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Up or out - The dynamics of star rating scores on Airbnb

This paper investigates the impact of dynamic processes, including survivorship, as a possible reason for the distribution skewness of star ratings in C2C platforms. We draw on actual Airbnb data covering a time frame of 19 months from October 2015 to May 2017, comprising information on the listings’ number of ratings and average rating scores. Building on research approaches from empirical finance, we find that rating distributions vary markedly when differentiated by underlying volume. While for few ratings, basically the entire bandwidth of possible scores is represented, the distribution becomes narrower for larger numbers of ratings. Interestingly, this is not associated with changes in average rating scores. Also, we observe higher churn rates for listings with lower rating scores. The market is growing and exhibits high turnover rates of about 7% per month. Overall, we find that Airbnb’s rating score skewness is caused by a multiplicity of influences, including survivorship and the constantly high market share of new arrivals. We discuss our findings in view of the important role of star ratings as a popular design element within the digital platform economy.