Six months after year-end, the email finally comes in.
A tenant has questions about their CAM reconciliation. The numbers don’t line up with last year. A cap looks wrong. A charge feels out of place.
The asset manager pulls up the spreadsheet. The accounting team pulls up their version. Someone digs through a shared drive to find the lease.
No one is sure who’s right — only that the conversation is happening after the statements went out.
This is how most CAM problems start. Quietly. Late. And far too confidently.
Consider a value-add retail center acquired mid-year.
The leases come in thick binders and PDFs — original agreements, amendments, side letters. CAM caps vary by tenant. Some expenses are excluded. Others gross up differently depending on occupancy.
During onboarding, the most critical terms are abstracted. The rest live “for later.”
Accounting builds the CAM model based on what’s readily available. It looks right. It balances.
But buried in a third amendment is a subtle cap change tied to a specific expense category. Another tenant has a carve-out that only applies after a certain date. One amendment was never reflected at all.
On paper, the CAM rec is complete.
In reality, it’s wrong.
Overcharges get attention. Tenants push back. Audits happen.
Undercharges don’t.
They sit quietly in the background — eroding NOI one line item at a time. No alerts. No red flags. Just revenue that never materializes.
Most operators will never know how much CAM they failed to recover, because there’s no obvious moment where something “breaks.”
The math worked.
The data didn’t.
CAM reconciliations don’t fail because accounting teams are careless or under-resourced.
They fail because the lease data feeding the process was never structured to begin with.
Spreadsheets rely on interpretation.
Interpretation relies on memory.
Memory doesn’t scale.
Once inaccurate or incomplete lease logic is baked into a CAM model, every calculation downstream inherits that risk — no matter how polished the final statement looks.
Most CAM errors aren’t dramatic. They’re subtle.
A cap applied too broadly.
An exclusion missed.
A gross-up assumed instead of verified.
Each one feels small. Together, they compound — across tenants, across assets, across years.
And because the process looks controlled, teams walk away confident.
Until they shouldn’t be.
The problem isn’t CAM season.
It’s treating lease data like static paperwork instead of living infrastructure.
When CAM terms are centralized, structured, and always accessible, reconciliation becomes repeatable — not reactive.
That’s when CAM stops being a risk exposure and starts becoming a predictable part of portfolio operations.
Prophia was built for this exact moment — when teams realize that spreadsheets and PDFs can’t support the complexity of modern CAM recovery.
By transforming lease documents into structured, searchable data, Prophia allows teams to:
Apply CAM logic consistently across tenants and assets
Surface caps, exclusions, and expense rules automatically
Reduce reliance on manual interpretation
Defend CAM calculations with confidence
CAM errors aren’t inevitable.
They’re the result of outdated systems trying to do modern work.
If your CAM reconciliations still depend on spreadsheets, memory, and “best guesses,” there’s a strong chance your numbers are off — even if no one has complained yet.
And the most expensive CAM mistake is the one that never gets caught.