Anticipating and tracking vehicle price variations is vital for the auto vehicle industry. Within this thesis, we trained machine learning models that can predict the price changes based on different events associated with the vehicle models. We divide the price changes into four different categories. The four categories include price decrease, mild price increase, significant price increase or no change at all for a given vehicle model. All predictions are made by taking into consideration the percent pure price change of a vehicle that also takes into account if the overall value of the vehicle changes with price change. We introduce a new feature called inverse premium ratio for every model that indicates the brand value of the vehicle.