Why the iBuying algorithms failed Zillow, and what it says about the business world’s love affair with AI

(BigStock Image)

Just because a business process can be automated, doesn’t necessarily mean it should be automated. And maybe — just maybe — there are components of business that are not better served with AI algorithms doing the job.

That’s a key takeaway after Zillow Group made the unexpected decision on Tuesday to shutter its home buying business — a painful move that will result in 2,000 employees losing their jobs, a $304 million third quarter write-down, a spiraling stock price (shares are down 18% today), and egg on the face of co-founder and CEO Rich Barton.

Zillow’s move also represents a big loss for the algorithms that powered its nascent iBuying business, and it is a warning sign to other businesses — both in real estate and other industries — that rely heavily on the all mighty algorithm.

In Zillow’s third quarter conference call with investors and an appearance on CNBC on Tuesday, Barton made reference to the company’s lack of confidence in its home buying algorithm’s ability to accurately predict fluctuations in home prices.

He said Zillow could have blamed the iBuying failure on “Black Swan events” — the pandemic or unprecedented labor shortages, for example — and then “tweak” the company’s models and press on.

But Barton — one of the tech industry’s most respected and accomplished entrepreneurs — said that was too risky. And while the company eventually could have solved forecasting and operational problems with iBuying, it’s the algorithm itself where Barton placed the most uncertainty.

“But what we can’t solve is what the model is going to tell us about how much capital we need to raise, deploy and risk in the future in order to achieve a scale that we think is necessary to offer a fair price to customers for their homes in a competitive way,” he said.

In other words, the algorithm used to predict home prices just didn’t work to the level that Barton was going to risk the entire company on it.

And that gets into the bigger discussion: Are businesses too reliant on algorithms?

Research has long showed these computer models are loaded with biases and flaws. This is causing a backlash against AI and machine learning algorithms, and you can see it playing out in some of the chatter following Zillow’s decision.

A significant strategic blunder! Everything in our world cannot be managed with algorithms parsing big data! They failed to think through the implications of buying all those homes. A perfect HBS case study on bonehead! @zillow heads should roll!

— Ron Erickson (@ronerickson) November 2, 2021

And this comment from Seattle entrepreneur Galen Ward, who sold real estate tech startup Estately to Realogy in 2018.

It would be fascinating to find out where in the stack Zillow's failure lives.

Was it incorrect use of ML?
Too much trust in ML?
Aggressive management that wouldn't take "we aren't ready" for an answer?
Wrong KPIs?

— Galen Ward (@galenward) November 2, 2021

MoxiWorks CEO York Baur, whose Seattle company sells cloud-based software tools to residential real estate brokerages, said tech-powered iBuying companies are putting too much faith in machines to do what humans can do better, at least at this juncture.

“All the AI and machine learning in the world isn’t yet up to the task of the complexity of valuing a home in a rapidly changing market, and this move by Zillow is proof,” Baur told GeekWire. “They invented computer home valuation with the Zestimate 15 years ago, and it’s still not accurate after 15 years of refinement and billions of dollars invested.”

And it’s not just real estate where businesses may have overplayed the hand of algorithms. Take a look at some of the biggest bets in technology today:

  • Social media: Facebook’s recent problems are largely tied to whether its algorithm can properly serve information, and whether it’s tuned to inflame rather than empower.
  • Autonomous vehicles: Many of us were supposed to be riding along carefree in robot-powered vehicles by now, but it turns out this problem is much more complex than many technologists imagined.

So, where does that leave us? For Baur, the answer and lesson from Zillow’s recent failure is clear.

“What this says to me is that we need to stop over-applying technology in an effort to replace humans, and instead focus on applying technology to make humans better,” he said.

Zillow Offers is the company’s “iBuyer” service that aims to digitize the homebuying experience from start to finish. (Zillow Photo)

And that’s essentially where Zillow is headed in its next chapter.

In many ways, the company is falling back on its original premise — to empower real estate agents (the human kind) to do their jobs better. It’s a remarkable turnabout, considering Zillow’s big-time and surprising bet on iBuying.

One problem with Zillow’s new vision. Wall Street, which loves a good tech and automation story, isn’t liking what appears to be a less bold direction.

Zillow’s stock is down 50% in the past year, including 18% today.

“We acknowledge the stock may be temporarily put in the penalty box until investors get clarity on remaining balance sheet risk and whether to underwrite the changed strategy,” RBC Capital Markets Brad Erickson wrote in a report.

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Real estate Algorithm Ibuying Zillow Zillow group