In the automotive industry, it is very common for new vehicles to be leased rather than sold. This implies forecasting an accurate residual value for the vehicles, which is a major factor for determining monthly leasing rates. Either a systematic overestimation or underestima- tion of future residual values can incur large potential losses in resale value or, respectively, competitive disad- vantages. For the purpose of facilitating residual value related management decisions, an operative decision sup- port system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural net- works for this application is demonstrated in a case study based on more than 250,000 data sets of leasing contracts from a major German car manufacturer, completed between 2011 and 2017. The importance of determining price factors and the effect of different time horizons on forecasting accuracy are investigated and practical impli- cations are discussed. In addition, the authors neither found a significant explanatory nor predictive power of external economic factors, which underlines the importance of collecting and taking advantage of vehicle-specific data or, in more general terms, the exclusive data of corporations, which is often only available internally.
Gleue, Christoph; Eilers, Dennis; Mettenheim, Hans-Jörg von; and Breitner, Michael H.
"Decision Support for the Automotive Industry - Forecasting Residual Values Using Artificial Neural Networks,"
Business & Information Systems Engineering:
Vol. 61: Iss. 4, 385-397.
Available at: https://aisel.aisnet.org/bise/vol61/iss4/2