Paper: arXiv 2501.10535

Abstract

Short-term shifts in booking behaviors can disrupt forecasting in the travel and hospitality industry, especially during global crises. Traditional metrics like average or median lead times often overlook important distribution changes. This study introduces a normalized L1 (Manhattan) distance to assess Airbnb booking lead time divergences from 2018 to 2022, focusing on the COVID-19 pandemic across four major U.S. cities. We identify a two-phase disruption: an abrupt change at the pandemic’s onset followed by partial recovery with persistent deviations from pre-2018 patterns. Our method reveals changes in travelers’ planning horizons that standard statistics miss, highlighting the need to analyze the entire lead-time distribution for more accurate demand forecasting and pricing strategies. The normalized L1 metric provides valuable insights for tourism stakeholders navigating ongoing market volatility.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper’s core math is simple, relying on normalized L1 distance and standard descriptive statistics, but it demonstrates high empirical rigor by using real-world Airbnb data across multiple cities, time periods, and including a detailed methodology section suitable for backtesting.

Research Flowchart

  flowchart TD
  A["Research Goal<br/>Analyze Airbnb booking lead time<br/>dynamics during global upheavals<br/>(2018-2022)"] --> B["Data Input<br/>Airbnb booking data<br/>(4 major U.S. cities)"]
  B --> C["Methodology<br/>Calculate daily lead time distributions"]
  C --> D{"Computational Process<br/>Apply Normalized L1 (Manhattan)<br/>distance vs. pre-pandemic baseline"}
  D --> E["Key Findings<br/>Identified 2-phase disruption:<br/>Abrupt change & partial recovery"]
  E --> F["Outcome<br/>Revealed shifts in planning horizons<br/>missed by averages; enables better<br/>demand forecasting & pricing"]