Graph Embedding of Parcel Groups for Accurate Neighborhood Valuation
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Solution Overview
Problem
Conventional methods struggle to handle large volumes of geographic unit data efficiently, leading to computational expense and inaccurate results due to data loss and unintended comparisons, especially in artificial intelligence models like AVMs, which require numeric input and often produce inconsistent outputs when dealing with categorical data like census block groups.
Innovation Solution
A system that generates low-dimensional embedding vectors for geographic units by analyzing property-level data, forming graph models, and using AI models to reduce dimensions while retaining essential characteristics, thereby improving accuracy and reducing noise and multicollinearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to group geographical units, then computational processing requirements are high, but the results are inconsistent and inaccurate due to data loss
Solution Approach 1:
The patent introduces graph models as an intermediary between raw property-level data and AI models. These graph models encode geographic relationships and spatial context, serving as a mediator that preserves information while transforming data into a format suitable for AI processing. This intermediary layer prevents data loss by maintaining structural relationships that would otherwise be lost in conventional grouping methods.
Solution Approach 2:
The patent transforms categorical geographic data into continuous embedding vectors through dimensionality reduction. This parameter change from discrete categories to continuous vectors allows for more nuanced representation of geographic units, improving accuracy by capturing subtle variations and relationships that conventional categorical grouping methods lose.
2Measurement precision
If high-dimensional geographic data is processed directly by AI models, then computational complexity increases, but accuracy improves
Solution Approach 1:
The patent extracts essential geographic features and relationships from high-dimensional data through graph models and embedding vectors. By taking out only the most relevant information (spatial relationships, neighborhood characteristics) and representing it in a compressed vector format, the system reduces computational complexity while preserving the information needed for accurate predictions.
Solution Approach 2:
The patent applies dimensionality reduction to transform high-dimensional geographic data into lower-dimensional embedding vectors. This dimensionality change maintains the essential structure and relationships of the data while reducing the computational burden on AI models, achieving a balance between complexity and accuracy.
3Ease of operation
If categorical data is used to represent geographic units, then data interpretability is maintained, but handling effectiveness decreases
Solution Approach 1:
The patent transforms categorical geographic data into continuous embedding vectors, changing the parameter type from discrete to continuous. This transformation improves the reliability and consistency of results by allowing for more nuanced comparisons and reducing the arbitrary nature of categorical groupings, while the embedding process maintains interpretability through learned representations.
Data Source
AI summary
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.