SYSTEM AND METHOD FOR ANALYSIS AND PRESENTATION OF USED VEHICLE PRICING DATA

• US 20140114726A1
• Filed: 12/31/2013
• Published: 04/24/2014
• Est. Priority Date: 07/28/2011
• Status: Active Grant

• Current Assignee: TrueCar, Inc.
• Original Assignee: TrueCar, Inc.
• Inventors: Swinson, Michael D., Laughlin, Isaac Lemon, Semeniuk, Mikhail, Liu, Xingchu, Ramanuja, Meghashyam Grama
• Granted Patent: US 10,108,989 B2
• Time in Patent Office: Days
• US Class Current: 705/7.35
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 30/0278 Product appraisal

Abstract

Systems and methods for the aggregation, analysis, and display of data for used vehicles are disclosed. Historical transaction data for used vehicles may be obtained and processed to determine pricing data, where this determined pricing data may be associated with a particular configuration of a vehicle. The user can then be presented with an interface pertinent to the vehicle configuration utilizing the aggregated data set or the associated determined data where the user can make a variety of determinations. This interface may, for example, be configured to present the historical transaction data visually, with the pricing data such as a trade-in price, a list price, an expected sale price or range of sale prices, market low sale price, market average sale price, market high sale price, etc. presented relative to the historical transaction data.

Claims

1. A method for pricing a used vehicle, comprising:

receiving, by a process embodied on a non-transitory computer readable medium, user input data from a client device, the user input data comprising information about a used vehicle in a locale;

the process receiving or obtaining socio-economic data specific to the locale;

constructing regression variables tailored to the used vehicle based the information about the used vehicle in the locale and the socio-economic data specific to the locale;

based on historical transactions that occurred within a predetermined distance of the locale and that involved vehicles of a given year, make, and model that is same or similar to the used vehicle, determining an average residual value for the given year, make, and model;

determining a final price for the used vehicle using the average residual value from the historical transactions and the regression variables tailored to the used vehicle; and

communicating the final price for the used vehicle to the client device.

2. The method according to claim 1, further comprising:

presenting an interface on the client device, the interface configured for allowing a user to describe the used vehicle in detail, including year, make, model, trim, mileage, and condition for the used vehicle.

3. The method according to claim 1, further comprising:

subsequent to receiving the user input data from the client device, determining the historical transactions from a collection of temporally weighted historical observations.

4. The method according to claim 1, further comprising:

determining an average days-to-sell based on the historical transactions; and

including the average days-to-sell on a price report for the used vehicle.

5. The method according to claim 1, further comprising:

prior to determining the average residual value for the given year, make, and model, determining whether the historical transactions are sufficient to cover the predetermined distance of the locale for the given year, make, and model; and

if the historical transactions are insufficient, performing a clustering process to include additional historical transactions involving vehicle models most similar to the used vehicle.

6. The method according to claim 1, further comprising:

normalizing the final price for the used vehicle against a price ratio of vehicles within the predetermined distance of the locale.

7. The method according to claim 1, wherein regression coefficients for the regression variables are determined independently of the receiving the user input data from the client device.

8. A computer program product comprising at least one non-transitory computer-readable medium storing instructions executable by a processor for:

receiving user input data from a client device, the user input data comprising information about a used vehicle in a locale;

receiving or obtaining socio-economic data specific to the locale;

constructing regression variables tailored to the used vehicle based the information about the used vehicle in the locale and the socio-economic data specific to the locale;

based on historical transactions that occurred within a predetermined distance of the locale and that involved vehicles of a given year, make, and model that is same or similar to the used vehicle, determining an average residual value for the given year, make, and model;

determining a final price for the used vehicle using the average residual value from the historical transactions and the regression variables tailored to the used vehicle; and

communicating the final price for the used vehicle to the client device.

9. The computer program product of claim 8, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

presenting an interface on the client device, the interface configured for allowing a user to describe the used vehicle in detail, including year, make, model, trim, mileage, and condition for the used vehicle.

10. The computer program product of claim 8, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

subsequent to receiving the user input data from the client device, determining the historical transactions from a collection of temporally weighted historical observations.

11. The computer program product of claim 8, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

determining an average days-to-sell based on the historical transactions; and

including the average days-to-sell on a price report for the used vehicle.

12. The computer program product of claim 8, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

prior to determining the average residual value for the given year, make, and model, determining whether the historical transactions are sufficient to cover the predetermined distance of the locale for the given year, make, and model; and

if the historical transactions are insufficient, performing a clustering process to include additional historical transactions involving vehicle models most similar to the used vehicle.

13. The computer program product of claim 8, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

normalizing the final price for the used vehicle against a price ratio of vehicles within the predetermined distance of the locale.

14. A system for pricing a used vehicle, comprising:

a processor; and

at least one non-transitory computer-readable medium storing instructions executable by the processor for;

receiving user input data from a client device, the user input data comprising information about a used vehicle in a locale;

receiving or obtaining socio-economic data specific to the locale;

constructing regression variables tailored to the used vehicle based the information about the used vehicle in the locale and the socio-economic data specific to the locale;

based on historical transactions that occurred within a predetermined distance of the locale and that involved vehicles of a given year, make, and model that is same or similar to the used vehicle, determining an average residual value for the given year, make, and model;

determining a final price for the used vehicle using the average residual value from the historical transactions and the regression variables tailored to the used vehicle; and

communicating the final price for the used vehicle to the client device.

15. The system of claim 14, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

presenting an interface on the client device, the interface configured for allowing a user to describe the used vehicle in detail, including year, make, model, trim, mileage, and condition for the used vehicle.

16. The system of claim 14, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

subsequent to receiving the user input data from the client device, determining the historical transactions from a collection of temporally weighted historical observations.

17. The system of claim 14, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

determining an average days-to-sell based on the historical transactions; and

including the average days-to-sell on a price report for the used vehicle.

18. The system of claim 14, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

prior to determining the average residual value for the given year, make, and model, determining whether the historical transactions are sufficient to cover the predetermined distance of the locale for the given year, make, and model; and

if the historical transactions are insufficient, performing a clustering process to include additional historical transactions involving vehicle models most similar to the used vehicle.

19. The system of claim 14, wherein the at least one non-transitory computer-readable medium further stores instructions executable by the processor for:

normalizing the final price for the used vehicle against a price ratio of vehicles within the predetermined distance of the locale.

20. The system of claim 14, wherein regression coefficients for the regression variables are determined independently of the receiving the user input data from the client device.