PRICE SCORING FOR VEHICLES
Vehicle pricing such as used vehicle pricing is improved by supplementing statistical modeling techniques with additional algorithms to accommodate factors such as geography and dealer reputation that do not readily yield to regression analysis or similar tools that might be used to characterize a population.
1 . A method comprising:
receiving a request for vehicle information from a client;
ranking a number of vehicles responsive to the request based upon a relative value using a difference between a fair market price and a listing price for each of the number of vehicles, wherein the relative value is a dimensionless value normalized according to a standard deviation of prices for the number of vehicles, thereby providing a ranked list;
adjusting a position of one of the vehicles in the ranked list according to a dealer reputation for a dealer offering the one of the vehicles for resale, thereby providing an adjusted ranked list; and
transmitting one or more items in the adjusted ranked list to a client for display.
2 . The method of claim 1 wherein the fair market price for each of the number of vehicles is determined using a regression model.
3 . The method of claim 2 wherein the regression model uses a number of regression parameters including one or more of a vehicle type, a vehicle condition, a vehicle condition, and a vehicle trim.
4 . The method of claim 2 wherein the regression model uses a number of regression parameters including one or more of a vehicle fleet history, a repair history, and a flood damage history.
5 . The method of claim 2 further comprising retrieving vehicle listings from a plurality of online sources and creating the regression model using the vehicle listings.
6 . The method of claim 2 further comprising retrieving vehicle data for each one of the number of vehicles from one or more online sources.
7 . The method of claim 1 further comprising adjusting a position of each one of the vehicles in the ranked list according to a corresponding dealer reputation, thereby providing the adjusted ranked list.
8 . The method of claim 1 further comprising assigning a deal quality score to a portion of the adjusted ranked list and transmitting the deal quality score for one or more listings within the adjusted ranked list to the client.
9 . The method of claim 1 further comprising transmitting a number of surveys to a number of purchasers of vehicles and processing responses to the number of surveys to determine the dealer reputation for the dealer.
10 . The method of claim 1 wherein the request for vehicle information specifies at least one of a type, a trim, a year, and a mileage.
11 . A computer program product comprising computer executable code embodied in a non-transitory computer-readable medium that, when executing on one or more computing devices, performs the steps of:
receiving a request for vehicle information from a client;
ranking a number of vehicles responsive to the request based upon a relative value using a difference between a fair market price and a listing price for each of the number of vehicles, wherein the relative value is a dimensionless value normalized according to a standard deviation of prices for the number of vehicles, thereby providing a ranked list;
adjusting a position of one of the vehicles in the ranked list according to a dealer reputation for a dealer offering the one of the vehicles for resale, thereby providing an adjusted ranked list; and
transmitting one or more items in the adjusted ranked list to a client for display.
12 . The computer program product of claim 11 wherein the fair market price for each of the number of vehicles is determined using a regression model.
13 . The computer program product of claim 12 wherein the regression model uses a number of regression parameters including one or more of a vehicle type, a vehicle condition, a vehicle condition, and a vehicle trim.
14 . The computer program product of claim 12 wherein the regression model uses a number of regression parameters including one or more of a vehicle fleet history, a repair history, and a flood damage history.
15 . The computer program product of claim 12 further comprising retrieving vehicle listings from a plurality of online sources and creating the regression model using the vehicle listings.
16 . The computer program product of claim 12 further comprising retrieving vehicle data for each one of the number of vehicles from one or more online sources.
17 . The computer program product of claim 11 further comprising code that performs the step of adjusting a position of each one of the vehicles in the ranked list according to a corresponding dealer reputation, thereby providing the adjusted ranked list.
18 . A system comprising:
a database storing a regression model that characterizes a fair market value of a vehicle according to a number of regression parameters;
a server configured to receive a request from a client for vehicle information and to transmit to the client an adjusted ranked list responsive to the request; and
a processor configured to rank a number of vehicles responsive to the request based upon a relative value using a difference between a fair market price for each of the number of vehicles determined using the regression model and a listing price for each of the number of vehicles, wherein the relative value is a dimensionless value normalized according to a standard deviation of prices for the number of vehicles, thereby providing a ranked list, the processor further configure to adjust a position of one of the vehicles in the ranked list according to a dealer reputation for a dealer offering the one of the vehicles for resale, thereby providing the adjusted ranked list.
19 . The system of claim 18 further comprising a dealer evaluation module executable to transmit a survey to a purchaser of a vehicle and to process a survey response to determine the dealer reputation for the dealer.
20 . The system of claim 18 wherein the database stores a plurality of regression models for different vehicles, and wherein the processor is configured to select a best one of the plurality of regression models for a type of vehicle specified in the request.
21 - 40 . (canceled)