Abstract: 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.

Abstract: A system, method and computer program product cooperate to extract a building footprint from other data associated with a property. Imagery data of real property is input to a computing device, the imagery data containing a plurality of parcels. A processing circuit detects contrasts of candidate man-made structures on a parcel of the plurality of parcels. The candidate man-made structures are then associated with the parcel. A building footprint is then extracted by distinguishing a man-made structure on said parcel from natural terrain, recognizing that man-made structures when viewed from above generally show a strong contrast from background terrain. Remaining candidate man-made structures are removed by observing that they having features inconsistent with predetermined extraction logic.

Abstract: A system, method and computer program product cooperate to extract a building footprint from other data associated with a property. Imagery data of real property is input to a computing device, the imagery data containing a plurality of parcels. A processing circuit detects contrasts of candidate man-made structures on a parcel of the plurality of parcels. The candidate man-made structures are then associated with the parcel. A building footprint is then extracted by distinguishing a man-made structure on said parcel from natural terrain, recognizing that man-made structures when viewed from above generally show a strong contrast from background terrain. Remaining candidate man-made structures are removed by observing that they having features inconsistent with predetermined extraction logic.

Abstract: A system, method and computer program product cooperate to extract a building footprint from other data associated with a property. Imagery data of real property is input to a computing device, the imagery data containing a plurality of parcels. A processing circuit detects contrasts of candidate man-made structures on a parcel of the plurality of parcels. The candidate man-made structures are then associated with the parcel. A building footprint is then extracted by distinguishing a man-made structure on said parcel from natural terrain, recognizing that man-made structures when viewed from above generally show a strong contrast from background terrain. Remaining candidate man-made structures are removed by observing that they having features inconsistent with predetermined extraction logic.

Abstract: A system, method and computer program product cooperate to extract a building footprint from other data associated with a property. Imagery data of real property is input to a computing device, the imagery data containing a plurality of parcels. A processing circuit detects contrasts of candidate man-made structures on a parcel of the plurality of parcels. The candidate man-made structures are then associated with the parcel. A building footprint is then extracted by distinguishing a man-made structure on said parcel from natural terrain, recognizing that man-made structures when viewed from above generally show a strong contrast from background terrain. Remaining candidate man-made structures are removed by observing that they having features inconsistent with predetermined extraction logic.

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: A system, method and computer program product cooperate to extract a building footprint from other data associated with a property. Imagery data of real property is input to a computing device, the imagery data containing a plurality of parcels. A processing circuit detects contrasts of candidate man-made structures on a parcel of the plurality of parcels. The candidate man-made structures are then associated with the parcel. A building footprint is then extracted by distinguishing a man-made structure on said parcel from natural terrain, recognizing that man-made structures when viewed from above generally show a strong contrast from background terrain. Remaining candidate man-made structures are removed by observing that they having features inconsistent with predetermined extraction logic.

Abstract: An apparatus, method and computer program storage device determine a composite hazard index. An interface receives a first risk score for a first hazard and a second risk score for a second hazard. The first risk score is in a first range of scores and the second risk score is in a second range of scores. A processing circuit emphasizes at least some scores in at least one of the first range of scores and the second range of scores. The processing circuit also normalizes the first risk score with respect to the first range of scores and second range of scores, and normalizes the second risk score with respect to the first range of scores and second range of scores. The processing circuit also combines a normalized first risk score with a normalized second risk score to form at least a component of a composite risk index. The first risk score and second risk score being specific to a common property.