The grounded workflow

Claude for commercial real estate underwriting

Yes, Claude by Anthropic can support commercial real estate underwriting, and it is at its best when grounded in the actual deal documents: the OM, the T-12, and the rent roll. The approach PSV builds on is simple: Claude reads your files, fills your underwriting template, and cites the page or line behind every number, and a person verifies the work before it reaches committee. Ungrounded, no general model should be trusted with committee-ready figures. Grounded and cited, Claude is a strong first pass on the documents, with every number traceable to its source.

The workflow

Underwrite a deal with Claude, step by step

01
Give Claude the deal documents
Upload the actual OM, T-12, and rent roll. The model must work from your files, not from general knowledge of what a deal like this usually looks like.
02
State your rules and your template
Spell out your underwriting assumptions and thresholds, and name the exact template to fill. The output arrives in your format, not the model's.
03
Require a citation per figure
Every rent, expense, and assumption gets the page or line it came from. A number without a source is a number you cannot use.
04
Verify before committee
A person checks the citations, chases the anomalies, and signs off. Claude drafts the first pass. The underwriter owns the numbers.

This is how PSV builds CRE AI on Claude by Anthropic: cited truth as a system property, in a workflow your team owns. The same setup is taught in CRE AI training and deployed for firms.

Division of labor

What Claude handles well, and what stays yours

Well-suited to Claude

Long documents: a full deal package read in one pass, first page to last
Rule-following: detailed, multi-step instructions held across the whole job
Abstraction: a messy rent roll turned into your clean, consistent template

Stays human-owned

The go or no-go decision
The assumptions behind the model
The negotiation

Weighing ChatGPT for real estate underwriting instead? The same grounding rules apply to any model. See the full Claude vs ChatGPT for CRE comparison.

Failure modes

Where AI underwriting goes wrong

Ungrounded answers

Asked about a deal without the documents, a model answers from general knowledge, and it sounds right. The fix is structural: give it the files and restrict it to them.

Unverifiable numbers

A figure with no trail cannot be checked, so it cannot be trusted. A required citation per figure makes every number traceable to its source document.

Template drift

Left loose, outputs arrive in a different shape every time. A stated template and standing rules keep the work consistent, and keep the workflow yours.

All three trace to the same root: an ungrounded model with no obligation to show its work. The grounded setup removes the root, which is why it comes first in CRE AI done properly.

Frequently asked

Claude for underwriting, answered

Can Claude underwrite a commercial real estate deal?: Claude can support underwriting: reading the OM, T-12, and rent roll, filling an underwriting template, and citing the source behind each figure. It should not replace the underwriter. A person verifies the citations and owns the go or no-go decision.
How do I underwrite a deal with Claude step by step?: Upload the actual deal documents, state your underwriting rules and the exact template to fill, and require a source citation for every figure. Then verify the citations before the numbers reach committee. The setup, not the prompt wording, is what makes the output dependable.
What documents does Claude need to underwrite a deal?: At minimum the offering memorandum, the trailing 12-month financials, and the current rent roll. Loan terms, comps, and market reports help when the analysis calls for them. Actual files beat summaries, because the model can only cite what it can read.
How accurate is Claude for real estate underwriting?: Accuracy depends more on the setup than the model. Grounded in the real documents and required to cite a source per figure, every number is checkable, and a person checks it. No general model's unverified output should be treated as committee-ready.
Is ChatGPT or Claude better for real estate underwriting?: PSV builds on Claude for long, rule-bound deal documents, where steady instruction following across a full document set matters most. Either model improves sharply with grounding, stated rules, and required citations. The full comparison is on the Claude vs ChatGPT for CRE page.
Does AI underwriting replace analysts?: No. The model handles the document-heavy first pass: extraction, abstraction into the template, and flagging what does not reconcile. Analysts own the assumptions, the verification, and the recommendation, which is where underwriting judgment lives.

Keep reading

Claude vs ChatGPT for CRE AI agents for commercial real estate What is CRE AI?

Learn this workflow on a real deal

The CRE AI Institute, by PSV, teaches this exact underwriting workflow on a real practice deal, with Claude reading the documents and citing its sources. 7-day free trial. $0 today.

Explore the Institute What is CRE AI?

Claude for commercial real estate underwriting

Underwrite a deal with Claude, step by step

Give Claude the deal documents

Upload the actual OM, T-12, and rent roll. The model must work from your files, not from general knowledge of what a deal like this usually looks like.

State your rules and your template

Spell out your underwriting assumptions and thresholds, and name the exact template to fill. The output arrives in your format, not the model's.

Require a citation per figure

Every rent, expense, and assumption gets the page or line it came from. A number without a source is a number you cannot use.

Verify before committee

A person checks the citations, chases the anomalies, and signs off. Claude drafts the first pass. The underwriter owns the numbers.

This is how PSV builds CRE AI on Claude by Anthropic: cited truth as a system property, in a workflow your team owns. The same setup is taught in CRE AI training and deployed for firms.

What Claude handles well, and what stays yours

Well-suited to Claude

Long documents: a full deal package read in one pass, first page to last
Rule-following: detailed, multi-step instructions held across the whole job
Abstraction: a messy rent roll turned into your clean, consistent template

Stays human-owned

The go or no-go decision
The assumptions behind the model
The negotiation

Weighing ChatGPT for real estate underwriting instead? The same grounding rules apply to any model. See the full Claude vs ChatGPT for CRE comparison.

Where AI underwriting goes wrong

Ungrounded answers

Asked about a deal without the documents, a model answers from general knowledge, and it sounds right. The fix is structural: give it the files and restrict it to them.

Unverifiable numbers

A figure with no trail cannot be checked, so it cannot be trusted. A required citation per figure makes every number traceable to its source document.

Template drift

Left loose, outputs arrive in a different shape every time. A stated template and standing rules keep the work consistent, and keep the workflow yours.

All three trace to the same root: an ungrounded model with no obligation to show its work. The grounded setup removes the root, which is why it comes first in CRE AI done properly.

Claude for underwriting, answered

Can Claude underwrite a commercial real estate deal?

Claude can support underwriting: reading the OM, T-12, and rent roll, filling an underwriting template, and citing the source behind each figure. It should not replace the underwriter. A person verifies the citations and owns the go or no-go decision.

How do I underwrite a deal with Claude step by step?

Upload the actual deal documents, state your underwriting rules and the exact template to fill, and require a source citation for every figure. Then verify the citations before the numbers reach committee. The setup, not the prompt wording, is what makes the output dependable.

What documents does Claude need to underwrite a deal?

At minimum the offering memorandum, the trailing 12-month financials, and the current rent roll. Loan terms, comps, and market reports help when the analysis calls for them. Actual files beat summaries, because the model can only cite what it can read.

How accurate is Claude for real estate underwriting?

Accuracy depends more on the setup than the model. Grounded in the real documents and required to cite a source per figure, every number is checkable, and a person checks it. No general model's unverified output should be treated as committee-ready.

Is ChatGPT or Claude better for real estate underwriting?

PSV builds on Claude for long, rule-bound deal documents, where steady instruction following across a full document set matters most. Either model improves sharply with grounding, stated rules, and required citations. The full comparison is on the Claude vs ChatGPT for CRE page.

Does AI underwriting replace analysts?

No. The model handles the document-heavy first pass: extraction, abstraction into the template, and flagging what does not reconcile. Analysts own the assumptions, the verification, and the recommendation, which is where underwriting judgment lives.