12 highlights
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Sellers want their money quickly, often because they need to put it towards the next house. But it takes a typical buyer weeks to secure financing and do other due diligence—and sometimes deals fall through. Zillow was hoping to fill a gap between buyers and sellers.
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This is not intended to be a pure “investment” strategy; it’s more of a service, where, from the seller’s perspective, you expedite the house-selling process in exchange for a fee. It just so happens that the way you expedite it is by buying and briefly holding the house yourself. In the real estate industry, this service is called iBuying, but in more general terms it’s often called market making.
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But the job of a market maker is much harder in housing than in stocks.
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Zillow’s Zestimate®, like Yahoo!‘s fantasy football experts, is pretty smart, and pretty good at valuing things. But it’s not omniscient, and it certainly doesn’t know as much as the sellers. It values home prices fairly most of the time, but occasionally it overrates or underrates a home.
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The problem is that—much like my fantasy football team—Zillow’s formulaic purchasing strategy virtually guaranteed that it would get the slice of the inventory it had most overrated. Any time there’s a problem with the home not captured in the Zestimate, (but perhaps visible to a slower, more careful buyer), Zillow’s iBuying program was likely to overpay.
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This is a mix of problems that economists call “adverse selection” and “imperfect information,” most famously studied by George Akerlof in the 1970s. (His example was used cars.) Any time that you’re working with imperfect knowledge and trying to operate a business on a large scale, you’re likely to run into this kind of trouble.
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Do market makers in the stock market run into this problem? Absolutely, but it’s not nearly as severe. They are only trading a limited number of public stocks, and they can more quickly detect patterns that suggest mispricing.
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It is harder to do this with houses, since every house is different. Zillow may not be able to systematically detect the patterns in the houses it overrates, or adjust its algorithm to avoid stocking up on overrated homes.
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With housing, the inventory is costly to hold physically. You have to secure and keep up the home. And housing is very likely to be a bad investment while it’s vacant and waiting for sale, since it generates no rent. Each house is different and buyers are slow, so an aspiring market maker might get stuck holding it for a while.
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It’s easy to write a business model off when it fails, or when a problem with a market is described. Above I outline some serious issues with the iBuying model. But there’s also a clear need for market-making in housing.
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When economists first started examining the “market for lemons,” they didn’t conclude that the used car market would collapse completely because of people selling bad used cars. Instead, they concluded that the used car market would be less robust than it would if buyers could more easily determine car quality, and that buyers might have to do costly work to address their lack of information.
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It is likely that the iBuying market will endure, but it will be smaller and less efficient than its proponents had hoped.