The Role of Poisson Regression in Hotel Demand Forecasting: A Data-Driven Approach for Independent Hotels

In hospitality, demand forecasting is the backbone of revenue management, enabling hotels to optimize pricing, manage inventory, and maximize profitability (Leeuwen & Koole, 2021; Vives & Jacob, 2019). For independent hotels - particularly those without meeting spaces or full service restaurants - leveraging statistical models like Poisson regression can provide valuable insights into demand patterns and future booking trends (Sierag et al., 2017) without expensive revenue management systems (Bandalouski et al., 2018; Lee, 2025). My aim for this article is to explore how Poisson regression works, its real-world applications, and critical considerations for hotels navigating demand uncertainty.

Why Poisson Regression?

Poisson regression specializes in modeling count data - discrete, non-negative integers like daily bookings or cancellations. its mathematical foundation assumes events (eg. guest arrivals) occur independently at a constant rate over time (Lee, 2025). The model’s logarithmic link function ensures predicted counts remain positive, making it suitable for understanding booking arrivals, occupancy rates, and other key hotel metrics. 

log(λi)=β0+β1x1+…+βkxk

Here λi represents the expected count for observation (e.g. nightly bookings), while x1,….,xk are predictor variables like day-of-week or local events.

Key advantages of this model include:

1. Interpretability: Coefficients quantify the impact of each predictor on demand. For example, a coefficient of 0.3 for weekends implies a 35% increase in expected bookings (since e0.3≈1.35) (Lee, 2025).

2. Cost-Efficiency: Requires only historical booking data and basic statistical software, accessible for smaller hotels without dedicated data science teams.

3. Scalability: Adapts to variables including seasonality, pricing, promotions, and external events, offering a comprehensive perspective on factors influencing demand (Stasinakis, 2020).

Empirical Evidence from Hospitality Studies

Validating the Poisson Assumption 

A study of a 34-room Dutch hotel confirmed demand follows a non-homogeneous Poisson process, where arrivals vary by time but remain independent. This aligns with airline industry findings, reinforcing its applicability to hospitality demand forecasting (Sierag et al., 2017). However, smaller hotels face greater demand variations, highlighting the need for careful model calibration (Sierag et al., 2017).

Hybrid Models for Complex Scenarios

While pure Poisson regression struggles with overdispersion (variance exceeding the mean), combining it with other techniques improves forecast accuracy. 

• Negative Binomial regression for clustered arrival (e.g. group bookings)

• Machine learning integrations (e.g. neural networks) to capture non-linear patterns like event-driven demand surges (Choi et al., 2022) (Lee, 2025).

Real-World Applications

• Staffing Optimization: A Texas boutique hotel reduced labor costs by 15% by aligning staffing levels to Poisson-predicted check-in volumes (Lee, 2025)

•  Dynamic Pricing: Hotels near convention centers use Poisson models to hike rates by 20-30% during events, maximizing RevPAR (Lee, 2025).

Limitations and Mitigations

Case Study: Small Hotel Success

A 34-room independent hotel in the Netherlands used Poisson regression to:

1. Identify seasonal demand spikes (e.g., +50% bookings during summer).

2. Quantify day-of-week effects (weekends drove 40% of revenue).

3. Reduce forecast errors by 18% compared to moving averages.

The study emphasized exploratory data analysis (EDA) to validate model assumption often skipped but critical for small hotels (Sierag et al., 2017).

Poisson regression offers independent hotels a low-cost, high-impact tool for demand forecasting. By blending its interpretability with extensions like Negative Binomial models or machine learning, practitioners can navigate demand uncertainty, optimize pricing, and enhance overall revenue management strategies (Lee, 2025). However, success hinges on:

• Rigorous data validation to check Poisson assumptions (Sierag et al., 2017)

• Hybrid modeling to address overdispersion or zero-inflation (Choi et al., 2022)

• Continuous adaptation to shifting market conditions (Lee, 2025)

This is very important for resource-constrained hotels, mastering these techniques drives revenue growth and competitive advantage. As one study notes: “Demand uncertainty is a small hotel’s Achilles’ heel, but Poisson regression turns data into a shield (Sierag et al., 2017).

References

Bandalouski, A. M., Kovalyov, M. Y., Pesch, E., & Tarim, Ş. A. (2018). An overview of revenue management and dynamic pricing models in hotel business. In RAIRO - Operations Research (Vol. 52, Issue 1, p. 119). EDP Sciences. https://doi.org/10.1051/ro/2018001

Choi, J.-G., Yi-wei, Z., Nadzri, N. I. B. M., Baymuminova, N., & Xu, S.-N. (2022). A Review of Forecasting Studies for the Hotel Industry: Focusing on results, contributions and limitations [Review of A Review of Forecasting Studies for the Hotel Industry: Focusing on results, contributions and limitations]. GLOBAL BUSINESS & FINANCE REVIEW, 27(5), 65. https://doi.org/10.17549/gbfr.2022.27.5.65

Lee, S. (2025). 7 Key Poisson Regression Facts in Hospitality & Tourism. https://www.numberanalytics.com/blog/7-key-poisson-regression-facts-hospitality-tourism#google_vignette

Leeuwen, R. van, & Koole, G. (2021). Demand forecasting in hospitality using smoothed demand curves. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2102.04236

Sierag, D., Rest, J. P. van der, Koole, G., Mei, R. van der, & Zwart, B. (2017). A call for exploratory data analysis in revenue management forecasting: a case study of a small and independent hotel in The Netherlands. In International Journal of Revenue Management (Vol. 10, Issue 1, p. 28). Inderscience Publishers. https://doi.org/10.1504/ijrm.2017.084147

Stasinakis, A. (2020). Analyzing the effect of competition in the hospitality industry. http://www.diva-portal.org/smash/record.jsf?pid=diva2:1436145

Vives, A., & Jacob, M. (2019). Dynamic pricing in different Spanish resort hotels. Tourism Economics, 27(2), 398. https://doi.org/10.1177/1354816619870652