Snapshot approach for underwriting valuation of asset portfolios

• US 7,983,974 B2
• Filed: 08/14/2002
• Issued: 07/19/2011
• Est. Priority Date: 08/14/2002
• Status: Expired due to Fees

• Current Assignee: General Electric Company
• Original Assignee: GE Corporate Financial Services, Inc. (General Electric Company)
• Inventors: Doganaksoy, Murat, Keyes, Tim Kerry
• Primary Examiner(s): Kalinowski; Alexander
• Assistant Examiner(s): ONYEZIA, CHUKS N
• Application Number:
  US10/219,131
• Publication Number:
  US 20040034586A1
• Time in Patent Office: 3,261 Days
• Field of Search: 705/37
• US Class Current: 705/36.00R
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A method for valuing portfolio assets using a snapshot approach system is provided. The method includes segmenting portfolio assets into a predetermined number of segments based on financial attributes of each asset, selecting a representative sample of assets from each segment, valuing each asset in the representative asset sample, and calculating a value of the portfolio assets for bidding purposes based on the value of each asset in the representative asset sample.

Claims

1. A method for valuing a portfolio of assets having an undetermined value using a snapshot approach system, the snapshot approach system implemented using a computer coupled to a database, the computer configured to perform valuation process analytics, said method comprising:

assigning a plurality of predetermined financial attributes to each asset included within the portfolio;

categorizing the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

selecting a representative sample of assets having an undetermined value from each segment;

fully underwriting each asset included in the representative asset sample;

valuing each asset in the representative asset sample based on the underwriting process;

generating by the computer a valuation model based on the underwriting values and the financial attributes assigned to the assets included within the representative asset sample;

establishing a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determining the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determining the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculating a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determining by the computer a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria;

calculating by the computer a value of the portfolio of assets having an undetermined value for bidding purposes based on the generated valuation model if the stopping criteria is satisfied; and

if the stopping criteria is not satisfied, iteratively updating the generated valuation model by fully underwriting additional assets included within the portfolio but not included within the representative sample until the stopping criteria is satisfied.

2. The method according to claim 1 wherein categorizing the assets further comprises categorizing the assets into a predetermined number of segments based on the plurality of predetermined financial attributes of each asset wherein the financial attributes of each asset include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior.

3. The method according to claim 1 wherein categorizing the assets further comprises:

assigning a plurality of predetermined financial attributes to each asset included within the portfolio wherein the financial attributes include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior; and

categorizing each asset in the portfolio based on the financial attributes assigned to each asset.

4. The method according to claim 1 wherein selecting a representative sample of assets from each segment further comprises selecting a predetermined percentage of unpaid principal balance (“

UPB”

) of the assets.

5. The method according to claim 1 wherein valuing each asset in the representative asset sample further comprises performing an iterative and adaptive valuation in which each asset in the representative asset sample is individually valued.

6. The method according to claim 1 wherein calculating a value of the portfolio further comprises:

categorizing each asset in the representative asset sample based on asset characteristics such that each asset in the representative asset sample is categorized with assets included in the representative asset sample having similar asset characteristics;

determining that the stopping criteria is satisfied; and

valuing the portfolio for bidding purposes by comparing the asset characteristics of the assets in the representative asset sample to the assets included within the portfolio but not included within the representative sample and extrapolating the value of the assets included within the portfolio but not included within the representative sample from the value of each asset in the representative asset sample.

7. The method according to claim 6 further comprising storing the underwriting data in a centralized database.

8. The method according to claim 1 wherein fully underwriting each asset included in the representative asset sample further comprises:

collecting financial and business information relating to each asset in the representative asset sample;

performing collateral valuations for at least one asset in the representative asset sample; and

utilizing credit bureau information for at least one asset in the representative asset sample.

9. The method according to claim 1 wherein determining a predictive accuracy of the generated valuation model further comprises performing an R-Squared analysis on the generated valuation model to determine the predictive accuracy of the generated valuation model, wherein the R-Squared analysis calculates an amount of variation in the predictive results of the generated valuation model resulting from unknown causes.

10. The method according to claim 6 wherein determining that the stopping criteria is satisfied further comprises:

generating the valuation model for valuing the portfolio for bidding purposes by extrapolating the value of the assets included within the portfolio but not included within the representative sample from the value of the representative asset sample;

utilizing the valuation model to re-value the representative asset sample; and

determining whether the value of the representative asset sample based on underwriting data and the value of the representative asset sample based on the valuation model are substantially equal.

11. The method according to claim 1 wherein calculating a value of the portfolio for bidding purposes further comprises:

categorizing each asset in the representative asset sample based on asset characteristics such that each asset in the representative asset sample is categorized with assets having similar asset characteristics;

comparing the asset characteristics of the assets in the representative asset sample to the assets included within the portfolio but not included within the representative sample;

extrapolating the value of the assets included within the portfolio but not included within the representative sample from the value of each asset in the representative asset sample; and

calculating a total value of the portfolio based on the extrapolation.

12. A method for valuing a portfolio of assets having an undetermined value using a snapshot approach system, the snapshot approach system implemented using a computer coupled to a database, the computer configured to perform valuation process analytics, said method comprising:

assigning a plurality of predetermined financial attributes to each asset included within the portfolio, the plurality of financial attributes including an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior;

categorizing the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

selecting a representative sample of assets having an undetermined value from each segment;

performing by the computer an iterative and adaptive valuation in which each asset in the representative asset sample is individually valued, the iterative and adaptive valuation includes underwriting each asset in the representative asset sample to generate underwriting data, valuing each asset in the representative asset sample based on underwriting data, categorizing each asset in the representative asset sample based on asset characteristics such that each asset in the representative asset sample is categorized with assets included in the representative asset sample having similar asset characteristics, and generating a valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establishing a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determining the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determining the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculating a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determining by the computer a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria; and

valuing by the computer the portfolio of assets having an undetermined value for bidding purposes when the stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing the asset characteristics of the assets in the representative asset sample to the assets included within the portfolio but not included within the representative sample.

13. The method according to claim 12 further comprising:

increasing the size of the representative sample of assets when the stopping criteria is not satisfied;

performing an iterative and adaptive valuation in which each asset in the increased representative asset sample is individually valued, the iterative and adaptive valuation includes underwriting each asset in the increased representative asset sample to generate underwriting data, valuing each asset in the increased representative asset sample based on underwriting data, categorizing each asset in the increased representative asset sample based on asset characteristics such that each asset in the increased representative asset sample is categorized with assets included in the increased representative asset sample having similar asset characteristics, and generating an updated valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establishing a stopping criteria representing a predetermined amount of variance between an updated predicted value and an updated underwritten value of the increased representative asset sample, wherein the updated predicted value is determined from the generated updated valuation model, and wherein the updated underwritten value is determined from fully underwriting each asset in the increased representative asset sample;

determining a predictive accuracy of the generated updated valuation model including determining whether the generated valuation model satisfies the stopping criteria; and

valuing the portfolio of assets having an undetermined value for bidding purposes when the stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing the asset characteristics of the assets in the increased representative asset sample to the assets included within the portfolio but not included within the representative sample.

14. A portfolio valuation system for snapshot valuation of a portfolio of assets having an undetermined value, said system comprising:

a centralized database for storing information relating to the portfolio of assets having an undetermined value;

a server system coupled to said database and configured to perform valuation process analytics; and

at least one client system connected to said server system through a network, said server further configured to;

assign a plurality of predetermined financial attributes to each asset included within the portfolio;

categorize the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

select a representative sample of assets having an undetermined value from each segment;

prompt a user to fully underwrite each asset included in the representative asset sample;

value each asset in said representative asset sample based on the underwriting process;

generate a valuation model based on the underwriting values and the financial attributes assigned to the assets included within the representative asset sample;

establish a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determine the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determine the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculate a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determine a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria;

calculate a value of the portfolio of assets having an undetermined value for bidding purposes based on the generated valuation model if the stopping criteria is satisfied; and

if the stopping criteria is not satisfied, iteratively update the generated valuation model by fully underwriting additional assets included within the portfolio but not included within the representative sample until the stopping criteria is satisfied.

15. The system according to claim 14 wherein said server is further configured to categorize the assets into a predetermined number of segments based on the plurality of predetermined financial attributes of each asset wherein said financial attributes of each asset include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior.

16. The system according to claim 14 wherein said server is further configured to:

assign a plurality of predetermined financial attributes to each asset in said portfolio wherein said financial attributes include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior; and

categorize each asset in said portfolio based on said financial attributes assigned to each asset.

17. The system according to claim 14 wherein said server is further configured to select a representative sample of assets from each segment based on a predetermined percentage of unpaid principal balance (“

UPB”

) of said assets.

18. The system according to claim 14 wherein said server is further configured to perform an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued.

19. The system according to claim 14 wherein said server is further configured to:

categorize each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets in said representative asset sample having similar asset characteristics;

determine that the a stopping criteria is satisfied; and

value said portfolio for bidding purposes by comparing said asset characteristics of said assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample and extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample.

20. The system according to claim 19 wherein said server is further configured to store said underwriting data in said centralized database.

21. The system according to claim 19 wherein said server is further configured to fully underwrite each asset included in said representative asset sample by:

collecting financial and business information relating to each asset in said representative asset sample;

performing collateral valuations for at least one asset in said representative asset sample; and

utilizing credit bureau information for at least one asset in said representative asset sample.

22. The system according to claim 14 wherein said server is further configured to perform an R-Squared analysis on the generated valuation model to determine the predictive accuracy of the generated valuation model, wherein the R-Squared analysis calculates an amount of variation in the predictive results of the generated valuation model resulting from unknown causes.

23. The system according to claim 19 wherein said server is further configured to apply a stopping criteria by:

generating the valuation model for valuing said portfolio for bidding purposes by extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of said representative asset sample;

utilizing said valuation model to re-value said representative asset sample; and

determining whether the value of said representative asset sample based on said underwriting data and the value of said representative asset sample based on said valuation model are substantially equal.

24. The system according to claim 14 wherein said server is further configured to calculate a value of said assets for bidding purposes by:

categorizing each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets having similar asset characteristics;

comparing the asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample;

extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample; and

calculating a total value of said portfolio based on said extrapolation.

25. A portfolio valuation system for snapshot valuation of a portfolio of assets having an undetermined value, said system comprising:

a centralized database for storing information relating to the portfolio of assets having an undetermined value;

a server system coupled to said database and configured to perform valuation process analytics; and

at least one client system connected to said server system through a network, said server further configured to;

assign a plurality of predetermined financial attributes to each asset included within the portfolio, the plurality of financial attributes including an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior;

categorize the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

select a representative sample of assets from each segment;

perform an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said representative asset sample to generate underwriting data, valuing each asset in said representative asset sample based on underwriting data, categorizing each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets included in said representative asset sample having similar asset characteristics, and generating a valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establish a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determine the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determine the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculate a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determine a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria; and

value said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample.

26. The system according to claim 25 wherein said server is further configured to:

increase the size of the representative sample of assets when said stopping criteria is not satisfied;

perform an iterative and adaptive valuation in which each asset in said increased representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said increased representative asset sample to generate underwriting data, valuing each asset in said increased representative asset sample based on underwriting data, categorizing each asset in said increased representative asset sample based on asset characteristics such that each asset in said increased representative asset sample is categorized with assets included in said increased representative asset sample having similar asset characteristics, and generating an updated valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establish a stopping criteria representing a predetermined amount of variance between an updated predicted value and an updated underwritten value of the increased representative asset sample, wherein the updated predicted value is determined from the generated updated valuation model, and wherein the updated underwritten value is determined from fully underwriting each asset in the increased representative asset sample;

determine a predictive accuracy of the updated generated valuation model including determining whether the generated valuation model satisfies the stopping criteria; and

value said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said increased representative asset sample to said assets included within said portfolio but not included within said representative sample.

27. A system for snapshot valuation of a portfolio of assets having an undetermined value, said system comprising a computer including a database of the portfolio and configured to enable valuation process analytics, said computer configured to:

assign a plurality of predetermined financial attributes to each asset included within a portfolio;

categorize the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

select a representative sample of assets having an undetermined value from each segment;

prompt a user to fully underwrite each asset included in the representative asset sample;

value each asset in said representative asset sample based on the underwriting process;

generate a valuation model based on the underwriting values and the financial attributes assigned to the assets included within the representative asset sample;

establish a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determine the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determine the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculate a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determine a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria;

calculate a value of the portfolio of assets having an undetermined value for bidding purposes based on the generated valuation model if the stopping criteria is satisfied; and

if the stopping criteria is not satisfied, iteratively update the generated valuation model by fully underwriting additional assets included within the portfolio but not included within the representative sample until the stopping criteria is satisfied.

28. The system according to claim 27 wherein said computer is configured to categorize the assets into a predetermined number of segments based on the plurality of predetermined financial attributes of each asset wherein said financial attributes of each asset include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior.

29. The system according to claim 27 wherein said computer is configured to categorize the assets by:

assigning a plurality of predetermined financial attributes to each asset in said portfolio wherein said financial attributes include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior; and

categorizing each asset in said portfolio based on said financial attributes assigned to each portfolio asset.

30. The system according to claim 27 wherein said computer is configured to select a representative sample of assets from each segment based on a predetermined percentage of unpaid principal balance (“

UPB”

) of said assets.

31. The system according to claim 27 wherein said computer is configured to perform an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued.

32. The system according to claim 27 wherein said computer is configured to:

categorize each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets having similar asset characteristics;

determine that the stopping criteria is satisfied; and

value said portfolio for bidding purposes by comparing said asset characteristics of said assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample and extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample.

33. The system according to claim 32 wherein said computer is configured to store said underwriting data in a centralized database.

34. The system according to claim 27 wherein said computer is configured to prompt a user to fully underwrite each asset included in said representative asset sample by:

collecting financial and business information relating to each asset in said representative asset sample;

performing collateral valuations for at least one asset in said representative asset sample; and

utilizing credit bureau information for at least one asset in said representative asset sample.

35. The system according to claim 27 wherein said computer is configured to perform an R-Squared analysis on the generated valuation model to determine the predictive accuracy of the generated valuation model, wherein the R-Squared analysis calculates an amount of variation in the predictive results of the generated valuation model resulting from unknown causes.

36. The system according to claim 32 wherein said computer is configured to apply a stopping criteria by:

generating the valuation model for valuing said portfolio for bidding purposes by extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of said representative asset sample;

utilizing said valuation model to re-value said representative asset sample; and

determining whether the value of said representative asset sample based on said underwriting data and the value of said representative asset sample based on said valuation model are substantially equal.

37. The system according to claim 27 wherein said computer is configured to calculate a value of said portfolio for bidding purposes by:

segmenting each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets having similar asset characteristics;

comparing the asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample;

extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample; and

calculating a total value of said portfolio based on said extrapolation.

38. A system for snapshot valuation of a portfolio of assets having an undetermined value, said system comprising a computer including a database of the portfolio and configured to enable valuation process analytics, said computer configured to:

assign a plurality of predetermined financial attributes to each asset included within a portfolio, the plurality of financial attributes including an amount of unpaid principle balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior;

categorize the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

select a representative sample of assets having an undetermined value from each segment;

perform an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said representative asset sample to generate underwriting data, valuing each asset in said representative asset sample based on underwriting data, segmenting each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets included in said representative asset sample having similar asset characteristics, and generating a valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establish a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determine the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determine the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculate a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determine a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria; and

value said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample.

39. The system according to claim 38 wherein said computer is configured to:

increase the size of the representative sample of assets when said stopping criteria is not satisfied;

perform an iterative and adaptive valuation in which each asset in said increased representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said increased representative asset sample to generate underwriting data, valuing each asset in said increased representative asset sample based on underwriting data, categorizing each asset in said increased representative asset sample based on asset characteristics such that each asset in said increased representative asset sample is categorized with assets included in said increased representative asset sample having similar asset characteristics, and generating an updated valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establish a stopping criteria representing a predetermined amount of variance between an updated predicted value and an updated underwritten value of the increased representative asset sample, wherein the updated predicted value is determined from the generated updated valuation model, and wherein the updated underwritten value is determined from fully underwriting each asset in the increased representative asset sample;

determine a predictive accuracy of the generated updated valuation model including determining whether the generated valuation model satisfies the stopping criteria; and

value said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said increased representative asset sample to said assets included within said portfolio but not included within said representative sample.

40. A non-transitory computer readable medium comprising a computer program embodied thereon for performing snapshot valuation of a portfolio of assets having an undetermined value, said computer program comprising computer code stored on the computer readable medium and executable by a computer system that:

assigns a plurality of predetermined financial attributes to each asset included within the portfolio;

categorizes the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

selects a representative sample of assets having an undetermined value from each segment;

prompts a user to fully underwrite each asset included in the representative asset sample;

values each asset in said representative asset sample based on the underwriting process;

generates a valuation model based on the underwriting values and the financial attributes assigned to the assets included within the representative asset sample;

establishes a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determines the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determines the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculates a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determines a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria;

calculates a value of the portfolio of assets having an undetermined value for bidding purposes based on the generated valuation model if the stopping criteria is satisfied; and

if the stopping criteria is not satisfied, iteratively updates the generated valuation model by fully underwriting additional assets included within the portfolio but not included within the representative sample until the stopping criteria is satisfied.

41. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that categorizes the assets into a predetermined number of segments based on the plurality of predetermined financial attributes of each asset wherein said financial attributes of each asset include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior.

42. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that categorizes the assets by:

assigning a plurality of predetermined financial attributes to each asset in said portfolio wherein said financial attributes include at least one of an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior; and

categorizing each asset in said portfolio based on said financial attributes assigned to each asset.

43. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that selects a representative sample of assets from each segment based on a predetermined percentage of unpaid principal balance (“

UPB”

) of said assets.

44. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that performs an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued.

45. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that:

categorizes each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets having similar asset characteristics;

determines that the stopping criteria is satisfied; and

valuing said portfolio for bidding purposes by comparing said asset characteristics of said assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample and extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample.

46. The non-transitory computer readable medium according to claim 45 wherein said computer program further comprises computer code that stores said underwriting data in a centralized database.

47. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that fully underwrites each asset included in said representative asset sample by:

collecting financial and business information relating to each asset in said representative asset sample;

performing collateral valuations for at least one asset in said representative asset sample; and

utilizing credit bureau information for at least one asset in said representative asset sample.

48. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that performs an R-Squared analysis on the generated valuation model to determine the predictive accuracy of the generated valuation model, wherein the R-Squared analysis calculates an amount of variation in the predictive results of the generated valuation model resulting from unknown causes.

49. The non-transitory computer readable medium according to claim 45 wherein said computer program further comprises computer code that applies a stopping criteria by:

generating the valuation model for valuing said portfolio for bidding purposes by extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of said representative asset sample;

utilizing said valuation model to re-value said representative asset sample; and

determining whether the value of said representative asset sample based on said underwriting data and the value of said representative asset sample based on said valuation model are substantially equal.

50. The non-transitory computer readable medium according to claim 40 wherein said computer program further comprises computer code that calculates a value of said portfolio for bidding purposes by:

categorizing each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets having similar asset characteristics;

comparing the asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample;

extrapolating the value of said assets included within said portfolio but not included within said representative sample from the value of each asset in said representative asset sample; and

calculating a total value of said portfolio based on said extrapolation.

51. A non-transitory computer readable medium comprising a computer program embodied thereon for performing snapshot valuation of a portfolio of assets having an undetermined value, said computer program comprising computer code stored on the computer readable medium and executable by a computer system that:

assigns a plurality of predetermined financial attributes to each asset included within the portfolio, the plurality of financial attributes including an amount of unpaid principal balance (“

UPB”

), a collateral type, whether the loan is secured, whether the loan is unsecured, real estate security, non-real estate security, a litigation status, a borrower operational status, and payment behavior;

categorizes the assets into a predetermined number of segments based on the assigned financial attributes of each asset;

selects a representative sample of assets having an undetermined value from each segment;

performs an iterative and adaptive valuation in which each asset in said representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said representative asset sample to generate underwriting data, valuing each asset in said representative asset sample based on underwriting data, categorizing each asset in said representative asset sample based on asset characteristics such that each asset in said representative asset sample is categorized with assets included in said representative asset sample having similar asset characteristics, and generating a valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establishes a stopping criteria that equals a predetermined amount of variance between a predicted value and an underwritten value of the representative asset sample;

determines the predicted value of the representative asset sample by applying the generated valuation model to the assets included within the representative asset sample;

determines the underwritten value of the representative asset sample from the underwriting values of the assets included within the representative asset sample;

calculates a model delta by comparing the predicted value of the representative asset sample to the underwritten value of the representative asset sample;

determines a predictive accuracy of the generated valuation model by comparing the model delta to the stopping criteria and determining whether the generated valuation model satisfies the stopping criteria; and

values said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said representative asset sample to said assets included within said portfolio but not included within said representative sample.

52. The non-transitory computer readable medium according to claim 51 wherein said computer program further comprises computer code that:

increases the size of the representative sample of assets when said stopping criteria is not satisfied;

performs an iterative and adaptive valuation in which each asset in said increased representative asset sample is individually valued, said iterative and adaptive valuation comprises underwriting each asset in said increased representative asset sample to generate underwriting data, valuing each asset in said increased representative asset sample based on underwriting data, categorizing each asset in said increased representative asset sample based on asset characteristics such that each asset in said increased representative asset sample is categorized with assets included in said increased representative asset sample having similar asset characteristics, and generating an updated valuation model based on the underwriting values and asset characteristics of the assets included within the representative asset sample;

establishes a stopping criteria representing a predetermined amount of variance between an updated predicted value and an updated underwritten value of the increased representative asset sample, wherein the updated predicted value is determined from the generated updated valuation model, and wherein the updated underwritten value is determined from fully underwriting each asset in the increased representative asset sample;

determines a predictive accuracy of the generated updated valuation model including determining whether the generated valuation model satisfies the stopping criteria; and

values said portfolio of assets having an undetermined value for bidding purposes when said stopping criteria is satisfied by applying the generated valuation model to the portfolio and by comparing asset characteristics of the assets in said increased representative asset sample to said assets included within said portfolio but not included within said representative sample.