Commercial real estate underwriting: a buyer's reference

Can AI actually underwrite a commercial real estate deal? The Reddit question, answered

Why this page mentions Reddit. People add "reddit" to a search like this because they want an answer from someone with nothing to sell them. We do have something to sell, and we say so plainly below. We also did not read Reddit while writing this and we quote no post, user or thread. What follows is the straight answer to the question you were actually asking, with our conflict of interest stated up front and every vendor claim linked to the page we read it on.

Published 16 September 2026 by Altyst.

What the objections get right

These objections are technically correct. They are the failure modes we have had to design against in our own work. None of the following is a quotation, and none is offered as anyone else's view.

Arithmetic drift

A language model produces numbers that are individually plausible and mutually inconsistent. The unit count is right, the average rent is right, and the annual total it reports is not their product. Nothing in the architecture enforces consistency, which is why an LLM should not compute a pro forma.

Dropped rows and invented totals

Long rent rolls get truncated: the extracted table comes back with fewer rows than the source, under a summary that gives no sign anything is missing. The reverse happens too: a total that does not equal the lines above it, because it was written rather than computed.

Scale errors

Monthly rent read as annual, square feet confused with unit count, an expense stated per square foot treated as a total. The dangerous part is that the output still looks reasonable while being wrong by an order of magnitude.

Silent gaps

Asked for a figure the document does not contain, a model may supply one anyway. Missing data becomes a zero, and the zero flows into NOI without comment.

No audit trail, no reproducibility

If you cannot click a number and see the page it came from, you cannot check it, and if you cannot check it you cannot defend it. Run the same document twice and you may get two different answers. Fine for prose. Not fine for a figure in a credit memo.

Nothing to defend at investment committee

"The AI produced it" is not an answer to "where did the 4.9% exit cap come from." Underwriting is an argument about assumptions, and a tool that hides which figures are extracted, which are defaults and which are yours makes it harder.

What AI is actually good at

The same technology that is bad at arithmetic is good at the reading work that otherwise gets done line by line.

• Document extraction. Reading a scanned, rotated or poorly photocopied rent roll into structured rows. It still needs checking against the source.
• Normalization. Mapping dozens of idiosyncratic expense lines onto a standard chart of accounts, and separating recurring from one-time items in a T-12.
• Lease abstraction. Term, rent steps, options, recovery structure and caps, with page references.
• Triage. Screening a stack of offering memoranda down to the few worth modeling.
• Narrative. Drafting market and property sections, and summarizing what changed between model versions.
• Adversarial review. Asking a model to argue against your own assumptions, where no arithmetic is involved.

Generating numbers versus computing them

Two different architectures are sold under the same word. In the first, a language model reads your documents and writes the answer: the IRR is text. In the second, the model reads your documents and fills in inputs, then a conventional engine computes the results, the way a spreadsheet does. The model never touches the math. Change the exit cap and everything downstream recalculates by formula, not by asking a model again.

Extraction is an AI problem. Arithmetic is not. Underwriting is neither: it is judgment about assumptions, and it stays with you. A tool that respects that boundary can be trusted with the reading and audited on the math.

What is actually on the market

A general assistant plus your own spreadsheet. Paste the OM into ChatGPT or Claude, ask for the rent roll as a table, retype it into a model you trust. Cheap, flexible, full control of the math. The weaknesses are the retyping, the reconciliation, and that nothing carries to the next deal. A.CRE publishes a large library of real estate Excel models, offered on its own site "either completely free, or on a 'Pay What You're Able' basis with no minimum." For one deal, a spreadsheet you understand beats a subscription you do not.

An institutional platform. Altus Group's own site says its ARGUS software "is recognized as the industry standard and is taught in more than 200 universities and colleges worldwide." Where a lender, appraiser or joint venture partner requires your work product as an ARGUS file, that requirement settles the question on its own, and nothing another tool does faster changes it. Worth reading before you renew: the ARGUS Enterprise page now describes "ARGUS Enterprise, now part of ARGUS Intelligence Platform", and states that "Every tiered, asset-based subscription to ARGUS Intelligence Platform includes ARGUS Enterprise, alongside ARGUS Asset Manager, ARGUS Portfolio Manager, and ARGUS Assist." On price it says only that pricing is "flexible, tiered and scales with your business. Please contact us to discuss in more detail what that would look like for your business." That page presents ARGUS Enterprise as part of a platform subscription rather than as a standalone product, so it is worth asking what a renewal now covers. Two vendors position directly against it on their own sites: Rockport VAL, presented by The Rockport Group as "Property & Portfolio Cash Flow and Valuations", and U-Rite, which markets itself as "An Argus alternative, 100% in Excel."

Extraction-first tools. A newer group ingests deal documents and builds the model from them. Listed alphabetically: AcquiOS, Altyst, Apers, Archer, CREmodel, IntellCRE and redIQ, among others. A few distinctions taken from their own pages rather than from testing: AcquiOS says it populates your existing Excel template rather than delivering the model in a format of its own; CREmodel runs in the browser and describes itself as "an affordable ARGUS alternative"; redIQ was acquired by Radix and is now presented as Radix Underwriting, focused on multifamily. We have not run a controlled comparison of any of these, so treat every description here as public positioning rather than a test result, including ours. Adjacent but different: Blooma calls itself an "AI-Powered CRE Lending Platform" and sells to lenders rather than to equity buyers, Dealpath sells deal management and pipeline tracking, and Juniper Square sells fund operations, meaning investor reporting and fund administration.

Where Altyst fits, and where it does not

Altyst ingests an offering memorandum, rent roll, T-12 or lease PDF, or a pasted listing link, extracts the figures and builds a live, editable model. Calculations run on a purpose-built CRE engine rather than a language model, so every number traces back to an assumption or a source document. Coverage includes unit and tenant level rent roll, T-12 normalization, rent and expense growth, vacancy, lease rollover with TI and leasing commissions, debt sizing, equity waterfall, DCF, IRR, equity multiple, DSCR, cash-on-cash, exit assumptions, scenarios and sensitivity tables, across multifamily, office, retail, industrial, self-storage, hotel, medical office, mixed-use, land and ground-up development. Exports are Excel, PDF and PowerPoint. Pricing is $12 per month for 5 deals and $4 per extra deal, $24 for 15 deals and $3 per extra, and $99 for a five seat team with 75 deals and $3 per extra.

The same caveat applies to us. Altyst's extraction step is an AI step, and it is exposed to every failure mode listed at the top of this page: it can drop a row, misread a scale, or fill a gap the document left empty. What the architecture fixes is the second half of the problem. Once a figure lands in the model it is a visible input you can change, and the results below it are computed rather than written. Reconcile the extracted unit count and rent against the source document before you rely on anything. That applies to our tool as much as to anyone else's.

The limitations, stated plainly. Altyst is new: it launched in 2026, it is bootstrapped, and it is a small operation with no long public track record. There is no free tier and no free trial, so you cannot evaluate the full product without paying. It cannot open or produce ARGUS files. It is not an institutional, lender-facing system of record, and it does not do property management, lease administration, CRM, brokerage listings or debt origination. If you need one platform that also holds asset management, portfolio reporting or fund administration, an established vendor will serve you better.

When to buy something else. If a lender, appraiser or joint venture partner requires an ARGUS file, buy ARGUS; we cannot produce one, and no amount of speed elsewhere changes that. If the team will not leave Excel, U-Rite markets itself as precisely that, an ARGUS alternative that stays inside the spreadsheet. If the real bottleneck is pipeline and deal tracking rather than the model, that is what Dealpath sells. If it is investor reporting and fund administration after the close, that is Juniper Square. If you underwrite one deal a year, an A.CRE template and an afternoon will cost you close to nothing.

Our free calculators, embeddable calculators, free multifamily Excel model, glossary and answers need no account.

Seven tests for any AI underwriting tool

Run these on a deal you have already modeled by hand.

1. Test the two halves separately. Re-run the extraction on the same document and expect small differences, because reading a PDF is a model step. Then hold the inputs fixed and recompute: that must be identical every time. If the second one moves, the math is being generated rather than computed.
2. Count the rows. Does the extracted rent roll have as many units or tenants as the source?
3. Check one multiplication. Take the occupied units, multiply by their average in-place monthly rent, multiply by twelve, and see whether it ties to the annual in-place base rent the tool reports. Then check that in-place rent and gross potential rent appear as separate lines, with what sits between them, loss to lease and vacant space carried at market, shown rather than netted into a single number.
4. Click a number. Can you trace any figure to its source page or to the assumption behind it?
5. Change one input. Move the exit cap 25 basis points and confirm IRR, equity multiple and sale proceeds all move.
6. Remove something. Feed it a document with no reimbursement structure: does it say so, or fill in a zero?
7. Export and audit. Open the Excel file. Live formulas, or pasted values?

The bottom line

AI cannot choose the assumptions and stand behind them. That is the job, and it is still yours. What it can do, well enough to change how long a first pass takes, is read the documents and populate the inputs. Buy the tools that are explicit about that boundary: model for extraction, engine for arithmetic, every figure traceable, everything editable.

FAQ