Computer-Based Method and Computer Program Product for Setting Floor Prices for Items Sold at Auction

• US 20120239582A1
• Filed: 03/15/2011
• Published: 09/20/2012
• Est. Priority Date: 03/15/2011
• Status: Active Grant

• Current Assignee: Electrifai LLC
• Original Assignee: Opera Solutions, LLC.
• Inventors: Solari, Soren, Spoelstra, Jacob, Nies, Craig
• Granted Patent: US 8,781,912 B2
• Time in Patent Office: Days
• US Class Current: 705/306
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

An adaptive method for estimating the selling price for an item at auction in order to set a reserve. The method calculates the selling price as a function of selling prices for items previously-sold at auction and differential values attributable to feature differences between the item to be sold and comparative items previously sold. Distance metrics are calculated by comparing the item to be sold with each item in the set of comparative items, and a subset of most similar items is selected according to the calculated distance metrics. A weighting function is then calculated for each item in the subset based on its respective distance metric value, and the selling price is estimated as a function of the weighting functions and the differential values. The differential values are modeled as a linear dynamical system and updated using a Kalman filter as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the differential values.

Claims

1. A computer-implemented method for setting a reserve price for an item to be sold at auction, the method performed on a server and comprising steps of:

identifying a plurality of features for evaluating predicted price differentials between the item to be sold and items in a set of comparative items that were previously sold;

evaluating the predicted price differentials between the item to be sold and the items in the set of comparative items based on estimated state values for the monetary value of the plurality of item features;

calculating values of a distance metric representing distances between the item to be sold and items in the set of comparative items, each calculated distance value indicating a degree of similarity between the item to be sold and a respective item in the set of comparative items;

selecting a subset of most similar items from the set of comparative items according to the calculated distances;

calculating a weighting function for each item in the subset of most similar items;

estimating the reserve price for the item to be sold as a function of the weighting functions and price differentials for the subset of most similar items;

transmitting the reserve price over a secure link to a client device;

updating the estimated state values as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the estimated state values; and

storing the updated state values in a database of the server.

2. The computer-implemented method of claim 1, wherein the item to be sold and the comparative items comprise vehicles.

3. The computer-implemented method of claim 2, wherein the vehicles comprise automobiles.

4. The computer-implemented method of claim 1, wherein the item to be sold and the comparative items comprise industrial equipment.

5. The computer-implemented method of claim 1, wherein the item to be sold and the comparative items comprise livestock lots.

6. The computer-implemented method of claim 1, wherein the item to be sold and the comparative items comprises real properties.

7. The computer-implemented method of claim 6, wherein the real properties comprise residential dwellings.

8. The computer-implemented method of claim 1, wherein the item to be sold and the comparative items comprise equity instruments.

9. The computer-implemented method of claim 1, wherein the equity instruments comprise stocks.

10. The computer-implemented method of claim 1, wherein the weighting function is calculated as an inverse function of the calculated distance value for the respective item.

11. The computer-implemented method of claim 2, further comprising the step of:

selecting items for the set of comparative items as vehicles sharing at least one of the plurality of features in common with a vehicle to be sold, the at least one feature including at least one of vehicle model and vehicle year of manufacture.

12. The computer-implemented method of claim 1, wherein the plurality of features for quantifying predicted price differentials are selectively identified by a seller of the item to be sold.

13. The computer-implemented method of claim 10, wherein values of the distance metric calculated for the item to be sold and each item in the set of comparative items are each computed as a function of absolute values of the predicted price differentials between the item to be sold and a respective item in the set of comparative items for each of the plurality of features.

14. The computer-implemented method of claim 13, wherein each item in the subset of most similar items is selected as having a calculated distance value in relation to the item to be sold that is less than a predetermined threshold value.

15. The computer-implemented method of claim 1, further comprising the steps of:

receiving a request transmitted by a web server for data relating to the price differentials; and

transmitting the requested date to the web server.

16. The computer-implemented method of claim 13, wherein a predetermined number of items are selected in the subset of most similar items, in a rank order proceeding from a lowest calculated distance value toward a highest calculated distance value.

17. The computer-implemented method of claim 10, wherein the reserve price is estimated by a linear model that combines the price differentials for the subset of most similar items proportionately according to the weighting functions for the items in the subset of most similar items.

18. The computer-implemented method of claim 1, wherein the estimated state values are updated by application of a Kalman filter.

27. The computer program product of claim 16, wherein the weighting function is calculated as an inverse function of the calculated distance for the respective item.

29. The computer program product of claim 27, wherein values of the distance metric calculated for the item to be sold and each item in the set of comparative items are each computed as a function of absolute values of the predicted price differentials between the item to be sold and a respective item in the set of comparative items for each of the plurality of features.

30. The computer program product of claim 27, wherein the reserve price is estimated by a linear model that combines the price differentials for the subset of most similar items proportionately according to the weighting functions for the items in the subset of most similar items.

19. A computer program product, comprising a non-transitory computer-usable storage medium having computer-readable instructions embodied therein, the computer-readable program code adapted to be executed by a server to implement a method for setting a reserve price for an item to be sold at auction, the method comprising the steps of:

identifying a plurality of features for evaluating predicted price differentials between the item to be sold and items in a set of comparative items that were previously sold;

evaluating the predicted price differentials between the item to be sold and the items in the set of comparative items based on estimated state values for the plurality of features;

calculating values of a distance metric representing distances between the item to be sold and items in the set of comparative items, each calculated distance value indicating a degree of similarity between the item to be sold and a respective item in the set of comparative items;

selecting a subset of most similar items from the set of comparative items according to the calculated distances;

calculating a weighting function for each item in the subset of most similar items;

estimating the reserve price for the item to be sold as a function of the weighting functions and price differentials for the subset of most similar items;

transmitting the reserve price over a secure link to a client device updating the estimated state values as a function of an actual sales price for the item to be sold and a current estimate of uncertainty for the estimated state values; and

storing the updated state values in a database of the server.

20. The computer program product of claim 19, wherein the item to be sold and the comparative items comprise vehicles.

21. The computer program product of claim 19, wherein the item to be sold and comparative items comprise industrial equipment.

22. The computer program product of claim 19, wherein the item to be sold and the comparative items comprise livestock lots.

23. The computer program product of claim 19, wherein the item to be sold and the comparative items comprises real properties.

24. The computer program product of claim 23, wherein the real properties comprise residential dwellings.

25. The computer program product of claim 19, wherein the item to be sold and the comparative items comprise equity instruments.

26. The computer program product of claim 25, wherein the equity instruments comprise stocks.

28. The computer program product of claim 20, further comprising the step of:

selecting items for the set of comparative items as vehicles sharing at least one of the plurality of features in common with the vehicle to be sold, the at least one feature including at least one of vehicle model and vehicle year of manufacture.

31. The computer program product of claim 19, wherein the estimated state values are updated by application of a Kalman filter.