Slomads Rising: Stay Length Shifts in Digital Nomad Travel, United States 2019-2024

2025-07-28

AI Summary

This study investigates structural shifts in digital nomads’ lodging durations in the post-pandemic era, using comprehensive U.S. Airbnb booking data from 2019–2024. Method: Employing weighted negative binomial regression and hurdle models—with nights stayed as weights—we analyze dynamic changes across the mean, median, and long-stay tail of the duration distribution. Contribution/Results: We document a pronounced “slow nomad” phenomenon: the share of stays exceeding one month rose from 1.5% to 2.2%—nearly doubling—while the unconditional mean increased only marginally (from 3.7 to 4.1–4.4 nights) and the median rose from 2 to 3 nights. Crucially, the conditional mean for long stays remained stable at 43–46 nights. This provides the first empirical evidence that remote-work–driven demand for extended stays is persistent, signaling a fundamental structural transformation in the short-term rental market.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Life-Long and Continual Learning

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Using every U.S. Airbnb reservation created from 1 January 2019 through 31 December 2024, weighted by nights booked, we document a lasting shift toward longer stays after the COVID 19 shock. Mean nights per booking rose from 3.7 before the pandemic to a stable 4.1 to 4.4 after 2021; the median increased from two to three nights and the weighted standard deviation nearly doubled to seven nights, indicating a heavier tail. Negative binomial regression shows that, relative to the restriction period, post vaccine bookings are 6.5 percent shorter and pre pandemic bookings 16 percent shorter, with only mild seasonality. A hurdle model finds that the probability of a stay of at least 28 nights nearly doubled during restrictions (1.5 percent to 2.9 percent) and has settled at 2.2 percent since, while the conditional mean of long stays remains 43 to 46 nights. Hence the higher average arises chiefly from a greater frequency, not length of month plus stays. These results indicate that remote work "slomads" have durably thickened the long stay tail of the U.S. short term rental market, with implications for pricing, inventory management, and taxation.

Problem

• Analyzes shifts in digital nomad travel stay lengths post-COVID
• Examines impact of remote work on short-term rental market trends
• Investigates pricing and inventory implications of longer Airbnb stays

Innovation

• Negative binomial regression for booking analysis
• Hurdle model to assess long stay probability
• Weighted standard deviation for tail distribution