System and method for evaluating residual values of products

• US 7,546,273 B2
• Filed: 08/28/2002
• Issued: 06/09/2009
• Est. Priority Date: 08/30/2001
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Blanchard, Jean Louis, Messatfa, Hammou, Lorin, Stephane, Pavillon, Christelle
• Primary Examiner(s): Kramer; James
• Assistant Examiner(s): Khattar; Rajesh
• Application Number:
  US10/229,540
• Publication Number:
  US 20030074296A1
• Time in Patent Office: 2,477 Days
• Field of Search: 705 35- 45
• US Class Current: 705/40
• CPC Class Codes: G06Q 20/102 Bill distribution or payments
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A method and system to evaluate residual value of leased products, comprising a learning phase and an application phase. Each phase has two steps. The first consists in analyzing lease or product main parameter while the second uses other lease conditions or product characteristics. Analysis is based on a grid wherein several specific points are determined during the learning phase. An extrapolation of these points according to the parameters during the application phase gives a first approximation of residual value, which is adjusted using lease conditions or product characteristics. During the learning phase, the system determines the relevant lease conditions or product characteristics and the associated corrections used during the application phase.

Claims

1. A method for use in a data processing system to learn residual value factors of leased products, using a learning database containing lease durations and lease mileage and resale value factors associated therewith, comprising the steps of:

selecting by the data processing system at least one lease duration and lease mileage of said learning database;

specifying by the data processing system values of said at least one lease duration and lease mileage, determining residual value factors associated with said specified values of said at least one lease duration and lease mileage from said learning database and building a first table storing said specified values of said at least one lease duration and lease mileage and said residual value factors associated therewith;

estimating by the data processing system first residual value factors for records of said learning database from said first table;

selecting by the data processing system at least one modality comprising a lease condition or product characteristic of said learning database, determining residual value factor corrections associated with the selected modality of the lease condition or product characteristic and said first residual value factors, and building at least one other table storing the selected modality and said residual value factor corrections associated therewith; and

determining by the data processing system a residual value factor of a leased product by adding a first residual value factor estimation from the first table with at least one residual value factor correction from the at least one other table, the at least one residual value factor correction being based upon a modality of the leased product,

wherein said first and at least one other table characterize residual value factor behavior of the products contained in said learning database, and

wherein said steps of determining residual value factors and building said first table comprise the steps of;

setting a window value for each of said at least one lease duration and lease mileage;

for each of said specified values of said at least one lease duration and lease mileage;

storing said specified value in said first table;

selecting records of a learning database whose values according to said at least one lease duration and lease mileage belong to a range defined by said specified value and said window values;

extrapolating a residual value factor RV(D,M) from the residual value factors associated with said selected records according to the equation

$\begin{array}{cc}\mathrm{RV}<br><br>\left(D,M\right)={\mathrm{RV}}^{\mathrm{″<br><br>}}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right)·<br><br>\frac{d<br><br>\mathrm{xRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)\right)}·<br><br>\frac{d<br><br>\mathrm{yRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)\right)}+{\mathrm{RV}}^{\mathrm{″<br><br>}}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)·<br><br>\frac{d<br><br>\mathrm{xRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)\right)}·<br><br>\frac{d<br><br>\mathrm{yRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)\right)}+{\mathrm{RV}}^{\mathrm{″<br><br>}}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)·<br><br>\frac{d<br><br>\mathrm{xRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)\right)}·<br><br>\frac{d<br><br>\mathrm{yRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)\right)}+{\mathrm{RV}}^{\mathrm{″<br><br>}}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}+\right)·<br><br>\frac{d<br><br>\mathrm{xRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}+,j^{\mathrm{\prime <br><br>}}-\right)\right)}·<br><br>\frac{d<br><br>\mathrm{yRV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right)}{d<br><br>\left(\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}-\right),\mathrm{RV}<br><br>\left(i^{\mathrm{\prime <br><br>}}-,j^{\mathrm{\prime <br><br>}}+\right)\right)}& \left(14\right)\end{array}$

wherein

RV″

(D,M) is a provisional residual value factor corresponding to a lease duration D and a lease mileage M,

i and j are index value integers defining a cell of lease duration value and a lease mileage value having a specified range defined by said window values, respectively,

i−

corresponds to the maximum lease duration value of the boundary of the cell that is less than the input lease duration,

i+ corresponds to the minimum lease duration value of the boundary of the cell that is greater than input lease duration,

j−

corresponds to the maximum lease mileage value of the boundary of the cell that is less than the input lease mileage,

j+ corresponds to the minimum lease mileage value of the boundary of the cell that is greater than the input lease mileage,

dxRV(i,j) is the signed distance along the lease duration axis between the point for which the residual value factor is being evaluated and point (i,j), expressed in years,

dyRV(i,j) is the signed distance along the lease mileage axis between the point for which the residual value factor is being evaluated and point (i,j), expressed in miles,

d(RV(i,j),RV(k,l)) is the distance between points (i,j) and (k,l), expressed in (years²+miles²)^1/2and

storing by the data processing system said extrapolated residual value factor in said first table in association with said specified value.

2. The method of claim 1, wherein said extrapolated residual value factor is a median value of the residual value factors associated with said selected records.

3. The method of claim 1, wherein said step of estimating first residual value factors comprises the steps of:

for each record of said learning database;

selecting specified values of said first table bordering the value of said record corresponding to said at least one lease duration and lease mileage; and

,

extrapolating a first residual value factor from the residual value factors associated to said selected specified values of said first table.

4. The method of claim 3, wherein said extrapolation is a linear extrapolation.

5. The method of claim 1, wherein said step of estimating first residual value factors comprises the steps of:

for each record of said learning database, comparing the value of said record corresponding to said at least one lease duration and lease mileage with said specified values of said first table; and

,

when said value of said record corresponding to said at least one lease duration and lease mileage is bordered on specified values of said first table, determining a first residual value factor of said record by performing a linear extrapolation on residual value factors associated with specified values of said first table bordering said value of said record corresponding to said at least one lease duration and lease mileage,

else, determining a first residual value factor of said record by performing a linear extrapolation on residual value factors associated with specified values of said first table bordering said value of said record corresponding to said at least one lease duration and lease mileage wherein first residual value factors of missing bordering specified values of said first table are estimated.

6. The method of claim 1, wherein said step of selecting at least one modality comprising a lease condition or product characteristic of said learning database, determining residual value factor corrections associated with the selected modality and said first residual value factors and building at least one other table storing the selected modality and said residual value factor corrections associated therewith, comprises the steps of:

determining an error criterion to evaluate lease duration and lease mileage influence on residual value factor correction;

setting an error variable to the greatest reachable error value;

selecting a first variable to be tested;

selecting a modality of said selected first variable to be tested and the learning database records that correspond to said selected modality;

determining a residual value factor correction according to a difference between first residual value factor estimations and resale value factors of said selected records corresponding to said selected modality;

repeating the preceding two steps until all modalities of said selected variable to be tested have been selected;

estimating the error associated with said selected variable;

when said estimated error is lower than said error variable;

setting said error variable to said estimated error;

storing a reference of said selected variable;

storing modalities of said selected variable and associated correction in said at least one other table; and

,

repeating the above steps until all the variables to be tested have been selected.

7. The method of claim 6, wherein said determined residual value factor correction is a median value of the differences between first residual value factor estimations and resale value factors of said selected records corresponding to said selected modality.

8. The method of claim 1, further comprises a step analyzing said learning database records to detect aberrant records, and isolating the aberrant records from said learning database.

9. The method of claim 1, wherein said specified values of said at least one lease duration and lease mileage are determined so that learning database records are uniformly distributed in said first table.

10. The method of claim 1, further comprising the steps of:

inputting a value of said leased product corresponding to said at least one lease duration and lease mileage;

determining a first residual value factor estimation from said first table;

inputting the value of said leased product corresponding to said at least one other lease condition or product characteristic; and

determining at least one residual value factor correction from said at least one other table.

11. The method of claim 10, wherein said step of determining a first residual value factor from said first table, comprises the steps of:

selecting specified values of said first table bordering the value of the leased product corresponding to said at least one lease duration and lease mileage; and

,

extrapolating a first residual value factor from the residual value factors associated with said selected specified values of said first table.

12. The method of claim 11, wherein said extrapolation is a linear extrapolation.

13. The method of claim 10, wherein said step of determining a first residual value factor from said first table, comprises the steps of:

comparing the value of said leased product corresponding to said at least one lease duration and lease mileage with said specified values of first table; and

,

when said value of said leased product corresponding to said at least one lease duration and lease mileage borders specified values of said first table, determining a first residual value factor of said leased product by performing a linear extrapolation on residual value factors associated with specified values of said first table bordering said value of said leased product corresponding to said at least one lease duration and lease mileage;

else, determining a first residual value factor of said leased product by performing a linear extrapolation on residual value factors associated with specified values of said first table bordering said value of said leased product corresponding to said at least one lease duration and lease mileage wherein residual value factors of missing bordering specified values of said first table are estimated.

14. The method of claim 1, wherein said leased products are vehicles.