Optimal price setting in peer-to-peer markets featuring online ratings requires incorporating interactions between prices and ratings. Additionally, recent literature reports that online ratings in peer-to-peer markets tend to be inflated overall, undermining the reliability of online ratings as a quality signal. This study proposes a two-period model for optimal price setting that takes (potentially inflated) ratings into account. Our theoretical findings suggest that sellers in the medium-quality segment have an incentive to lower first-period prices to monetize on increased second-period ratings and that the possibility on monetizing on second-period ratings depends on the reliability of the rating system. Additionally, we find that total profits and prices increase with online ratings and additional quality signals. Empirically, conducting Difference-in-Difference regressions on a comprehensive panel data set from Airbnb, we can validate that price increases lead to lower ratings, and we find empirical support for the prediction that additional quality signals increase prices. Our work comes with substantial implications for sellers in peer-to-peer markets looking for an optimal price setting strategy. Moreover, we argue that our theoretical finding on the weights between online ratings and additional quality signals translates to conventional online markets.
Neumann, Jürgen and Gutt, Dominik, (2017). "A HOMEOWNER’S GUIDE TO AIRBNB: THEORY AND EMPIRICAL EVIDENCE FOR OPTIMAL PRICING CONDITIONAL ON ONLINE RATINGS". In Proceedings of the 25th European Conference on Information Systems (ECIS), Guimarães, Portugal, June 5-10, 2017 (pp. 997-1010). ISBN 978-989-20-7655-3 Research Papers.