The paper investigates statistical models for forecasting the resale prices of used cars. An empirical study is performed to explore the contributions of different degrees of freedom in the modeling process to the forecast accuracy. First, a comparative analysis of alternative prediction methods provides evidence that random forest regression is particularly effective for resale price forecasting. It is also shown that the use of linear regression, the prevailing method in previous work, should be avoided. Second, the empirical results demonstrate the presence of heterogeneity in resale price forecasting and identify methods that can automatically overcome its detrimental effect on the forecast accuracy. Finally, the study confirms that the sellers of used cars possess informational advantages over market research agencies, which enable them to forecast resale prices more accurately. This implies that sellers have an incentive to invest in in-house forecasting solutions, instead of basing their pricing decisions on externally generated residual value estimates.