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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, revised Feb 2026.
    • Handle: RePEc:arx:papers:2601.12175
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    References listed on IDEAS

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    1. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    2. Whitney Newey & Kenneth West, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    3. Katz, Harrison & Brusch, Kai Thomas & Weiss, Robert E., 2024. "A Bayesian Dirichlet auto-regressive moving average model for forecasting lead times," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1556-1567.
    4. Apostolos Ampountolas, 2025. "Predicting hotel booking cancellations: a comprehensive machine learning approach," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(6), pages 539-550, December.
    5. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    6. Kulendran, N. & King, Maxwell L., 1997. "Forecasting international quarterly tourist flows using error-correction and time-series models," International Journal of Forecasting, Elsevier, vol. 13(3), pages 319-327, September.
    7. Nicolau, Juan L. & Masiero, Lorenzo, 2017. "Determinants of advanced booking," Annals of Tourism Research, Elsevier, vol. 67(C), pages 78-82.
    8. Anna Maria Fiori & Ilaria Foroni, 2019. "Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability," Sustainability, MDPI, vol. 11(5), pages 1-24, February.
    9. ChihChien Chen, 2016. "Cancellation policies in the hotel, airline and restaurant industries," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 270-275, July.
    10. Masiero, Lorenzo & Viglia, Giampaolo & Nieto-Garcia, Marta, 2020. "Strategic consumer behavior in online hotel booking," Annals of Tourism Research, Elsevier, vol. 83(C).
    11. Yang, Yang & Mao, Zhenxing, 2020. "Location advantages of lodging properties: A comparison between hotels and Airbnb units in an urban environment," Annals of Tourism Research, Elsevier, vol. 81(C).
    12. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    13. Sainaghi, Ruggero & Baggio, Rodolfo, 2020. "Substitution threat between Airbnb and hotels: Myth or reality?," Annals of Tourism Research, Elsevier, vol. 83(C).
    14. Daniel Guttentag, 2015. "Airbnb: disruptive innovation and the rise of an informal tourism accommodation sector," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(12), pages 1192-1217, December.
    15. Fuchs, Galia & Reichel, Arie, 2011. "An exploratory inquiry into destination risk perceptions and risk reduction strategies of first time vs. repeat visitors to a highly volatile destination," Tourism Management, Elsevier, vol. 32(2), pages 266-276.
    16. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    17. Andrews, Donald W K, 1991. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Econometrica, Econometric Society, vol. 59(3), pages 817-858, May.
    18. Stepan Chalupa & Martin Petricek, 2025. "Application of revenue management practices in short-term rental management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 24(4), pages 326-333, August.
    19. Harrison Katz, 2026. "Directional-Shift Dirichlet ARMA Models for Compositional Time Series with Structural Break Intervention," Papers 2601.16821, arXiv.org, revised Apr 2026.
    20. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    21. Sigala, Marianna, 2020. "Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research," Journal of Business Research, Elsevier, vol. 117(C), pages 312-321.
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