Predictive machine learning models
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and applying a machine learning model. One of the methods includes the actions of obtaining a collection of training data, the training data comprising collection of data points associated with a labeled set of real property parcels; training a machine learning model using the training data, the machine learning model being trained to generate a likelihood with respect to a parameter from input data associated with a specific parcel of real property, wherein training includes optimizing the model using a Markov chain optimization that seeks to minimize error in the model where the model is underpinned by one or more non-differentiable functions; receiving a plurality of data points associated with an input parcel of real property; and using the optimized model to generate a likelihood for the parameter for the input parcel of real property.
1. A method comprising:
obtaining, 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, wherein the particular parameter being predicted is a likelihood that one or more potentially open mortgages attached to the specified parcel of real property are actually open, and wherein the machine learning model is trained using a training set comprising a collection of data points associated with a set of real property parcels distinct from the specified parcel of real property, wherein each real property parcel of the training set includes information about each mortgage attached to the parcel; and
based on the prediction, classifying the specified parcel of real property according to a determination of whether the predicted value of the parameter satisfies a threshold value.
2. The method of claim 1 , wherein the obtained plurality of data points comprises a variety of data from a variety of data sources.
3. The method of claim 1 , wherein in response to determining that the predicted likelihood that the mortgage is open satisfies the threshold, considering the mortgage closed.
4. The method of claim 1 , wherein the data points associated with the specified parcel of real property input to the machine learning model include dates associated with the recordation of one or more mortgages and transaction data indicating dates in which ownership of the parcel changed.
5. The method of claim 4 , wherein the data points include an identification of potentially open mortgages both directly identified from parcel data or indirectly identified from the parcel data.
6. The method of claim 5 , wherein indirectly identified mortgages include determining the presence of an unrecorded mortgage based on a recorded subordinate mortgage.
7. 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, 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, wherein the particular parameter being predicted is a likelihood that one or more potentially open mortgages attached to the specified parcel of real property are actually open, and wherein the machine learning model is trained using a training set comprising a collection of data points associated with a set of real property parcels distinct from the specified parcel of real property, wherein each real property parcel of the training set includes information about each mortgage attached to the parcel; and
based on the prediction, classifying the specified parcel of real property according to a determination of whether the predicted value of the parameter satisfies a threshold value.
8. The system of claim 7 , wherein the obtained plurality of data points comprises a variety of data from a variety of data sources.
9. The system of claim 7 , wherein in response to determining that the predicted likelihood that the mortgage is open satisfies the threshold, considering the mortgage closed.
10. The system of claim 7 , wherein the data points associated with the specified parcel of real property input to the machine learning model include dates associated with the recordation of one or more mortgages and transaction data indicating dates in which ownership of the parcel changed.
11. The system of claim 10 , wherein the data points include an identification of potentially open mortgages both directly identified from parcel data or indirectly identified from the parcel data.
12. The system of claim 11 , wherein indirectly identified mortgages include determining the presence of an unrecorded mortgage based on a recorded subordinate mortgage.
13. 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, 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, wherein the particular parameter being predicted is a likelihood that one or more potentially open mortgages attached to the specified parcel of real property are actually open, and wherein the machine learning model is trained using a training set comprising a collection of data points associated with a set of real property parcels distinct from the specified parcel of real property, wherein each real property parcel of the training set includes information about each mortgage attached to the parcel; and
based on the prediction, classifying the specified parcel of real property according to a determination of whether the predicted value of the parameter satisfies a threshold value.
14. The one or more non-transitory computer-readable storage media of claim 13 , wherein the obtained plurality of data points comprises a variety of data from a variety of data sources.
15. The one or more non-transitory computer-readable storage media of claim 13 , wherein in response to determining that the predicted likelihood that the mortgage is open satisfies the threshold, considering the mortgage closed.
16. The one or more non-transitory computer-readable storage media of claim 13 , wherein the data points associated with the specified parcel of real property input to the machine learning model include dates associated with the recordation of one or more mortgages and transaction data indicating dates in which ownership of the parcel changed.
17. The one or more non-transitory computer-readable storage media of claim 16 , wherein the data points include an identification of potentially open mortgages both directly identified from parcel data or indirectly identified from the parcel data.
18. The one or more non-transitory computer-readable storage media of claim 17 , wherein indirectly identified mortgages include determining the presence of an unrecorded mortgage based on a recorded subordinate mortgage.