Siamese Network Automated Valuation Model
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for determining the value of real-estate properties are inefficient and inaccurate due to their complexity, requiring separate models for identifying similar properties, computing weights, and adjusting prices, which leads to time-consuming and often incorrect valuations.
Innovation Solution
A machine learning technique, such as a Siamese Network, is trained to jointly establish a relationship between weights and value adjustments of comparable properties, allowing for accurate prediction of a property's value by combining these factors in a single model, reducing the need for multiple separate models and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple separate models are used for identifying similar properties, computing weights, and adjusting prices, then each function can be specialized, but the system complexity increases and valuation time increases
Solution Approach 1:
The patent combines multiple separate models (property identification, weight computation, and price adjustment) into a single integrated automated valuation model. This unified model processes all functions simultaneously, reducing system complexity while maintaining valuation accuracy through joint training on comprehensive datasets.
Solution Approach 2:
The automated valuation model is designed as a universal system that performs multiple functions: identifying comparable properties, computing their weights, and adjusting prices based on various factors. This single multi-functional model replaces the need for multiple specialized models, simplifying the overall system architecture.
2Measurement precision
If multiple separate models are used for property valuation, then each model can be optimized for its specific task, but the overall valuation process becomes time-consuming
Solution Approach 1:
By merging property identification, weight computation, and price adjustment into a single integrated model, the system processes all valuation tasks simultaneously rather than sequentially through multiple separate models, significantly reducing valuation time while maintaining precision through comprehensive joint optimization.
Solution Approach 2:
The model performs preliminary processing of all input data (property features, comparable properties, market factors) simultaneously during a single training and inference process, eliminating the need for repeated data processing across multiple separate models and reducing overall valuation time.
3Adaptability or versatility
If manual analysis of similar property sales is performed, then sellers can estimate property value, but the process is very time consuming and resource intensive
Solution Approach 1:
The patent replaces the manual mechanical process of analyzing similar property sales with an automated machine learning model that processes property data, identifies comparables, computes weights, and determines valuations automatically, dramatically increasing valuation efficiency while maintaining adaptability to different property types and market conditions.
Solution Approach 2:
The automated valuation model enables the system to perform property valuation independently without human intervention, automatically gathering data, processing information, and generating valuations, thereby freeing sellers from time-consuming manual analysis while providing accurate, adaptable valuation decisions.
4Ease of operation
If manual analysis of similar property sales is performed, then sellers can understand market conditions, but the accuracy is often insufficient leading to poor decisions
Solution Approach 1:
The automated model replaces manual analysis with algorithmic processing that systematically evaluates all relevant factors (property features, comparable sales, market conditions) without human cognitive limitations, improving valuation accuracy while maintaining ease of operation through automated decision support.
Solution Approach 2:
The model incorporates feedback mechanisms by continuously learning from transaction data and market outcomes, adjusting its valuation calculations based on actual market performance, thereby improving accuracy over time while providing sellers with reliable, data-driven decision-making support.
Data Source
AI summary
Systems and methods are disclosed for automatically determining property value, the systems and methods perform operations comprising: receiving, by a server, subject real-estate property listing information associated with a subject real-estate property; identifying a plurality of comparable real-estate property listings based on attributes of the subject real-estate property listing information; processing the subject real-estate property listing information together with the plurality of comparable real-estate property listings using a trained machine learning technique to predict a value for the subject real-estate property, the trained machine learning technique being trained to jointly establish a relationship between weights assigned to a set of training comparable real-estate property listings and value adjustments of the set of training comparable real-estate property listings and a value of a real-estate property of interest; and performing an action with respect to the subject real-estate property based on the predicted value of the subject real-estate property.