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Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnb

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  • Harrison E. Katz
  • Jess Needleman
  • Liz Medina

Abstract

We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day's allocation across 0-365 days as a compositional vector. The data span 2,557 days from January 2019 through December 2025 in a large North American region. Three findings emerge. First, GBV concentrates more heavily in mid-range horizons: beyond 90 days, GBV tail mass typically exceeds Nights by 20-50%, with ratios reaching 75% at the 180-day threshold during peak seasons. Second, Gamma and Weibull distributions fit comparably well under interval-censored cross-entropy. Gamma wins on 61% of days for Nights and 52% for GBV, with Weibull close behind at 38% and 45%. Lognormal rarely wins (

Suggested Citation

  • Harrison E. Katz & Jess Needleman & Liz Medina, 2026. "Distributional Fitting and Tail Analysis of Lead-Time Compositions: Nights vs. Revenue on Airbnb," Papers 2601.12175, arXiv.org.
    • Handle: RePEc:arx:papers:2601.12175
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