TECHNICAL PAPERS
Jul 1, 2006

Statistical Considerations for Predicting Residual Value of Heavy Equipment

Publication: Journal of Construction Engineering and Management
Volume 132, Issue 7

Abstract

Residual value needs to be considered in owning cost calculations for used heavy construction equipment. Its dependency on factors such as manufacturer and model, equipment age, and condition rating can best be examined by analyzing real market data from equipment auctions. Macroeconomic indicators can also be included to examine any potential influence of the overall economy on auction prices. This paper discusses statistical considerations for performing such a residual value analysis. Considerations include the study type, data properties, identifying outlier observations, regression assumptions, and formulating and selecting an appropriate regression model using the adjusted coefficient of determination. A second-order polynomial of equipment age with additive factors appears promising as the final regression model. Adjusted confidence and prediction intervals are created to correctly display residual value. Cross-validation using randomly split halves of the dataset is performed. Actual data for medium track dozers are used to illustrate the validity of the methodology.

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Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 132Issue 7July 2006
Pages: 723 - 732

History

Received: May 3, 2005
Accepted: Dec 21, 2005
Published online: Jul 1, 2006
Published in print: Jul 2006

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Authors

Affiliations

Gunnar Lucko, A.M.ASCE
Assistant Professor, Dept. of Civil Engineering, The Catholic Univ. of America, Washington, DC 20064 (corresponding author). E-mail: [email protected]
Christine M. Anderson-Cook
Technical Staff Member, Statistical Sciences Group, Los Alamos National Laboratory, Los Alamos, NM 87544. E-mail: [email protected]
Michael C. Vorster, M.ASCE
David H. Burrows Professor, The Charles E. Via, Jr. Dept. of Civil and Environmental Engineering, Virginia Polytechnic Institute and State Univ., Blacksburg, VA 24061. E-mail: [email protected]

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