ARGUS Enterprise (Altus Group) is the institutional standard for commercial real estate cash-flow modeling and valuation. It runs discounted cash-flow (DCF) analysis, lease-by-lease modeling, direct capitalization, and investment return analysis on income-producing assets — the cash-flow output that lenders, equity partners, and institutional buyers expect when they underwrite a large commercial deal.
ARGUS-grade cash flows are the lingua franca of institutional CRE underwriting. A REPE acquisitions team, a debt fund, and an institutional buyer all expect to see the same lease-by-lease DCF — and almost every large deal touches ARGUS at some point. The problem is what happens after the model is built: an analyst exports the ARGUS cash flow to Excel, retypes the key numbers into an IC memo, copies the valuation into a portfolio tracker, and re-keys the assumptions into the firm's house Excel model. That re-keying is slow, error-prone, and happens every time a model is updated during a months-long diligence process.
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Parse exported native ARGUS files (.sf) to extract the model's cash-flow schedule, valuation, return metrics, and key assumptions into structured data. No connection to ARGUS itself — the file is uploaded or watched in a folder.
Your analyst exports the .sf the same way they do for a lender. The automation reads it instead of an analyst re-typing the 10-year cash flow into a memo or tracker.
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When an analyst finishes an ARGUS model and exports it (Excel or .sf), this workflow ingests the export, extracts the 10-year cash flow, valuation, return metrics, and key assumptions, and drafts the cash-flow and valuation sections of the firm's Investment Committee memo. The analyst opens a memo with the ARGUS numbers already populated and an AI-written first-pass narrative summarizing the valuation thesis — instead of a blank template and a wall of cash-flow rows to transcribe.
1n8n detects a new ARGUS export in the watched folder (file-watch trigger — there is no ARGUS webhook)
2A parsing node reads the export: annual cash flows, NOI, valuation conclusion, going-in and exit cap, unleveraged and leveraged IRR, equity multiple, hold period
3A second pass extracts the modeling assumptions: market rent, rent growth, vacancy, renewal probability, TI/LC, discount rate
4AI node drafts the IC-memo cash-flow narrative and flags any assumption that falls outside the firm's standard ranges (e.g., exit cap below going-in cap, rent growth above a configured ceiling)
The IC memo's hardest, most transcription-heavy section is drafted in minutes from the ARGUS export. Analysts spend their time stress-testing the assumptions the AI flagged, not retyping cash-flow rows — and every memo is formatted identically.
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Fires when a new ARGUS export (.sf, Excel, or PDF) appears in a watched cloud folder (Google Drive, SharePoint, Dropbox). This is a file-watch trigger on your storage — not an ARGUS webhook, which does not exist.
An analyst saves an ARGUS Excel export to the deal's Drive folder; the watch trigger starts the IC-memo drafting workflow automatically.
Fires when an ARGUS export is uploaded through an intake form or portal, capturing the file along with deal metadata (asset name, deal stage).
A regional team uploads its ARGUS export through a simple intake form; the file is routed to the right deal record and parsed without IT touching shared folders.
Runs on a schedule (e.g., quarterly) and processes any ARGUS exports added to the portfolio valuation folder since the last scan.
Each quarter, scan the valuation folder and refresh the portfolio dashboard from every updated ARGUS model in one batch.
Read an ARGUS export and return the structured cash-flow schedule — annual (and where present, monthly) NOI build, capital reserves, and net cash flow over the hold period.
Pull the 10-year cash flow from an ARGUS Excel export to populate the cash-flow table in an IC memo.
Return the headline outputs from an ARGUS model: value conclusion, going-in and terminal cap rate, unleveraged and leveraged IRR, equity multiple, and discount rate.
Write the value conclusion and return metrics from a refreshed ARGUS export to the asset's row in a portfolio dashboard.
Isolate the assumption set — market rent, rent growth, vacancy and credit loss, renewal probability, downtime, TI/LC, exit cap, hold period — as a structured object.
Sync the assumptions from an ARGUS export into the input tab of the firm's house Excel underwriting model.
Read the lease-by-lease detail carried in an ARGUS export — tenant, area, in-place rent, expiry, options, and modeled renewal assumptions — into a structured rent-roll table.
Reconcile the lease assumptions in the ARGUS model against the actual rent roll and estoppels during diligence.
Diff two exports of the same asset and return the assumption-level and value-level changes, with the lease lines that drove the difference.
Produce a diligence change-log entry showing how the model moved between the first screen and final pricing.
Use the extracted ARGUS data to populate a downstream document or system — IC-memo section, portfolio dashboard row, or house-model input tab.
Auto-fill the cash-flow and valuation sections of an IC-memo template from a freshly ingested ARGUS export.
Get started in approximately 1-2 hours for the first export-to-memo pipeline; a half-day for the full memo + dashboard + model-sync suite, depending on export consistency
Decide which export format is your source of truth — the ARGUS Excel cash-flow workbook is the most parse-friendly; .sf carries the most detail; PDF is the fallback when that's all you receive. Agree on a folder structure (one folder per deal or per portfolio) and a file-naming convention that encodes the asset identifier, so the automation can route each export to the right record.
Have analysts always export the same Excel template from ARGUS. Consistent sheet/tab names make the parsing dramatically more reliable than ad-hoc exports.
In n8n, add a trigger on your cloud storage that fires when a new file lands in the watched ARGUS folder. Because ARGUS exposes no webhook or event API, this storage watch (or a scheduled folder scan) is how the pipeline starts. Confirm the trigger fires by dropping a test export into the folder.
Use a dedicated 'inbox' subfolder for fresh exports and move processed files to an 'archive' subfolder at the end of the run, so the watch never re-processes the same file.
Add the parsing step that reads the export and emits a structured object: cash flows, valuation, return metrics, and assumptions. Validate it against several real ARGUS exports from your own deals — different asset types and lease structures — and confirm the extracted numbers match the model exactly. This validation step is the foundation; everything downstream trusts it.
Build a small reconciliation check that re-sums the extracted annual cash flows and compares to the export's own NOI total. If they don't tie, the parse failed and the run should halt rather than push bad numbers downstream.
Point the structured output at its destination: an IC-memo template that gets populated, a portfolio dashboard row that gets written, or a house Excel model whose named input cells get updated. Map each extracted field to its target explicitly, and keep the mapping in one reusable sub-workflow so memo, dashboard, and model sync all share the same field definitions.
For the house-model sync, write only to named input cells and never to formula cells — that keeps your model's logic intact while the inputs refresh.
Add an AI node that summarizes the model and flags assumptions outside your firm's standard ranges, then route a notification (Slack or email) to the deal lead with a link to the artifact and the flagged items. Monitor the first several runs and tune the flag thresholds to your buy-box.
Log a one-line result for every run ('Asset X parsed: value $42.1M, IRR 14.2%, 2 assumptions flagged'). It makes it obvious when a parse silently degrades after an ARGUS export-format change.
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