Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing

TL;DR

This paper introduces a novel demand function modeling price elasticity for hotel occupancy prediction, improving accuracy in dynamic pricing by learning elasticity coefficients from multiple factors and addressing data challenges.

Contribution

It proposes a new hotel demand model with elasticity learning modules and a multi-task framework, advancing demand estimation in hotel dynamic pricing.

Findings

Outperforms state-of-the-art baselines in occupancy prediction.

Demonstrates effectiveness of elasticity modeling in revenue optimization.

Validates approach on real-world datasets.

Abstract

Demand estimation plays an important role in dynamic pricing where the optimal price can be obtained via maximizing the revenue based on the demand curve. In online hotel booking platform, the demand or occupancy of rooms varies across room-types and changes over time, and thus it is challenging to get an accurate occupancy estimate. In this paper, we propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction, and design a price elasticity prediction model to learn the dynamic price elasticity coefficient from a variety of affecting factors. Our model is composed of carefully designed elasticity learning modules to alleviate the endogeneity problem, and trained in a multi-task framework to tackle the data sparseness. We conduct comprehensive experiments on real-world datasets and validate the superiority of our method over the state-of-the-art baselines for both occupancy prediction and dynamic pricing.