Paper: arXiv 2507.21298

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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