Lead Times in Flux: Analyzing Airbnb Booking Dynamics During Global Upheavals (2018-2022)

TL;DR

This paper introduces a normalized L1 distance metric to analyze Airbnb booking lead time changes from 2018 to 2022, revealing disruptions and recovery patterns during the COVID-19 pandemic across major U.S. cities.

Contribution

It proposes a novel normalized L1 metric for analyzing booking lead time distributions, capturing shifts overlooked by traditional statistics during crises.

Findings

Identified a two-phase disruption in booking behaviors during COVID-19.

Revealed persistent deviations from pre-pandemic patterns.

Provided insights for demand forecasting and pricing strategies.

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.