Property Rent Estimation Using Distance-Weighted Comparable Analysis
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Solution Overview
Problem
Current methods for estimating property rental values are subjective, lack objectivity, and fail to consider multiple factors simultaneously, leading to inconsistencies and discrepancies, and do not allow for real-time updates or customization according to user needs, with limited integration of modern technologies like machine learning and cloud computing.
Innovation Solution
A cloud-based system employing machine learning techniques that receives user inputs, leverages a property database, and applies data analysis to provide an objective final rent estimate, including filtering and sorting comparable properties, generating confidence scores, and offering customizable reports, while utilizing encryption for data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional human-based methods are used for property rent estimation, then subjective judgement and experience can be applied, but the results are susceptible to errors, bias, and inconsistencies
Solution Approach 1:
The patent replaces the mechanical system of human judgment and subjective assessment with an automated computational system that uses machine learning algorithms and data processing. This substitution eliminates human bias and inconsistency while providing objective, repeatable rent estimates based on multiple property factors and market data.
Solution Approach 2:
The system enables self-service by automatically gathering property data, analyzing comparable properties, and generating rent estimates without requiring human intervention. The automated process independently performs data collection, analysis, and estimation, providing consistent results without human error or bias.
2Adaptability or versatility
If existing rent estimation systems are used, then basic market research can be conducted, but they lack the ability to consider multiple factors simultaneously and objectively
Solution Approach 1:
The system implements multi-functionality by integrating multiple analysis capabilities into a single platform. It simultaneously evaluates property characteristics, location factors, market conditions, and comparable properties using various data sources and analytical methods, providing comprehensive rent estimation that considers all relevant factors together.
Solution Approach 2:
The patent combines multiple data sources, analysis methods, and property factors into a composite estimation model. It integrates structured property data, unstructured market information, historical trends, and comparable property analyses to create a comprehensive rent estimate that leverages the strengths of each component.
3Productivity
If traditional rent estimation methods are used, then simple processes can be maintained, but they do not allow real-time updates and adjustments based on current market trends
Solution Approach 1:
The system performs preliminary actions by pre-establishing data collection mechanisms, analysis frameworks, and update protocols. It proactively monitors market data sources and automatically updates estimates when new information becomes available, eliminating the need for manual refreshes and enabling real-time adaptability.
Solution Approach 2:
The system implements feedback loops that continuously monitor market conditions, property data changes, and estimation accuracy. This feedback mechanism automatically triggers updates and adjustments based on current market trends, ensuring estimates remain current without manual intervention while managing complexity through automated control.
4Adaptability or versatility
If existing estimation systems are used, then standard processes can be followed, but they do not allow for easy customization and adjustment according to user needs
Solution Approach 1:
The system implements dynamics by allowing flexible configuration of estimation parameters, data sources, and analysis methods according to user needs. Users can dynamically adjust weights for different property factors, select specific comparable properties, and customize output formats without requiring complex technical knowledge, making the system adaptable to diverse requirements.
5Ease of operation
If current systems are used for rent estimation, then basic data processing can be performed, but they lack an efficient and convenient means of presenting and sharing the information generated
Solution Approach 1:
The system creates multiple copies of the estimation results in different formats and levels of detail. It generates comprehensive reports, summary views, and shareable data exports that preserve all underlying analysis and data while presenting information in user-friendly formats. This copying approach ensures information is not lost but made accessible in various forms for different user needs.
Data Source
AI summary
The present disclosure relates to a method for estimating a final rent estimate (FRE) of a property. The method enables receiving, at a computing device, a dwelling input from a user. A remote server acquires the received dwelling input from the computing device and analyses a property database to determine an initial rent estimate (IRE) based on each property located in a geological grid and each adjacent geological grid. The server identifies the multiple comparable properties based on the determined IRE, acquired dwelling input and at least one filter, which can be selected from a spatial filter, a temporal filter, a configuration filter and a variance filter. The FRE can be calculated based on the rental value of the identified multiple comparable properties, wherein a rental value contribution of each comparable property is inversely proportional to a distance from the location input. The calculated FRE is displayed at the computing device.