Abstract: A system, and method perform compliance checking of computer records for MLS services. The compliance checking includes detecting multiple-listing-service (MLS) contract compliance with a MLS, gathering MLS-related data regarding an MLS subscriber from at least one database, checking for a non-compliance incident of the MLS-related data with the MLS service requirements, and in response to detection of a non-compliance incident, trigger a computer-based resolution process to resolve the non-compliance incident.

Abstract: A computer system and associated processes for grouping similar real estate properties into contiguous neighborhoods and generating neighborhood-specific models capable of estimating property values within their neighborhoods. An artificial intelligence system directed to using a graph neural network framework to identify relationships between different parcel groups based on similar property features and embed the parcel groups into low dimensional space vectors. The method can include generating a graph and features relevant to the parcel groups that can train an embedding function that generate an embedding vector for each parcel group in a geographic unit grouping, such as a census tract. Embedding vectors of two or more parcel groups can then be compared to each other to determine whether the parcel groups are similar or to determine a housing valuation of a parcel group.

Abstract: An improved living area estimation system is described herein that can obtain aerial images of structures and property data corresponding to structures. The improved living area estimation system can then perform image processing on the aerial images to estimate a living area of each structure. The improved living area estimation system can group property records for the various structures based on one or more criterion and, for each group of property records, determine the average difference between living area calculated using aerial images and living area included in the property data. For a particular structure, the improved living area estimation system can process the aerial image(s) to estimate living area and then modify this value by the average difference determined for the group to which the structure belongs to determine an estimated living area of the structure.

Abstract: A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

Abstract: A system, and method perform compliance checking of computer records for MLS services. The compliance checking includes detecting multiple-listing-service (MLS) contract compliance with a MLS, gathering MLS-related data regarding an MLS subscriber from at least one database, training an AI engine with input data containing as features MLS service requirements that correspond to contractual obligations of the MLS subscriber under a MLS license, applying the MLS-related data to the AI engine, after the AI engine is trained, and checking for a non-compliance incident of the MLS-related data with the MLS service requirements, and in response to detection of a non-compliance incident, trigger a computer-based resolution process to resolve the non-compliance incident.

Abstract: A method using a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

Abstract: An improved parcel growth prediction system that uses parcel data, population data, and artificial intelligence to predict the growth of a geographic area at a micro level (e.g., a real estate parcel level) is described herein. For example, the improved parcel growth prediction system may generate a graph model and apply the graph model as an input to an artificial intelligence model to predict the likelihood that a particular parcel may be developed some time in the future. Ultimately, implementing the improved parcel growth prediction system described herein may lead to more precise placements of infrastructure projects and/or to infrastructure projects that more precisely support the needs of the population of a geographic area as time passes.

Abstract: Systems and methods for property valuation using automated valuation models are described herein, which involve a complex series of workflows for data processing, automated valuation modeling, and error detection—each of which are capable of leveraging machine learning algorithms/techniques and can be combined and operated together in a specific manner in order to improve data availability and data quality, improve the accuracy of generated property value estimates, improve the performance of the models over time, and allow the property value estimation to be adapted to various use cases. Estimation of property value may involve an ensemble model which reconciles the outputs of different sub-models based on a use case, with the sub-models using different approaches that have their own strengths and weaknesses.

Abstract: A delinquent payment portal system is described herein. The delinquent payment portal system can obtain transformed assessor and/or property tax data (e.g., assessor data and/or property tax data converted from one format to another format compatible with the delinquent payment portal system) from a parcel data store. The delinquent payment portal system can then use some or all of the retrieved data to generate user interface data that, when rendered by a user device, causes the user device to display a graphical user interface (GUI) depicting any of the data or features described herein, including property tax delinquency data details by land type, city, zip code, and/or other geographical area (e.g., county, state, etc.), property tax delinquency data map details, and/or property tax delinquency data year over year concurrently and/or overlapping within a single view.

Abstract: Systems and methods are disclosed for the generation and interactive display of three-dimensional (3D) models that accurately represent buildings and other large, complex objects possessing both internal and external features. More specifically, an exterior mesh can be generated that captures all the external features of the building. An interior mesh can also be generated that captures all the internal features of the building. The exterior mesh and the interior mesh may be layered and combined using geographical coordinates (e.g., global positioning system (GPS) data) in order to create a combined mesh that can be used to render a 3D model of the object. Any images taken of external or internal features may also be appended to the 3D model or even overlaid on the 3D model (like textures), based on the location and directional view associated with those images.

Abstract: A system and method exploit information gained by observing abnormal commonality factors from multiple accounts over a predetermined time frame. The presence of an abnormal commonality factor serves as an indication that a group of accounts may have been compromised by a common actor via information gained by successful phishing of multiple users' information to improperly gain access to their respective accounts. Once this commonality associated with phishing is detected, the system provides a mechanism and process to rapidly respond to the phishing-originated attack to minimize information security damage to the affected accounts.

Abstract: A development management system is described herein that may provide a unified portal that depicts an interactive user interface accessible to a developer and various stakeholders in a region at which a development is planned. Within the unified portal, the developer can provide and/or access certain sets of data. In addition, other stakeholders in the region at which a development is planned can access the unified portal and provide access-controlled data that may otherwise be unavailable to the developer. The unified portal may allow the developer and/or the stakeholders to monitor the progress of a development, provide input as to various design elements that may comprise the development, to provide or request additional information as needed, to select a location at which the development will occur with the aid of artificial intelligence, and to review and revise a layout of the proposed development graphically depicted via the user interface.

Abstract: A system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

Abstract: A flood footprint estimation system that can generate a requested flood footprint is described herein. For example, the flood footprint estimation system can estimate water surface elevations at various points along a stream or river. The flood footprint estimation system can then perform a smoothing operation on the estimated water surface elevations to generate a trend line or curve of the estimated water surface elevations in which each point of the trend line or curve corresponds to a water surface elevation value that is no greater than any of the water surface elevation values of the previous points of the trend line or curve. The flood footprint estimation system can use the trend line or curve to perform an iterative water expansion to determine which geographic locations may flood. Results of the iterative water expansion can be provided to a user device in the form of a user interface.

Abstract: An improved living area estimation system is described herein that can control and/or use data obtained by a residential robotic device that operates within a structure to estimate the living area. In particular, a residential robotic device may navigate along a predetermined or dynamically-determined route on one floor of the structure. As the residential robotic device travels along the route, the residential robotic device can use one or more sensors to track the area covered. The living area estimation system can obtain a traversed area map directly or indirectly from the residential robotic device, use image processing techniques to enhance the traversed area map, and estimate a living area of a floor on which the residential robotic device operated using the enhanced traversed area map. The living area estimation system can then use artificial intelligence and the estimated floor living area to estimate the living area of a structure.

Abstract: A system, apparatus, computer program product, and method use a convolutional neural network to auto-determine a first floor height (FFH) and a FFH elevation (FFE) of a building. The FFH, and FFE of the building are determined with respect to the terrain or surface of the parcel of land on which the building is located. In turn, by knowing the FFH and/or FFE of the building on the parcel, it is possible to use that information while performing a flood risk assessment to a property without requiring a personal inspection of the parcel by a human.

Abstract: Systems and methods are disclosed for the generation and interactive display of three-dimensional (3D) models that accurately represent buildings and other large, complex objects possessing both internal and external features. More specifically, an exterior mesh can be generated that captures all the external features of the building. An interior mesh can also be generated that captures all the internal features of the building. The exterior mesh and the interior mesh may be layered and combined using geographical coordinates (e.g., global positioning system (GPS) data) in order to create a combined mesh that can be used to render a 3D model of the object. Any images taken of external or internal features may also be appended to the 3D model or even overlaid on the 3D model (like textures), based on the location and directional view associated with those images.

Abstract: Systems and methods for property valuation using automated valuation models are described herein, which involve a complex series of workflows for data processing, automated valuation modeling, and error detection—each of which are capable of leveraging machine learning algorithms/techniques and can be combined and operated together in a specific manner in order to improve data availability and data quality, improve the accuracy of generated property value estimates, improve the performance of the models over time, and allow the property value estimation to be adapted to various use cases. Estimation of property value may involve an ensemble model which reconciles the outputs of different sub-models based on a use case, with the sub-models using different approaches that have their own strengths and weaknesses.

Abstract: Systems and methods are disclosed for the generation and interactive display of three-dimensional (3D) building models, for the determination and simulation of risk associated with the underlying buildings, for the prediction of changes to the underlying buildings associated with the risk, for the generation and display of updated 3D building models factoring in the risk or predicted changes, and for the collection and display of relevant contextual information associated with the underlying building while presenting a 3D building model. The 3D building model can be displayed in an augmented reality (AR) view, and a user can interact with the 3D building model via controls present in the AR view and/or by moving within the real-world location in which the user is present.

Abstract: A hazard visualization system that can use artificial intelligence to identify locations at which hazards have occurred and a cause therein and to predict locations at which hazards may occur in the future is described herein. As a result, the hazard visualization system may reduce the likelihood of structural damage and/or loss of life that could otherwise occur due to natural disasters or other hazards. For example, the hazard visualization system can train an artificial intelligence model to predict the date, time, type, severity, path, and/or other conditions of a hazard that may occur at a geographic location. As another example, the hazard visualization system can train an artificial intelligence model to identify equipment or other infrastructure depicted in geographic images.