{"id":4896,"date":"2017-04-04T21:41:37","date_gmt":"2017-04-05T01:41:37","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/opendoor-applying-big-data-to-the-home-selling-process\/"},"modified":"2017-04-04T21:41:38","modified_gmt":"2017-04-05T01:41:38","slug":"opendoor-applying-big-data-to-home-selling","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/opendoor-applying-big-data-to-home-selling\/","title":{"rendered":"OpenDoor: applying big data to home selling"},"content":{"rendered":"
\"\"<\/a>
Ex. 1 – 2016 average number of days homes are listed on Zillow.com by state<\/figcaption><\/figure>\n
\"\"<\/a>
Ex. 2 – Zillow estimate errors<\/figcaption><\/figure>\n

Selling a house can be a source of deep frustration. According to real estate website, Zillow.com, the average US home spends three months listed on the market (see ex. 1).[1]<\/a> A large cause of the delay is the difficulty of information discovery. Home buyers need to physically visit the properties, assess the various features, and somehow conjure up a price. It\u2019s a huge challenge for any individual buyer. But what if you could utilise the data from thousands of previous transactions? That\u2019s where OpenDoor comes in.<\/p>\n

OpenDoor uses the power of big data to calculate home values, fast. They offer to buy properties from sellers quickly in exchange for a fee. OpenDoor then resells the houses, keeping the fee and a small margin.<\/p>\n

Value Creation and Capture<\/strong><\/p>\n

Real estate investors and lenders have been using automated valuation
\nmodels (AVMs) to model house prices for quite some time. These\u00a0AVMs usually consist of two parts: a repeat-sales index<\/strong>\u00a0(which estimates house prices changes over time) and\u00a0a hedonic pricing model<\/strong> (which estimates a property’s value by piecing together its constituents – bedrooms, bathrooms, etc.). The literature on both methods is extensive.[2]<\/a>\u00a0Nonetheless, building an accurate AVM is still a real challenge. For example, in Zillow\u2019s infamous Zestimate, 55% of valuations are more than 5% off from the actual sale price (see ex. 2).[3]<\/a>\u00a0CoreLogic, a leading provider of AVMs, is more than 15% off from the sale price in 10%+ of cases.[4]<\/a><\/a><\/p>\n

To enhance the accuracy of its AVM, OpenDoor\u2019s team of data scientists have had to do things differently:[5]<\/a><\/p>\n

    \n
  1. Getting super specific on inputs<\/strong> \u2013 in addition to considering standard house features, OpenDoor\u2019s model also uses very granular (often proprietary) inputs such as the property\u2019s proximity to freeways, curb appeal, and kitchen countertop material[6]<\/a><\/li>\n
  2. Narrowing focus to where the model is most reliable<\/strong> \u2013 OpenDoor only works with single-family houses built after 1960, priced between $125,000-500,000 in cities where housing is more homogenous (e.g., Phoenix, Dallas)[7]<\/a><\/li>\n
  3. Understanding second-order relationships<\/strong> \u2013 rather than just consider the direct impact of inputs on price, OpenDoor attempts to understand how inputs interact with each other, for example, a pool\u00a0adds more or less value depending on the quality of the neighbourhood<\/li>\n<\/ol>\n

    The resulting reliability gives OpenDoor the confidence to make offers to sellers in as little as 24 hours.[8]<\/a>\u00a0Providing sellers this speed and convenience then allows OpenDoor to capture value in three ways:<\/p>\n

      \n
    1. Charging a 6% service fee<\/strong> akin to a real estate agent<\/li>\n
    2. Pricing slightly below market value (OpenDoor estimates their offers are 1-3% below market<\/strong>,[9]<\/a>\u00a0others estimate 6%[10]<\/a>)<\/li>\n
    3. Adding 0-6% in fees for resale risk<\/strong>, based on market conditions[11]<\/a>[12]<\/a><\/a><\/li>\n<\/ol>\n

      Unlike other bulk house buyers such as We Buy Ugly Houses, OpenDoor doesn\u2019t focus on major renovations and \u2018flipping\u2019 the properties. Instead, their model is one of scale \u2013 buy and sell, earning a small sliver on each transaction.<\/p>\n

      \"\"<\/a>
      Many fast, bulk purchasers of houses exist but these companies focus on buying at a substantial discount to market and ‘flipping’ the properties<\/figcaption><\/figure>\n

      Challenges and Opportunities<\/strong><\/p>\n

      Few others have dared to utilise such a purely data-driven approach in real estate. Getting the model right is an enormous challenge. Accessing accurate and granular data at scale is difficult (many inputs required physical validation), and even small errors on such a large capital base can be devastating. Moreover, the risks associated with wide-scale systemic downturns is nearly impossible to factor in.[13]<\/a>\u00a0As it expands, OpenDoor will face additional challenges such as whether it can offload the properties acquired quickly enough and whether its model can be adjusted for new markets.<\/p>\n

      However, the potential upside from cracking this nut is immense: real estate broker revenues top $100bn a year in the US alone.[14]<\/a> OpenDoor also has the opportunity to take a slice of other components of the real estate transaction: generating leads for preferred lenders; operating SaaS for appraisers; sharing information with real estate databases.<\/p>\n

      If OpenDoor is successful, maybe one day we\u2019ll see an efficient market for real estate emerge, and selling a house will be as easy as clicking a button.<\/p>\n

       <\/p>\n

      \n

       <\/p>\n

      [1]<\/a> https:\/\/www.zillow.com\/research\/data\/ Days on Zillow dataset; average of 2016<\/p>\n

      [2]<\/a>\u00a0One Texas study calculated that golf courses increase nearby property values by 26% http:\/\/journals.humankinetics.com\/doi\/pdf\/10.1123\/jsm.21.4.555<\/p>\n

      [3]<\/a> http:\/\/www.realestatedecoded.com\/zillows-typical-error\/<\/p>\n

      [4]<\/a>\u00a0https:\/\/www.corelogic.com\/imgs\/international\/retrospective-avms.pdf<\/p>\n

      [5]<\/a>\u00a0https:\/\/www.forbes.com\/sites\/amyfeldman\/2016\/11\/30\/home-shopping-networkers-opendoor-is-upending-the-way-americans-buy-and-sell-homes\/#1dc273c3430c<\/p>\n

      [6]<\/a> OpenDoor asks sellers to provide specific inputs not available in existing databases. A physical inspection is also conducted, though the offer price is typically made before this inspection.<\/p>\n

      [7]<\/a> Interestingly, the three cities where OpenDoor currently operates, Phoenix, Dallas, and Las Vegas, are in states where the average listed time is below the US average. This lower listing time may reflect the more \u2018standardised\u2019 nature of housing there<\/p>\n

      [8]<\/a> https:\/\/www.opendoor.com\/faq\/seller\/the-opendoor-offer-seller<\/p>\n

      [9]<\/a> https:\/\/techcrunch.com\/2016\/06\/07\/a-startup-that-pays-cash-to-buy-homes-now-offers-money-back-guarantee\/<\/p>\n

      [10]<\/a> https:\/\/www.inman.com\/2017\/03\/22\/opendoor-cost-markup-fee-las-vegas\/<\/p>\n

      [11]<\/a> https:\/\/stratechery.com\/2016\/opendoor-a-startup-worth-emulating\/<\/p>\n

      [12]<\/a> https:\/\/www.opendoor.com\/pricing<\/p>\n

      [13]<\/a> Some speculate that OpenDoor will increase its \u2018risk fee\u2019 in a market downturn but there are clear limits to this approach<\/p>\n

      [14]<\/a> https:\/\/www-statista-com.ezp-prod1.hul.harvard.edu\/statistics\/295475\/revenue-real-estate-sales-and-brokerage-in-the-us\/<\/p>\n","protected":false},"excerpt":{"rendered":"

      Using the power of data analytics, OpenDoor will offer you a price for your house in under 24 hours<\/p>\n","protected":false},"author":1088,"featured_media":4897,"comment_status":"open","ping_status":"closed","template":"","categories":[134,29,1360,16],"class_list":["post-4896","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-analytics","category-big-data","category-hedonic-pricing","category-real-estate"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-and-analytics-as-digital-assets\/","yoast_head":"\nOpenDoor: applying big data to home selling - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/opendoor-applying-big-data-to-home-selling\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OpenDoor: applying big data to home selling - 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