Abstract: In an illustrative embodiment, an automated system determines similarities between properties at a location using customized features sets. The system can generate feature sets for properties from property attribute data received from external and internal sources. The system can transform one or more elements of attribute data of combined internal and external data into customized property attributes that enhance property attribute comparisons. The combined attribute data can be applied to a machine learning algorithm that identifies a portion of the combined attribute data for using in property similarity calculations. Using the identified features, the system can calculate similarity scores identifying an amount of similarity between properties at the location and a property identified in a comparable property query, which can be output to an external device.

Abstract: In an illustrative embodiment, an automated system determines rental income and location quality for geographic regions using customized data sets. The system can generate customized location feature sets for applying to trained location quality prediction models where the feature sets include hybrid features combining aspects of two or more items of the location feature data into a single hybrid feature and the geographic regions are sized to a predetermined granularity level. Each feature set associated with a geographic region can be applied to a machine learning data model trained to predict rental property income at the respective predefined region from the customized location feature sets. Using the output data from the machine learning data model, the system can calculate location metrics quantifying a rental property income and the location quality for the geographic region.