Reviews and reputation scores are increasingly important for decision-making, especially in the case of online marketplaces. Sixty-eight percent of respondents in a 2013 Nielsen survey said that they trusted consumer opinions posted online. However, online reviews may not provide an accurate depiction of the characteristics of a product, either because many people do not leave reviews or because some reviewers omit salient information. We study the causes and magnitude of bias in online reviews by using large-scale field experiments that change the incentives of buyers and sellers to honestly review each other.
Our setting is Airbnb, a prominent online marketplace for accommodations where guests (buyers) stay in the properties of hosts (sellers). Reputation is particularly important for transactions on Airbnb because guests and hosts interact in person, often in the primary home of the host. Guests must trust that hosts have accurately represented their property on the website, while hosts must trust that guests will be clean, rule abiding, and respectful.
We find that there are two mechanisms by which we lose information in the review system: first, guests and hosts with worse experiences are less likely to leave reviews and, second, guests omit negative feedback from publicly displayed reviews. The fear of a retaliatory review plays a comparatively minor role for public reviews. We find that by simultaneously revealing the contents of the guest and host reviews and offering increased incentivize to guests unlikely to leave a review, we are able to decrease the bias in reviews and create a more informative review system.
Dave Holtz is a data scientist at Airbnb focusing on online reputation and pricing. Previously, he worked as a data science engineer at Yub (acquired by Coupons.com) and as a data scientist and Product Manager at TrialPay. He is the instructor for Udacity’s Introduction to Data Science course.
Dave holds an MA in Physics from The Johns Hopkins University, and a Bachelor’s degree in Physics and Theater from Princeton. In addition to data science, Dave is passionate about cosmology, smart cities, music, theater, and improv comedy.
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