Abstract: In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).

Abstract: A method and apparatus for valuing every property in a predetermined geographic region at regular intervals and storing those valuations for ready access later in a layered data stratum, using customary sources of property valuation data to create a new layers of the data stratum and using the data stored in the one or more layers of the data stratum for the creation of tables, spreadsheets and maps for evaluations of changes and trends in property valuation.

Abstract: In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).

Abstract: A computer-based method for validating an appraisal report includes receiving appraisal data for a property, receiving property data for the property, verifying the appraisal data meets an appraisal guideline, providing a list of appraisal items that are present in the appraisal data, providing a medium discrepancy amount and a high discrepancy amount for the list of appraisal items, calculating a difference amount between the appraisal data and the property data for each appraisal item, and comparing the difference amount to the medium discrepancy amount and the high discrepancy amount.

Abstract: In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).

Abstract: A computer model of tax delinquency risk is generated by analyzing historical data, including mortgage loan data, associated with real estate properties that have become property tax delinquent. The model is used to generate property-specific scores representing the likelihood that the corresponding properties will become tax delinquent (absent lender or servicer intervention) within a selected time period, such as six months. The scores may, for example, be used by a mortgage lender or servicer to identify loans/properties for which to take preemptive action to avoid tax delinquency.

Abstract: A computer-based method for validating an appraisal report includes receiving appraisal data for a property, receiving property data for the property, verifying the appraisal data meets an appraisal guideline, providing a list of appraisal items that are present in the appraisal data, providing a medium discrepancy amount and a high discrepancy amount for the list of appraisal items, calculating a difference amount between the appraisal data and the property data for each appraisal item, and comparing the difference amount to the medium discrepancy amount and the high discrepancy amount.

Abstract: Computer-based processes are disclosed for mining and analyzing owner association (OA) data associated with a plurality of real estate properties and generating an OA amount model that can be used to estimate OA amounts for a subject property. The OA data may also be analyzed to identify OA ratings, OA recommendations, OA delinquency actions, OA contacts information, OA services information, etc. The disclosed processes can also use the generated OA amount model to identify OA amount trends, and determine valuations and investment metrics for real estate properties.

Abstract: Systems and methods are disclosed that can enable a user to search for real estate properties by specifying search criteria that may exclude personal information. The systems and methods then can subsequently conduct a search for real estate property listings based partly on the specified search criteria and access loans associated with the matched properties. The system and methods then can perform analysis of the loans to identify loan and consumer characteristics and generate an electronic report that includes a representation of the loan and consumer characteristics.

Abstract: System and methods are disclosed that attempt to verify data related to a parcel against mapping and addressing data. If the data cannot be verified, then an enhanced normalization process may be performed. The enhanced normalization process may attempt to group the parcel with some other parcels based on parcel characteristics. If the grouping is successful, then normalization may be performed on the grouping. Normalization may involve checking whether the grouping satisfies a set of grouping criteria.

Abstract: Computer-based systems and methods are disclosed for automatically identifying taxes for real estate properties. The systems and methods can automatically identify the tax agency and/or tax identifier for real estate properties without substantial user interaction. In some embodiments, the systems and methods can automatically identify the real estate property and/or parcel based at least in part on received tax information without substantial user interaction. A confidence score and/or error rate may also be calculated based on one or more business rules to provide information about the relative error rate in any of the identifications provided.

Abstract: Computer-based systems and methods are disclosed for modeling and predicting short sale fraud risks for short sale transactions related to real estate properties. In some embodiments, the systems and methods can predict short sale fraud risks for real estate properties by considering a variety of factors, including AVM analytics characteristics, relationship analysis characteristics, and/or marketing analysis characteristics.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: Computer-based systems and methods are disclosed for modeling and predicting flash flood risks for real estate properties. In some embodiments, the systems and methods can predict flash flood risks for real estate properties by considering a variety of factors, including watershed hydrology characteristics, land surface characteristics, meteorological characteristics, and/or property characteristics. In some embodiments, the systems and methods can improve determination of investment metrics or automated valuations by considering flash flood risks in determining the investment metrics or automated valuations.

Abstract: Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

Abstract: In various embodiments, a flood risk score may be determined for a property point that provides a comprehensive assessment of the property point's risk of flooding. Determining the flood risk score may include determining a flood risk characteristic for the property point and assigning a flood risk score that corresponds to the flood risk characteristic. In some embodiments, flood risk characteristics may include a difference in elevation between the elevation of the property point and an elevation of a calculated point (e.g., on a known flood risk zone boundary). Flood risk characteristics may also include a flood zone determination for the property point and/or proximity of the property point to a known flood risk zone boundary or a flood source. In some embodiments, flood risk scores may be provided on flood risk score reports.

Abstract: Data analytics are provided in loan treatment. Various sources of data may be used to optimize or predict value for a loan. Using machine-learning and/or statistical analysis, loans or treatment best suited for a particular borrower may be determined. Due to the large amounts of data available, borrower behavior may be learned from previous behavior of others and mapped to a predictive model. Machine-learning indicates the most relevant factors in loan treatment, providing a matrix for predicting loan value or treatment success. A given borrower may be classified into one of many classes of borrower based on credit information, property information, desired loan information, real estate market information, and/or other data. Tens, hundreds, or even thousands of variables may be used to predict the optimum treatment.

Abstract: In some embodiments, scripts may be used to perform parcel data acquisition, conversion, and clean-up/repair in an automated manner and/or through graphical user interfaces. The scripts may be used, for example, to repair geometries of new parcel data, convert multi-part parcel geometries to single part parcel geometries (explode), eliminate duplicate parcel geometries, append columns, create feature classes, and append feature classes. These scripts may be executed in a predetermined manner to increase efficiency. In some embodiments, different combinations of attributes may be appended to stored parcel data. In some embodiments, a tracking application may be used to track information about sources of data. In some embodiments, a tracking application may be used to track which system users are assigned to specific tasks (e.g., in a data acquisition project).

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).

Abstract: In various embodiments, old flood maps may be compared to new flood maps to determine which areas of the flood map have changed. These changed areas may be correlated against geographic area descriptions that are within changed areas of the flood map. The changed areas may also be analyzed to determine whether each area has had a change in. status (e.g., from a high risk flood zone to a non-high risk flood zone or vice versa) or a change in zone within a status (e.g., from one flood zone to another flood zone). The information on type of change (or no change) may be used to populate a database that includes geographic area description identifiers. In some embodiments, detection of certain types of changes may initiate a manual comparison of the old and new flood maps to verify the change.