Abstract: This disclosure describes systems, methods, and devices related to linear regression models and machine learning models for generating vehicle prices. A device may receive information associated with a vehicle, information associated with a dealer selling the vehicle, and market information indicative of previous sales of similar vehicles. The device may generate probabilities of sale of the vehicle at respective price positions based on the information. The device may generate a price elasticity curve for the vehicle based on the probabilities of sale. The device may generate a vehicle score represented by a supply line that is associated with the vehicle based on the information. The device may identify a first intersection of the supply line and the price elasticity curve, generate a market rank based on the intersection, and generate a recommended price for the vehicle based on the market rank as an input to a machine learning model.

Abstract: This disclosure describes systems, methods, and devices related to predictive modeling for evaluating vehicles. A device may receive vehicle information including a make, model, and year of a first vehicle. The device may determine a competitive set of vehicles that are of the same make, model, and year as the first vehicle, and that have with an odometer reading and a daily sale rate during a period of time. The device may determine, based on the odometer reading, an odometer-adjusted Cost to Market for the first vehicle. The device may determine, based on the daily sale rate during the period of time, a Market Day's Supply of the first vehicle. The device may determine, based on the Market Day's Supply and the odometer-adjusted Cost to Market, a vehicle score of the first vehicle. The device may send presentation data indicative of the vehicle score.