Coupled Supply and Demand Forecasting in Platform Accommodation Markets

2026-02-27

AI Summary

Traditional tourism demand forecasting often treats accommodation supply as exogenous and fixed, overlooking the elasticity, endogeneity, and policy responsiveness of supply in platform-based short-term rental markets, which leads to model failure under supply fluctuations. This study proposes a supply-demand coupled forecasting framework that explicitly models the dynamic interaction between supply and demand through three dimensions: agent behavior, information dynamics, and policy interventions, thereby addressing the observational truncation caused by inventory sell-outs. For the first time, it systematically incorporates supply endogeneity into tourism demand prediction by integrating revenue management principles, two-sided market theory, and Bayesian time series methods. Through simulation, the work reveals the identification bias inherent in conventional models and establishes a new paradigm for jointly forecasting supply and demand.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Relational Probabilistic Models
• Search and Optimization: Other Foundations of Search & Optimization

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Tourism demand forecasting is methodologically mature, but it typically treats accommodation supply as fixed or exogenous. In platform-mediated short-term rentals, supply is elastic, decision-driven, and co-evolves with demand through pricing, information design, and interventions. I reframe the core issue as endogenous stock-out censoring: realized booked nights satisfy B_{k,t} <= min(D_{k,t}, S_{k,t}), so booking models that ignore supply learn a regime-specific ceiling and become fragile under policy changes and supply shocks. This narrated review synthesizes work from tourism forecasting, revenue management, two-sided market economics, and Bayesian time-series methods; develops a three-part coupling framework (behavioral, informational, intervention); and illustrates the identification failure with a toy simulation. I conclude with a focused research agenda for jointly forecasting supply, demand, and their compositions.

Problem

• endogenous supply
• demand forecasting
• platform accommodation
• stock-out censoring
• two-sided markets

Innovation

• endogenous stock-out censoring
• coupled forecasting
• platform accommodation markets
• elastic supply
• two-sided market