Price factor analysis model for used smartphone products based on machine learning

Abstract

In recent years, more and more used smartphones have been bought and sold through online sales services in the used smartphone market, and it is desirable to utilize the large amount of transaction data accumulated in conjunction with these transactions when listing and purchasing used smartphones. Used equipment buyers can use this data to analyze market price trends and the factors that determine those prices, which can lead to optimal purchase strategies and pricing. However, used smartphones are handled by various sales services. For such a target problem, it would be possible to understand which factors contribute to the selling price if a prediction model could be constructed to explain the selling price based on various characteristics. In this study, we analyze price determinants using a model that incorporates the gradient boosting method, which is a model with high accuracy and interpretability, with the help of explainable AI. In this analysis, it is undesirable to apply a single pricing factor analysis model that could not consider differences in sales services, which has been the focus of previous analyses of the used equipment market. Therefore, we proposes an analytical model that employs the technique of explainable AI for the different price determinants among sales services. The proposed model is applied to analyze actual sales data of used smartphones and discuss the results.