Analysis of Circular Price Prediction Strategy for Used Electric Vehicles

Shaojia Huang, Yisen Zhu, Jingde Huang, Enguang Zhang and Tao Xu ()
Additional contact information
Shaojia Huang: School of Intelligent Manufacturing & Aeronautics, Zhuhai College of Science and Technology, Zhuhai 519041, China
Yisen Zhu: School of Electronics and Information Engineering, Wuyi University, Jiangmen 529000, China
Jingde Huang: School of Intelligent Manufacturing & Aeronautics, Zhuhai College of Science and Technology, Zhuhai 519041, China
Enguang Zhang: School of Intelligent Manufacturing & Aeronautics, Zhuhai College of Science and Technology, Zhuhai 519041, China
Tao Xu: Department of Biomedical Engineering, Shantou University, Shantou 515063, China

Sustainability, 2024, vol. 16, issue 13, 1-19

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.

Keywords: used electric vehicles price prediction; lasso regression; regression tree; support vector machine; random forest; gradient boosting regression tree; k-nearest neighbor; price updating strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/13/5761/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/13/5761/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:13:p:5761-:d:1429890

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().