Agreeing with other commentors here who don't buy into the superficial 'silly Zillow's ML folks didn't consider the possiblity that the model might fail to predict the real world' narrative. Below I'll outline what Zillow learned from the 'iBuying failure' from my perspective having worked in real estate tech.
As someone who worked as a senior Data Scientist at one of the Silicon Valley companies involved in iBuying some years ago (5+ years ago) I see the recent Zillow iBuying spree as a mechanism to test how much market pressure needed to be applied to historically not-so-competitive residential real estate markets to induce 'FOMO' / social contagion behaviors of large-ticket items AND as a way to produce a dataset on the actual dollar amount that (residential) property sellers would need to abandon the 'safe', 'we've always done it this way' process of selling a piece of real estate through a real estate agent / broker. The upside (for real estate tech companies) in removing the middle[wo]man - the real estate agent - in a residential home transaction is that the real estate platform can now control both sides of information asymmetry in the real estate transaction. They can also start offering (like Zillow does) mortage services and other ancillary financial services, allowing them to earn millions of dollars in fees by capturing the home-buying financial services markets.
In my work at the $real_estate_tech_company I mainly developed lead-generation data products which were used by $real_estate_tech_company market to get less-desirable single family homes to be bought up by high-net-worth indiviudals who invest in real estate. The end goal of this process was to get the foreclosure and pre-foreclosure single family homes (SFHs) off of the bank's ledgers and leave someone else holding the (debt) bag. HNW individuals would buy up the assets, the banks would have someone with sufficient collateral now in possession of the single family home, and the HNW individual could rent out the home to less-likely-to-default-than-the-original-homeowners family / renter. According to what I read about Zillow's iBuying model, Zillow focused on buying up assets (SFHs) in markets with strong, diversified economies, i.e., economies that are less sensitive to economic downswings. (Blackstone is doing the same thing and is also buying up trailer parks / mobile home communities near tech hubs.) As someone who builds data products for a living the datapoints that Zillow was able to gather are, in my opinion:
- A hard number, in USD, of the amount of money needed to get humans to abandon the process of selling their home in the traditional way: through a real estate agent, broker, etc. Because Zillow has home buyer and seller data they now know what that switching cost trigger is, in USD, for homeowners with an income of x, a mortgage of y, and a debt-to-income ratio of z. Anecdotally, from reading posts on Twitter and other sites from people who sold their SFH's to Zillow in the iBuyer program it appears that in economically depressed regions of the United States (the Midwest, rural places within 1-2 hours of a medium-sized city, etc.) that 'cash-in-hand' amount that the iBuyer program offered homeowners to sell their houses to Zillow is only $15,000 - $30,000 over list price per property. People who sold their houses to Zillow via the iBuyer program were talking about how they could 'pay off their new car and have a bit left over to buy new appliances in their new house'. These sums are rounding errors to Zillow's business mode even when multiplied by the thousands of properties that Zillow bought. But to the home sellers, $30,000 or $50,000 is written about as thought it is some life-changing sum of money. I say this not to mock, demean nor poke fun at the homeowners, having grown up in one of these economically-depressed regions of the United States. A good portion of these homeowners are selling their modest homes and taking on risky levels of debt in a real estate bubble. I hope that I'm wrong about the risk that they're incurring - all the while celebrating getting $30,000 cash-in-hand from Zillow - but I don't think that I am misreading the situation.
- Hard numbers on how much (or how little) a given housing market's supply need to be (artificially) constrained before home prices skyrocket. Real estate markets in different regions behave differently: rural Iowa's market is nothing like Santa Barbara's market which is different from Boston's market. Until Zillow undertook large-scale coordinated (artifical) reduction in supply *at a time of unprecendented _physical_ mobility of workers due to remote work status during covid19* we really had no way to model which markets would be more resistent to large upticks in housing prices, which markets would see meteoric growth quickly and in a sustained fashion ('pent-up demand').
So if I were running Zillow's iBuying experiment 'failure' as a data scientist I would be delighted in the new data points gleaned from the 'failed experiment', namely:
- What's the exact dollar amount that causes single family homeowners to abandon the 'sticky' process of selling their home through a human (broker, real estate agent)? Answer: it's pretty damn low for most folks: less than $100,000 over the Zestimate price or price that their real estate agent quoted them. And home sellers talked about that being some huge windfall enabling them to pay off a new car, or buy all new appliances in their new home (that they probably overpaid for).
- To what degree do I have to (artifically) constrain the housing supply in different regions to induce a 'feeding frenzy' / FOMO / social-contagion-like behavior? By manipulating public perception of the real estate market in their area can I induce irrational / deleterious individual behaviors that then spread to others in their geographic area and social circles?
To me Zillow's iBuying experiment mirrors what Facebook allowed researchers to do in the mid-2010s when they manipulated content in user's feeds to see if they could induce positive or negative emotional states: https://www.theguardian.com/technology/2014/jun/29/facebook-... Until recently there has never been a way to leverage mechanisms of social contagion in the nation-wide housing market for the middle class. Five plus years ago when working at the real estate tech company I would have loved to get my hands on a dataset of linked behavioral data like this, especially a dataset that had reduced geographic buying pressure (due to remote work) as the dataset would have revolutionalized supply- and demand-side real estate data product development.