The Future of Commercial Real Estate Valuation
Who we are
Michael Woodard
- Senior Mortgage Banker with Atlantic Bay Mortgage Group
- Georgetown University: Master's in Real Estate, December 2026
- Leadership roles across multiple industries during professional career
Nick Spiers
- 15+ years in software development and technical leadership
- CTO experience across emerging, high growth technology industries
- Passionate about scalable AI and cloud infrastructure solutions
The AI Renaissance
- AI is bringing the tsunami of change to a very antiquated industry
- Speed and efficiency will be catapulted into the 21st century
The biggest hurdle in the transaction process: Appraisal
- Too individualistic
- Subjective and inconsistent
- Outdated amount of emphasis on backward-looking data
Appraisals: Art vs. Science
- Appraiser has full discretion on how market sentiment is derived
- Appraiser's individualistic style disproportionately skews values
Appraisal industry headwinds
- Aging demographic (average age of a commercial appraiser is over 60)
- Commoditization of the appraisal process and high barriers to entry have disincentivized younger generation from career path
- Pipeline of trainees not big enough to sustain the number of expected retirements in the next 5-10 years
A Modern Approach to determining market sentiment: Smitty (CRESI)
- CRESI: Commercial Real Estate Sentiment Index (dubbed "Smitty")
- Proprietary agentic AI economist trained to determine market sentiment
Difference between generative and agentic
Generative AI: Creates images/narratives (think ChatGPT)
Agentic AI: Takes independent actions to accomplish goals and learns from the results
Consistent and methodical approach
- Foundation of Smitty is rooted in a consistent and methodical approach to determining sentiment
- CRESI eliminates subjectivity and ensures every valuation is based on the same rigorous methodology
- Removes feelings out of the valuation adjustment
CRESI index converted to adjustment to value
Index layers:
- Macroeconomic Factors
- Microeconomic Factors
- Geographic Considerations
- Capital Markets Signals
- Demographics & Migration Patterns
- Sentiment Analysis
Layers for CRESI index are specifically weighted for each asset class. Each layer is assessed and scored.
Agentic learning process
- Memory bank: Stores all historical data and valuation outcomes
- Self-correcting: Continuously refines its models based on new information
- Constantly improving: Gets smarter with every valuation
How it comes together
Accepted Valuation Methodology
DCF, Cap Rate Analysis, Comp Analysis
+
Smitty Score Adjustment
Market Sentiment, Risk Factors, Regulatory Considerations
= CREstimate Value
Comprehensive, Consistent, Data-Driven
Lenders,
Regulators,
Private Market
Who is our user?
- Banks/lenders – potential to license to banking software firms
- Regulators – Appeal to appraisal sub-committee and Fannie Mae/Freddie Mac
- Private market – Investment firms, REIT's, Brokers
Business Model
- Subscription based model
- Value available with free account
- One-time fee for full property report
- Scalable subscription for paid users to have unlimited access