Work

Opportunities and pitfalls of regression algorithms for predicting the residual value of heavy equipment — A comparative analysis

Year: 2024

Type: article

Abstract: The residual value of heavy equipment is essential for financial and economic considerations in the construction industry. In practice, empirical methods are frequently used to determine the residual value of a given piece of equipment. Here, various regression methods are compared based on a real-world dataset of used heavy equipment sales from a construction company. The results show that the prediction performance of traditional methods is clearly worse when compared to machine learning models not yet employed for this purpose. For the latter, preprocessing and parameter tuning are essential, and the article guides through these steps. Further, the article demonstrates how a variable importance value comparable across all methods can be obtained. These findings may also be useful in other applications.

Source: Engineering Applications of Artificial Intelligence

DOI: 10.1016/j.engappai.2024.109599

Authors: Marco Huymajer, Peter Filzmoser, Alexandra Mazak-Huemer, Leopold Winkler, Hans Kraxner

Institutions: Montanuniversität Leoben, TU Wien, Johannes Kepler University of Linz

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,270