Spatial price mimicking on Airbnb: Multi-host vs single-host

Boto‐García, David; Mayor, Matías; Vega, Pablo de la

doi:10.1016/j.tourman.2021.104365

Cited by 23 publications

(17 citation statements)

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“…Second, Airbnb guests use sustainable transportation more intensively, defined here as services able to reduce their carbon footprints, such as public transportation or renting a bicycle ( Önder et al, 2019 ). While previous studies have analyzed the effects generated by being located close to public transportation ( Deboosere et al, 2019 ), train stations, and subway stops ( Boto-García et al, 2021 ), no study has considered the distance to rentable bicycles.…”

Section: Introductionmentioning

confidence: 99%

“…Airbnb providers are therefore more densely located in some areas than in others ( Gyódi and Nawaro, 2021 ; Sainaghi, 2020a ). Airbnb listings located close to museums and monuments charge higher rates ( Boto-García et al, 2021 ). These locational patterns have an impact on both competition levels and agglomeration economies between Airbnb providers ( Xie et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…These locational patterns have an impact on both competition levels and agglomeration economies between Airbnb providers ( Xie et al, 2019 ). Furthermore, mimicking strategies can occur, and single-listing hosts tend to follow the pricing choices implemented by multi-listing hosts ( Boto-García et al, 2021 ). However, few papers have explored the spatial spillover effect (Zhou, Wen, Sutherland, & Lee, 2020), which largely remains an enigma.…”

Section: Introductionmentioning

confidence: 99%

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The effects of location before and during COVID-19

Sainaghi

Chica-Olmo

2022

Annals of Tourism Research

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The effects of location before and during COVID-19

Sainaghi

Chica-Olmo

2022

Annals of Tourism Research

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“…In recent years, the accommodation-sharing rental market has experienced particularly strong growth [1,2]. For instance, Airbnb, one of the most popular and frequently discussed accommodation-sharing models, has over 7 million listings in 100,000 cities and 220 countries worldwide as of January 2020.…”

Section: Introductionmentioning

confidence: 99%

“…[3]). Professional hosts are hosts who hold two or more listings, and nonprofessional agents refer to those who hold a single listing [2][3][4]. Hosts in accommodation-sharing markets decide whether they want to offer their properties, the quantity, and the price.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Professional and Nonprofessional Hosts’ Pricing Behaviors on Accommodation-Sharing Market Outcome

Jia

Wang

2021

Sustainability

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Nonprofessional hosts in the P2P accommodation-sharing markets have been demonstrated to be inferior in pricing. The sharing market is increasingly recruiting more professional hosts but is bothered by the disharmony from nonprofessionals’ feelings of being cast aside in this drive. To respond to this practice and disharmony, we develop a modeling framework with price-sensitive customers and earning-based hosts to investigate how varying ratios of professional and nonprofessional hosts affect pricing and impact sharing-market outcomes according to contemporary and long-term success indicators. This study is one of the first attempts to examine whether more professional hosts as supply decision makers is more beneficial to the sharing market. Numerical experiments for model analysis led to two primary managerial implications. A high ratio of professional hosts does not necessarily maximize indicators of hosts’ earnings, platform’s profit, or supply size, indicators that measure the accommodation-sharing market’s contemporary and long-term success. In addition, the market improves with magnified differences in the unique features of two types of hosts and they can cater to customers’ experiences and expectations, differentiating the market positioning of the two types of hosts.

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Is peer-to-peer demand cointegrated at the listing level?

Pérez-Rodríguez,

Rachinger,

Suárez-Vega

2023

Empir Econ

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In this paper, we analyse the long-run equilibrium demand of the peer-to-peer sharing economy. Our panel data demand model relates occupancy rates to relative prices of Airbnb and HomeAway listings, prices of competitors (hotels and apartments) and a proxy for income of tourists visiting the Canary Islands (Spain). We use a fractional heterogeneous panel data model which allows for a more general persistence and cointegration relationship and incorporates individual and interactive fixed effects. We find some evidence for (fractional) cointegration in P2P at the listing level. Regarding elasticities, classic cointegration methods give larger estimates for individual slopes and mean group coefficients than the fractional integrated heterogeneous model. Finally, own-price elasticities are inelastic, and the cross-price elasticity indicates that P2P listings and hotels are substitute goods. Income elasticity is lower than 1 and is not statistically significant, indicating that the demand for tourism in the Canary Islands is not sensitive to the economic conditions in the origin countries.

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Spatial price mimicking on Airbnb: Multi-host vs single-host

Cited by 23 publications

References 50 publications

The effects of location before and during COVID-19

The effects of location before and during COVID-19

Investigating the Impact of Professional and Nonprofessional Hosts’ Pricing Behaviors on Accommodation-Sharing Market Outcome

Is peer-to-peer demand cointegrated at the listing level?

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