Research: Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing

• URL: http://arxiv.org/abs/2208.03135v1
• Date: Thu, 4 Aug 2022 13:58:04 GMT
• Title: Research: Modeling Price Elasticity for Occupancy Prediction in Hotel Dynamic Pricing
• Authors: Fanwei Zhu, Wendong Xiao, Yao Yu, Ziyi Wang, Zulong Chen, Quan Lu, Zemin Liu, Minghui Wu and Shenghua Ni
• Abstract summary: We propose a novel hotel demand function that explicitly models the price elasticity of demand for occupancy prediction. 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.
• Score: 13.768319677863259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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