Knowledge Graphs for Real Estate Valuation Accuracy
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Solution Overview
Problem
Current automated valuation models for real estate rely on outdated relational databases and geographic averaging, leading to inaccurate property value assessments due to inadequate representation of complex relationships between properties.
Innovation Solution
The implementation of knowledge graph technology to aggregate and interpret data from multiple sources, creating advanced data structures that represent complex relationships between properties, thereby improving home valuation accuracy and utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional relational databases are used to store property data, then data storage is straightforward, but the system cannot accurately represent complex relationships between properties and entities
Solution Approach 1:
The patent segments the data representation into distinct components: entities (properties, neighborhoods, features) are separated from their relationships. This segmentation allows complex relationships to be represented through structured connections between discrete entities, improving accuracy while managing complexity through modular organization.
Solution Approach 2:
The patent transitions from traditional two-dimensional relational tables to a multi-dimensional knowledge graph structure. This dimensional expansion enables representation of complex relationships through multiple attributes and connections simultaneously, allowing the system to capture nuanced property relationships that would be cumbersome in traditional databases.
2Productivity
If multiple queries are executed on relational databases to retrieve data for valuation modeling, then data can be retrieved, but query execution time and computational overhead increase significantly
Solution Approach 1:
The patent merges multiple data retrieval operations into a single knowledge graph query. By representing all required property relationships within the graph structure, the system can retrieve comprehensive data through one efficient traversal operation, eliminating the need for multiple sequential database queries and significantly reducing total execution time.
Solution Approach 2:
The knowledge graph serves as an intermediary layer between the data storage system and the valuation modeling process. This intermediary structure pre-organizes data relationships, allowing rapid retrieval without requiring complex multi-step queries through traditional relational databases, thus reducing computational overhead and query time.
3Quantity of substance
If relational databases store all relationships between entities, then complete relationship data is available, but storage requirements and computational overhead become unsustainable at scale
Solution Approach 1:
The patent applies local quality by representing only the specific relationships needed for valuation modeling within the knowledge graph, rather than storing all possible entity relationships. This selective representation maintains necessary data quantity while reducing unnecessary computational overhead associated with processing and maintaining complete relationship datasets at scale.
Data Source
AI summary
This disclosure relates to knowledge generation and implementation. A knowledge graph system comprises at least one processor, at least one database communicatively connected to the at least one processor, and a memory storing executable instructions. When executed, the instructions cause the at least one processor to aggregate, from the at least one database, entity data for a plurality of homes. Attribute information identifying geographic locations of the plurality of homes and relationships between pairs of the plurality of homes is extracted from the aggregated data. Knowledge graph data structures are populated with the extracted attribute information. A home knowledge graph is built, having nodes corresponding to the plurality of homes and edges corresponding to the identified relationships. A hierarchical cluster tree structure of the plurality of homes is outputted, wherein levels of the hierarchical cluster tree correspond to clusters of homes determined based in part on the knowledge graph edges.