Why I'm finally paying attention to "Days to Pay"
Over the past ten years of owning and managing small commercial properties, I’ve had four tenants go bad.
In each case, the ending looked different — some moved out quietly, others required eviction — but the financial result was nearly identical. I lost roughly $2,000–$3,000 each time. Painful, yes, but not catastrophic. And that’s exactly why I missed the warning signs.
Those losses never felt large enough to demand a process change. What I failed to see was that each one followed the same slow, predictable pattern, but I never knew what that pattern was or how to identify it: payment drift.
What is "Payment Drift"?
Payment drift is the gradual, almost imperceptible change in a tenant’s payment behavior over time. Rent doesn’t suddenly stop; instead, it arrives a little later each month — five days late becomes ten, ten becomes fifteen — until “late” quietly becomes “normal.” Because payments are still eventually made, the risk feels manageable and easy to rationalize. But like a car sliding just a few degrees off its racing line, drift compounds: the longer it goes uncorrected, the harder it is to recover, and by the time the loss is obvious, the damage is already done.
Payment drift is identified by analyzing an individual tenant's Days To Pay trend over time. Most of my tenants pay early, particularly if they schedule payments with their bank on a recurring basis. Those that pay manually, typically pay within 1-2 days.
- Phase 1 - Stable Payer: payments made on time or early (-2 to +2 days)
- Phase 2 - Drift: persistent but small lateness (5 - 30 days)
- Phase 3 - Acceleration: payments creep into >30 days delinquency
This chart shows Days to Pay for a single tenant (Tenant 1) over time, anonymized and indexed by invoice date.
At no point did this tenant “suddenly” fail. There is no cliff. No dramatic spike. Instead, the line slowly slopes upward over several months.
Five days late becomes eight.
Eight becomes twelve.
Twelve becomes twenty.
At the time, I treated each late payment as a one-off event. Seeing it plotted out like this makes the reality unavoidable: the failure was already underway long before it was visible operationally.
Why I Didn’t See This in Real Time
When rent eventually arrived, I mentally reset the clock. My tracking stopped at paid vs. unpaid, not how long payment behavior was changing.
Because I wasn’t aggregating or visualizing Days to Pay, I had no way to distinguish:
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noise from signal
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exception from trend
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patience from denial
In isolation, each late payment was explainable. In sequence, they were predictive.
Rolling Average Days to Pay (Early Warning Signal)
Had I been monitoring this at the time, it would have triggered a clear question:
“Why is this tenant taking longer to pay every month?”
That question alone could have led to earlier action — tighter terms, firmer conversations, or earlier exit — when losses were still minimal.
The difference isn’t the occasional late payment. Good tenants have those too.
The difference is variance and direction:
Stable tenants fluctuate but revert to a baseline
Failing tenants drift upward and never recover
Once you see this pattern, it’s hard to unsee.
Drift vs Cliff Events
It's important to note that not every tenant will follow this gradual accelerated pattern of drift. Some tenants might exhibit a sudden and dramatic increase in Days to Pay which are unavoidable. You can see in this second example.
This type of default is shock-driven default — something external happened:
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loss of income
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health issue
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business disruption
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legal / personal event
There was no long runway of gradual deterioration.
The tenant remained stable paying within 10 days every month before experiencing a sudden disruption that led to immediate and severe lateness. Both patterns result in bad debt, but only the gradual accelerated drift is meaningfully predictable.
Why Predictable Drift Matters for Small Landlords
Large property managers have automated aging reports, dashboards, and teams dedicated to credit monitoring. Small landlords rely on memory, inboxes, and intuition.
That works — until it doesn’t.
The irony is that small landlords feel losses more acutely, yet tolerate warning signs longer, because each loss feels isolated rather than systemic.
Days to Pay gives small operators a lightweight early-warning system without adding complexity or overhead.
What I’ll Do Differently Going Forward
Going forward, Days to Pay won’t be a retrospective metric. It will be a trigger.
Specifically:
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I will track rolling averages per tenant
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I will flag sustained upward drift, not single late payments
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I will intervene earlier — when conversations are easier and options are broader
The goal isn’t to eliminate risk. That’s impossible.
The goal is to recognize drift early enough that losses stay small — or never materialize at all.
Practical Policy You Can Implement Tomorrow
Internal Rule Set (Simple & Defensible)
3 months >5 days late → written notice
Any payment >10 days late → tighten terms
Any payment >30 days late → pre-collections
Any payment >45 days late → assume default risk
This is not aggressive — it’s data-driven.
After ten years, four bad debts, and a few expensive lessons that never felt quite expensive enough at the time, this feels like a necessary evolution.
