial intelligence and machine learning represent the next step in the process, where the streamlined systems can be analyzed in order to facilitate an even easier buying/selling process for agents and their clients.
Benutech was another one of this year’s API sponsors, allowing our hackathon participants to access their data solutions to use within the participants’ projects. Benutech’s products allow for the easy sharing of multiple data platforms, allowing for seamless integration of the data and a one-stop solution for industry professionals to find out important property information when buying/selling homes.
Benutech tracks a variety of sources – from divorce filings to property tax defaults – in order to help agents generate leads. By tracking all these variables, instead of just one, real estate agents can see all the possible leads in their area instead of scrambling to put together data from different sources. Benutech’s strengths lie in their partnerships with other data providers – including some of our other hackathon sponsors – which not only widens the scope of their searches, but allows for sources to be easier to track and use. One of the biggest complaints we hear in the lab is that all this technology and data comes from so many sources that it’s hard to keep track of, which is why we were interested in working with Benutech. Their collaborations allow them to lead the way for all data providers to work with each other, instead of against. That was also the spirit of our hackathon – using artificial intelligence and machine learning to make real estate more streamlined, and getting groups together to collaborate to create innovative new solutions to the complaints we hear about from Realtors every day when it comes down to adopting technology into their business practices.
We thank Benutech for their sponsorship, and know that they were instrumental in making our first hackathon a success for us and our participants.
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!