After years of false starts and regulatory confusion, a few serious projects are pointing toward what this technology can actually deliver for real estate
Underwriting has always been a discipline built on judgment, speed, and pattern recognition. Traditionally, that meant manually reviewing offering memoranda, building out models, normalizing assumptions, and stress-testing deals line by line. The process was slow, but it imposed a kind of rigor that investment teams came to rely on.
That workflow is beginning to change.
AI is starting to reshape how deals are screened, how documents are analyzed, and how risks are surfaced. Tasks that once consumed hours can now be completed in minutes. Scenarios can be tested faster and more consistently. Yet speed alone is not the point. The real question facing most firms is how to use these tools without lowering standards, introducing hidden risks, or mistaking automation for judgment.
So we're offering our first-ever deep dive workshop into the growing role of AI in real estate underwriting. We'll help you understand how to utilize these new tools and incorporate them seamlessly into your business.
The AI in Underwriting Workshop
Our live, interactive workshop on February 20 covers how AI is being applied across real estate underwriting, from deal screening through investment committee preparation. The session is designed for investors, developers, lenders, and operators who want a clear, practical understanding of what these tools can do today and how they fit into real underwriting processes.
This is a hands-on two-hour working session built around real underwriting decisions: how documents are processed, how assumptions are generated, and how teams can incorporate AI without weakening discipline, governance, or trust in the numbers.
Participants will work through real examples, examine practical frameworks, and leave with tools they can apply directly to their own underwriting workflows.
Understand how modern AI systems actually work, including the difference between models, applications, and automation workflows, explained in terms relevant to investment teams
Apply AI to underwriting tasks, including deal intake, document abstraction, rent comps, expense normalization, and memo preparation
See real underwriting workflows in practice, with examples of AI-assisted screening, modeling support, and risk analysis
Evaluate where AI improves speed and where human judgment remains essential, and how to maintain rigor as processes accelerate
Prepare your data, models, and team, including how to structure assumptions and integrate AI into Excel-first workflows
Anticipate what’s coming next, including automated modeling, document intelligence, and AI-supported IC processes
Throughout the sessions, these ideas are grounded in examples drawn from acquisitions, development, and credit underwriting.
What We’ll Cover
The workshop walks step by step through how AI fits into the underwriting process, including:
Automating deal summaries and abstracting offering memoranda, rent rolls, and T12s
Supporting financial modeling through assumption generation and sensitivity analysis
Identifying potential blind spots in underwriting and stress-testing scenarios
Drafting and standardizing investment committee materials
Evaluating implementation strategies, including governance, controls, and data quality
Mapping AI tools to each step of the underwriting workflow and assessing build-versus-buy decisions
We’ll also discuss how underwriting roles are evolving as these tools mature, and what that means for investment teams over the next several years.
Format & Access
Date: February 20, 2026 Time: 12:00–2:00 PM Eastern Platform: Zoom
The session is interactive and will be recorded and shared with all participants. Participants receive:
Pre-read materials, including framing decks and underwriting-specific examples
Post-workshop access to the Thesis Driven Circle community, including tools, vendor lists, and implementation guides
Cost: $299
Who Is This Workshop For?
It's designed for investors, developers, lenders, and operators who are actively involved in underwriting or investment decisions, as well as professionals who want a grounded understanding of how AI is changing the mechanics of deal evaluation and risk analysis.
Underwriting will remain a human discipline. But the tools available to support that discipline are improving quickly, and the teams that learn to use them well will have an advantage in speed, consistency, and decision quality.
Brad is the founder and editor of Thesis Driven (www.thesisdriven.com), a publication covering innovation in the built world. Over the past decade he has co-founded General Assembly and Common which generated a combined $500M+ in enterprise value.
