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New research published in Marketing Science finds that while Zillow’s widely used "Zestimate" home-valuation algorithm boosts efficiency in the residential real estate sector, it also significantly benefits lower-income neighborhoods, even in situations where the algorithms are sometimes less accurate in those areas.

The study, "Unequal Impact of Zestimate on the Housing Market," is authored by Runshan Fu of New York University; Yan Huang of Carnegie Mellon University; Nitin Mehta of the University of Toronto; Param Vir Singh of Carnegie Mellon University; and Kannan Srinivasan of Carnegie Mellon University.

The researchers used detailed Zillow and home-sales data from 4,027 properties across 140 neighborhoods in Pittsburgh, where the researchers built a structural model of the housing market. They sought to measure how Zestimate affects listing decisions, selling prices, time on market, buyer surplus and seller profit.

In the end, they found that because the Zestimate reduces uncertainty surrounding actual property values, it enables sellers to reduce the likelihood of selling their properties below market value. It also helps buyers to match with the homes they truly value, leading to purchases that better fit their specific needs and preferences. This has the effect of increasing the extra value (buyer surplus) by 5.94%, and seller profit by 4.36% on average.

"Even when Zestimates under- or overvalue a home, they reduce uncertainty about the property’s true market value and help clarify expectations for both the list price and the final offer," said Huang. "Buyers and sellers make better-matched decisions, and the market as a whole becomes more efficient."

The researchers found that the Zestimate algorithm tends to be less accurate in lower-income neighborhoods, but surprisingly, they benefit the most from its existence. This is due to the fact that those neighborhoods face higher baseline uncertainty about property values, so the Zestimate helps alleviate that uncertainty even with room for error.

The researchers found that seller profits increase by 4.78% in poorer neighborhoods, compared to 4.21% in wealthier neighborhoods. They found that the buyer surplus tends to increase by 9.09% in low-income neighborhoods, compared to 7.26% in wealthy neighborhoods.

"These results surprised even us," said Mehta. "Zestimates actually narrow information gaps and reduce inequality by helping the buyers and sellers start the process with better information, more clarity and more reasonable expectations of what they can achieve."

Singh added, "If Zestimate accuracy in poorer neighborhoods were improved to the level of richer neighborhoods, the total economic surplus could increase by another 31%. That’s a substantial opportunity for improving fairness and efficiency in residential real estate."

On the matter of accuracy, the study showed that 73% of properties with overvalued Zestimates still generated a higher buyer surplus; and 44% of properties with undervalued Zestimates still produced higher seller profits.

"These algorithms create a win-win environment for both seller and buyer, and they don’t need to be perfect to create real value," said Kannan. "Markets respond strongly to any signal that reduces uncertainty, and Zestimates do exactly that."

More information: Runshan Fu et al, Unequal Impact of Zestimate on the Housing Market, Marketing Science (2025). DOI: 10.1287/mksc.2022.0451

Citation: New research finds Zillow’s Zestimate reduces uncertainty and improves outcomes for both buyers and sellers (2025, December 15) retrieved 15 December 2025 from https://phys.org/news/2025-12-zillow-zestimate-uncertainty-outcomes-buyers.html

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