AI-Accelerated Deal Underwriting for Real Estate
Turn raw deal documents into investment-ready underwriting and memos in hours—not weeks. Automate data extraction, standardize models, and generate decision-ready outputs.
Why Is Deal Underwriting Still So Manual?
Underwriting should drive decisions, yet most of the process is spent preparing data and relying on manual processes:
Pulling data manually from OMs, rent rolls, T12s, and operating statements
Hours (or days) lost retyping PDFs into Excel
Models built differently across teams with inconsistent assumptions and outputs
Investment memos assembled from scattered sources
Copy-paste errors, narrative mismatches, and version control issues
As a result:
Analyst time is consumed by data preparation
Hours (or days) lost retyping PDFs into Excel
Output quality varies across teams
Scaling deal volume requires additional headcount
How Does AI-Accelerated Underwriting Work?
From raw documents to decision-ready output—automatically.
Instead of rekeying data and building models manually, teams review structured outputs and refine investment insights.
How It Works
What happens
- Upload deal documents (OMs, rent rolls, T12s)
- Review underwriting inputs
- Draft investment memo
- Validate assumptions
What the system does
- Extracts and structures key data automatically
- Populates standardized models with firm-specific assumptions
- Generates complete first draft with narrative, comps, risks, and exhibits
- Tracks every override, input, and source with full auditability
How This Changes Everything
Before
Analysts spend 60–70% of time on data extraction
Models vary by analyst
Memos built manually from multiple sources
Scaling requires more analysts
After
Analysts review structured data and focus on decisions
Standardized, firm-approved models populate in minutes
Investment memos generated as complete first drafts
Teams evaluate 3–5x more deals with existing teams
Where Does AI-Accelerated Underwriting Fit?
This approach enhances core real estate investment workflows:
One workflow connects documents, models, and outputs — without switching tools.
What Changes When You Underwrite in Hours?
Underwriting becomes faster, more consistent, and easier to scale across the pipeline.
Underwriting cycles reduced by 70–80%
Deal throughput increased by 3–5x
Analysts focus on judgment, scenarios, and risk
Standardized outputs improve investment committee decisions
What Could You Underwrite If It Took Hours Instead of Weeks?
Start with one real deal from your pipeline.
We’ll show how raw documents turn into a fully structured model and investment memo — using your own use case.
Common Questions About AI in Deal Underwriting
Do we need to change our existing models?
No. The workflow is tailored to your firm’s models, assumptions, and templates, and integrates with Excel, Argus, deal platforms, and data rooms.
How accurate is the extracted data?
Data extraction is paired with validation workflows and confidence scoring, allowing teams to review and confirm key inputs.
Can this support our investment memo format?
Yes. Memo outputs follow your firm’s structure, including executive summary, market context, financials, risks, and supporting exhibits.
Who uses this — analysts or senior team members?
Both. Analysts gain efficiency in data preparation, while senior team members benefit from faster access to structured, decision-ready outputs.
How quickly can this be implemented?
Initial workflows can be configured quickly using existing deal documents, models, and data sources.
What Deals Could You Move Faster — With the Same Team?
We’ll show how AI-accelerated underwriting works on your pipeline.
Try it with your use case
We’ll show you how to get the answer instantly — with your own use case.