Method and apparatus for constructing a forecast standard deviation for automated valuation modeling

• US 20060085234A1
• Filed: 09/17/2004
• Published: 04/20/2006
• Est. Priority Date: 09/17/2004
• Status: Abandoned Application

• Current Assignee: CoreLogic Information Solutions Incorporated (CoreLogic Incorporated)
• Original Assignee: First American Real Estate Solutions LLC (CoreLogic Incorporated)
• Inventors: Cagan, Christopher L.
• Application Number:
  US10/944,593
• Publication Number:
  US 20060085234A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06Q 30/0278 Product appraisal
• CPC Class Codes: G06Q 50/16 Real estate

Abstract

A method and apparatus for deriving Sigmas or forecast standard deviations for valuations of properties valued by an automated valuation model without reference to the underlying mathematical architecture and without reference to a particular data structure of the automated valuation model and for providing right-tail and responsive confidence scores consistent with these Sigmas for each property valued by an automated valuation model.

Claims

1. A computer-based method of calculating a forecast standard deviation for at least one property evaluated by an automated valuation model and located in a predetermined geographic area comprising the steps of:

categorizing a plurality of properties into at least one group of properties in said predetermined geographic area; and

calculating a standard deviation for said at least one property from individual reference values associated with said plurality of properties in said at least one group to thereby calculate a forecast standard deviation.

2. The method of claim 1, further comprising the step of applying said standard deviation for said at least one group to each of said plurality of properties.

3. The method of claim 1, wherein said plurality of properties are categorized into at least one group using a raw confidence score of said at least one property.

4. The method of claim 1, wherein said plurality of properties are categorized into at least one group using the confidence scores of said plurality of properties.

5. The method of claim 1, wherein said plurality of properties are categorized into at least one group using the state of said at least two properties.

6. The method of claim 1, wherein said plurality of properties are categorized into at least one group using the county of said at least two properties.

7. The method of claim 1, wherein said plurality of properties are categorized into at least one group using the land-use type of said at least two properties.

8. The method of claim 1, wherein said plurality of properties are categorized into at least one group using the economic tier of said at least two properties.

9. The method of claim 1, wherein said individual reference value is a property sale price.

10. The method of claim 1, wherein said individual reference value is an appraised value.

11. The method of claim 1, wherein said calculating step further comprises the validation of said forecast standard deviation for accuracy.

12. The method of claim 11, wherein said validation step comprises:

expressing the variances between the valuations generated by an automated valuation model and the reference values of properties in Sigma units;

deriving a measure of dispersion of said variances in Sigma units;

comparing said measure of dispersion of said variances in Sigma units to an accuracy range;

correcting said forecast standard deviation using said measure of dispersion; and

returning a validated forecast standard deviation.

13. The method of claim 12, wherein said measure of dispersion is a standard deviation.

14. The method of claim 11, wherein said correcting step is accomplished by multiplying said forecast standard deviation by said measure of dispersion.

15. The method of claim 1, wherein said calculating step uses the equation:

Forecast Standard Deviation=√

{[Σ

(v−

0)²]/(n−

1)}

Wherein

v is the Individual Valuation Variances described by the equation v=(x−

p)/p;

x is the automated valuation of each individual property in said group of properties;

p is a reference value for each individual property in said group of properties; and

n is the total number of properties in said group.

16. The method of claim 1, further comprising the step of presenting said forecast standard deviation aggregate data in terms of percentiles.

17. A computer-based method of generating a right-tail confidence score for a valuation of a subject property evaluated using an automated valuation model comprising the steps of:

obtaining a forecast standard deviation;

dividing a right-tail cutoff number by said forecast standard deviation to compute a corresponding right-tail cutoff number in Sigma units; and

correlating said corresponding right-tail cutoff number in Sigma units with a right-tail confidence score using a table of percentiles.

18. The method of claim 17, wherein said correlating step is accomplished using aggregate valuation variance data in Sigma units presented as percentiles.

19. The method of claim 17, wherein said obtaining step is accomplished by computing said forecast standard deviation in terms of a percentage.

20. A computer-based method of generating a responsive confidence score for a valuation of a subject property evaluated using an automated valuation model comprising the steps of:

obtaining at least one user input suggested value for the subject property;

obtaining at least one automated valuation model valuation for said subject property;

calculating a right tail cutoff number in terms of Sigma units based on said at least one user input suggested value of said subject property; and

using a table of percentiles to correlate said cutoff number in Sigma units with a responsive confidence score.

21. The method of claim 20, wherein said calculating step is accomplished using the formula:

automated valuation model variance>

[(1+b)/(1+a)]−

1

wherein

a is the percentage, represented in decimal notation, of difference between said user input suggested value and said automated valuation model valuation of said subject property; and

b is the percentage, represented in decimal notation, of said right-tail cutoff number.

22. The method of claim 20, wherein said correlating step is accomplished using aggregate forecast standard deviation data presented as percentiles.

23. The method of claim 20, wherein said correlating step is accomplished using aggregate valuation variance data in sigma units presented as percentiles.

24. A computer-based method of calculating a forecast standard deviation for a plurality of properties each evaluated by an automated valuation model and each located in a predetermined geographic area comprising the steps of:

categorizing said plurality of properties into at least one group of properties in said predetermined geographic area; and

calculating a standard deviation for the variances of the valuations of said plurality of properties from a reference value associated with each of said plurality of properties in said at least one group to thereby calculate a forecast standard deviation.

25. The method of claim 24, wherein said standard deviation is calculated using the following equation:

forecast standard deviation=√

{[Σ

(v−

0)²]/(n−

1)}

wherein

v is the individual valuation variance described by the equation v=(x−

p)/p;

x is the automated valuation of each individual property in said group properties;

p is a reference value for each individual property in said group of properties; and

n is the total number of properties in said group.

26. The method of claim 24, wherein said plurality of properties are categorized into a group of properties each having the same confidence score.

27. The method of claim 24, wherein said plurality of properties are categorized into a group of properties each having the same raw confidence score.

28. The method of claim 24, wherein said plurality of properties are categorized into a group of properties each having the same land-use type.

29. The method of claim 24, wherein said plurality of properties are categorized into a group of properties each having the same economic tier.

30. The method of claim 24, wherein said predetermined geographic area is a state in which said plurality of properties are located.

31. The method of claim 24, wherein said predetermined geographic area is a county in which said plurality of properties are located.

32. The method of claim 24, wherein the said individual reference values include at least one sales price of properties in said predetermined geographic area.

33. The method of claim 24, wherein said individual reference values are sales prices of properties in said predetermined geographic area.

34. The method of claim 24, wherein said individual reference values are appraisal values of properties in said predetermined geographic area.

35. The method of claim 24, further comprising the step of validation of said standard deviation for accuracy.

36. The method of claim 35, wherein said validation step comprises:

expressing the variances between the valuations generated by an automated valuation model and the reference values of properties in Sigma units;

deriving a measure of dispersion of said variances in Sigma units;

comparing said measure of dispersion of said variances in Sigma units to an accuracy range;

correcting said forecast standard deviation using said measure of dispersion; and

returning a validated forecast standard deviation.

37. The method of claim 36, wherein said measure of dispersion is a standard deviation.

38. The method of claim 36, wherein said measure of dispersion is a forecast standard deviation.

39. The method of claim 36, wherein said correcting step is accomplished by multiplying said forecast standard deviation by said measure of dispersion.

40. A computer-based apparatus for calculating a forecast standard deviation for at least one property evaluated by an automated valuation model and located in a predetermined geographic area comprising:

data storage means for storing data of characteristics of a plurality of properties evaluated by an automated valuation model;

categorization means connected to said data storage means for categorizing a plurality of properties into at least one group of properties in said predetermined geographic area;

calculation means connected to said categorization means for calculating a forecast standard deviation for said at least one property from individual reference values associated with said plurality of properties in said at least one group; and

output means connected to said calculating means for providing forecast standard deviation output data.

41. The apparatus of claim 40, further comprising application means for applying said standard deviation for said at least one group to each of said plurality of properties.

42. The apparatus of claim 40, wherein said plurality of properties are categorized into at least one group using a raw confidence score of said plurality of properties.

43. The apparatus of claim 40, wherein said data storage means includes a means for storing a confidence score associated with each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the confidence score of said plurality of properties.

44. The apparatus of claim 40, wherein said data storage means includes a means for storing a confidence score associated with each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the raw confidence score of said plurality of properties.

45. The apparatus of claim 40, wherein said categorization means includes means for categorizing each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the state of said plurality of properties.

46. The apparatus of claim 40, wherein said categorization means includes means for categorizing each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the county of said plurality of properties.

47. The apparatus of claim 40, wherein said categorization means includes means for categorizing each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the land-use type of said plurality of properties.

48. The apparatus of claim 40, wherein said categorization means includes means for categorizing each of said plurality of properties and said categorization means categorizes each of said plurality of properties into at least one group using the economic tier of said plurality of properties.

49. The apparatus of claim 40, wherein said individual reference value is a property sale price.

50. The apparatus of claim 40, wherein said individual reference value is an appraised value.

51. The apparatus of claim 40, wherein said calculation means further comprises a validation means connected to said calculation means for validating said forecast standard deviation for accuracy.

52. The apparatus of claim 40, wherein said calculation means uses the equation:

forecast standard deviation=√

{[Σ

(v−

0)²]/(n−

1)}

wherein

v is the individual valuation variances described by the equation v=(x−

p)/p;

x is the automated valuation of each individual property in said group of properties in said predetermined geographic area;

p is a reference value for each individual property in said group of properties; and

n is the total number of properties in said group.

53. The apparatus of claim 40, further comprising a presentation means connected to said output means for presenting forecast standard deviation aggregate data in terms of percentiles.

54. The apparatus of claim 40, further comprising a presentation means connected to said output means for presenting valuation variance aggregate data in sigma units in terms of percentiles.

55. The apparatus of claim 48, wherein said validation means validates said forecast standard deviation by:

expressing the variances between the valuations generated by an automated valuation model and the reference values of properties in Sigma units;

deriving a measure of dispersion of said variances in Sigma units;

comparing said measure of dispersion of said variances in Sigma units to an accuracy range;

correcting said forecast standard deviation using said measure of dispersion; and

returning a validated forecast standard deviation.

56. The method of claim 55, wherein said measure of dispersion is a standard deviation.

57. The method of claim 55, wherein said measure of dispersion is a forecast standard deviation.

58. The method of claim 55, wherein said means for correcting, corrects by multiplying said trial forecast standard deviation by said measure of dispersion.

59. A computer-based apparatus for generating a right-tail confidence score for a valuation of a subject property evaluated using an automated valuation model comprising:

data storage means for storing data of characteristics of said subject property;

obtaining means connected to said data storage means for obtaining a forecast standard deviation;

calculating means connected to said obtaining means including a dividing means for dividing a right-tail confidence score cutoff number by said forecast standard deviation to compute a corresponding right-tail cutoff number in Sigma units; and

correlating means connected to said calculating means and said dividing means for correlating said corresponding right- tail cutoff number in Sigma units with a right-tail confidence score.

60. The apparatus of claim 59, wherein said correlating means uses aggregate valuation variance data in Sigma units presented as percentiles.

61. The apparatus of claim 59, wherein said obtaining means obtains said forecast standard deviation as a percentage.

62. The apparatus of claim 59, wherein said calculating means calculates said forecast standard deviation and said obtaining means obtains said forecast standard deviation from said calculating means.

63. A computer-based apparatus for generating a responsive confidence score for a valuation of a subject property evaluated using an automated valuation model comprising:

input means for inputting at least one user input suggested value for the subject property;

data storage means connected to said input means for obtaining at least one automated valuation model valuation for said subject property;

calculating means connected to said data storage means for calculating a valuation variance in Sigma units based on said at least one user input suggested value of said subject property; and

correlating means connected to said calculating means for correlating said valuation variance in Sigma units with a responsive confidence score.

64. The method of claim 63, wherein said calculating means uses the formula:

automated valuation model variance>

[(1+b)/(1+a)]−

1

wherein

a is the percentage, represented in decimal notation, of difference between said user input suggested value and said automated valuation model valuation of said subject property; and

b is the percentage, represented in decimal notation, of a right-tail cutoff number.

65. The method of claim 63, wherein said correlating means uses aggregate forecast standard deviation data presented as percentiles.

66. The method of claim 63, wherein said correlating means uses aggregate valuation variance data measured in Sigma units presented as percentiles.

67. A computer-based apparatus for calculating a forecast standard deviation for a plurality of properties each evaluated by an automated valuation model and each located in a predetermined geographic area comprising:

data storage means for storing data of characteristics of a plurality of properties each evaluated by an automated valuation model;

categorizing means connected to said data processing means for receiving data of characteristics of said plurality of properties each evaluated by an automated valuation model to categorize said plurality of properties into at least one group of properties in said predetermined geographic area;

calculating means connected to the output of said categorizing means for calculating said forecast standard deviation for said plurality of properties from references values each associated with one of said plurality of properties in said at least one group; and

output means connected to said calculating means for providing forecast standard deviation output data.

68. The apparatus of claim 67, wherein said forecast standard deviation is calculated using the following equation:

forecast standard deviation=√

{[Σ

(v−

0)²]/(n−

1)}

wherein

v is the individual valuation variances described by the equation (x−

p)/p;

x is the automated valuation of each individual property in said group of properties;

p is a reference value for each individual property in said group of properties; and

n is the total number of properties in said group.

69. The apparatus of claim 67, wherein said categorizing means categorizes said plurality of properties into a group of properties each having the same confidence score.

70. The apparatus of claim 67, wherein said categorizing means categorizes said plurality of properties into a group of properties each having the same raw confidence score.

71. The apparatus of claim 67, wherein said categorizing means categorizes said plurality of properties into a group of properties each having the same land-use type.

72. The apparatus of claim 67, wherein said categorizing means categorizes said plurality of properties into a group of properties each having the same economic tier.

73. The apparatus of claim 67, wherein said predetermined geographic area is a state in which said plurality of properties are located.

74. The apparatus of claim 67 wherein said predetermined geographic area is a county in which said at least one property is located.

75. The apparatus of claim 67, wherein the said individual reference values include at least one sales price of properties in said predetermined geographic area.

76. The apparatus of claim 67 wherein said individual reference values are sales prices of properties in said predetermined geographic area.

77. The apparatus of claim 67 wherein said individual reference values are appraisal values of properties in said predetermined geographic area.

78. The apparatus of claim 67, further comprising validation means connected to said calculating means for validating the accuracy of said forecast standard deviation.

79. The apparatus of claim 78, wherein said validation means includes a means for validating the accuracy of said forecast standard deviation by:

expressing the variances between the valuations generated by an automated valuation model and the reference values of properties in Sigma units;

deriving a measure of dispersion of said variances in Sigma units;

comparing said measure of dispersion of said variances in Sigma units to an accuracy range;

correcting said forecast standard deviation using said measure of dispersion; and

returning a validated forecast standard deviation.

80. The apparatus of claim 79, wherein said measure of dispersion is a standard deviation.

81. The apparatus of claim 79, wherein said measure of dispersion is a forecast standard deviation.

82. The apparatus of claim 79, wherein said correcting step is accomplished by multiplying said trial forecast standard deviation by said measure of dispersion.