System and method for estimating forward retail commodity price within a geographic boundary

• US 8,019,694 B2
• Filed: 02/12/2008
• Issued: 09/13/2011
• Est. Priority Date: 02/12/2007
• Status: Expired due to Fees

• Current Assignee: Pricelock, Inc.
• Original Assignee: Pricelock, Inc.
• Inventors: Gros, Thomas D., Painter, Scott, Fell, Robert M., Bonsignore, Michael R., Magnuson, Gary A., Reed, Brian P.
• Primary Examiner(s): Erb; Nathan
• Application Number:
  US12/030,086
• Publication Number:
  US 20080313013A1
• Time in Patent Office: 1,309 Days
• Field of Search: 705/400
• US Class Current: 705/400
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Embodiments disclosed herein provide a new way to generate estimated forward retail prices for a retail commodity within a geographic boundary that represents a target market. Using estimates for local retail prices, combined with knowledge of current and historical wholesale prices, embodiments disclosed herein enable the creation of a forward estimate of retail prices on fuels for a specific location, time period, and fuel grade. In some embodiment, the process of creating a forward estimate of retail prices on fuels comprises performing a predictive modeling utilizing wholesale gasoline prices, rack markup, retail markup, and taxes on a location, time period, and fuel grade basis. In some cases, the estimated forward retail prices thus generated can be used in a pricing model for price protection services for that retail commodity in that target market.

Claims

1. A method for determining a forward retail price of a commodity, comprising:

determining, by a computer system comprising a processor and a non-transitory computer-readable medium, a current retail price of the commodity for a specific location within a geographic boundary, wherein the current retail price of the commodity comprises a wholesale price component, a total markup component, and a tax component, and wherein the total markup component comprises a rack markup component and a retail markup component;

subtracting, by the computer system, an estimated total markup of the commodity for the specific location from an aggregated composite retail price of the commodity for the specific location to generate an estimated current wholesale price of the commodity for the specific location; and

creating, by the computer system, a forward market curve indicating forward retail price of the commodity for a corresponding period of time within the geographic boundary, wherein creating the forward market curve comprises;

determining, by the computer system, a rate of change between retail pricing and wholesale pricing for the commodity over a past period of time for each location within the geographic boundary on a monthly basis;

forecasting, by the computer system, future wholesale prices and estimated total markup for each location within the geographic boundary on a monthly basis;

calculating, by the computer system, an estimated forward retail price for each location within the geographic boundary on a monthly basis based on a plurality of factors, wherein the plurality of factors includes the current retail price of the commodity, the estimated current wholesale price of the commodity, and the rate of change; and

aggregating, by the computer system, the estimated forward retail price, location by location, within the geographic boundary on a monthly basis.

2. The method of claim 1, wherein the plurality of factors includes market conditions, wherein the market conditions include contango and backwardation, wherein contango describes a market where spot prices of the commodity are lower than future prices of the commodity, and wherein backwardation describes a market where spot prices of the commodity are higher than future prices of the commodity.

3. The method of claim 1, wherein the commodity is a specific grade of motor fuel.

4. The method of claim 1, wherein the total markup component is determined by extrapolating, by the computer system, historical differences between retail pricing and wholesale pricing for the commodity based on historical data from a plurality of markets over a past period of time.

5. The method of claim 1, wherein the total markup component is determined by estimating, by the computer system, a difference between the wholesale price component and an aggregated composite retail price for the commodity.

6. The method of claim 1, wherein the commodity is gasoline, further comprising weighting, by the computer system, the estimated forward retail price based on gas purchase volumes by fuel type per each location within the geographic boundary.

7. The method of claim 1, further comprising determining, by the computer system, an additional markup applicable to the estimated forward retail price for each location within the geographic boundary.

8. The method of claim 7, wherein at least one additional markup is determined based on a user-defined event with respect to an event location.

9. The method of claim 7, further comprising applying, by the computer system, a weighting factor to the additional markup.

10. A non-transitory computer-readable medium storing program instructions executable by a processor to perform the steps comprising:

determining a current retail price of a commodity for a specific location within a geographic boundary, wherein the current retail price of the commodity comprises a wholesale price component, a total markup component, and a tax component, and wherein the total markup component comprises a rack markup component and a retail markup component;

subtracting an estimated total markup of the commodity for the specific location from an aggregated composite retail price of the commodity for the specific location to generate an estimated current wholesale price of the commodity for the specific location; and

creating a forward market curve indicating forward retail price of the commodity for a corresponding period of time within the geographic boundary, wherein creating the forward market curve comprises;

determining a rate of change between retail pricing and wholesale pricing for the commodity over a past period of time for each location within the geographic boundary on a monthly basis;

forecasting future wholesale prices and estimated total markup for each location within the geographic boundary on a monthly basis;

calculating an estimated forward retail price for each location within the geographic boundary on a monthly basis based on a plurality of factors, wherein the plurality of factors includes the current retail price of the commodity, the estimated current wholesale price of the commodity, and the rate of change; and

aggregating the estimated forward retail price, location by location, within the geographic boundary on a monthly basis.

11. The non-transitory computer-readable medium of claim 10, wherein the retail commodity is gasoline and wherein the program instructions are further executable by the processor to perform estimating the wholesale price changes per fuel type or grade.

12. The non-transitory computer-readable medium of claim 10, wherein the program instructions are further executable by the processor to perform adjusting the estimated forward retail price based on one or more market conditions.

13. The non-transitory computer-readable medium of claim 10, wherein the program instructions are further executable by the processor to perform weighting the estimated forward retail price based on gas purchase volumes by fuel type per each location within the geographic boundary.

14. The non-transitory computer-readable medium of claim 10, wherein the program instructions are further executable by the processor to perform adding at least one additional markup to the estimated forward retail price for each location within the geographic boundary based on a user-defined event with respect to an event location.

15. The non-transitory computer-readable medium of claim 14, wherein the program instructions are further executable by the processor to perform applying a weighting factor to the at least one additional markup.

16. The non-transitory computer-readable medium of claim 10, wherein the program instructions are further executable by the processor to generate a forward market curve of an expected retail price for the retail commodity within the geographic boundary for at least one year based on the estimated forward retail price aggregated location by location within the geographic boundary on a monthly basis.

17. A system comprising:

a processor; and

a non-transitory computer-readable medium, communicatively coupled to the processor, storing program instructions executable by the processor to perform the steps comprising;

determining a current retail price of a commodity for a specific location within a geographic boundary, wherein the current retail price of the commodity comprises a wholesale price component, a total markup component, and a tax component, and wherein the total markup component comprises a rack markup component and a retail markup component;

subtracting an estimated total markup of the commodity for the specific location from an aggregated composite retail price of the commodity for the specific location to generate an estimated current wholesale price of the commodity for the specific location; and

creating a forward market curve indicating forward retail price of the commodity for a corresponding period of time within the geographic boundary, wherein creating the forward market curve comprises;

determining a rate of change between retail pricing and wholesale pricing for the commodity over a past period of time for each location within the geographic boundary on a monthly basis;

forecasting future wholesale prices and estimated total markup for each location within the geographic boundary on a monthly basis;

calculating an estimated forward retail price for each location within the geographic boundary on a monthly basis based on a plurality of factors, wherein the plurality of factors includes the current retail price of the commodity, the estimated current wholesale price of the commodity, and the rate of change; and

aggregating the estimated forward retail price, location by location, within the geographic boundary on a monthly basis.

18. The system of claim 17, wherein the retail commodity is gasoline and wherein the program instructions are further executable by the processor to perform estimating the wholesale price changes per fuel type or grade.

19. The system of claim 17, wherein the program instructions are further executable by the processor to perform adjusting the estimated forward retail price based on one or more market conditions.

20. The system of claim 17, wherein the program instructions are further executable by the processor to perform weighting the estimated forward retail price based on gas purchase volumes by fuel type per each location within the geographic boundary.