Collection Loss Calculator
The formula
What the rent you can’t bank tells you
Collection loss separates late-but-paid (cash timing) from written-off non-pay (true loss) — they need different fixes.
Calculation: Collection loss = billed − collected; recoverable vs structural split. Enter your inputs and every substitution is shown below the result.
Reading the result
680 shortfall with 60% arriving later means only ~270/mo is bleeding away — that’s the number to police.
Rules of thumb: Underwrite 1–3% credit loss atop vacancy
Limitations and best practice
Reporting collections against scheduled rents hides a 5–10 point loss in EGI line items.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Healthy collection rate?
Does online payments fix it?
When to forgive vs evict?
Source & methodology
Multifamily EGI construction (vacancy + credit).
Collection Loss: the number, the method and the caveats
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. Collection Loss is the kind of number people argue about from memory. This page ends that argument with arithmetic you can watch happen: enter your figures, get Collection loss rate in real time, and see every substitution printed below.
It helps to separate the mechanics from the interpretation. The mechanics are fixed: Collection loss = billed − collected; recoverable vs structural split. Anyone, anywhere, entering the same inputs will get the same 11.50 %. The interpretation is where experience counts: whether 11.50 % is good, normal or alarming depends on comparisons covered below, and that is the part most articles about Collection Loss never explain.
A calculation like this one is worth roughly as much as a careful sentence: it forces several vague considerations into one comparable quantity. Once Collection Loss is on the table, the argument can stop being about impressions.
Nothing below is stock text pasted around a number: the worked example computes collection loss rate = 11.50 % from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Monthly billed first, at ≈85.5 % of the headline for a ten-percent error either way in that input. Work down the page and by the end you could re-derive the result in a spreadsheet from memory.
How Collection Loss is computed, line by line
Here is the complete model this tool runs — Collection loss = billed − collected; recoverable vs structural split — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.
Monthly billed ($) (example value 12,670) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Monthly collected ($) (example value 11,213) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Late-then-paid share of shortfall (%) (example value 62) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Eviction write-offs this month ($) (example value 472) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Use the value as of today, not as of the last time you looked — time fields drift silently.
The default Collection Loss calculation, written out in full
Rather than describe the calculation, here it is end to end — every substitution in order, starting from the inputs above. Starting figures: Monthly billed ($), Monthly collected ($), Late-then-paid share of shortfall (%), Eviction write-offs this month ($). The endpoint of that chain is 11.50 % of collection loss rate, sitting in the “Above the healthy ~5% line: this is a leasing-quality problem wearing a bookkeeping costume” band.
- Loss = $12,670 − $11,213 = $1,457
- Split: timing 62% / hard rest
- Rate = 11.50% of billed
Monthly shortfall — $1,457. This is the number experienced users quote when the headline alone would mislead.
Timing (eventually collected) — $903.34. Read next to the headline, this is the figure that shows the shape of the trade-off.
Structural loss — $553.66. Read next to the headline, this is the figure that shows the shape of the trade-off.
Annualized at this pace — $17,484. Compare it against the main result of 11.50 % before drawing conclusions.
Re-running with your own figures is instant; re-running by hand once is the best way to know the tool is telling you the truth.
Scenario table: Collection Loss at ±10 % and ±20 % of the key input
Single-point answers invite false confidence, so the same engine was run across a band around the input that matters most here. Each row is a full recalculation of Collection Loss, not an interpolation.
| Monthly billed setting | Collection loss rate |
|---|---|
| 80 % of the example value | -10.63 % |
| 90 % of the example value | 1.67 % |
| example inputs | 11.50 % |
| 110 % of the example value | 19.55 % |
| 120 % of the example value | 26.25 % |
Across 5 settings of Monthly billed, the result spans -10.63 % to 26.25 %. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Monthly billed alone; the substitution steps will follow your figures exactly.
Sensitivity: which inputs change Collection Loss the most
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the collection loss rate. Read it as a map of where Collection Loss is fragile — drawn for the example inputs, which is the map you want when deciding what to measure carefully.
- Monthly billed +10 % → Collection Loss = 19.55 % · −10 % → 1.67 % (the collection loss rate shifts ≈85.5 %)
- Monthly collected +10 % → Collection Loss = 2.65 % · −10 % → 20.35 % (the collection loss rate shifts ≈77.0 %)
Monthly billed is the dominant lever here: ten percent either way moves the result by about 85.5 %, from 1.67 % to 19.55 %. That is where a careless figure does the most damage, so check it against a source, and treat Monthly collected as the second priority (≈77.0 % per ten percent). The remaining fields can sit at safe defaults without materially changing the answer — knowing which is which is what separates a careful calculation from an anxious one.
Interpreting the Collection Loss verdict
The value above only becomes useful the moment you know what it is being measured against. The default case lands in the “Above the healthy ~5% line: this is a leasing-quality problem wearing a bookkeeping costume” band, and bands are deliberately wide because real situations overlap. The sensitivity list shows how far a modest input error moves you along that ladder: for Monthly billed, ±10 % covers 1.67 % to 19.55 %. The practical reading rules: read the band as a starting hypothesis, then test it: change the two dominant inputs by ten percent and see whether the verdict survives. Verdicts that survive stress are the ones worth acting on.
Placing this figure in context: calculators of this kind differ less in arithmetic than in what they choose to ignore. This one keeps Monthly billed ($) explicit so that the omissions are visible rather than hidden in a preset.
How people get Collection Loss wrong
Reporting collections against scheduled rents hides a 5–10 point loss in EGI line items. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Treating the example inputs as a recommendation. They are a demonstration of the mechanism — a decision-grade Collection Loss begins the moment your own figures replace them.
One mistake is specific to this engine: assuming every input matters equally. The computation above shows Monthly billed dominating — a careful figure there beats false precision everywhere else.
A final trap is presentation: reporting Collection Loss to more decimal places than the inputs support. The tool prints what the arithmetic gives; you should quote only what your least precise field justifies.
Collection Loss verification checklist
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Compare the result with one independent method or published benchmark.
- Put Monthly billed under a magnifier; the sensitivity table above is the reason why.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Repeat the ±10 % test on the inputs that matter and see whether the decision — not just the number — still holds.
Step by step: getting a trustworthy Collection Loss out of this tool
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — start from evidence. The inputs behind Collection Loss are monthly billed ($), monthly collected ($), late-then-paid share of shortfall (%), eviction write-offs this month ($). Write them down with their source next to each one; if you cannot name where a figure came from, that is the input most likely to be wrong. Rounding early is the quietest way to introduce a permanent error.
Step 2 — start from the case you can defend. Enter pessimistic-but-honest figures first; the optimistic run is a ceiling, not a plan. A conclusion that holds on conservative inputs holds everywhere. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. The point of transparency is that a reader can disagree with a specific line rather than with you.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn Collection Loss from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. A figure nobody can reproduce is a figure nobody will act on twice.
Step 5 — write the one-line memo. “Collection loss rate = 11.50 % on this date, from these inputs.” That line is the entire deliverable; everything else on this page is how you earned the right to write it. If the answer changes your behaviour, it deserves a second run with different assumptions.
Who actually uses Collection Loss — and when
In Property Management the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. Most surprises in numbers are surprises about inputs, not about formulas.
The audience for this page is people who need one trustworthy number before their next decision. In professional Property Management practice the pattern is always the same — screen with Collection Loss, then invest slow analysis only in whatever survives the screen. Re-running a number costs seconds; retracting a decision costs considerably more.
There is a personal-administration use people overlook: periodic self-checking. Collection Loss quietly drifts as life changes — rates move, bodies change, costs compound — and re-running it a few times a year catches drift while correction is still cheap. One decimal place more than your data supports is one decimal place of fiction.
Collection Loss — frequently asked questions
What affects Collection Loss the most?
Collection Loss is driven above all by Monthly billed. Perturbing only that field by ten percent either way produced 19.55 % against the 11.50 % baseline — a ≈85.5 % response, while Monthly collected manages ≈77.0 %.
Which matters more: monthly collected or monthly billed?
By the numbers, Monthly billed: a ten-percent change in it moves the result ≈85.5 %, versus ≈77.0 % for Monthly collected. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Collection Loss?
With the example inputs on this page the result is 11.50 %, labelled “Above the healthy ~5% line: this is a leasing-quality problem wearing a bookkeeping costume”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Collection Loss by hand?
Three steps: write the formula (Collection loss = billed − collected; recoverable vs structural split), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Collection Loss than any number of calculator runs.
What happens to Collection Loss if monthly billed changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 1.67 % to 19.55 % across the ten-percent probe, i.e. ≈85.5 % sensitivity on the collection loss rate.
Is the Collection Loss result of 11.50 % good or bad?
The tool answers “what”, not “whether”: 11.50 % carries the verdict “Above the healthy ~5% line: this is a leasing-quality problem wearing a bookkeeping costume” for the default case, which is a starting interpretation rather than a verdict on your situation. Re-run with your own figures and compare the bands.
Is Collection Loss accurate?
The arithmetic is exact: Collection loss = billed − collected; recoverable vs structural split is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the 11.50 % result as the correct answer to the question you typed — and check the assumptions section before acting on it.
How do I calculate Collection Loss manually?
Write down the inputs, then follow the printed steps in order — Collection loss = billed − collected; recoverable vs structural split. A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
Healthy collection rate?
96–99% of billed in class A/B; below 92% is a screening failure.
Does online payments fix it?
It fixes forgetfulness, not inability — screen to 3× rent, then the hard-loss line drops.
Does this Collection Loss tool store my numbers?
No. The entire calculation runs in your browser session; nothing entered above is transmitted or saved, which is the correct behaviour for sensitive Real Estate & Property figures and the reason you can re-run this page as often as you like.
What the labels on this page mean
- Monthly shortfall
- $1,457 in the default Collection Loss calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Timing (eventually collected)
- $903.34 in the default Collection Loss calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Structural loss
- $553.66 in the default Collection Loss calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Collection loss rate
- The primary output of this tool: 11.50 % for the default inputs, computed exactly from Collection loss = billed − collected; recoverable vs structural split.
- Worked example
- Every substitution in order, from the example inputs to 11.50 % — the audit trail for the headline figure.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Above the healthy ~5% line: this is a leasing-quality problem wearing a bookkeeping costume”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Collection Loss moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Methodology, sources and limits
There is no black box on this page — the engine evaluates Collection loss = billed − collected; recoverable vs structural split. Source of record: Multifamily EGI construction (vacancy + credit). Specialist software layers extra coefficients on top of the same algebra; none are hidden here, and that transparency is what makes a disagreement traceable to a specific input instead of a black box.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The promise to the reader is narrow and testable: same inputs, same output, no silent rounding, and no figure presented without the formula that produced it.
Turning this Collection Loss into a decision
Re-run the calculation with your own figures, stress the two most sensitive inputs by ±10 %, and only then act on the result.
This page was written for people who need one trustworthy number before their next decision, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.