Customized Regression Model for Airbnb Dynamic Pricing

ABSTRACT

This paper describes the pricing strategy model deployed at Airbnb, an online marketplace for sharing home and experience. The goal of price optimization is to help hosts who share their homes on Airbnb set the optimal price for their listings. In contrast to conventional pricing problems, where pricing strategies are applied to a large quantity of identical products, there are no "identical" products on Airbnb, because each listing on our platform offers unique values and experiences to our guests. The unique nature of Airbnb listings makes it very difficult to estimate an accurate demand curve that's required to apply conventional revenue maximization pricing strategies.

Our pricing system consists of three components. First, a binary classification model predicts the booking probability of each listing-night. Second, a regression model predicts the optimal price for each listing-night, in which a customized loss function is used to guide the learning. Finally, we apply additional personalization logic on top of the output from the second model to generate the final price suggestions. In this paper, we focus on describing the regression model in the second stage of our pricing system. We also describe a novel set of metrics for offline evaluation. The proposed pricing strategy has been deployed in production to power the Price Tips and Smart Pricing tool on Airbnb. Online A/B testing results demonstrate the effectiveness of the proposed strategy model.

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Index Terms

• Applied computing

  • Enterprise computing

    • Business process management

• Computing methodologies

  • Artificial intelligence

    • Planning and scheduling

  • Machine learning

    • Learning paradigms

      • Supervised learning

        • Supervised learning by regression

• Social and professional topics

  • Professional topics

    • Management of computing and information systems

      • Implementation management

        • Pricing and resource allocation

• Theory of computation

  • Theory and algorithms for application domains

    • Algorithmic game theory and mechanism design

      • Computational pricing and auctions