*2.1. Efficient Pricing*

Pricing tools play a crucial role in the accommodation sector's revenue management, and efficient pricing has become a popular research field in recent years. Many hotels rely on cost-based, competition-driven and customer-driven pricing strategies (Tong and Gunter 2020), while others use dynamic pricing, namely, adjusting prices upward or downward over time (Leoni and Nilsson 2021). According to Vives and Jacob (2020), two dynamic pricing models are currently widely applied in the hotel industry to maximise revenue. The first is a deterministic model that sets different prices across booking horizons, while the second is a stochastic model that segments demand into different classes in order to determine market responses and demand's sensitivity to price variations. A combination of both dynamic pricing models is often used.

These models take advantage of consumers' willingness to pay more as the date of their stay approaches. Companies set the price of accommodations according to the time horizon between booking and travel dates and their hotels' capacity at any given time. Empirical research has shown that the probability is extremely high that the price will increase as the travel date approaches and the number of rooms available decreases (Leoni and Nilsson 2021).

HPM theory posits that prices depend on each product's features and their effects, which determine that item's consumption utility. HPM models have long been used to analyse the relationship between various product characteristics and their prices and to study heterogeneous features' impact on prices (Liang and Yuan 2021). Soler-García et al. (2019) report that HPM models have been extensively used in both tourism and hospitality studies to assess the influence of specific destination and hotel factors on room rates. To ensure efficient pricing, hotel managers need to know customers' propensity to pay for particular amenities and their hotel's set of services, so services' impact on overall customer satisfaction and the associated costs need to be analysed (Soler-García et al. 2019). HPM models facilitate the estimation of goods or services' prices based on previously defined variables. For hotels, prices are mainly determined by various tangible factors such as hotel category and geographic location, but type of accommodations and hotel chain membership are also important.

In addition, destinations' characteristics must be incorporated into hotel room rates (Soler-García and Gémar-Castillo 2018). Another external feature considered is the time of year, especially in sun-and-sea destinations, due to seasonality (Coenders et al. 2003; Rigall i Torrent et al. 2011); day of the week, especially in destinations with higher occupation rates on weekends; or special event periods (Soler-García and Gémar-Castillo 2017). 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).

Inefficient pricing can contribute to financial losses in every business activity, especially in the holiday rental sector. Hotels have trained professionals, price management

programmes and industry benchmarking reports, but vacation rental units are usually managed by people without specific training in pricing strategies and with limited access to pricing tools (Gibbs et al. 2018). Airbnb has made some attempt to develop pricing tools that the hosts can use to set their listings' prices. However, the first tool launched in 2012 was quite basic, as it only focused on simple factors including, among others, the number of rooms, neighbouring properties and amenities such as parking (Gibbs et al. 2018; Hill 2015).

A second, more elaborate pricing tool, Smart Pricing, was released a few years later, which takes both property characteristics and demand into account. The tool uses machine learning to provide hosts with a suggested price for a specific date that hosts may accept or change according to their perception (Gibbs et al. 2018; Hill 2015). Smart Pricing thus has a purely advisory function, so it may have no real influence on holiday rentals' price because most hosts do not use the tool (Tong and Gunter 2020).

As hosts are responsible for setting their listed properties' price, analyses of which factors affect vacation rental rates are of great importance to the sharing economy (Voltes-Dorta and Sánchez-Medina 2020). A significant number of studies have found that property, host and location factors have the strongest impact on prices (Voltes-Dorta and Sánchez-Medina 2020). Significant property features usually include the number of beds, bedrooms and bathrooms (Fearne 2021; Gibbs et al. 2018; Gunter and Önder 2018; Voltes-Dorta and Sánchez-Medina 2020) and online photos (Tong and Gunter 2020). 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).

While most research on sharing economy accommodation pricing has focused on Airbnb, a few investigations have taken Booking.com into account. For example, Gyódi (2017) compared Airbnb and Booking.com listings in Warsaw, finding evidence that Airbnb provides cheaper accommodation alternatives in all price segments. 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).


**Table 1.** Variables, descriptive statistics and description.


**Table 1.** *Cont.*

Note: Var. = variable; SD = standard deviation; NA = not available. Source: (Booking.com 2018, 2019); (Google Maps 2018, 2019); (Tinsa 2018); (INE-PT (Instituto Nacional de Estatística) 2019).
