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). 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’s a huge challenge for any individual buyer. But what if you could utilise the data from thousands of previous transactions? That’s where OpenDoor comes in.
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.
Value Creation and Capture
Real estate investors and lenders have been using automated valuation
models (AVMs) to model house prices for quite some time. These AVMs usually consist of two parts: a repeat-sales index (which estimates house prices changes over time) and a hedonic pricing model (which estimates a property’s value by piecing together its constituents – bedrooms, bathrooms, etc.). The literature on both methods is extensive. Nonetheless, building an accurate AVM is still a real challenge. For example, in Zillow’s infamous Zestimate, 55% of valuations are more than 5% off from the actual sale price (see ex. 2). CoreLogic, a leading provider of AVMs, is more than 15% off from the sale price in 10%+ of cases.
To enhance the accuracy of its AVM, OpenDoor’s team of data scientists have had to do things differently:
- Getting super specific on inputs – in addition to considering standard house features, OpenDoor’s model also uses very granular (often proprietary) inputs such as the property’s proximity to freeways, curb appeal, and kitchen countertop material
- Narrowing focus to where the model is most reliable – 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)
- Understanding second-order relationships – 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 adds more or less value depending on the quality of the neighbourhood
The resulting reliability gives OpenDoor the confidence to make offers to sellers in as little as 24 hours. Providing sellers this speed and convenience then allows OpenDoor to capture value in three ways:
- Charging a 6% service fee akin to a real estate agent
- Pricing slightly below market value (OpenDoor estimates their offers are 1-3% below market, others estimate 6%)
- Adding 0-6% in fees for resale risk, based on market conditions
Unlike other bulk house buyers such as We Buy Ugly Houses, OpenDoor doesn’t focus on major renovations and ‘flipping’ the properties. Instead, their model is one of scale – buy and sell, earning a small sliver on each transaction.
Challenges and Opportunities
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. As 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.
However, the potential upside from cracking this nut is immense: real estate broker revenues top $100bn a year in the US alone. 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.
If OpenDoor is successful, maybe one day we’ll see an efficient market for real estate emerge, and selling a house will be as easy as clicking a button.
 https://www.zillow.com/research/data/ Days on Zillow dataset; average of 2016
 One Texas study calculated that golf courses increase nearby property values by 26% http://journals.humankinetics.com/doi/pdf/10.1123/jsm.21.4.555
 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.
 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 ‘standardised’ nature of housing there
 Some speculate that OpenDoor will increase its ‘risk fee’ in a market downturn but there are clear limits to this approach