A statistical framework for Airbnb hosts and Superhosts


Abstract


We propose a statistical framework in order to investigate the Airbnb hosts activities. We aim to propose an extended model able rstly to comprehend which variables can impact on the hosts' activity; secondly to identify a guide that can support the hosts in the constant eort to reach the best performances
and to become a Superhost. The framework use two dierent models, the logistic model and the bivariate odds ratio model. Three groups of variables are taken into account. They are the attributes that Airbnb uses to assign the Superhost badge, the managerial aspects and the characteristics of the accommodations. The analysis is focused on the hosts operating in the Italian most visited cities. Our ndings show the capacity of the framework to identify the variables, as for instance the number of reviews, the services, and the typology of the rented accommodation, that aect the hosts' performance.
The results show how the framework can be used as a managerial support for the hosts.

DOI Code: 10.1285/i20705948v15n1p211

Keywords: Airbnb; hosts; superhost badge; VGAM; logistic model

References


Aznar, J. P., Sayeras, J. M., Rocafort, A., and Galiana, J. (2016). The irruption of Airbnb and its effects on hotel profitability: An analysis of Barcelona’s hotel sector. Intangible Capital,, 13(1):147–159.

Ben ́ıtez-Aurioles, B. (2018). Why are flexible booking policies priced negatively? Tourism Management, 67:312–325.

Choi, K. H., Jung, J. H., Ryu, S. Y., Do Kim, S., and Yoon, S. M. (2015). The relationship between Airbnb and the hotel revenue: in the case of korea. Indian Journal of Science and Technology, 8(26).

Contu, G., Conversano, C., Frigau, L., and Mola, F. (2019). Identifying factors affecting the status of Superhost: evidence from Sardinia and Sicily. Quality & Quantity, pages 1–21.

Dud ́as, G., Boros, L., Kovalcsik, T., and Kovalcsik, B. (2017). The visualisation of the spatiality of Airbnb in Budapest using 3-band raster represeantation. Geographia Technica, 12(1):23–30.

Edelman, B. G. and Luca, M. (2014). Digital discrimination: The case of Airbnb. com. Harvard Business School, 14-054.

Fagerstrøm, A., Pawar, S., Sigurdsson, V., Foxall, G. R., and Yanide Soriano, M. (2017). That personal profile image might jeopardize your rental opportunity! on the relative impact of the seller’s facial expressions upon buying behavior on Airbnb. Computers in Human Behavior, 72:123–131.

Gunter, U. (2018). What makes an Airbnb host a Superhost? empirical evidence from San Francisco and the bay area. Tourism Management, 66:26–37.

Gunter,U.andO ̈nder,I.(2018).Determinants of Airbnb demand in Vienna and their implications for the traditional accommodation industry. Tourism Economics, 24(3):270– 293.

Guti ́errez, J., Garc ́ıa-Palomares, J. C., Romanillos, G., and Salas-Olmedo, M. H. (2017). The eruption of airbnb in tourist cities: Comparing spatial patterns of hotels and peer- to-peer accommodation in Barcelona. Tourism Management, 62:278–291.

Guttentag, D. (2015). Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector. Current issues in Tourism, 18(12):1192–1217.

Guttentag, D., Smith, S., Potwarka, L., and Havitz, M. (2018). Why tourists choose Airbnb: A motivation-based segmentation study. Journal of Travel Research, 57(3):342–359.

Lalicic, L. and Weismayer, C. (2018). A model of tourists’ loyalty: the case of Airbnb. Journal of Hospitality and Tourism Technology, 9(1):80–93.

Lee, S. and Kim, D.-Y. (2018). The effect of hedonic and utilitarian values on satisfaction and loyalty of airbnb users. International Journal of Contemporary Hospitality Management, 30(3):1332–1351.

Leeper, T. J. (2017). Interpreting regression results using average marginal effects with r’s margins. Available at the comprehensive R Archive Network (CRAN).

Liu, S. Q. and Mattila, A. S. (2017). Airbnb: Online targeted advertising, sense of power, and consumer decisions. International Journal of Hospitality Management, 60:33–41.

Lutz, C. and Newlands, G. (2018). Consumer segmentation within the sharing economy: The case of airbnb. Journal of Business Research, 88:187–196.

Malazizi, N., Alipour, H., and Olya, H. (2018). Risk perceptions of Airbnb hosts: Evidence from a Mediterranean island. Sustainability (2071-1050), 10(5).

Neumann, J. and Gutt, D. (2017). A homeowner’s guide to Airbnb: theory and empirical evidence for optimal pricing conditional on online ratings. Association for Information SystemsAIS Electronic Library (AISeL).

Powers, D. and Xie, Y. (2008). Statistical methods for categorical data analysis. Emerald Group Publishing.

Quattrone, G., Proserpio, D., Quercia, D., Capra, L., and Musolesi, M. (2016). Who benefits from the sharing economy of Airbnb? In Proceedings of the 25th international conference on world wide web, pages 1385–1394. International World Wide Web Conferences Steering Committee.

Varma, A., Jukic, N., Pestek, A., Shultz, C. J., and Nestorov, S. (2016). Airbnb: Exciting innovation or passing fad? Tourism Management Perspectives, 20:228–237.

Xie, K. and Mao, Z. (2017). The impacts of quality and quantity attributes of Airbnb hosts on listing performance. International Journal of Contemporary Hospitality Management, 29(9):2240–2260.

Yee, T. W. (2008). Vgam family functions for bivariate binomial responses.

Yee, T. W. (2015). Vector generalized linear and additive models: with an implementation in R. Springer. Yee, T. W. et al. (2010). The Vgam package for categorical data analysis. Journal of Statistical Software, 32(10):1–34.

Zervas, G., Proserpio, D., and Byers, J. (2015). A first look at online reputation on Airbnb, where every stay is above average.


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