The Illusion of Trust: How Airbnb’s Rating System Breaks the Market

 

Airbnb as a Business 

Airbnb’s rating system appears less reliable than it seems. When booking a place based on its rating, the old saying “don’t judge a book by its cover” becomes relevant. If every Airbnb with 4.8-star ratings is “perfect”, then none of them actually are. This is what is called ‘Ratings inflation’ where gradual ratings over time can render the original ratings scale less meaningful. This happens as both sides quietly agree to keep the system look impressive though it’s average. Hosts fear losing bookings while guests fear receiving a bad stay from the booking. This strategic self-interest or mutual politeness reveals a classic case of asymmetric information & signaling that has gone wrong in the digital marketplace.

 To find out how bad the airbnb rating system is, according to Airbnb, 2023, it shows that Airbnb has a clear problem of “rating inflation.” The Study and report suggest that the average Airbnb rating is around 4.7 to 4.8 stars, and more than 80% of reviews are five-star.‌ And the research from Chen, 2020 we can see that in Airbnb Development in New York City most listings receive extremely high review scores, often above 90 out of 100, with only a very small number of low-rated outliers. Which means, this may look like all listings are high quality, and the real issue is that when almost everything is rated near perfect, the rating system loses its ability to tell the difference, which means that ratings are supposed to act as a signal to help users understand quality. But when everyone gives high scores, this signal becomes weak and less useful. Users can no longer clearly tell which listings are truly good, and which are just average. As a result, it becomes harder to make good decisions, and the problem of asymmetric information gets worse. Instead of reflecting real quality, ratings are increasingly influenced by social pressure and strategic behavior, which slowly reduces trust in the platform.

The Review
Problems for Hosts and Guests The Airbnb market is distorted by asymmetric information: guests rely on host-provided photos and descriptions while hosts exaggerate positive qualities to boost sales and demand (Akkurt, 2025, pp. 12–13). This creates false signalling about property quality.  After stays, guests are incentivised to give five-star ratings to receive one themselves, or out of empathy despite this being economically irrational by distorting market signals and inflating host demand. Guests getting five-star ratings in return leads to hosts being at risk of receiving problematic guests. False signalling also affects local economies by distorting perceived profitability, leading to local businesses overestimating visitor numbers and switching focus from residents to visitors (Colomb and Moreira de Souza, 2021).		Asymmetric Information and Signalling However, this classic case of asymmetrical information also appears across many digital platforms that use ratings to showcase their products or services. Research shows platforms like Uber, Deliveroo, and Airbnb all construct digital reputation scores to build trust, but these systems are not always reliable, as it depends on user-generated reviews to build trust between strangers (Christiaens, 2025). Bolt, for instance, faces its own version of a disrupted ratings system where passengers choose not to rate at all, creating a scoring system that is incomplete and meaningless. For the Amazon marketplace, sellers sometimes nudge customers for positive reviews, making it difficult for buyers to judge a genuine seller. Across these platforms, high ratings don't always reflect uniformly high quality; instead, it's a buildup from social pressure, strategic incentives or platform-design constraints, which these patterns are documented across Uber, Airbnb, Turo, Lyft and other gig-economy platforms (Keyser, Christophe Lembregts and Schepers, 2024). Thus, this broader pattern shows that digital intermediaries consistently struggle to produce trust for customers, showcasing why the rating system in Airbnb becomes increasingly inefficient.

Other Online Reputation Systems and Digital Intermediaries So, what’s the solution? Instead of relying only on opinions, platforms can also use real, verifiable data. This relates to the idea of signal information, which helps users to judge the quality when they cannot directly observe it. While star ratings are a common signal, but they are often biased as nowadays people for many different purposes are mostly giving the high Star ratings. In my opinion, I think data such as cancellation rates, number of complaints, response time, and repeat bookings are the more reliable signals because they are harder to manipulate and better reflect actual performance. Let's use some real-life examples like Uber. It does not only display star ratings but also tracks how often drivers accept and complete trips. These indicators provide users with a clearer understanding of the quality of the services, rather than just popularity. This combined reviews and objective data helps improve the quality of signals in the market. According to (Tadelis, 2016), integrating real data with user reviews can reduce misleading information and improve trust. When platforms rely more on factual data and less on biased opinions, users can make better decisions, and the overall market becomes more efficient.

Conclusion:

In the end, not every highly rated Airbnb is inflated. Accumulating ratings that are too good to be true creates asymmetric information between hosts and guests. Using objective data, such cancellation & complaints, would provide clearer signals that will rebuild trust across Airbnb and other digital platforms

Bibliography

Airbnb (2023) Airbnb global quality report. Available at: https://news.airbnb.com/airbnb-global-quality-report/ (Accessed: 19 April 2026)

Akkurt, H.S. (2025) Reducing information asymmetry: Economic value of Airbnb signals in a small college town. Master’s thesis. University of South Carolina. 

Chen, Z. (2020). Chapter 5 Results to the Research Questions | EDAV Project: Airbnb Research. [online] Github.io. Available at: https://joseph-zhuo.github.io/airbnb_in_depth_research/results-to-the-research-questions.html? [Accessed  20 Apr. 2026].

Christiaens, T. (2025). Trust and power in Airbnb’s digital rating and reputation system. Ethics and Information Technology, 27(2). doi:https://doi.org/10.1007/s10676-025-09825-6.

Colomb, C. and Moreira de Souza, T. (2021). The Airbnb Effect – Part 1: How Do Short Term Vacation Rentals Impact People and places? [online] Rics.org. Available at: https://www.rics.org/news-insights/wbef/the-airbnb-effect-part-1-how-do-short-term-vacation-rentals-impact-people-and-places [Accessed 18 Apr. 2026].

Keyser, A.D., Christophe Lembregts and Schepers, J. (2024). Research: How Ratings Systems Shape User Behavior in the Gig Economy. [online] Harvard Business Review. Available at: https://hbr.org/2024/04/research-how-ratings-systems-shape-user-behavior-in-the-gig-economy?utm_source=copilot.com [Accessed 23 Apr. 2026].

Tadelis, S. (2016) ‘Reputation and feedback systems in online platform markets’, Annual Review of Economics, 8, pp. 321–340.[Accessed 23 Apr. 2026]