It’s not all about price. The determinants of occupancy rates in peer-to-peer accommodation

Abstract

Purpose This paper aims to identify the key drivers of occupancy rates in peer-to-peer accommodation. Design/methodology/approach The applied methodology fits the specific characteristics of this market segment: the peculiar distribution of the occupancy rate (a ratio characterised by a large share of zeros) requires the adoption of a mixed discrete-continuous model; the insidious issue of price endogeneity is dealt with a control function approach; the econometric specification takes into account the monopolistic competition, the relevant market regime in the hospitality industry. The model is tested on Airbnb listings in the Balearic Islands (Spain). Findings The occupancy rate of peer-to-peer properties in the Balearic Islands strongly depends on their geographical location and online reputation. There is a qualitative difference between two groups: listings with positive occupancy rates, which demand tends to be inelastic, and listings with zero occupancy. The authors found that the price is a not a statistically significant determinant of the latter group membership. Originality/value This paper applies a zero-inflated beta model, never used in previous analyses of occupancy rates, to provide a benchmark for future studies. This procedure allows the estimation of unbiased marginal effects. It, thus, offers important technical and managerial implications, as a wrong understanding of how occupancy depends on price would deliver ineffective managerial decisions. This paper highlights the importance of methodological choices, as coefficients are highly sensitive to misspecifications of the model.

Staying in a hotel or peer-to-peer accommodation sharing? A discrete choice experiment with online reviews and discount strategies

“…From the review of published literature reviews, existing studies have found that the volume and rate scores of online reputations are key and steady determinants influencing consumers' booking behaviors (Leoni et al , 2020; Purnawirawan et al , 2015; Zhao et al , 2015). Dynamic pricing and price conscientiousness were also widely investigated in the online accommodation booking context (Guizzardi et al , 2019).…”

“…Discount strategy includes three tiers in our study, including direct (Huang, 2016) and price-break discounts (Kim and Hwang, 1988) and coupons (Guimond et al , 2011). Online review number connotes the number of reviews the listing has accumulated (Leoni et al , 2020). Online review number also includes three different tiers: high, medium and low.…”

Listing popularity on the peer-to-peer accommodation platform: the heuristic-systematic and uncertainty reduction perspectives

“…The fourth stream highlights the impact of P2P accommodation on hotel revenue (Xie and Kwok, 2017). The fifth stream highlights the determinants of P2P accommodation performance (Leoni et al , 2020; Ranjbari et al , 2020). Hence, our literature review makes two prominent observations.…”

Determinants of price and revenue for peer-to-peer hosts. The state of the art

“…Therefore, new models based on more complex patterns are needed. Other promising avenues of research are the use of locational models (Zhang et al, 2017), multivariate regression models that are able to apply selected independent and control variables to more than one outcome variable and non-linear models (Leoni et al, 2020).…”