Tourist accommodation pricing through peer-to-peer platform: evidence from Seville (Spain)

Abstract

The expansion of holiday rentals’ worldwide makes it relevant to confirm what are the determinants of these accommodations’ daily rates. This research aims to compare two models on estimating holiday rentals’ daily rate through variables that influence it; using artificial neural networks and hedonic pricing method, with the same cross-sectional dataset and variables with data obtained from Booking.com listings from Seville (Spain), a ‘cultural tourism’ large European city. Artificial neural networks estimations adapt better than the hedonic pricing method due to non-linear relations involved, although hedonic estimators have a clearer economic interpretation. Variables related to size, location and amenities appear as the most relevant in the models, including also seasonal and special events factors. The models presented, not only help to clarify these variables but also allow estimating a rental price congruent with the characteristics of the dwelling and season, being useful as an objective valuation method for the main agents of the accommodation sector: Owners, clients and peer-to-peer platforms. This study wants to highlight the convenience of using Booking.com listings as the main data source, as two variables presented as relevant for the models (size and location) are not available in other peer-to-peer platforms like Airbnb.