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.

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.