关键词: 共享经济, Airbnb, 机器学习, 变量选择, 预测, 价格影响因素

Abstract: Airbnb is one of the largest accommodation rental platforms. This paper comprehensively uses a variety of machine learning methods to predict Airbnb price in Beijing and examine the impacts of six groups of explanatory variables: characteristics of listings, location of listings, amenities and services, rentalrules, attributes of hosts,and reputation of listings. Findings find that: (a)Characteristics of listings have the greatest impact on the Airbnb price. (b)The performance of the nonlinear method is significantly better than the linear method. (c)Contrary to previous findings, instant booking and high price are related. (d)Among all variables, capacity of listings, room type, and the distance between listings and the city center are the most important variables. This paper provides new perspectives for the study of Airbnb pricing, and can help hosts to set price for their listings more accurately. The authors have made possible explanations from the theoretical and practical perspective.

Key words: sharing economy, airbnb, machine learning, variable selection, prediction, price determinants