DVSA records show 237,360 initial MOT tests for the Nissan Micra in 2025. 63.4% passed at the first attempt, while 36.6% did not pass on the initial inspection. The average valid odometer reading was 63,035 miles. Explore Odometer readings, Vehicle age, Common faults and Annual history.
Initial normal tests (NT) recorded as pass, fail or repaired and passed (PRS) in 2025.
| Initial MOT tests | 237,360 |
|---|---|
| First-time passes | 63.4% |
| Failed | 67,449 (28.4%) |
| Repaired and passed (PRS) | 19,538 |
| Average recorded odometer | 63,035 mi |
Approximate age at the test date for this model in 2025. Unknown vehicle ages are reported separately.
| Age at test | Initial MOT tests | First-time pass | Initial failure |
|---|---|---|---|
| 0–2 years | 4,659 | 90.8% | 9.2% |
| 3–5 years | 19,779 | 84.3% | 15.7% |
| 6–9 years | 44,080 | 70.9% | 29.1% |
| 10–14 years | 61,355 | 64.2% | 35.8% |
| 15–19 years | 69,321 | 55.8% | 44.2% |
| 20+ years | 38,166 | 52.7% | 47.3% |
Distribution of valid readings in 2025. These are test readings, not unique vehicles or distance driven during the year.
| Recorded odometer range | Valid readings | Share of valid readings |
|---|---|---|
| Under 50,000 miles | 98,598 | 41.6% |
| 50,000–99,999 miles | 102,034 | 43.0% |
| 100,000–149,999 miles | 31,890 | 13.5% |
| 150,000–199,999 miles | 4,097 | 1.7% |
| 200,000–249,999 miles | 362 | 0.2% |
| 250,000 miles or more | 52 | 0.0% |
Average recorded reading: 63,035 miles. 237,033 valid odometer readings in the model summary. Source mileage bins are grouped into 50,000-mile bands.
Failure-item records in 2025. Occurrences do not equal the number of distinct vehicles or tests affected.
| Recorded failure item | Severity | Occurrences |
|---|---|---|
| not workingRfR 32041 · Class 4 | Major | 13,418 |
| pin or bush excessively wornRfR 31322 · Class 4 | Major | 13,239 |
| does not clear the windscreen effectivelyRfR 30858 · Class 4 | Major | 11,937 |
| fractured or brokenRfR 31227 · Class 4 | Major | 10,196 |
| projected beam image is obviously incorrectRfR 32017 · Class 4 | Major | 10,083 |
| inoperative in the case of a single lamp or all lampsRfR 31086 · Class 4 | Major | 9,717 |
| tread depth below requirements of 1.6mmRfR 31194 · Class 4 | Dangerous | 6,946 |
| ball joint excessively wornRfR 31321 · Class 4 | Major | 6,941 |
| incorrect colourRfR 31002 · Class 4 | Major | 5,237 |
| too lowRfR 30898 · Class 4 | Major | 4,511 |
| provides insufficient washer liquidRfR 30861 · Class 4 | Major | 4,478 |
| not workingRfR 30968 · Class 4 | Major | 4,381 |
Each year is an independent count of initial tests, not a distinct set of vehicles. Additional published years will appear automatically.
| Year | Initial MOT tests | First-time pass | Initial failure | Average odometer |
|---|---|---|---|---|
| 2025 | 237,360 | 63.4% | 36.6% | 63,035 mi |
Explore other published model families from 2025, ordered by the number of initial MOT tests. The current model is excluded.
| Model | Initial MOT tests | First-time pass | Initial failure |
|---|---|---|---|
| Qashqai | 600,104 | 69.0% | 31.0% |
| Juke | 328,244 | 69.8% | 30.2% |
| Note | 146,826 | 63.1% | 36.9% |
| X-Trail | 88,162 | 69.5% | 30.5% |
| Navara | 70,769 | 67.1% | 32.9% |
| Leaf | 53,146 | 80.3% | 19.7% |
| NV200 | 24,769 | 55.3% | 44.7% |
| Pulsar | 20,882 | 70.7% | 29.3% |
| Elgrand | 13,869 | 66.9% | 33.1% |
| Almera | 11,249 | 55.7% | 44.3% |
| Pixo | 10,729 | 63.7% | 36.3% |
| Primastar | 9,744 | 52.1% | 47.9% |
| Cabstar | 8,854 | 50.0% | 50.0% |
| NV400 | 7,944 | 62.7% | 37.3% |
| 350 | 4,856 | 73.4% | 26.6% |
| Pathfinder | 4,727 | 55.7% | 44.3% |
| NV300 | 3,302 | 68.8% | 31.2% |
| Serena | 3,025 | 74.5% | 25.5% |
| Cube | 2,765 | 67.8% | 32.2% |
| Primera | 2,490 | 57.2% | 42.8% |
Other Nissan models ranked by their first-time pass rate, using at least 1,000 initial tests per model in 2025. Test outcomes are not an adjusted measure of model reliability.
| Model | Initial MOT tests | First-time pass | Initial failure |
|---|---|---|---|
| GT-R | 2,378 | 95.3% | 4.7% |
| Ariya | 1,568 | 91.5% | 8.5% |
| Skyline | 2,018 | 86.2% | 13.8% |
| Leaf | 53,146 | 80.3% | 19.7% |
| Serena | 3,025 | 74.5% | 25.5% |
| 350 | 4,856 | 73.4% | 26.6% |
| Pulsar | 20,882 | 70.7% | 29.3% |
| Juke | 328,244 | 69.8% | 30.2% |
| X-Trail | 88,162 | 69.5% | 30.5% |
| Qashqai | 600,104 | 69.0% | 31.0% |
| NV300 | 3,302 | 68.8% | 31.2% |
| NV250 | 2,245 | 68.6% | 31.4% |
| Cube | 2,765 | 67.8% | 32.2% |
| Navara | 70,769 | 67.1% | 32.9% |
| Elgrand | 13,869 | 66.9% | 33.1% |
| Interstar | 2,338 | 64.3% | 35.7% |
| Pixo | 10,729 | 63.7% | 36.3% |
| Note | 146,826 | 63.1% | 36.9% |
| NV400 | 7,944 | 62.7% | 37.3% |
| Terrano | 1,137 | 59.8% | 40.2% |
We analyse official DVSA MOT records, link model names to Department for Transport reference data and publish independently calculated results. Figures refer to MOT tests, not unique vehicles. .
Published years: 2025. Latest completed year: 2025.
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MOTSTATUS is an independent UK vehicle information and MOT centre directory. We bring official records, carefully calculated statistics and local comparisons together to make MOT information easier to understand and use.
We work with published DVSA MOT records and test centre information, vehicle reference data from DfT, and location data from Ordnance Survey. The original authorities remain the source of their data.
We normalise vehicle labels conservatively, calculate test-based pass rates and mileage summaries, and compare centres using mapped locations. Statistics are not an assessment of any particular vehicle or garage.
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Definitions, calculations and limitations.
We process anonymised DVSA annual MOT extracts, retain the published outcomes and derive aggregated results. The make and model catalogue uses DfT/DVLA reference mappings, with additional normalisation limited to unambiguous, verified formatting matches. Ambiguous labels are not assigned automatically.
P is an initial pass, F a fail and PRS a repaired-and-passed result. Other test types are excluded from this denominator.
This is the share that passed without an initial failure or repair-at-test outcome.
Repaired-and-passed tests count as initial failures, not first-time passes.
We exclude missing and zero readings. This is the average reading at an MOT test, not distance travelled during the year.
Rates are weighted by included test counts. We never average model percentages without their denominators.
One test can contain several faults. Fault occurrences are not a count of unique vehicles, and their sum must not be treated as a failure rate.
We calculate approximate straight-line distance from mapped British National Grid coordinates: d (miles) = √[(E₂−E₁)² + (N₂−N₁)²] / 1,609.344. Road distances, where separately calculated and cached, are different. Coordinates may represent postcode reference points, not garage entrances.
We group the nearest centres already retrieved for a profile by their imported place identifier. The count and combined vehicle classes refer to that sample, not every centre in the locality. Distance is to the nearest included station for each place.
Class labels come from the station record and are translated into broad vehicle categories for readability. Eligibility still depends on vehicle weight, seating, construction and the station’s current approval.
Published prices are statutory maximum MOT test fees from GOV.UK, not quotes from an individual MOT centre. Our fee-check tool can compare the GOV.UK schedule with our published table; unrecognised source changes must be reviewed before replacement.
We show results only from completed and published annual imports, with the latest successfully normalised year selected automatically. Subsequent years are shown as distinct reporting periods. Our model names and coverage reflect conservative reference matching. Coverage is not identical across makes and should not be interpreted as the full registered fleet.
Age groups are estimated from the available registration information. Mileage ranges aggregate source readings; unknown or invalid readings are not imputed as zero. Changes to MOT regulations and fault classification, particularly around 2018, can reduce comparability with older years.
These are descriptive statistics, not adjusted causal measures of reliability. Vehicle age, usage, maintenance and the tested fleet composition all affect results. See for the official sources.
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