Analysis of Circular Price Prediction Strategy for Used Electric Vehicles

Abstract

As the car price war has intensified in China from 2023, the continuous decline in prices of new cars for both conventional fuel vehicles and electric vehicles (EVs) has led to a sharp decline in used cars. In particular, the EV market appears more vulnerable as the prime cost of battery raw materials has decreased since January 2023. And thus, a second-hand EV price prediction system is urgent. This study compares several methods for used EVs in China. We find that the random forest method and the gradient boosting regression tree (GBRT) method have good effects on predicting used EV prices in respecting price ranges. Timed EV data capture is applied to guarantee the real-time property of our prediction system. Then, we propose the concept of circular pricing, which means that the obsolete data for the priced car will be repriced according to the latest data. In this way, such a system can guide the used car dealers to adjust the price in time.