Neural Network Home Valuation System
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Solution Overview
Problem
Conventional home valuation methods, such as comparative market analysis, are time-consuming, expensive, and lack accuracy due to manual processes and reliance on incomplete MLS data, making it difficult to scale and repeat accurately.
Innovation Solution
A system utilizing multiple neural network models to analyze images of a home, determining the type of space and identifying predictive features, which are then used by a machine learning model to estimate the home's value without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparative market analysis is used to determine home value, then accuracy can be achieved through expert judgment, but the process is time-consuming and expensive
Solution Approach 1:
The patent replaces the manual mechanical process of comparative market analysis with an automated machine learning system. Multiple neural network models process home images and features automatically, substituting human expert judgment with algorithmic analysis. This maintains valuation accuracy through sophisticated multi-model architecture while eliminating time loss associated with manual processes.
Solution Approach 2:
The machine learning system performs self-service by automatically analyzing home images, extracting features, and determining property values without requiring human intervention. The multi-model neural network system independently completes the entire valuation process, from image processing to value determination, making the system self-sufficient and eliminating dependency on manual appraisers.
2Measurement precision
If manual comparative market analysis is used, then detailed expert assessment can be performed, but the process lacks scalability
Solution Approach 1:
The patent replaces manual analysis with automated machine learning models that can process multiple home images simultaneously. The system maintains detailed assessment capabilities through multiple neural network models that analyze various features, while achieving high productivity by automatically valuing numerous properties without additional time investment or human resources.
Solution Approach 2:
The machine learning system achieves universality by designing multi-functional models that can handle diverse home types, styles, and features through a single automated platform. The neural network models are trained to recognize and evaluate various property characteristics, enabling the system to accurately value different kinds of homes while maintaining high productivity across all property types.
3Ease of operation
If MLS data is used for valuation, then readily available information can be utilized, but the data is incomplete and lacks accuracy
Solution Approach 1:
The patent segments the valuation process into multiple independent neural network models, each specializing in analyzing specific home features from images. This segmentation allows the system to go beyond incomplete MLS data by extracting detailed visual information about property conditions, renovations, and unique characteristics that are not captured in standard database listings, thereby improving accuracy while maintaining ease of operation.
Solution Approach 2:
The machine learning system acts as an intermediary between available data sources and valuation determination. It supplements incomplete MLS data by processing home images through multiple neural network models that extract additional features and characteristics. This intermediary process enriches the data foundation with visual information about property conditions, improving accuracy while building upon readily accessible MLS information.
Data Source
AI summary
Techniques for determining value of a home by applying one or more neural network models to images of spaces in the home. The techniques include: obtaining at least one image of a first space inside or outside of a home; determining a type of the first space by processing the at least one image of the first space with a first neural network model; identifying at least one feature in the first space by processing the at least one image with a second neural network model different from the first neural network model and trained using images of spaces of a same type as the first space; and determining a value of the home at least in part by using the at least one feature as input to a machine learning model different from the first neural network model and the second neural network model.