This paper uses quantile regression modelling to provide a broad description of the relationship between tourism demand and its theoretical determinants across the peer-to-peer (P2P) demand distribution. Specifically, we use a panel data unconditional quantile regression with high-dimensional fixed effects to infer the effects of heterogeneous elasticity on unconditional demand. Our empirical analysis comprises a case study of the Canary Islands (Spain) using microeconomic information based on Airbnb listings. The results suggest that P2P demand behavior (measured by total booked days) is heterogeneous among quantiles. We show that the effects of low, medium, and high demand differ from each other with a 1% increase in average revenue, the average relative price of P2P competitors, and the average price of hotel competitors.
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