Analysis of price determinants in the case of Airbnb listings

Abstract

Nowadays, the role of the sharing economy in tourism increases, the number of people involved as guests or hosts rising day by day. This dynamic generates a viable alternative to the traditional services, allowing tourists to customise their trips and enrich their experiences. This paper focuses on accommodation services, investigating the factors influencing the prices established by Airbnb hosts. Using structural equation modelling, the authors analyse the influence of different categories of factors (listing's characteristics, hosts' involvement, listing's reputation, listing's location, and rental policies) on the average daily rate. The results emphasise that the hosts establish the listing's price based on listing's characteristics and on their involvementthe owners managing only one listing and the ones charging a security deposit value more their involvement, while the other hosts focus more on the listings' characteristics. The location of the listing counts for experienced owners, while for opportunist owners it has no importance. The listings' reputation has a negative impact on the price, contrary to the conclusions achieved in other studies, an aspect that supports the idea that price determinants differ across regions.

Focal Points in Recent Tourism Price Research

“…Early research on hedonic pricing research by Rosen (1974) was later followed by a broader application of hedonic pricing in tourism, as shown by Latinopoulus (2018) and . In addition to hedonic analysis, there is some research comparing the ubiquitous application of hedonic pricing research with the alternative method of artificial neural networks, which shows some strengths in pricing research (Solano-Sánchez et al, 2023), complementing hedonic pricing research with clustering method, which allows to classify pricing decisions (Espinet-Rius et al, 2021), quantile regression (Falk et al, 2019), hedonic pricing models with quantile regression (Wang & Nicolau, 2017;Wang et al, 2019), advanced machine learning regression models (Razavi & Israeli, 2019), structural equation method (Toader et al, 2022), multivariate adaptive regression splines (Mitra, 2020a). An artificial intelligence-based framework is already being used in pricing research to predict the prices of Airbnb listings (Ghosh et al, 2023).…”

Does Price Matter in Mainland China? Examine the Factors Influencing Broiler Chicken Purchase Intention

“…The broiler business is the largest poultry meat industry in the world and is an important part of mainland China's livestock industry [22]. This study builds on the prior research to establish a formative indicator model that identifies the factors that influence the price-setting behavior of enterprises in the broiler industry [68]. The pricing and attribute information for broilers were obtained from major supermarkets.…”

Holiday Rentals in Cultural Tourism Destinations: A Comparison of Booking.com-Based Daily Rate Estimation for Seville and Porto

“…Host characteristics, reputation, experience, responsiveness and 'superhost' status are specifically referred to in research on Airbnb (Gunter and Önder 2018;Voltes-Dorta and Sánchez-Medina 2020). Extremely important location factors for pricing holiday rentals are similar to those for hotels, namely, distance to the city centre, bus or train stations, airports, beaches or other hotspots (Gunter and Önder 2018;Gyódi and Nawaro 2021;Santos et al 2021;Toader et al 2021;Voltes-Dorta and Sánchez-Medina 2020).…”