Nordic Real Estate Services

05/13/2026

I think the CRE industry is about to learn a painful lesson:

An unknown number of multimillion-dollar deals have already been priced with AI-generated numbers no one can reproduce.

Not because people are careless.
Because the output looked clean.

That is the line I’m trying to draw in this piece:

AI is great for research, summarization, market color, and first-pass narrative.
But the numbers that price deals, size debt, and drive LP reporting need to be deterministic, traceable, and auditable.

If you can’t answer “where did this NOI assumption come from?” in one click, that is a real risk.

I’m not anti-AI.
I’m anti using language models for load-bearing math.

I wrote this after watching too many teams blur that boundary, buying into “AI black box” tools or prompt-only workflows for underwriting.

If you can’t trace a number to a source and reproduce it on demand, it doesn’t belong in a multimillion-dollar deal model.

AI is transforming how CRE professionals research, draft, and summarize. But underwriting, debt sizing, waterfall distributions, and LP reporting need engineered software — not generated paragraphs. Here's why.

04/18/2026

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04/10/2026

Most people overcomplicate the proforma, or oversimplify it.

The overcomplicated version: a 47-tab Excel workbook where one broken
cell reference silently wrecks your IRR.

The oversimplified version: NOI ÷ cap rate, call it a day.

Neither one actually helps you decide if a deal works.

A good proforma is really just seven steps in sequence:

1. Build revenue from the rent roll (contract rent + market rent for vacancy)
2. Apply vacancy and credit loss
3. Model every operating expense with its own inflation rate
4. Calculate NOI year over year
5. Layer in debt service and check your DSCR
6. Run cash flow through the hold period
7. Model the exit — reversion value, sale costs, equity multiple, IRR

The part most people get wrong isn't the formula. It's the compounding.

Every expense line escalates independently. Leases roll at different
times. Step increases hit on different anniversaries. Vacancy between
tenants compounds into your revenue line differently depending on
when it happens.

By Year 3 of a 10-year hold, a spreadsheet with manual escalation
is almost certainly drifting from reality — and you can't tell
where it broke.

I wrote a full walkthrough with worked examples — revenue build,
expense modeling, NOI, debt service, cash flow, return metrics,
and exit analysis. No theory, just the actual math.

Link in comments.

What's the section of the proforma that gives you the most trouble?

Learn how to build a commercial real estate proforma from scratch — revenue projections, expense modeling, NOI, debt service, cash flow, and return metrics explained with examples.

04/07/2026

I just published a practical guide on how I analyze a rent roll before I ever touch a proforma.

Most bad deals do not look bad in the OM. The risk usually shows up in lease rollover, tenant concentration, occupancy quality, and recovery structure.

In the piece, I break down:
physical vs economic occupancy,
lease expiration risk,
tenant concentration,
and a few red flags that can change the whole underwriting story.

If you work on CRE acquisitions or asset management, this should be useful.

Learn how to read and analyze a commercial real estate rent roll — what to look for, red flags to catch, key metrics to calculate, and how rent roll analysis feeds your proforma.

03/12/2026

We have officially completed our APP! we are currently looking for beta testers in order to work out the kinks. If anyone is interested in participating, please connect with us. All beta testers will receive a permanent discount for their assistance.

https://app.nordicrealestateservices.com/login

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