AUTOMATED-VALUATION-MODEL TRAINING-DATA OPTIMIZATION SYSTEMS AND METHODS
First Claim
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.
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Accused Products
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.
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Citations
18 Claims
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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:
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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. - View Dependent Claims (2, 3, 4, 5, 6)
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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:
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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. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, configure the processor to:
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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. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification