How Does Algorithmic Trust Affect Pricing Decisions? Evidence from Zillow
AI-driven valuation models are now widespread. But what happens when trust in these algorithms breaks down?
AI-driven valuation models have become widespread in the U.S. housing market. Zillow, one of the country’s largest online real estate platforms, provides proprietary machine-learning-based home value estimates—known as Zestimates—for over 110 million properties. Since launching in 2006, Zillow has offered these estimates free of charge on its website.
See the figure below for an example: the property sold on 4/30/25 for $1.23 million, while Zillow’s estimate that day was $1.324 million.
Buyers, sellers, and agents routinely consult Zestimates when setting list prices or making offers, and research shows that these estimates can influence final sale prices. In other words, market participants generally place trust in Zillow’s algorithmic guidance. But what happens when that trust breaks down? A recent working paper addresses this question.
An exogenous shock to trust
In November 2021, Zillow shut down Zillow Offers, its algorithm-driven “iBuyer” program, which purchased homes with minimal inspection, renovated them, and then resold them. Confronted with an average gross loss of $80,771 per home in Q3 2021, Zillow’s CEO admitted that “the unpredictability in forecasting home prices far exceeds what we anticipated.”
It is reasonable to expect that this news undermined public confidence in Zestimates. The shutdown received widespread coverage in both traditional and social media, and a follow-up survey confirms that it significantly reduced consumers’ trust in Zillow’s valuations.
In standard product-recall settings, diminished trust typically reduces demand, leading to lower prices. On a two-sided platform that provides algorithmic tools, however, a loss of confidence can influence the behavior of both buyers and sellers, even without directly altering overall demand.
In the housing market, increased uncertainty about true market values should lead to greater dispersion in listing prices, resulting in larger absolute gaps—positive or negative—between listing prices and their corresponding Zestimates. Models on the pricing of “one-of-a-kind” goods (such as houses) also predict that sellers would err on the high side when uncertain, thus becoming more likely to set list prices above Zestimates.
In summary, the predictions are that:
The absolute gap between listing prices and Zestimates increases after the iBuyer shutdown.
Sellers set listings prices above the Zestimate more frequently.
Testing the predictions
The paper analyzes approximately 28,000 listings in Boston (MA) and Pittsburgh (PA) between June 2019 and May 2022. To identify the causal effect of the shutdown of the iBuyer program—and the associated shock to trust—it compares changes in outcomes in 2021–22 (the treated period) with seasonally matched changes in 2019–20 and 2020–21 (control periods).
The figure below shows estimated monthly changes—relative to the control periods—in the absolute deviation between listing prices and Zestimates, spanning five months before and after the shutdown of the iBuyer program in November 2021 (indicated by the red dashed line). Deviations remain near zero prior to the shutdown but increase noticeably afterward. This pattern supports the hypothesis that diminished trust in Zestimates leads to greater absolute divergence between list prices and Zillow’s valuations.
To give a sense of magnitudes, the absolute difference between listing prices and Zestimates increases by an average of 1.1 percentage points following the iBuyer shutdown. This effect is substantial—it represents a 26.8% increase relative to the average absolute deviation in the pre-shutdown period.
Next, the same approach is used to examine whether home sellers became more likely to set listing prices above or below their Zestimates. Consistent with the paper’s hypothesis and with prior literature, the non-absolute deviation from Zestimates increases by 1.5 percentage points—indicating that sellers, on average, priced their homes higher relative to Zillow’s estimates following the iBuyer shutdown.
What are the consequences for time-on-market and sales prices?
All else equal, higher initial listing prices should slow the arrival of buyer offers, leading to longer time on market, which is generally associated with lower final sale prices.
However, the observed changes in list prices stem from a decline in consumer trust in Zestimates and the resulting increase in pricing uncertainty affecting both buyers and sellers. Because the iBuyer shutdown influenced the expectations of both sides of the market, its impact on time on market and final sale prices is theoretically ambiguous.
The paper finds that the sale-price premium over list prices increased by 0.7 percentage points, while the average time on market declined by nearly 11.2%. Taken together, these results suggest that sellers ultimately benefited from reduced trust in Zestimates and the resulting increase in price uncertainty.
Takeaways
Algorithmic tools are pervasive, and their credibility has real market consequences. The findings in the paper discussed in this post show that when trust in Zillow’s pricing algorithm declined, list-price dispersion increased, sellers raised their asking prices, and—surprisingly—homes sold more quickly and at higher premiums. This suggests that reduced trust in AI can shift market outcomes, even in the absence of changes to underlying supply or demand.