Welcome to Five for Wednesday, our weekly roundup of interesting tech news. Today, the (accidental) theme is rethinking interaction.
- Amazon released new abilities for smart doorbells and cameras. Among the new features are motion sensing linked to routines – so, for example, if someone is approaching your house and your smart camera detects it, the lights could turn on. Developers can also use the new API to enable two-way conversation between a smart doorbell and an Echo device.
- Columbus, Ohio, adds to its smart city repertoire by adopting driverless shuttles. The shuttles are currently in a trial run, and should be accepting passengers by December. The shuttle route includes Center of Science and Industry, Smart Columbus Experience Center, Bicentennial Park and National Veterans Memorial and Museum.
- Toyota wants autonomous cars that know how you feel. The company’s CEO says they want people having more fun in their cars, and suggests autonomous vehicles should appeal to people’s emotions and interests. This can including suggesting stop offs at national parks for nature lovers, and other AI guided experiences while traveling by car.
- Sidewalk Labs (a division of Google), is working with the city of Toronto on several projects, and recently released a statement about urban data. Since the public is who contributes the data, they’ve decided that a public trust – not a private company – should own that data, and it should be accessible to all. This is a huge step on making sure private interests don’t benefit from the public’s data without informed consent.
- The Palm Phone is a new way to think about smart phone experiences. Instead of going bigger, what if your phone was smaller? I’m not sure I buy into what Palm is selling – firstly, in order to even use a Palm, you need to already have a smart phone (so its not replacing anything), but the conscious decision to try to minimize your screen time/use a phone more intuitively is definitely something I can get behind.
Bonus (I can’t seem to narrow down to 5 these days): If you’re interested in learning more about how design works, and hearing about use cases of human-centered design, check out the new podcast “Wireframe.” It takes a “This American Life” style approach to talking about design, and the first two episodes are out now. The first discusses the Three Mile Island disaster in terms of “bad” user interface design, and the second takes a look at the city of Boston’s efforts to create a 311 app.
Welcome to Five for Wednesday, our weekly round-up of tech-focused news. Lots of Google information this week!
- Time will tell if Google’s new approach to fighting all those annoying robocalls to your cell phone will pay off.
- Not wanting Apple to dominate the product announcement headlines, Google just held their most recent product event. Here’s the important stuff you need to know.
- Wondering how the new Pixel Slate tablet compares to Surface and iPad tablets? Stop wondering; we’ve got you covered.
- This will surely interest busy REALTORS®: the creator of Android is building a phone with serious AI capabilities to (among other things) “mimic the user and automatically respond to messages on their behalf.”
- We’ve said it for a while: augmented reality (AR) is here to stay, and here to shake up homebuying and staging. Apple’s acquisition of several companies demonstrates a serious interest on their part.
- 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.