Modelling and forecasting European Airbnb occupancy during the pandemic: The specific merits of panel-data and Markov-switching models

This study models European Airbnb occupancy from January 2017 to December 2020 for 43 European countries within an econometric panel-data framework. Besides typical economic covariates, the COVID-19 Stringency Index is employed to allow for the recent pandemic. The counterfactual observations created by two versions of the econometric model (one with and one without European aggregates of macroeconomic controls) are benchmarked against traditional univariate time-series models and a seasonal Markov-switching autoregression. For pandemic times, the latter performs very well in terms of several forecast accuracy measures due to its ability of (a) detecting the pandemic-induced structural break and (b) accounting for seasonal patterns in the data. The econometric models also predict well as they outperform the time-series models in the majority of cases. Hence, the panel-data approach is suitable when looking for economically interpretable Airbnb occupancy forecasts, even during the time of the pandemic.

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Publication Details

Journal
Tourism Economics
Published
2024-09-13
DOI
https://doi.org/10.1177/13548166241283510
Citations
2
Primary Topic
Sharing Economy and Platforms
Type
article
Field-Weighted Citation Impact
0.88
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article

Modelling and forecasting European Airbnb occupancy during the pandemic: The specific merits of panel-data and Markov-switching models

Bozana Zekan, Francesco Luigi Milone, Ulrich Gunter
2 citations
Tourism Economics
Sharing Economy and Platforms
0.88
article

Modelling and forecasting European Airbnb occupancy during the pandemic: The specific merits of panel-data and Markov-switching models

Bozana Zekan, Francesco Luigi Milone, Ulrich Gunter
article en
2 citations

Abstract

This study models European Airbnb occupancy from January 2017 to December 2020 for 43 European countries within an econometric panel-data framework. Besides typical economic covariates, the COVID-19 Stringency Index is employed to allow for the recent pandemic. The counterfactual observations created by two versions of the econometric model (one with and one without European aggregates of macroeconomic controls) are benchmarked against traditional univariate time-series models and a seasonal Markov-switching autoregression. For pandemic times, the latter performs very well in terms of several forecast accuracy measures due to its ability of (a) detecting the pandemic-induced structural break and (b) accounting for seasonal patterns in the data. The econometric models also predict well as they outperform the time-series models in the majority of cases. Hence, the panel-data approach is suitable when looking for economically interpretable Airbnb occupancy forecasts, even during the time of the pandemic.

Tourism EconomicsVol. 31(4)
MODUL University Vienna (AT), Politecnico di Torino (IT)
Openalex Percentile: Top 19%
Sharing Economy and Platforms
0.88
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