[2N5-GS-10] AI application

[2N5-GS-10-02] Implementation of a used car price determination support system

〇hiroyuki dan¹, yutaro hayashi¹, akira matsushita², atsushi iwasaki³ (1. AUCNET INC., 2. University of Tokyo, 3. University of Electro-Communications)

Keywords: used car trading, BtoB auction, price prediction, gradient boosting, MLOps

Aucnet, Inc. provides a used car appraisal support service for used car dealers in Japan.
Prior to 2020, prices were manually determined by experienced staff based on car information such as model, mileage, and body conditions. However, speed and accuracy are desired to improve due to increasing size of inquiry. In this study, we developed a price recommendation system using AutoML and compared its performance with human assessment. We used empirical data from Aucnet used car auctions to build a machine learning model that predicts winning bids or prices from precise car conditions.
Among the models generated by AutoML, LightGBM performed best with root mean squared errors.
We built a system on databricks and put it into actual operation in 2020, resulting in a 28.3\% reduction in response time on average over 100 thousand inquiries. The system also accurately predicted actual winning bids of subsequent auctions, with 73.1\% of the data falling within plus or minus 10\% of the price range suggested by the system.