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Enhancing Predictive Accuracy in European Agricultural Tractor Residual Value Estimation: A Double Square Root Regression Reappraisal

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  • Ivan Herranz-Matey

    (Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2, 28040 Madrid, Spain)

  • Luis Ruiz-Garcia

    (Departamento de Ingeniería Agroforestal, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro, 2, 28040 Madrid, Spain)

Abstract

Determining the residual value of tractors is imperative for comprehensive cost analyses within the agricultural machinery sector. Despite numerous studies offering various models and independent variables, the double square root regression approach, originally developed by Cross and Perry and adapted by ASABE for North American contexts, has been widely utilized. However, factors such as the complexity of OEM portfolios, steep price increases due to compliance with diesel emission regulations, and limited data availability in Europe and its market specificities necessitate improvements in predictive accuracy. This study evaluates different tractor cohort alternatives beyond engine horsepower to enhance predictive robustness. Incorporating brand and powertrain type alongside engine power significantly improved model performance and exhibited the strongest robustness, as evidenced by reduced the root mean square error (RMSE) and increased R-squared values. These findings contribute to the refinement of tractor residual value estimation models, offering valuable insights for stakeholders in the agricultural machinery industry.

Suggested Citation

  • Ivan Herranz-Matey & Luis Ruiz-Garcia, 2024. "Enhancing Predictive Accuracy in European Agricultural Tractor Residual Value Estimation: A Double Square Root Regression Reappraisal," Agriculture, MDPI, vol. 14(5), pages 1-19, April.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:654-:d:1381231
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    References listed on IDEAS

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    1. Perry, Gregory M & Glyer, J David, 1990. "Durable Asset Depreciation: A Reconciliation between Hypotheses," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 524-529, August.
    2. Jing Wu & Gregory M. Perry, 2004. "Estimating Farm Equipment Depreciation: Which Functional Form Is Best?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 483-491.
    3. Timothy L. Cross & Gregory M. Perry, 1995. "Depreciation Patterns for Agricultural Machinery," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(1), pages 194-204.
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    7. Ivan Herranz-Matey & Luis Ruiz-Garcia, 2023. "Agricultural Tractor Retail and Wholesale Residual Value Forecasting Model in Western Europe," Agriculture, MDPI, vol. 13(10), pages 1-21, October.
    8. Ivan Herranz-Matey & Luis Ruiz-Garcia, 2023. "Agricultural Combine Remaining Value Forecasting Methodology and Model (and Derived Tool)," Agriculture, MDPI, vol. 13(4), pages 1-15, April.
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