CRE underwriting tools read a deal's source documents (rent rolls, T-12s, operating statements, offering memoranda) and turn them into the figures a decision rests on: net operating income, cap rate, debt service coverage, internal rate of return, equity multiple. Acquisition teams, asset managers and lender credit desks use them to screen more deals than a spreadsheet can carry.
Underwriting a commercial real estate deal has two phases. Phase one is working out what the deal is worth. Phase two is finding the financing. Most underwriting software stops at phase one: once the DCF is done and the IRR looks right, you still have to work out which lender will finance it, on what terms, and how fast.
Here is how the major platforms compare, and where the financing layer picks up.
What do CRE underwriting tools actually do?
Underwriting software ingests rent rolls, T-12s, operating statements and offering memoranda, extracts the line items, and populates a model that returns NOI, cap rate, DSCR, IRR and equity multiple. The work it removes is re-keying, not judgement. Every platform below does some subset of extraction, modelling and market data.
The category has moved in three steps. Early tools were spreadsheet calculators. A second generation added structured data extraction, almost all of it multifamily-only. The current generation parses any document format, reconciles figures that disagree across sources, and pulls market data straight into the model without manual re-entry.
CRE Daily's roundup of must-have AI tools for CRE makes the same general point: AI is cutting manual work and sharpening decisions.
What none of it changes is the metric set. You still need to agree on how NOI is normalised, which expenses are recurring, and what debt yield the deal clears at, before any tool's output means anything. If you only need to sanity-check a single deal, our free underwriting calculator covers the same arithmetic without a subscription.
Who needs underwriting software, and who is fine in a spreadsheet?
Buy software when the volume of deals you screen, not the complexity of any one deal, is what breaks your process, because extraction tools save re-keying rather than analysis. One acquisition a quarter does not justify a subscription. A steady screening pipeline across several asset classes usually does.
The honest test is how many deals you kill. If most of your pipeline dies in the first hour, you are paying analyst time to type numbers into a model that was never going to matter, and extraction pays for itself. If you underwrite a handful of deals a year and each one gets weeks of attention, Excel plus a disciplined template is still the right answer.
How do the major CRE underwriting platforms compare?
The six platforms below split into three jobs (document extraction, cash-flow modelling, and deal or credit workflow), and almost none of them does all three well. Read each entry for what it is built to do and what it deliberately leaves to another tool.
Cactus
Cactus is an AI-native CRE underwriting platform that puts document extraction, real-time DCF modelling and live market data in one workflow. It ingests rent rolls, T-12s and offering memoranda from PDF, scanned images and Excel, and identifies unit mix, rent, square footage, lease dates and operating expenses. Raw inputs become a full DCF in seconds: cash flow projections, IRR, cash-on-cash yields and an equity waterfall, with scenario controls and sensitivity sliders for cap rates, hold periods and financing terms, plus bulk simulations run in parallel.
Market data is the third leg: rent comps, cap rates, vacancy analytics, sale transaction histories and submarket surveys feed the model without manual updates. Pricing is a flat monthly subscription rather than per seat or per deal, which suits teams whose deal count swings month to month.
| Strength | Detail |
|---|---|
| All-in-one | Doc extraction, DCF and live market data in one workflow |
| Speed | DCF in seconds; long documents processed in one pass |
| Price shape | Flat monthly subscription, no per-deal charges |
| Segments | Multifamily, self-storage, retail, industrial, office |
| Weakness | Newer entrant than Argus; no cell-level source citation |
It fits investors running a continuous screening pipeline who want extraction, modelling and market data without switching tools.
Argus Enterprise (Altus Group)
Argus Enterprise is the institutional standard for CRE cash flow modelling and asset valuation, used across owners, lenders and appraisers for DCF analysis, lease-by-lease modelling and portfolio reporting. It is the common language of CRE finance in North America, Europe and Australia: lenders accept Argus outputs, appraisers build Argus models, and investment committee packages are assembled in it.
The critical limitation is that Argus is not a document extraction tool. Analysts still key in lease data, rent rolls and operating assumptions by hand. At volume that is exactly the bottleneck the AI tools were built to remove, which is why Argus is so often paired with one of them.
| Strength | Detail |
|---|---|
| Institutional acceptance | Lenders, appraisers and IC all accept Argus outputs |
| Modelling depth | Multi-tenant lease structures, TI/LC, full hold period |
| Portfolio reporting | Asset management workflows |
| Weakness | Per-seat pricing plus training and implementation; steep learning curve; no doc extraction |
It fits institutional buyers modelling commercial assets for IC, lender packages, or long-hold DCF, usually alongside an extraction tool for document intake.
Primer (PropRise)
Primer is an AI document intelligence platform built for CRE acquisition teams. It ingests offering memoranda, rent rolls, T-12s and property-management reports, then maps the extracted data into your existing Excel underwriting model instead of asking you to abandon it.
The defining feature is cell-level source citation. Every populated cell traces back to the exact page, table and line in the source document, and when two documents disagree on the same figure Primer flags the conflict rather than silently picking one. There is no native DCF: Primer feeds your model, which is the right trade for teams with an established template.
| Strength | Detail |
|---|---|
| Source citation | Every cell cites source doc, page, table and line |
| Conflict detection | Flags discrepancies across docs instead of overriding them |
| Any asset class | Multifamily, storage, industrial and more |
| Excel plugin | Pulls outputs into a live spreadsheet without leaving Excel |
| Weakness | Newer; smaller comp database than RedIQ; no native DCF |
It fits acquisition teams with established Excel models who want AI extraction with a full audit trail.
RedIQ (Radix Software)
RedIQ is the most established multifamily-specific underwriting platform. DataIQ extracts and standardises rent rolls and T-12s, ValuationIQ generates a structured multifamily proforma, and QuickSync populates user templates. It is one of the longest-established platforms in its category and carries a historical deal database used for rent and expense benchmarking.
The limitation is scope. Multifamily only: no self-storage, industrial, office or retail. There is no cell-level source citation, and template re-mapping is required deal by deal.
| Strength | Detail |
|---|---|
| Multifamily depth | Historical deal database, rent benchmarking, embedded market reports |
| Track record | Long-established in the multifamily-only category |
| Weakness | Multifamily only; no source citation; custom pricing |
It fits multifamily-only acquisition teams that want market data integrated into underwriting.
Dealpath
Dealpath is a deal management platform, not a modelling or extraction tool. It tracks deal status, task ownership, document versions and approval workflows across a team. Where the operational pain is "who has the ball" rather than "what is this worth", it is best in class. It is not a substitute for underwriting software, and pairing it with one is the normal configuration.
Blooma
Blooma targets lender-side credit underwriting: DSCR automation, loan sizing and credit memo generation. It is a poor fit for equity acquisition teams and a strong one for bridge lenders, CMBS originators and bank credit desks processing a steady flow of loan submissions.
Which underwriting tool fits your team?
Pick by the job you need done, not by feature count, because every platform here is strong at one thing and deliberately thin at the others. Match extraction, modelling and workflow to the bottleneck you actually have, then accept that the financing step sits outside all of them.
| Platform | Best for | Price shape | AI extraction | Native DCF | Live market data | Source citation |
|---|---|---|---|---|---|---|
| Cactus | Mid-market investors | Flat monthly | Yes | Yes | Yes | No |
| Primer | Teams with custom Excel models | Flat monthly | Yes | No, feeds Excel | No | Yes, cell-level |
| RedIQ | Multifamily-only teams | Custom quote | Yes, multifamily | Yes, proforma | Yes, multifamily | No |
| Argus Enterprise | Institutional DCF | Per seat | No, manual entry | Yes, industry standard | No | N/A |
| Dealpath | Large deal pipelines | Custom quote | No | No | No | N/A |
| Blooma | CRE lenders | Custom quote | Yes, lender-side | No | No | N/A |
Read that table as a decision, not a scorecard. If you need extraction and modelling in one place, Cactus. If you have a custom Excel model that works and you only want AI to fill it in with an audit trail, Primer. If you do multifamily exclusively and want benchmarking data alongside the proforma, RedIQ. If you are packaging for an investment committee or a lender, Argus, usually with an extraction tool in front of it. If the problem is pipeline visibility across a large team, Dealpath. If you are on the credit side rather than the equity side, Blooma.
What does underwriting software not tell you about financing?
None of these tools tells you who will lend on the deal, at what price, or on what terms, because they model the asset rather than the debt market. That is a separate search, and the lender mix has moved enough that last year's assumptions no longer hold.
The composition of the lender pool is the part most models get wrong. CBRE Research, reported by CRE Daily, found that alternative lenders such as debt funds and credit companies took part in 38% of non-agency loan closings in the second quarter of 2026, up from 34% a year earlier, while the CBRE Lending Momentum Index eased to 1.0 from 1.3. Deal volume rose alongside it: US commercial real estate investment reached $250.3B year-to-date through Q2 2026, a 21% increase. More deals and a wider lender pool mean the right lender for a given file is less likely to be the one you called last time.
Leverage assumptions have moved too. In the same quarter, loan-to-value ratios averaged 59.6% on commercial deals and 63.3% on multifamily, per CBRE Research. If your template still carries a higher default LTV, the equity requirement in your model is understated before the first lender sees it. It is worth comparing loan terms across lenders on the same deal rather than assuming a house standard.
That second phase is where most investors slow down. Calling lenders one at a time is a multi-day manual process, and the deal ages while you do it.
YieldStack is a commercial mortgage brokerage, not a lender. It handles the step underwriting software leaves out: a 5-minute submit puts the file against 5,000+ loan programs and returns 5–8 matches across bridge, DSCR, CMBS, SBA and permanent debt, with a median first offer in under an hour. It costs $0 upfront to submit a deal and review offers; YieldStack's broker fee is 0.50–1.00% of the loan amount and is paid only at closing.
The sequence is simple: underwrite in Cactus, Argus, or your own Excel model, submit the finished deal, review the matched programs, and close with the best fit.
Submit your deal after you underwrite →
How do you keep underwriting assumptions from going stale?
The input that ages fastest in any model is the debt assumption, so pin your rate and spread to a dated public series rather than to whatever was saved in the template. Refresh it whenever a deal moves from screening to a lender submission, and record the date you used.
The 10-year Treasury is the usual anchor for permanent debt. The Federal Reserve Bank of St. Louis publishes it daily: the constant-maturity 10-year yield was 4.77% on 3 September 2026. Spreads on top of it move separately, which is why CBRE's observation that spreads tightened while LTVs fell matters more to your sizing than the headline index level.
Two habits are worth building into the workflow regardless of platform. Stamp every model with the date its rate assumption was set, so a stale file is obvious on sight. And re-run sizing at submission rather than at screening, because the gap between the two is where deals get repriced.
The bottom line
Underwriting tools answer what a deal is worth; none of them answers who will fund it, and treating those as one purchase is how teams end up paying twice. Choose the valuation tool that fits your volume and asset classes, then run the financing search as its own step.
Cactus if you want extraction and modelling together. Primer if your Excel model already works and you need an audit trail. RedIQ if you are multifamily-only. Argus if the output has to survive an investment committee. Dealpath or Blooma if the bottleneck is workflow or credit rather than valuation. Then take the finished numbers to the debt market as a separate exercise, with a rate assumption you can date.