What do artificial intelligence and real estate have in common? A lot, as it turns out. Real estate has been the most fascinating space to watch throughout the global pandemic, and the applications of artificial intelligence are growing faster than housing starts. Working from home has called the value of some commercial real estate into question, and residential housing values have shot through the roof.

Machines are not all-knowing, even when they have lots of data. Translators are needed to ensure the output makes sense and that the algorithms have no blind spots.

Zillow learned this lesson the hard way through its iBuying program. The program put an estimated value on homes and then solicited the purchase of those homes from their current owners through the Zillow Offers program. To make a long story short, Zillow bought too many homes in the wrong places at prices that were too high, and is now seeking to sell 7,000 homes for $2.8 billion, a loss of more than $300 million.

Zillow is underwater on perhaps two-thirds of the homes it bought. There is a human cost to this story, as those losses and the shuttering of the Zillow Offers program will cause Zillow to reduce its headcount by 25 percent. That is a considerable impact of model risk, both in financial and human terms. Apparently, the algorithms deployed by home buying systems Opendoor and Offerpad were able to dodge the pitfalls that Zillow landed in.

Trend followers know that models can struggle during turning points and volatile markets. It is nearly impossible to buy low and sell high, as it is hard to find a signal that identifies the highest or lowest prices, at least in real time. Markets have been volatile during COVID, and the residential real estate market has been highly impacted. A decades-long trend of the increased desirability of urban living abruptly reversed, as working from home during COVID lockdowns made homeowners desire more space in more affordable (often suburban or exurban) locations. Housing prices stabilized or fell in the most expensive cities, while more affordable and more distant locations with more space, skyrocketed in price. One turning point in the markets is when people started moving away from cities during the height of COVID fears, and a second turning point came when those fears subsided in some geographies with successful vaccination programs.

As they said in Top Gun, “hit the brakes, and he’ll fly right by.” Real estate pricing models may have anticipated continuing price gains even as real-world factors were mitigating this mass migration. The same thing happened to commodity prices in 2008 after China abruptly completed its urbanization program and quickly reduced demand for building materials while supply continued to rise.

What happened to Top Gun 2 anyway? I guess we’ll have to wait until at least May for the release, even though we really wanted to see it in July 2019. But I digress …

Keith Black, managing director, program director at FDP Institute.