After-Repair Value Calculator
The formula
What the after-repair value tells you
The blend for after-repair value: weighted mean with the weights shown explicitly, so nobody has to guess whether line one counted.
Calculation: weighted average = Σ(value × weight) ÷ Σ weights. Enter your inputs and every substitution is shown below the result.
Reading the result
Compare with the simple-mean line; a gap means the weighting is doing real work — decide if that is intended.
Limitations and best practice
Weights must share one unit (dollars, hours, kg); mixing units produces a number that is meaningless but still displays fine.
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
When does weighted beat simple?
Can weights be percentages?
Source & methodology
Weighted mean definition.
How After-Repair Value works — and what your result really means
This page was written for one job: turning your own inputs into a defensible figure, with the working shown. There are plenty of pages about After-Repair Value; almost none of them show the working. This one does: the formula, every input, a fully computed example, and a sensitivity analysis calculated live from this engine’s own defaults.
Most confusion about After-Repair Value comes from mixing up the input you control with the input you merely observe. The formula treats them identically, but your decisions should not: one deserves measurement, the other deserves a defensible estimate.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for After-Repair Value.
Nothing below is stock text pasted around a number: the worked example computes weighted average = 122.50 from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Third value (use twice for 2-item blends) first, at ≈4.4 % 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.
Every input in After-Repair Value, and what it does to the answer
The engine implements a single stated model: weighted average = Σ(value × weight) ÷ Σ weights. There is no hidden coefficient and no login-gated precision — the formula printed is the formula executed, which is also what makes the result citable.
First value (example value 80) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Second value (example value 115) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
Third value (use twice for 2-item blends) (example value 215) is a quantity rather than a category — precision here is cheap and worth having. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Weight of first (example value 2) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Weigh rather than estimate where you can — this field usually enters the formula multiplicatively.
Weight of second (example value 1) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. Weigh rather than estimate where you can — this field usually enters the formula multiplicatively.
Weight of third (example value 1) is a quantity rather than a category — precision here is cheap and worth having. Weigh rather than estimate where you can — this field usually enters the formula multiplicatively.
A fully worked After-Repair Value example
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: First value, Second value, Third value (use twice for 2-item blends), Weight of first. The computed weighted average comes out at 122.50, which the interpretation band labels “Wide component spread — the average hides a lot”.
- Σv·w = 160.0 + 115.0 + 215.0 = 490.0
- ÷ Σw = 4 → 122.50
Simple mean — 136.67. This is the number experienced users quote when the headline alone would mislead.
Weight spread — 1.0. If your inputs change, this is usually the first companion figure to move.
Range of components — 135.00. If your inputs change, this is usually the first companion figure to move.
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: After-Repair Value 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 After-Repair Value, not an interpolation.
| Third value (use twice for 2-item blends) setting | Weighted average |
|---|---|
| 80 % of the example value | 111.75 |
| 90 % of the example value | 117.13 |
| example inputs | 122.50 |
| 110 % of the example value | 127.88 |
| 120 % of the example value | 133.25 |
Across 5 settings of Third value (use twice for 2-item blends), the result spans 111.75 to 133.25. 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 Third value (use twice for 2-item blends) alone; the substitution steps will follow your figures exactly.
Where After-Repair Value is fragile — and where it is not
Generic advice says “it depends on your inputs”; this section says exactly how much. Each field was perturbed by ±10 % in isolation and the weighted average recomputed, so the ranking below is measured rather than asserted. For After-Repair Value the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Third value (use twice for 2-item blends) +10 % → After-Repair Value = 127.88 · −10 % → 117.13 (the weighted average shifts ≈4.4 %)
- First value: ten percent up gives 126.50, ten percent down gives 118.50 — ≈3.3 % of the weighted average either way
- Second value: ten percent up gives 125.38, ten percent down gives 119.63 — ≈2.3 % of the weighted average either way
- Nudge Weight of third by ±10 % and After-Repair Value lands between 120.13 and 124.76 (sensitivity ≈1.9 %)
- Nudge Weight of first by ±10 % and After-Repair Value lands between 120.48 and 124.74 (sensitivity ≈1.8 %)
- Weight of second +10 % → After-Repair Value = 122.32 · −10 % → 122.69 (the weighted average shifts ≈0.2 %)
Third value (use twice for 2-item blends) is the dominant lever here: ten percent either way moves the result by about 4.4 %, from 117.13 to 127.88. Spend your verification effort there first, and treat First value as the second priority (≈3.3 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Reading the result: bands, zones and thresholds
A number on its own is trivia; a number against a range is intelligence. The verdict attached to the default calculation — “Wide component spread — the average hides a lot” — is one band on a ladder your own figure may sit somewhere else on. How much of that band is luck? On Third value (use twice for 2-item blends) alone ten percent either way covers 117.13 to 127.88. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Where After-Repair Value sits next to similar measures: this tool answers “how much is the After-Repair Value”, while the related calculators answer “what do I do about it”. Reading the result alone tends to over-weight a single snapshot; pairing it with the companion tools above turns the number into a decision.
Why After-Repair Value results go wrong — and how to prevent it
Weights must share one unit (dollars, hours, kg); mixing units produces a number that is meaningless but still displays fine. 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.
Reusing an After-Repair Value from last season. In Real Estate & Property the inputs are the thing that drifts; the formula does not.
This model has its own particular trap: Third value (use twice for 2-item blends) carries ≈4.4 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
A final trap is presentation: reporting After-Repair Value 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.
Before you trust your After-Repair Value: a short checklist
- Confirm the example values are plausible for your own situation before replacing them.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Check Third value (use twice for 2-item blends) before anything else: it is the measured dominant input, ≈4.4 % of After-Repair Value per ten percent.
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Recompute once with the dominant input deliberately set low, and see whether the decision changes.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
Step by step: getting a trustworthy After-Repair Value 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 — gather before you type. After-Repair Value depends on first value, second value, third value (use twice for 2-item blends), weight of first, weight of second, weight of third. Collect them from a source document — a statement, a report, an invoice, a log — rather than from memory, because recalled inputs are the single largest cause of wrong results. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Step 2 — compute the version you would not be embarrassed by. If a stranger audited your inputs, which set would you hand over? Enter that set, read the result, and treat everything else as upside. Note the assumption you are least sure about; that is where the real risk lives.
Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. One decimal place more than your data supports is one decimal place of fiction.
Step 4 — stress before you commit. A decision that survives a ten-percent squeeze on its two biggest inputs needs no luck to be sound. One that does not survive is a decision about hope, not about arithmetic. Write the assumptions in the same file as the result, and future arguments take minutes instead of meetings.
Step 5 — schedule the next run. In Real Estate & Property the inputs drift quietly. Put a reminder a quarter out, re-enter the same fields, and read the change rather than the level. A figure with a date beside it becomes evidence; without one it stays an anecdote.
Who actually uses After-Repair Value — and when
Most Real Estate Investment disagreements are arithmetic disagreements in disguise. When Weighted average of 122.50 sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. Note the assumption you are least sure about; that is where the real risk lives.
Professionals in Real Estate Investment use this style of calculation as a screening filter before spending real money or effort: a quick After-Repair Value check eliminates weak options in minutes, and the survivors earn the deeper, slower analysis. When two sources disagree, it is almost never the arithmetic that differs.
Nothing here is stored or transmitted, so the habit of checking is free: open the page, re-enter current figures, compare with the number you saved last time. Suspiciously tidy answers deserve the same scrutiny as untidy ones.
After-Repair Value — frequently asked questions
What affects After-Repair Value the most?
After-Repair Value is driven above all by Third value (use twice for 2-item blends). Perturbing only that field by ten percent either way produced 127.88 against the 122.50 baseline — a ≈4.4 % response, while First value manages ≈3.3 %.
Which matters more: first value or third value (use twice for 2-item blends)?
For After-Repair Value, Third value (use twice for 2-item blends) wins the head-to-head (≈4.4 % against ≈3.3 % per ten-percent change). Knowing which input dominates is the difference between careful work and merely slow work.
What is a typical After-Repair Value?
There is no honest universal average for After-Repair Value, but there is a reference point: the defaults on this page produce 122.50 (Wide component spread — the average hides a lot). Replace the inputs with your own and compare — that comparison is the useful meaning of “typical”.
How do I calculate After-Repair Value by hand?
weighted average = Σ(value × weight) ÷ Σ weights is the entire method. Work the inputs in that order — Σv·w = 160.0 + 115.0 + 215.0 = 490.0 — and you will reach 122.50 on the default figures, the same number this tool prints.
What happens to After-Repair Value if third value (use twice for 2-item blends) changes by 10 %?
On these inputs a ±10 % change in third value (use twice for 2-item blends) takes After-Repair Value to 127.88 against the 122.50 example — around 4.4 % of the headline. The same test on every other field is in the sensitivity section.
Is the After-Repair Value result of 122.50 good or bad?
Neither, on its own — the label “Wide component spread — the average hides a lot” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Third value (use twice for 2-item blends) alone moves it by ≈4.4 %.
What affects After-Repair Value the most?
Sensitivity depends on where you start, but the inputs First value and Second value typically move After-Repair Value further than the rest because they enter the formula multiplicatively. If you can only measure two things well, measure those and hold the others at conservative defaults.
Can After-Repair Value be negative or empty?
Yes, and that is informative rather than broken: a negative After-Repair Value usually means the inputs encode an outflow or a shortfall, and the verdict band above labels exactly that situation instead of hiding it.
When does weighted beat simple?
Whenever components differ in importance or size — averaging three test scores of 1 and 4 credits needs the weights.
Can weights be percentages?
Yes — only the ratios matter; Σw need not equal 100.
Does this After-Repair Value 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
- Simple mean
- 136.67 in the default After-Repair Value calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Weight spread
- 1.0 on the example inputs — a companion figure printed beside the headline of After-Repair Value so that no result is read in isolation.
- Range of components
- 135.00 on the example inputs — a companion figure printed beside the headline of After-Repair Value so that no result is read in isolation.
- Weighted average
- The primary output of this tool: 122.50 for the default inputs, computed exactly from weighted average = Σ(value × weight) ÷ Σ weights.
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “Wide component spread — the average hides a lot”, and your inputs may not.
- Sensitivity (±10 %)
- How far the After-Repair Value moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
Method, sources and what this tool cannot tell you
Everything above runs on one stated formula, applied without hidden coefficients: weighted average = Σ(value × weight) ÷ Σ weights. Source of record: Weighted mean definition. Any other implementation of this model should return the same figure for the same inputs. If one does not, the difference is in its assumptions — which this page prints and that one probably does not.
This page is an estimation aid, not professional advice. The design rule is simple: two people entering identical figures must get identical answers. 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 After-Repair Value 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.