Cash Flow-Adjusted Comparables Valuation System
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Solution Overview
Problem
Current valuation approaches in commercial real estate, such as the replacement cost approach, comparable sales approach, and discounted cash flow (DCF) approach, are flawed due to reliance on human intuition, manual processes, and inaccurate assumptions about future market conditions and tenant behavior.
Innovation Solution
The Cash Flow-Adjusted Comparables (CFAC) AVM approach, which separates cash flows into deterministic and stochastic components, eliminates the need for human-driven assumptions by modeling asset- and location-level factors to predict future rent and asset sales prices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional valuation approaches (replacement cost, comparable sales, DCF) are used, then asset valuation can be performed, but the accuracy and objectivity are reduced due to reliance on human intuition and manual processes
Solution Approach 1:
The patent replaces manual, intuition-based valuation processes with an automated machine learning system. The ML model objectively processes asset data, location data, and comparable asset information to generate valuations, eliminating human bias and manual errors while maintaining systematic rigor.
Solution Approach 2:
The valuation system performs self-service by automatically gathering data, processing information through the ML model, and generating valuations without requiring human intervention at each step. The system autonomously handles data collection, feature extraction, model inference, and result generation.
2Productivity
If traditional valuation approaches are used, then asset valuation can be performed, but resource-intensive manual efforts are required
Solution Approach 1:
The patent substitutes manual valuation processes with an automated machine learning system that rapidly processes asset data, location data, and comparable asset information. This automation dramatically reduces the time and resources required for valuation while increasing throughput and productivity.
Solution Approach 2:
The ML-based valuation system operates continuously and automatically, processing valuation requests without interruption or manual intervention. The system maintains continuous operation, rapidly handling multiple valuations in sequence without the breaks and delays inherent in manual processes.
3Reliability
If traditional valuation approaches are used, then asset valuation can be performed, but unbiased and scientific appraisal is compromised due to human-driven assumptions
Solution Approach 1:
The patent replaces human-driven assumption-making with machine learning model inference. The ML model objectively processes asset data, location data, and comparable asset information without human bias, generating reliable and reproducible valuation results through systematic algorithmic processing.
Solution Approach 2:
The system incorporates feedback loops where the ML model continuously learns from and adjusts to patterns in asset data, location data, and comparable asset information. This feedback mechanism enhances the reliability and objectivity of valuations by systematically incorporating new information and correcting previous biases.
Data Source
AI summary
A method for managing valuation of an asset includes: inferring, by an engine and using a trained model, a future rent and asset sale price (FRASP) value of an asset based on an inferencing dataset received from an analyzer; upon receiving the FRASP value, appending, by the analyzer, the FRASP value to the inferencing dataset to generate an inferred FRASP value output; generating, by the analyzer, an asset valuation value for the asset based on the FRASP value and a net present value (NPV) of a known cash flow; and initiating, by the analyzer, notification of an administrator about the asset valuation value for the asset using a graphical user interface (GUI).