Residual Value Forecasting Using Asymmetric Cost Functions

TL;DR

This paper develops and evaluates resale price forecasting models for car leasing, emphasizing the importance of asymmetric error costs, and demonstrates that incorporating these costs improves decision-making accuracy.

Contribution

It consolidates previous work on asymmetric error functions, systematically compares approaches, and shows that accounting for asymmetric costs enhances leasing decision support.

Findings

Incorporating asymmetric error costs reduces decision costs by about 8%.

Higher cost asymmetry leads to larger improvements in forecast quality.

Systematic evaluation demonstrates the effectiveness of the proposed approach.

Abstract

Leasing is a popular channel to market new cars. Pricing a leasing contract is complicated because the leasing rate embodies an expectation of the residual value of the car after contract expiration. To aid lessors in their pricing decisions, the paper develops resale price forecasting models. A peculiarity of the leasing business is that forecast errors entail different costs. Identifying effective ways to address this characteristic is the main objective of the paper. More specifically, the paper contributes to the literature through i) consolidating and integrating previous work in forecasting with asymmetric cost of error functions, ii) systematically evaluating previous approaches and comparing them to a new approach, and iii) demonstrating that forecasting with asymmetric cost of error functions enhances the quality of decision support in car leasing. For example, under the assumption that the costs of overestimating resale prices is twice that of the opposite error, incorporating corresponding cost asymmetry into forecast model development reduces decision costs by about eight percent, compared to a standard forecasting model. Higher asymmetry produces even larger improvements.