Real Estate Data Aggregation and Curating System
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Solution Overview
Problem
Individual databases storing real estate records are limited in scope, often containing errors, omissions, and inconsistencies, leading to incomplete and unreliable property information when accessed for evaluations.
Innovation Solution
A system and method that aggregates property information from multiple databases, curating and generating suggested values by dynamically extracting and comparing data to provide complete, unrestricted, and comprehensive property information through an interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If property information is retrieved from individual databases, then access is simple and direct, but the information is incomplete and restricted to particular record types and geographic regions
Solution Approach 1:
The patent merges multiple individual real estate databases into a single comprehensive aggregated database. The system collects property records from various sources including electronic appraisals, tax records, and multiple geographic regions, combining them into one unified data structure that provides complete property information without the limitations of individual databases.
Solution Approach 2:
The aggregated database system serves multiple functions: it stores diverse record types (electronic appraisals, tax records, etc.), covers multiple geographic regions, and provides comprehensive property information for various evaluation purposes. This multi-functional system replaces the need for multiple separate database accesses.
2Adaptability or versatility
If property information is stored in individual databases with finite records, then data storage is manageable, but the information is restricted to particular record types and markets
Solution Approach 1:
The system expands the scope of property information coverage by adding dimensions of geographic regions and record types to the database structure. The aggregated database organizes data across multiple dimensions including different geographic areas, various record types (electronic appraisals, tax records), and multiple property characteristics, enabling comprehensive access without managing the complexity of separate databases.
3Reliability
If property information from multiple databases is aggregated, then complete and comprehensive data is achieved, but errors and inconsistencies from source databases are duplicated
Solution Approach 1:
The system implements feedback mechanisms through curating algorithms that automatically detect and resolve errors, omissions, and inconsistencies in the aggregated property information. The curating process analyzes data from multiple source databases, identifies discrepancies, and applies correction rules to generate accurate property information, thereby eliminating duplicated errors while maintaining comprehensive data coverage.
4Measurement precision
If individual databases store limited records, then storage requirements are controlled, but property evaluations may be faulty due to incomplete data
Solution Approach 1:
The system performs preliminary aggregation and curating of property information from multiple databases before the actual property evaluation takes place. By pre-processing and organizing comprehensive data from various sources into a ready-to-use format, the system ensures accurate property evaluations without requiring users to manually access and compile data from multiple individual databases at the time of evaluation.
Data Source
AI summary
A system and method may aggregate into a data structure property information from multiple databases, e.g., from all records included in those databases, and upon selection of any subject property or a characteristic thereof curates the often imperfect and discrepant property information to create suggested values. Similarly, the system and method may perform an on-the-fly evaluation of any characteristic of a selected record, when the property information for that characteristic includes an unlikely value, by triggering from the data structure a dynamic extraction of property information corresponding to a subject property of the selected record, where the dynamic extraction further generates and compares a suggested value to the unlikely value.


