Paper Info
Title
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.
Year
DOI
Venue
2018
10.1145/3219819.3219830
KDD
Keywords
Field
DocType
Price Optimization,Customized Regression Model,Dynamic Pricing
Data mining,Revenue maximization,Binary classification,Regression analysis,Dynamic pricing,Computer science,Operations research,Demand curve,Price optimization,Pricing strategies,Personalization
Conference
ISBN
Citations 
PageRank 
978-1-4503-5552-0
7
0.63
References 
Authors
9
8
Name
Order
Citations
PageRank
Peng Ye149631.43
Julian Qian270.63
Jieying Chen3116.43
Chen-Hung Wu4121.11
Yitong Zhou5292.21
Spencer De Mars670.63
Frank Yang7132.52
Li Zhang870.97