How iBuyers are changing real estate racial disparities and individual homeownership rates in one major city

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How iBuyers are changing real estate ******* disparities and individual homeownership rates in one major city

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University of Washington researchers investigated how iBuyers—companies that use automated algorithms to quickly buy and sell homes—have affected the well-documented ******* bias against ****** home sellers. In Mecklenburg County, North Carolina, they found that on average iBuyers paid more equal prices to ****** and white home sellers than individual buyers, largely because iBuyers paid white sellers significantly less on average than an individual buyer. Credit: Blake Wheeler/Unsplash

Instant buyers, also known as iBuyers, rapidly buy and sell homes using automated models to set prices. These companies, such as Opendoor and Offerpad, can turn around cash offers in a matter of hours, and they’ve captured more than 5% of the real estate market in some U.S. cities.

Since new tech often replicates or exacerbates existing societal biases, University of Washington researchers wanted to investigate how iBuyers have affected the well-documented ******* bias in home appraisals—particularly bias against ****** homeowners.

The team homed in on Charlotte, North Carolina, where an estimated 35% of the population is ******, and where in 2021, iBuyers held more than 8% market share. Based on an analysis of five years of property transactions in Mecklenburg County (which contains Charlotte), researchers found that on average, compared to individual buyers, iBuyers paid more equal prices to ****** and white home sellers. That’s largely because iBuyers paid white sellers significantly less on average than an individual buyer would.

The team also discovered that iBuyers were then significantly less likely to sell homes to individual buyers. Instead, these companies were more likely to sell homes to institutions, such as large rental companies that’ve been tied to high eviction rates and rent gouging.

The team

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its research in June at the

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, held in Rio de Janeiro.

“It’s easy for bias to seep into automated models if they’re trained on data that is itself biased,” said lead author Isaac **********, a UW doctoral student in the Information School.

“The models that iBuyers use are essentially proprietary ****** boxes. Given the long history of housing discrimination in the ******* States, we were concerned that historical biases might be influencing these models behind the scenes, without the public being aware.”

The researchers pulled 50,000 publicly available property transfer records from 2018 to 2023 for Mecklenburg County, population 1.1 million in the last census. The team cross-referenced these transfer records with North Carolina voter rolls, which list each person’s race.

Controlling for 50 factors, including home size and neighborhood ****** rate, the team found that on average white-owned homes sold to private buyers for $36,051 more than ******-owned homes. But when homes sold to iBuyers, that difference shrank to $4,436, because iBuyers paid ****** homeowners $4,376 more on average, while paying white homeowners $27,239 less.

“There’s very little reason for us to believe that there’s some purposeful intervention going on here,” said senior author Nic Weber, a UW associate professor in the iSchool. “iBuyers are paying ****** homeowners a little bit more, but not significantly more. Rather, iBuyers don’t seem to be willing to pay white homeowners what they might be able to earn if they sold through a traditional broker.”

In going through the data, the team also found aberrations in who purchased homes from iBuyers. When iBuyers sold homes in Mecklenburg, institutions—frequently real estate investment trusts—bought 25% of the homes. Yet when an individual (not an iBuyer) sold the home, institutions bought just 15%.

The team also found ******* differences in this shift. When iBuyers bought and resold homes, both originally white-owned and ******-owned homes were bought up at greater rates by institutions. But the increase in institutional ownership for white-owned homes (from 9% for individuals to 17% for iBuyers) was greater than the increase for ******-owned homes (from 33% to 36%).

Conversion to institutionally owned real estate is associated with negative outcomes, including rent-gouging and higher eviction rates.

“These real estate investment trusts tend to look for cheap homes that they can buy and convert to rentals so that they can profit over decades,” Weber said. “So this change in conversion rate from people to institutions is troubling because in the U.S. one of the substantial ways that people gain wealth and transfer it between generations is through homeownership.”

The researchers plan to take the method and apply it to other areas—such as Maricopa County, Arizona, and Orange County, Florida—with large amounts of iBuyers, available data on home sales and race, and demographic diversity. They also plan to interview people who’ve sold homes to iBuyers to learn what the experience is like.

“iBuyers are offering a service. They’re making the home ***** process faster and simpler,” ********** said. “While our analysis in Mecklenburg suggests iBuyers are extending some disadvantages that ****** home sellers tend to face to white home sellers as well, we don’t know that people are experiencing these sales as generally harmful or whether they’re aware of the tradeoffs that are involved.”

Eva Maxfield Brown, a doctoral student in the iSchool, is also a co-author on this paper.

More information:
Isaac ********** et al, The Impact of iBuying is About More Than Just ******* Disparities: Evidence from Mecklenburg County, NC, The 2024 ACM Conference on Fairness, Accountability, and Transparency (2024).

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How iBuyers are changing real estate ******* disparities and individual homeownership rates in one major city (2024, July 25)
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