Cargando…
VGLM proportional odds model to infer hosts’ Airbnb performance
We investigated aspects of host activities that influence and enhance host performance in an effort to achieve best results in terms of the occupancy rate and the overall rating. The occupancy rate measures the percentage of reserved days with respect to available days. The overall rating identifies...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584234/ https://www.ncbi.nlm.nih.gov/pubmed/36285335 http://dx.doi.org/10.1007/s11135-022-01550-2 |
_version_ | 1784813218624962560 |
---|---|
author | Contu, Giulia Frigau, Luca Ortu, Marco |
author_facet | Contu, Giulia Frigau, Luca Ortu, Marco |
author_sort | Contu, Giulia |
collection | PubMed |
description | We investigated aspects of host activities that influence and enhance host performance in an effort to achieve best results in terms of the occupancy rate and the overall rating. The occupancy rate measures the percentage of reserved days with respect to available days. The overall rating identifies the satisfaction level of guests that booked an Airbnb accommodation. We used the proportional odds model to estimate the impact of the managerial variables and the characteristics of the accommodation on host performance. Five different levels of the occupancy and the overall rating were investigated to understand which features impact them and support the effort to move from the lowest to the highest level. The analysis was carried out for Italy’s most visited cities: Rome, Milan, Venice, and Florence. We focused on the year 2016. Moreover, we investigated different impact levels in terms of the overall rating during the COVID-19 pandemic to evaluate possible differences. Our findings show the relevance of some variables, such as the number of reviews, services, and typology of the rented accommodation. Moreover, the results show differences among cities and in time for the relevant impact of the COVID-19 pandemic. |
format | Online Article Text |
id | pubmed-9584234 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-95842342022-10-21 VGLM proportional odds model to infer hosts’ Airbnb performance Contu, Giulia Frigau, Luca Ortu, Marco Qual Quant Article We investigated aspects of host activities that influence and enhance host performance in an effort to achieve best results in terms of the occupancy rate and the overall rating. The occupancy rate measures the percentage of reserved days with respect to available days. The overall rating identifies the satisfaction level of guests that booked an Airbnb accommodation. We used the proportional odds model to estimate the impact of the managerial variables and the characteristics of the accommodation on host performance. Five different levels of the occupancy and the overall rating were investigated to understand which features impact them and support the effort to move from the lowest to the highest level. The analysis was carried out for Italy’s most visited cities: Rome, Milan, Venice, and Florence. We focused on the year 2016. Moreover, we investigated different impact levels in terms of the overall rating during the COVID-19 pandemic to evaluate possible differences. Our findings show the relevance of some variables, such as the number of reviews, services, and typology of the rented accommodation. Moreover, the results show differences among cities and in time for the relevant impact of the COVID-19 pandemic. Springer Netherlands 2022-10-20 /pmc/articles/PMC9584234/ /pubmed/36285335 http://dx.doi.org/10.1007/s11135-022-01550-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Contu, Giulia Frigau, Luca Ortu, Marco VGLM proportional odds model to infer hosts’ Airbnb performance |
title | VGLM proportional odds model to infer hosts’ Airbnb performance |
title_full | VGLM proportional odds model to infer hosts’ Airbnb performance |
title_fullStr | VGLM proportional odds model to infer hosts’ Airbnb performance |
title_full_unstemmed | VGLM proportional odds model to infer hosts’ Airbnb performance |
title_short | VGLM proportional odds model to infer hosts’ Airbnb performance |
title_sort | vglm proportional odds model to infer hosts’ airbnb performance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9584234/ https://www.ncbi.nlm.nih.gov/pubmed/36285335 http://dx.doi.org/10.1007/s11135-022-01550-2 |
work_keys_str_mv | AT contugiulia vglmproportionaloddsmodeltoinferhostsairbnbperformance AT frigauluca vglmproportionaloddsmodeltoinferhostsairbnbperformance AT ortumarco vglmproportionaloddsmodeltoinferhostsairbnbperformance |