Short-term rental market occupancy - daily time series for 2017-2022 on 500 markets worldwide

Abstract

Short-term vacation rentals, as promoted by platforms such as Airbnb, Homeaway, Vrbo, etc., are a growing component of the travel industry. This paper provides a unique, large dataset on global market occupancy for the short-term rental market using data from the American company Wheelhouse. The dataset consists of data for $500$ markets around the world. For each market, a daily occupancy time series from January $2017$ to December $2022$ is provided, allowing for studies of local and global patterns in the evolution of the short-term rental market. Additionally, the dataset includes curves representing the booking trajectory of each market and stay date up to one year prior to the stay date. This large dataset comprises a unique combination of time series and survival analysis data, and is suitable as a methodological benchmark for both classical statistical and machine learning models.

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BibTeX RIS

BibTeX

@misc{aastveit2026shortterm,
  title = {{Short-term rental market occupancy - daily time series for 2017-2022 on 500 markets worldwide}},
  author = {Marthe Elisabeth Aastveit and Andreas Buschermöhle and Alex Lenkoski and Thordis Thorarinsdottir},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.27445},
  eprint = {2609.27445},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.27445},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Short-term rental market occupancy - daily time series for 2017-2022 on 500 markets worldwide
AU  - Marthe Elisabeth Aastveit
AU  - Andreas Buschermöhle
AU  - Alex Lenkoski
AU  - Thordis Thorarinsdottir
PY  - 2026
DA  - 2026/09/23
PB  - arXiv
DO  - 10.48550/arxiv.2609.27445
UR  - https://arxiv.org/abs/2609.27445
AB  - Short-term vacation rentals, as promoted by platforms such as Airbnb, Homeaway, Vrbo, etc., are a growing component of the travel industry. This paper provides a unique, large dataset on global market occupancy for the short-term rental market using data from the American company Wheelhouse. The dataset consists of data for $500$ markets around the world. For each market, a daily occupancy time series from January $2017$ to December $2022$ is provided, allowing for studies of local and global patterns in the evolution of the short-term rental market. Additionally, the dataset includes curves representing the booking trajectory of each market and stay date up to one year prior to the stay date. This large dataset comprises a unique combination of time series and survival analysis data, and is suitable as a methodological benchmark for both classical statistical and machine learning models.
ER  -

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