New Research Finds Algorithm Meant to Eliminate Racial Disparities in Airbnb Revenue Fails to Enhance Revenue Equality

INFORMS Journal Marketing Science Study Key Takeaways:

• Racial disparities are identified in Airbnb pricing tool based on revenue earned by Black and white hosts.
• An algorithm meant to solve this problem has failed due to lack of adoption by hosts.
• Incorporating racial factors into the pricing tool will help to reduce disparities and make pricing and revenue more equal.

CATONSVILLE, MD, October 12, 2021 – Airbnb created a free, smart-pricing tool for its Airbnb hosts. But the tool meant to promote racial equality has done little to solve the problem. New research in the INFORMS journal Marketing Science finds the algorithm’s price recommendations are not affected by the host’s race, but rather the tool’s race blindness has resulted in unintended consequences that may lead to suboptimal pricing for Black hosts. 

Before Airbnb introduced the algorithm, white hosts earned $12.16 more in daily revenue than Black hosts. Conditional on its adoption, the revenue gap between white and Black hosts should have decreased by more than 71%. However, Black hosts were significantly less likely than white hosts to adopt the algorithm, so at the population level, the revenue gap increased after the introduction of the algorithm. 

“If Black and white hosts face different demand curves (as our data suggest), then a race-blind algorithm may set prices that are suboptimal for both Black and white hosts, meaning that the revenue of both groups could be improved by the incorporation of race into the algorithm,” says Shunyuan Zhang, Harvard University. 

The study, “Frontiers: Can an Artificial Intelligence Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb,” conducted by Zhang alongside Nitin Mehta of the University of Toronto and Param Vir Singh and Kannan Srinivasan both of Carnegie Mellon University, finds that Airbnb can further reduce the revenue gap between Black and white hosts by incorporating race into the tool either directly or indirectly via closely correlated socioeconomic characteristics. 

When a host “turns on” the algorithm, it automatically adjusts the property’s nightly rate to optimize revenue based on the season, demand and characteristics of the property. Among those who adopted the algorithm, the average nightly rate decreased by 5.7%, but average daily revenue increased by 8.6%. This seems good, but not everyone used the tool. 

“We segmented hosts into four socioeconomic status (SES) quartiles at the neighborhood level. We speculate that hosts with more advanced degrees are more proficient at setting optimal prices for their properties without the algorithm’s assistance. So, if Airbnb wishes to address the revenue gap by encouraging algorithm adoption, it would be best to target Black hosts in the lower SES quartiles,” continued Zhang, a professor in the Harvard Business School. 

The best way to enhance the fairness of the tool is to incorporate the host’s race into the pricing algorithm. Right now, the consideration of a protected attribute, such as race, is not currently permitted by U.S. policy. 

“We join other recent studies in raising awareness of this counterintuitive reality; a race-blind approach to algorithmic decision-making may worsen racial disparities,” concludes Zhang.

Link to full study.

About INFORMS and Marketing Science

Marketing Science is a premier peer-reviewed scholarly marketing journal focused on research using quantitative approaches to study all aspects of the interface between consumers and firms. It is published by INFORMS, the leading international association for operations research and analytics professionals. More information is available at www.informs.org or @informs.

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