Benchmarking of Airbnb listings: How competitive is the sharing economy sector of European cities?

Zekan, Bozana; Önder, Irem; Gunter, Ulrich

doi:10.1177/1354816618814349

Cited by 27 publications

(31 citation statements)

References 52 publications

(54 reference statements)

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“…From the tourism economics perspective, there are many other issues to tackle. It is interesting to understand if such new market structure has increased the number of arrivals or travel frequency (Tussyadiah and Pesonen, 2016), Airbnb pricing (Ö nder et al, 2019), Airbnb efficiency (Zekan et al, 2019), or if such new arrivals correspond to a new market niche in terms of party composition, length of stay (Tussyadiah and Pesonen, 2016), or tourist expenditure per night. Any significant change in this sense will have an impact on aggregate expenditure, gross domestic product, and employment.…”

Section: P2p Spatial Competitionmentioning

confidence: 99%

On the determinants of Airbnb location and its spatial distribution

2019

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This article explores Airbnb accommodation spatial distribution and it estimates the main determinants of its location choice. It employs spatial bivariate correlations and spatial econometrics to understand the heterogeneous spatial relationship between established hotels and Airbnb for three kinds of local tourism destinations: sun and beach, nature-based, and city. The case study concerns the Canary Islands where a good mixture of these attractions can be found. The main conclusion drawn is that Airbnb regulation needs to distinguish the kind of tourism. More precisely, Airbnb supply overlaps established hotels in city tourism, but it does not so clearly in sun and beach nor nature-based destinations. Airbnb supply matches tourist visits spatial distribution better than established hotels in city and nature-based destinations, but not in sun and beach destinations, where the incumbent hotels are closer to the tourism resources. Finally, the results from the spatial econometrics model shows that population size and the number of tourist visits matters as determinants of Airbnb location. However, the main determinant is price, which has got a much larger elasticity.

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Section: P2p Spatial Competitionmentioning

confidence: 99%

On the determinants of Airbnb location and its spatial distribution

2019

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“…unesco.org/). These cities also belong to the European Premier League of city destinations (Zekan et al, 2019).…”

Section: Conceptual Backgroundmentioning

confidence: 99%

Hotels benefit from stricter regulations on short-term rentals in European cities

Falk

Yang

2020

Tourism Economics

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The aim of this study is to examine whether the introduction of stricter rules on short-term rentals (STRs) in some European cities will have an impact on hotel overnight stays. The treatment group consists of cities with the strictest regulations in Europe (Amsterdam, Barcelona, Berlin, Paris and London). The coarsened exact matching method is used to select an appropriate control group consisting of cities where there are no similar regulations but which are of a similar size in relation to population and relevant tourist space. Weighted fixed-effect models are used to estimate the average treatment effect on the treated. The specification also includes variables controlling for accommodation prices, value-added tax (VAT) rate on accommodation, terrorist attacks, real income of the destination country and time. Results show that stricter regulations for STRs in the five cities lead to an average increase in overnight stays of around 9%. This suggests that STRs such as Airbnb are substitutes for hotel accommodations and thus strong competitors. In addition, terrorist attacks lead to a decline in overnight stays, while an increase in VAT on accommodation reduces hotel overnight stays.

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“…Chiu and Wu (2010) apply a context-dependent DEA model to assess the performance of international tourist hotels in Taiwan. In the context of Airbnb, Zekan et al (2019) apply DEA model to assess the performance of the European cities. Min and Peng (2012) propose an entropy-based TOPSIS method to investigate the competitiveness of various tourism destinations of China and to assess the current emotional intelligence training level of tour leaders.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated TOPSIS-MOORA-based performance evaluation methodology for the key service providers in sharing economy: case of Airbnb superhosts

Majumdar

Adhikari

2020

BIJ

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PurposeIn the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts.Design/methodology/approachThis work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb.FindingsFrom the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category.Practical implicationsThe proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms.Originality/valueThis is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

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Benchmarking of Airbnb listings: How competitive is the sharing economy sector of European cities?

Cited by 27 publications

References 52 publications

On the determinants of Airbnb location and its spatial distribution

On the determinants of Airbnb location and its spatial distribution

Hotels benefit from stricter regulations on short-term rentals in European cities

An integrated TOPSIS-MOORA-based performance evaluation methodology for the key service providers in sharing economy: case of Airbnb superhosts

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