Gleue, ChristophEilers, DennisMettenheim, Hans-JörgBreitner, Michael H.2019-08-142019-08-1420192019http://dx.doi.org/10.1007/s12599-018-0527-3https://dl.gi.de/handle/20.500.12116/24455In 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 underestimation of future residual values can incur large potential losses in resale value or, respectively, competitive disadvantages. For the purpose of facilitating residual value related management decisions, an operative decision support system is introduced with emphasis on its forecasting capabilities. In the paper, the use of artificial neural networks 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 implications 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.Artificial neural networksBusiness intelligenceCar leasingDecision support systemsResidual value forecastsText/Journal Article10.1007/s12599-018-0527-31867-0202