VGLM proportional odds model to infer hosts’ Airbnb performance
Bibliographic Data
| ID | 21377660 |
|---|---|
| Authors | Giulia Contu (0000-0001-9750-1896, University of Cagliari), Luca Frigau (0000-0002-6316-4040, University of Cagliari, corresponding author), Marco Ortu (0000-0003-4191-5058, University of Cagliari) |
| Year | 2023 |
| Volume | 57 |
| Issue | 5 |
| Pages | 4069-4094 |
| Publication date | 2023-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quality & Quantity (JOURNAL) |
| Journal identifiers | ISSN: 0033-5177 • E-ISSN: 1573-7845 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11135-022-01550-2 |
| PMID | 36285335 |
| OpenAlex | W4306888761 |
| Language | EN |
| References cited | 57 |
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
Accommodation · Biology · Business · Coronavirus disease 2019 (COVID-19) · Demographic economics · Econometrics · Economics · Geography · Host (biology) · Logistic regression · Occupancy · Odds · Ordered logit · Pandemic · Sociology · Statistics · Typology · Demography · Ecology · Engineering · Mathematics · Medicine · Psychology · Sharing Economy and Platforms · Smart Parking Systems Research · Transportation and Mobility Innovations
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| Citation velocity | historical |
|---|---|
| Highly cited | No |