AUTOMATED-VALUATION-MODEL TRAINING-DATA OPTIMIZATION SYSTEMS AND METHODS

• US 20150088766A1
• Filed: 09/26/2013
• Published: 03/26/2015
• Est. Priority Date: 09/26/2013
• Status: Active Grant

• Current Assignee: Greenfield Advisors LLC
• Original Assignee: Greenfield Advisors LLC
• Inventors: KRAUSE, Andy, LIPSCOMB, Clifford A., KILPATRICK, John A.
• Granted Patent: US 9,582,819 B2
• Time in Patent Office: Days
• US Class Current: 705/306
• CPC Class Codes: G06Q 30/0278 Product appraisal
• CPC Class Codes: G06Q 50/16 Real estate

Abstract

To optimize training data used by a predictive real-estate valuation model, a search space having multiple dimensions may be defined. Each search dimension corresponds to a range of candidate values for a search criterion for selecting subsets of sales-transaction records. The multiple dimensions include a temporal dimension and a geographic dimension. An accuracy-optimized subset of a multiplicity of sales-transaction records is identified by evaluating points that vary along each dimension within the multi-dimension search space. A statistical measure of model accuracy is used to evaluate each candidate point. The accuracy-optimized subset of the multiplicity of sales-transaction records is provided to a predictive model to generate an automated value prediction for a subject real-estate property as of an effective date.

Claims

1. A server-device-implemented method for optimizing training data for developing a predictive model to automatically value a subject real-estate property, the method comprising:

obtaining, by said server device, an indication to provide an automated value prediction for the subject real-estate property as of an effective date;

in response to obtaining said indication, defining, by said server device, a search space having multiple dimensions, each corresponding to a range of candidate values for a search criterion for selecting subsets of a multiplicity of sales-transaction records, said multiple dimensions including at least a temporal dimension that measures distance in time before the effective date, and a geographic dimension that measures distance in space away from the subject real-estate property;

evaluating, by said server device, a multiplicity of points within said multi-dimension search space according to a statistical measure of model accuracy, said multiplicity of points varying along at least said temporal dimension and said geographic dimension;

selecting, by said server device based at least in part on evaluating said multiplicity of points within said multi-dimension search space, an accuracy-optimized subset of said multiplicity of sales-transaction records; and

developing, by said server device, the predictive model according to said accuracy-optimized subset of said multiplicity of sales-transaction records to generate said automated value prediction for the subject real-estate property as of the effective date.

2. The method of claim 1, wherein defining said multi-dimension search space comprises:

sampling a temporal range to obtain a plurality of candidate temporal-distance values; and

sampling a geographic range to obtain a plurality of candidate geographic-distance values.

3. The method of claim 2, wherein evaluating a candidate point of said multiplicity of points according to said statistical measure of model accuracy comprises:

determining a multi-dimension search criteria set comprising a candidate temporal-distance value selected from said plurality of candidate temporal-distance values and a candidate geographic-distance value selected from said plurality of candidate geographic-distance values;

selecting a distinct subset of said multiplicity of sales-transaction records corresponding to said multi-dimension search criteria set;

developing the predictive model according to at least a majority of said distinct subset of sales-transaction records; and

statistically measuring an accuracy of the predictive model thereby developed.

4. The method of claim 1, wherein said statistical measure of model accuracy comprises a forecast standard deviation statistical measure.

5. The method of claim 1, wherein said statistical measure of model accuracy is selected from a group consisting of:

a mean average percent error statistical measure;

a mean absolute error statistical measure; and

a root mean square error statistical measure.

6. The method of claim 1, wherein the predictive model is selected from a group consisting of:

a regression model;

an adaptive estimation model; and

a neural network model.

7. A computing apparatus for optimizing training data for developing a predictive model to automatically value a subject real-estate property, the apparatus comprising a processor and a memory storing instructions that, when executed by the processor, configure the apparatus to:

obtain an indication to provide an automated value prediction for the subject real-estate property as of an effective date;

in response to obtaining said indication, define a search space having multiple dimensions, each corresponding to a range of candidate values for a search criterion for selecting subsets of a multiplicity of sales-transaction records, said multiple dimensions including at least a temporal dimension that measures distance in time before the effective date, and a geographic dimension that measures distance in space away from the subject real-estate property;

evaluate a multiplicity of points within said multi-dimension search space according to a statistical measure of model accuracy, said multiplicity of points varying along at least said temporal dimension and said geographic dimension;

select, based at least in part on evaluating said multiplicity of points within said multi-dimension search space, an accuracy-optimized subset of said multiplicity of sales-transaction records; and

develop the predictive model according to said accuracy-optimized subset of said multiplicity of sales-transaction records to generate said automated value prediction for the subject real-estate property as of the effective date.

8. The apparatus of claim 7, wherein the instructions that configure the apparatus to define said multi-dimension search space further comprise instructions configuring the apparatus to:

sample a temporal range to obtain a plurality of candidate temporal-distance values; and

sample a geographic range to obtain a plurality of candidate geographic-distance values.

9. The apparatus of claim 8, wherein evaluating a candidate point of said multiplicity of points according to said statistical measure of model accuracy comprises:

determine a multi-dimension search criteria set comprising a candidate temporal-distance value selected from said plurality of candidate temporal-distance values and a candidate geographic-distance value selected from said plurality of candidate geographic-distance values;

select a distinct subset of said multiplicity of sales-transaction records corresponding to said multi-dimension search criteria set;

develop the predictive model according to at least a majority of said distinct subset of sales-transaction records; and

statistically measure an accuracy of the predictive model thereby developed.

10. The apparatus of claim 7, wherein the instructions that configure the apparatus to Said statistical measure of model accuracy further comprise instructions configuring the apparatus to a forecast standard deviation statistical measure.

11. The apparatus of claim 7, wherein said statistical measure of model accuracy is selected from a group consisting of:

a mean average percent error statistical measure;

a mean absolute error statistical measure; and

a root mean square error statistical measure.

12. The apparatus of claim 7, wherein the predictive model is selected from a group consisting of:

a regression model;

an adaptive estimation model; and

a neural network model.

13. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, configure the processor to:

obtain an indication to provide an automated value prediction for a subject real-estate property as of an effective date;

in response to obtaining said indication, define a search space having multiple dimensions, each corresponding to a range of candidate values for a search criterion for selecting subsets of a multiplicity of sales-transaction records, said multiple dimensions including at least a temporal dimension that measures distance in time before the effective date, and a geographic dimension that measures distance in space away from the subject real-estate property;

evaluate a multiplicity of points within said multi-dimension search space according to a statistical measure of model accuracy, said multiplicity of points varying along at least said temporal dimension and said geographic dimension;

select, based at least in part on evaluating said multiplicity of points within said multi-dimension search space, an accuracy-optimized subset of said multiplicity of sales-transaction records; and

develop a predictive model according to said accuracy-optimized subset of said multiplicity of sales-transaction records to generate said automated value prediction for the subject real-estate property as of the effective date.

14. The non-transitory computer-readable storage medium of claim 13, wherein defining said multi-dimension search space comprises:

sample a temporal range to obtain a plurality of candidate temporal-distance values; and

sample a geographic range to obtain a plurality of candidate geographic-distance values.

15. The non-transitory computer-readable storage medium of claim 14, wherein evaluating a candidate point of said multiplicity of points according to said statistical measure of model accuracy comprises:

determine a multi-dimension search criteria set comprising a candidate temporal-distance value selected from said plurality of candidate temporal-distance values and a candidate geographic-distance value selected from said plurality of candidate geographic-distance values;

select a distinct subset of said multiplicity of sales-transaction records corresponding to said multi-dimension search criteria set;

develop the predictive model according to at least a majority of said distinct subset of sales-transaction records; and

statistically measure an accuracy of the predictive model thereby developed.

16. The non-transitory computer-readable storage medium of claim 13, wherein said statistical measure of model accuracy comprises a forecast standard deviation statistical measure.

17. The non-transitory computer-readable storage medium of claim 13, wherein said statistical measure of model accuracy is selected from a group consisting of:

a mean average percent error statistical measure;

a mean absolute error statistical measure; and

a root mean square error statistical measure.

18. The non-transitory computer-readable storage medium of claim 13, wherein the predictive model is selected from a group consisting of:

a regression model;

an adaptive estimation model; and

a neural network model.