Machine learning using multiple input data types

A system comprising:

• one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• obtaining, in real-time and from one or more sources, a plurality of data points associated with a specified parcel of real property;
• using a machine learning model to generate a prediction from the obtained plurality of data points, the prediction indicating a likelihood that the real property will satisfy a particular parameter, the particular parameter indicating a likelihood that the real property has a title defect, and a predicted scope for the parameter, wherein the machine learning model is trained using a training set comprising a collection of data points associated with a labeled set of a plurality of real property parcels distinct from the specified parcel of real property, wherein the data points for each real property parcel comprises one or more data types, the data types including statistical information about the real property parcel and a retail history of the property, and wherein each label indicates the actual historical occurrence and value for the particular parameter for each real property parcel of the training set such that the training set includes one or more real property parcels labeled as having a title defect of specified value and one or more real property parcels labeled as not having a title defect;
• and
• based on the prediction indicating the likelihood that the real property has a title defect and the predicted scope for the title defect, classifying the specified parcel of real property according to a determination of whether the predicted scope satisfies a threshold value.

One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• obtaining, in real-time and from one or more sources, a plurality of data points associated with a specified parcel of real property;
• using a machine learning model to generate a prediction from the obtained plurality of data points, the prediction indicating a likelihood that the real property will satisfy a particular parameter, the particular parameter indicating a likelihood that the real property has a title defect, and a predicted scope for the parameter, wherein the machine learning model is trained using a training set comprising a collection of data points associated with a labeled set of a plurality of real property parcels distinct from the specified parcel of real property, wherein the data points for each real property parcel comprises one or more data types, the data types including statistical information about the real property parcel and a retail history of the property, and wherein each label indicates the actual historical occurrence and value for the particular parameter for each real property parcel of the training set such that the training set includes one or more real property parcels labeled as having a title defect of specified value and one or more real property parcels labeled as not having a title defect;
• and
• based on the prediction indicating the likelihood that the real property has a title defect and the predicted scope for the title defect, classifying the specified parcel of real property according to a determination of whether the predicted scope satisfies a threshold value.