Abstract: Systems and methods for property valuation using automated valuation models are described herein, which involve a complex series of workflows for data processing, automated valuation modeling, and error detection—each of which are capable of leveraging machine learning algorithms/techniques and can be combined and operated together in a specific manner in order to improve data availability and data quality, improve the accuracy of generated property value estimates, improve the performance of the models over time, and allow the property value estimation to be adapted to various use cases. Estimation of property value may involve an ensemble model which reconciles the outputs of different sub-models based on a use case, with the sub-models using different approaches that have their own strengths and weaknesses.

Abstract: Systems and methods for property valuation using automated valuation models are described herein, which involve a complex series of workflows for data processing, automated valuation modeling, and error detection—each of which are capable of leveraging machine learning algorithms/techniques and can be combined and operated together in a specific manner in order to improve data availability and data quality, improve the accuracy of generated property value estimates, improve the performance of the models over time, and allow the property value estimation to be adapted to various use cases. Estimation of property value may involve an ensemble model which reconciles the outputs of different sub-models based on a use case, with the sub-models using different approaches that have their own strengths and weaknesses.

Abstract: Embodiments of systems and methods can determine comparable real estate properties for use in the valuation of a subject real estate property. For example, a geographic area surrounding the subject property can be divided into smaller regions and a set of one or more regions having property characteristics that most closely match the characteristics of the subject property can be identified. Comparable properties can be selected from this set of regions rather than from the overall geographic area as a whole, which may lead to identifying better-matching comparable properties as compared to selecting comparable properties from the overall geographic area (which generally will include regions that are poor matches to the subject property).