Residential Property Valuation Using Explainable ML Pricing Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating the price of new properties lack in efficiency and require domain knowledge, and traditional approaches fail to provide a comprehensive and explainable method for determining the price of new residential properties.
Innovation Solution
A method using Machine Learning Algorithms to analyze and predict the base price of the new residential property by inputting features such as square footage, lot area, number of beds, number of baths, number of floors, garage size, and geographic location, utilizing decision tree-based algorithms like Random Forest or Gradient Boost to determine the relationship between features and prices, and providing an explainable model for the price prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual methods are used to estimate property prices, then domain expertise can be applied, but the process is time-consuming and lacks efficiency
Solution Approach 1:
The patent replaces manual mechanical analysis methods with machine learning algorithms. The system automatically processes property features and historical data through ML models (Random Forest, Gradient Boost) to generate price predictions, eliminating the need for manual domain expertise while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service property valuation by allowing users to input property features and receive automated price estimates without requiring domain knowledge. The ML model independently analyzes the data and provides predictions with explanations, making the service accessible to anyone with basic input capabilities.
2Adaptability or versatility
If traditional spreadsheet filtering methods are used for Competitive Market_analysis, then simple comparisons can be made, but comprehensive analysis and explainability are lost
Solution Approach 1:
The patent implements feedback mechanisms where the ML model not only predicts prices but also provides explanations for the predictions. The system feeds back feature importances, model confidence scores, and comparative analysis results to help users understand the pricing rationale, making the black-box model interpretable and actionable.
Solution Approach 2:
The system transforms the analysis from simple spreadsheet filtering to multi-parameter ML modeling. It considers numerous features simultaneously (square footage, lot area, bedrooms, bathrooms, floors, garage size, location) and uses advanced algorithms to weigh their interactions, providing comprehensive adaptability beyond traditional threshold-based filtering.
3Ease of operation
If manual extrapolation methods are used, then simple price estimates can be generated, but they require high-level domain knowledge and lack transparency
Solution Approach 1:
The patent introduces an intermediary explanation layer between the complex ML model and the user. This layer translates model predictions into human-understandable feature importances and comparisons, maintaining operational simplicity while handling the complexity of the underlying algorithms transparently.
Data Source
AI summary
The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non-transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.