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Real Estate Search Needs a Kick in the Butt and I Think RealScout Can Do the Kicking

People that know me know that it’s hard to impress me when it comes to real estate tech. There’s no shortage of solutions for problems REALTORS® don’t have, which tends to be the name of the game when it comes to real estate technology pitches; but every now and then something comes across my desk that helps the real estate buy/sale experience for clients, solves a real estate problem for REALTORS® and truly blows me away. 2016 REach® class-member RealScout does just that.

Infusing client/agent collaboration with insight of homes, beyond IDX search, their collaboration platform puts REALTORS® at the heart of the search while adding next generation experiences for clients giving them data and context that go beyond the laptop and mobile search – with “machine learning.” Google, Facebook, and all the other online companies you know, love, and kind of creep you out, are getting better and better at “learning” about the wants and needs of you and your clients based on the information absorbed online. In real estate, it’s about where they want to live, HOW they want to live, and the information given to them – which, right now, is wholly incomplete. REALTORS® need to go beyond the “3 bedrooms, 2 baths, 2 car garage” conversation and get to the heart of what their client want, and sometimes DON’T KNOW what they need, to maximize the experience with you. Something that RealScout’s machine learning solution hopes to help with.

Read on for an in-depth write up on what machine learning is directly from RealScout CEO – a former REALTOR® – Andrew Flachner and learn where real estate search and client collaboration of the future is going. More importantly, sign up to be a mentor to RealScout and the other equally awesome members of the Reach Accelerator Class of 2016. Hang on to your hats!  Real estate tech nerds are getting better and better at their craft. NAR is a part of the conversation.  Where are you?

Computer vision is reshaping real estate search

  • How machine learning will enable a deeper, more intuitive home search experience
  • By teaching computers how to understand the visual world, MLS photos will soon unlock a treasure trove of listing data.
  • In the future, computer vision may be used to automatically predict marketability of listings and sale price.

Home buyers don’t measure “home” in square feet. I learned that during my time as an agent. Instead, my buyer clients described their home preferences in terms of large backyards, open floor plans and lots of natural light. But the language they used to describe their ideal house didn’t translate into any online search experience available at the time. This frustration was, in part, the impetus for founding RealScout and building a brokerage-branded home search platform that aligns with homebuyer wants and needs.

Last year, we took a major step in realizing this central mission by becoming the first in our industry to implement a powerful new technology called computer vision. Real estate listing photos carry vast amounts of valuable information for homebuyers – information that, until recently, was not understandable by computers and thus not easily searchable. Computer vision can analyze listing photos and automatically identify hundreds of features, giving consumers the ability to filter searches with this and other buyer-centric information.

Computer Vision Technology: All Around us Now and in the Future

The application of computer vision technology is familiar to most of us and will become increasingly so. Google’s autonomous vehicles use computer vision to identify objects and to distinguish pedestrians from bike riders and countless other factors in a dynamic road environment. Facebook uses it to suggest that the woman next to you in the photo you recently posted is your college friend.

Google’s autonomous vehicles use computer vision to identify objects:

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We’re particularly excited about this capability at RealScout because it provides agents with valuable information that’s never been available, such as consumers’ neighborhood-specific feature preferences and trends, based on true interests and preferences discovered from search behavior. This technology, when paired with an agent’s expertise, is setting a new standard in the consumer home buying experience. Only human agents using the best technology and machine intelligence can most successfully guide their clients through the complex home buying process.

What is Computer Vision?

Computer vision is the science of teaching computers how to understand the visual world. Since it’s so easy for people to glance at photographs of a home and accurately identify and describe what they see – room type, open layout, natural lighting, new appliances – it might be surprising that computers have only recently begun to do the same.

Recent advances in computer vision have been driven by innovations in deep learning, a technique where computers learn to solve problems and improve over time, similar to the process experienced with the human brain. Just as parents teach children by pointing out objects in pictures and saying “this is a cat,” so too are programmers training computers by providing examples.

The same tech that powers Facebook’s face-tagging is starting to change the face of home search. Teaching computers how to understand pictures is a lot of work. In real estate, it’s currently accepted that almost 100,000 manually tagged photographs are needed to help computers identify the 9 different room types in a house – kitchen, dining room, living room, bedroom, bathroom, etc. — as accurately as humans. Additionally, each subcategory – oven, refrigerator, countertop material, etc. – requires thousands more hand annotated photos.

Delighting Clients with Computer Vision

In 2012, we started manually creating the types of experiences that we’d eventually scale using computer vision. In the same way Google and Facebook have trained their models with mind-boggling amounts of data, we built a vast data-set tailored to real estate. We have used human taggers to label more than 7 million listing photos with dozens of elements ranging from room type to features, attributes and other key information.

Computer Vision and the Future of Real Estate Search

Whether you find it helpful or creepy when Facebook’s face-tagging system suggests the name of a friend or family member in a posted photo, this same technology is starting to change the face of real estate and home search.

The use cases above represent only the tip of the iceberg of innovative user experiences that will be made available through computer vision. Fundamentally, computer vision unlocks a treasure trove of visual information and makes it searchable, manipulable and consumable, not only increasing the overall quantity of property data, but increasing its quality.

This means that soon, agents can expect computer vision to enhance every part of their business. For example, property insights extracted from photos can power highly detailed CMAs for pricing properties, or enable high accuracy look-alike search for agents looking for homes with similar features. Even information like square footage, construction quality, and neighborhood conditions can be extracted through computer vision techniques, increasing the transparency of information for agents and clients.

What questions will the future of computer vision be able to answer for real estate search?

  • What are the fine-grained room classifications: wine cellar/poker room/nursery/deck/patio?
  • What are the fine-grained feature classifications: granite/marble/concrete/tile counter tops?
  • What is the exact size of every room?
  • What will the dining room look like if I add french doors to the back patio?
  • Has the house been professionally staged?
  • What is the architectural style?
  • When will I need to repair/replace the roof?
  • What direction is the house facing? What is the solar orientation?
  • Is the house on a cul-de-sac? Is there a sidewalk?
  • How green is the surrounding neighborhood?
  • How large is the front yard? Backyard?
  • How big is the driveway? How much snow will I have to shovel?

How will answers to those questions will help our industry:

  • Provide the best online exploration experience for home buyers
  • Predict days on market and sale price
  • Make suggestions to listing agents on how to optimize the sale price
  • Make suggestions to buyers agents on how to craft compelling offers

What does this mean for agents and brokers?

Does computer vision represent a threat to the agent value proposition? Absolutely not. Increased property information, in fact, empowers agents to be even greater domain experts. Making sense of data and, more importantly, making informed decisions through data, are subtle and highly personal skills machines cannot acquire. Agents and brokers who effectively integrate these technologies will be able to better cater to their clients’ needs, and will naturally be in higher demand.

Computer vision is still in its early days, and the full impact of the technology is unknown. But one thing is for sure, pioneering the use of this technology is a rare opportunity for our industry to take the lead in adopting bleeding edge innovation that drives real value for our clients.

 

Nobu Hata

Nobu Hata is the Director of Digital Engagement for the National Association of REALTORS®. An industry veteran since 1996, Nobu is a student of marketing, communications trends, social media, and technology in the real estate industry, having implemented and adapted various new school techniques to his successful brick and mortar business in Minneapolis, Minnesota for Edina Realty until July 2012. An accidental speaker, instructor, and volunteer member leader since 2009 (NAR YPN, Communications, Equal Opportunity/Diversity, Strategic Planning Committees), Nobu brings to NAR a skill set that includes insight and context of agent, brokerage and association issues up the pipeline, and delivers value-added information down, both in-person and through the digital domain.

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