Corelogic Solutions, LLC

Jon Arthur Wierks

Wei Geng

Bin He

Kien Trong Trinh

Mark A Spieckerman

Wei Liu

Sankar Bokka

Bryan Byron Craver

Matt Cannon

Roderick Maclan

Tricia J Murray

Howard A Botts

Duncan Chen

David Stiff

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.

Artificial intelligence model surveillance system

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&#x2014;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.

Multi-use artificial intelligence-based ensemble model

A computer system and associated processes are disclosed for grouping similar real estate properties into contiguous neighborhoods, and for generating neighborhood-specific models capable of estimating property values within their respective neighborhoods. A clustering component uses various sources of property-level data to group properties based on measures of property similarity. For example, the clustering component may use features extracted from property images to identify properties with similar characteristics. As another example, the clustering component may measure property similarity based on how frequently specific properties are designated as comparable in appraisal reports. A model generator uses a machine learning process to determine, for specific neighborhoods, correlations between property attributes and values, and uses these correlations to generate the neighborhood specific models.

Computer processes for clustering properties into neighborhoods and generating neighborhood-specific models

Systems and methods for determining a boundary from evaluation records for objects are disclosed. In some embodiments, evaluation records for objects (e.g., appraisal records for real estate properties) are accessed. The evaluation records can be used to identify objects with similar characteristics. By representing objects identified as nodes in a graph, edges between nodes representing the identified objects can be established. A network generated from the nodes and edges can be used to identify a boundary comprising objects having similar characteristics.

Data mining data records to determine networks and boundaries

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Tustin, CA, United States of America

Jon Arthur Wierks