Valuation Models for Holiday Rentals’ Daily Rates: Price Composition Based on Booking.com

Abstract

In recent years, the number of sharing economy accommodations has grown exponentially due to the Internet and peer-to-peer networks, which has made researchers increasingly interested in analysing this new type of lodging. This study sought to develop models that determine the significant variables for the daily price of staying in holiday rentals based on data extracted from Booking.com and other sources. The hedonic pricing method (HPM) was selected to conduct the research as this methodology has been widely used in real estate valuation and hotel daily rate determination; however, the HPM is still rarely used for holiday rentals. The study focused on the city of Seville, where a notable increase in holiday rentals has been observed in recent years. Variables related to the accommodation typology, including location, size and equipment, as well as seasonality, are the most influential factors in the proposed models. These results are of interest to both owners and users of holiday rentals and can help these individuals to determine if the price of a stay is what would commonly be offered in the market under normal circumstances.

Tourism Economics

“…However, the cited study included Booking.com's complete offer of hotels, hostels and apartments, so the focus was not exclusively on the sharing economy. Santos et al (2021) subsequently proposed a new HPM model for Booking.com holiday rentals using an extensive set of variables developed by Solano- Sánchez et al (2019) that were also used in the present comparative study (see Table 1).…”

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

“…Typical accommodation characteristics that influence prices are distance to the beach, the city centre, tourism hotspots, train stations or airports (Castro and Ferreira 2018;Gunter and Önder 2018;Soler-García and Gémar-Castillo 2018), as well as reputational factors such as hotel brand, number of stars and customer ratings (Castro and Ferreira 2018;Soler-García et al 2019). Additional features affecting prices are hotel category; availability of a swimming pool, fitness centre or sport facilities (Castro and Ferreira 2018); pet admission (Santos et al 2021); spa; parking; accommodations' size (Chen and Rothschild 2010;Santos et al 2021;Voltes-Dorta and Sánchez-Medina 2020); the inclusion of a restaurant, bar or terrace; and room amenities such as Wi-Fi, television (TV), minibar or room service (Castro and Ferreira 2018).…”

“…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).…”

“…However, the cited study included Booking.com's complete offer of hotels, hostels and apartments, so the focus was not exclusively on the sharing economy. Santos et al (2021) subsequently proposed a new HPM model for Booking.com holiday rentals using an extensive set of variables developed by Solano-Sánchez et al (2019) that were also used in the present comparative study (see Table 1).…”

Categorical Variable Problem In Real Estate Submarket Determination With Gwr Model

“…Mass real estate valuation is not a simple issue. It requires overcoming various types of difficulties(C. F. Chen & Rothschild, 2010;Santos et al, 2021). One such problem that arises when using multiple regression models is not considering spatial effects.…”