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

ArXiv ID: 2507.21298 “View on arXiv”

Authors: Harrison Katz, Erica Savage

Abstract

Using all U.S. Airbnb reservations created in 2019-2024 (booking-count weighted), we quantify pandemic-era shifts in nights per booking (NPB) and the mechanism behind them. The mean rose from 3.68 pre-COVID to 4.36 during restrictions and stabilized near 4.07 post-2021 (about 10% above 2019); the booking-weighted median moved from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, indicating heavy tails. A negative-binomial model with month effects implies post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-COVID bookings are 16% shorter. In a two-part model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post); conditional means among long stays were about 55-60 nights. Thus the higher average reflects more long stays rather than longer long stays. A SARIMA(0,1,1)(0,1,1)12 with pandemic-phase dummies improves fit (LR=8.39, df=2, p=0.015), consistent with a structural level shift.

Keywords: Log-Normal Distribution, Negative-Binomial Model, SARIMA Model, Structural Level Shift, Time Series Analysis, Real Estate (Short-Term Rentals)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced statistical modeling including SARIMA, negative-binomial regression, and distributional fitting (log-normal) with AIC/BIC comparisons, while using the entire U.S. Airbnb booking dataset (2019–2024) with booking-weighted data and multiple robustness checks.

  flowchart TD
    A["Research Goal: Quantify pandemic-era shifts<br>in Airbnb booking length (2019-2024)"] --> B["Data: U.S. Airbnb Reservations<br>(Booking-Count Weighted)"]
    B --> C["Methodology: Distributional &<br>Time-Series Analysis"]
    C --> D["Statistical Modeling"]
    
    subgraph D ["Computational Processes"]
        D1["Log-Normal Fit<br>Heavy Tails"] --> D2["Negative-Binomial<br>Post-vaccine effect: -6.5%"]
        D1 --> D3["Two-Part Model<br>>28 nights: Share ↑"]
        D1 --> D4["SARIMA (0,1,1)(0,1,1)12<br>Structural Level Shift"]
    end
    
    D --> E["Key Findings"]
    
    subgraph E ["Outcomes"]
        F["Mean NPB Rose 10%<br>(3.68 → 4.07)"]
        G["Median Shifted<br>(2 → 3 nights)"]
        H["Increase driven by<br>more long stays, not longer tails"]
    end