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The sharing economy has allowed people from all over the world to more effectively utilize their assets. Owners or controllers of assets in the sharing economy are free to set any price they want subject to prevailing market demand as long as they operate in an imperfectly competitive market environment. This paper examines how various characteristics of an Airbnb listing (size, number of photos, ratings, host responsiveness, superhost status, distance from city centre, etc.) affect the prices of accommodation and determines which factors strongly affect price using weighted least squares (WLS) and quantile regression. A hedonic pricing model was developed and applied to data from the cities of Barcelona, Madrid, and Seville to determine how the different characteristics of an Airbnb listing affect the price of accommodation in these major three Spanish tourist cities. The estimation results, which are resilient to various robustness checks, indicate that overall rating as well as characteristics indicative of the size of the accommodation have the strongest positive influence on price, while the number of reviews and distance from the city centre have the strongest negative influence on price. [ABSTRACT FROM AUTHOR]

Airbnb has a major role to play in the competitiveness of the overall accommodation sector of individual destinations and it is rather unlikely that this role will diminish in the post-COVID-19 recovery of the tourism industry. Therefore, the present study motivates the Airbnb sector to look back at its past performance for insights that can be used in setting post-pandemic targets. In particular, this research assesses competitiveness of the Airbnb listings of 28 European cities by including hotel-related data as uncontrollable input variables within interactive data envelopment analysis modeling. The contribution lies in joining Airbnb listings and hotels into the benchmarking discussion and efficiency analysis, along with looking beyond the cumulative number of listings by dissecting the overall sector into commercial and private listings—something that has not been attempted as of yet, in spite of the ever-growing body of literature on the sharing economy. [ABSTRACT FROM AUTHOR]

Using data on Airbnb listings from San Francisco and the Bay Area, the present study investigates the relative importance of the four criteria that need to be fulfilled to obtain the Airbnb superhost status. In order to quantify the marginal contributions of the four criteria, different index models of binary response (logit, probit, and IV probit, which allows for the endogeneity of Airbnb demand) are applied. The results, which are consistent across models, show that in San Francisco and the Bay Area obtaining (and maintaining) excellent ratings is, by far, the most important criterion, followed by reliable cancellation behavior of the host, host responsiveness, and sufficient Airbnb demand. Moreover, commercial Airbnb providers are more likely to obtain the superhost status. [ABSTRACT FROM AUTHOR]

This study identifies key determinants of Airbnb demand and quantifies their marginal contributions in terms of demand elasticities. A comprehensive cross-sectional data set of all Viennese Airbnb listings that were active between July 2015 and June 2016 is examined. Estimation results, which are obtained by cluster-robust ordinary least squares, show that Airbnb demand in Vienna is price-inelastic. Significant positive drivers include listing size, number of photos and responsiveness of the host. Significant negative drivers include listing price, distance from the city center and response time of the host. Implications for the traditional accommodation industry are that, on the one hand, it should better communicate its sought-after advantages (e.g. lower average minimum duration of stay). On the other hand, it should increase its offer of bigger and better equipped hotel rooms since hosting more than two guests at a time is one of the major benefits of Airbnb. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a major shock to the global tourism industry. Given its peculiarity, this paper analyzes one of the most intriguing questions in the Airbnb literature – the pricing of Airbnb listings – by taking advantage of a difference-in-differences methodology that largely draws on variations in country-level policy responses to the pandemic. Relying on a dataset containing weekly information from 130,999 continuously active listings across 27 European countries from 2019 to 2020, this study first investigates the exogenous impact of response policies (proxied by the COVID-19 Stringency Index) on demand. Secondly, accounting for the endogeneity of both demand and prices, this research analyzes pricing responses to demand variations. Results show that: i) increases in the COVID-19 Stringency Index cause significant declines in Airbnb demand; ii) increases in demand cause, on average, increases in Airbnb prices; and iii) pricing strategies between commercial and private hosts differ substantially. [ABSTRACT FROM AUTHOR]