🗓 Submission Timing Intelligence
When is the best month — and weekday — to submit your planning application? Real approval-rate + decision-speed data from 25,848 dated decisions across UK LPAs.
📊 National (all LPAs)
Months below refer to when you submit, not when the decision lands. Each application is bucketed by its received_date month — but the approval rate measures the final outcome, whenever it landed (60-90 days later typically). The lag is already baked into the signal: "submit in December = 100% approval" already accounts for the fact that those decisions arrive in Feb/March. The month-by-month table below shows the lag explicitly so you can see the full cycle.
🎯 Inverse view — "I want a decision in [month], when do I submit?"
Based on this LPA's typical decision-lag of 84 days (~3 months), the table below shows when to submit if you want your decision to land in a specific month.
| If you want a decision in... | Submit by (approx.) | That submit month's approval rate |
|---|---|---|
| Jan | Submit by end of Oct | 79.5% |
| Feb | Submit by end of Nov | 76.2% |
| Mar | Submit by end of Dec | 70.4% |
| Apr | Submit by end of Jan | 69.7% |
| May | Submit by end of Feb | 81.8% |
| Jun | Submit by end of Mar | 74.5% |
| Jul | Submit by end of Apr | 83.9% |
| Aug | Submit by end of May | 79.3% |
| Sep | Submit by end of Jun | 83.5% |
| Oct | Submit by end of Jul | 63.2% |
| Nov | Submit by end of Aug | 65.4% |
| Dec | Submit by end of Sep | 78.9% |
Inverse calculation is approximate — actual decision time varies (range typically ±30 days). Use the Decision Time Predictor for tighter forecasts.
⏱ Decision speed by application type
How long different application types actually take at National (all LPAs). Householder apps are statutorily 8 weeks; full/major are 13 weeks — but the real numbers usually drift. This is what the data says, not what the statute says.
→ Fastest: Outline (39 days). Slowest: Listed Building (102 days).
| Application type | Decisions | Approval rate | Avg days | Range | |
|---|---|---|---|---|---|
| Householder | 4,349 | 86.8% | 67 d (10 wk) | 1–365 d | Filter ▸ |
| Full / Major | 3,984 | 74.5% | 84 d (12 wk) | 1–365 d | ✕ clear |
| Lawful Development | 2,341 | 84.4% | 47 d (7 wk) | 0–337 d | Filter ▸ |
| Conservation Area | 1,255 | 87.3% | 50 d (7 wk) | 0–365 d | Filter ▸ |
| Listed Building | 1,013 | 90.2% | 102 d (15 wk) | 7–356 d | Filter ▸ |
| Advertisement | 943 | 69.8% | 73 d (10 wk) | 5–349 d | Filter ▸ |
| Prior Approval | 786 | 67.4% | 42 d (6 wk) | 0–345 d | Filter ▸ |
| Outline | 141 | 91.5% | 39 d (6 wk) | 0–361 d | Filter ▸ |
| Discharge Conditions | 126 | 91.3% | 47 d (7 wk) | 3–337 d | Filter ▸ |
| Change of Use | 78 | 66.7% | 84 d (12 wk) | 23–319 d | Filter ▸ |
| Reserved Matters (low sample) | 6 | 83.3% | 151 d (22 wk) | 15–361 d | Filter ▸ |
ⓘ Month-by-month stats above are filtered to Full / Major only. The table above always shows all types so you can compare.
📅 Month-by-month breakdown — full submit→decide cycle
Each row shows the complete journey for applications received in that month: how long they took, when the decision actually landed, and what % were approved. The approval rate already accounts for everything that happens between submission and decision — the lag is part of the signal.
| Submit month | Decisions | Approval rate | Median lag | Decision typically lands in | Range |
|---|---|---|---|---|---|
| January | 772 |
69.7%
|
57d (~8wk) | → March | 22–356d |
| February | 258 |
81.8%
|
64d (~9wk) | → April | 11–361d |
| March | 51 |
74.5%
|
188d (~27wk) | → September | 1–365d |
| April | 93 |
83.9%
|
143d (~20wk) | → September | 41–365d |
| May | 169 |
79.3%
|
107d (~15wk) | → September | 16–317d |
| June | 139 |
83.5%
|
147d (~21wk) | → November | 5–294d |
| July | 57 |
63.2%
|
212d (~30wk) (outlier) | → February | 8–323d |
| August | 26 |
65.4%
|
182d (~26wk) | → February | 126–328d |
| September | 133 |
78.9%
|
148d (~21wk) | → February | 98–305d |
| October | 400 |
79.5%
|
116d (~17wk) | → February | 59–248d |
| November | 844 |
76.2%
|
75d (~11wk) | → February | 38–210d |
| December | 1,042 |
70.4%
|
65d (~9wk) | → February | 23–225d |
Read across each row: submit in [month] → wait [median lag] → decision lands in [target month] → outcome [approval rate]. The approval rate is the final outcome of the whole cycle, not a snapshot.
🗓 Day-of-week patterns
| Day received | Decisions | Approval rate | Avg days |
|---|---|---|---|
| Sunday | 21 | 71.4% | 133 |
| Monday | 988 | 72.7% | 107 |
| Tuesday | 862 | 78.8% | 111 |
| Wednesday | 866 | 72.3% | 113 |
| Thursday | 742 | 77.8% | 108 |
| Friday | 763 | 77.5% | 108 |
| Saturday | 55 | 69.1% | 67 |
🌍 National picture — all 25,848 dated decisions
The broader pattern: March has the highest approval rate in our corpus; July the lowest. Pick an LPA above to see council-specific timing.
| Received in | Decisions | Approval rate | Avg days |
|---|---|---|---|
| January | 4,142 |
83.5%
|
46 |
| February | 2,191 |
87.1%
|
41 |
| March | 584 |
89.6%
|
53 |
| April | 301 |
86.4%
|
122 |
| May | 726 |
86.6%
|
82 |
| June | 821 |
90.5%
|
82 |
| July | 380 |
86.6%
|
135 |
| August | 295 |
87.1%
|
180 |
| September | 560 |
90%
|
146 |
| October | 1,342 |
86.4%
|
111 |
| November | 3,590 |
84.1%
|
70 |
| December | 4,530 |
81.5%
|
56 |
📊 Data sources & freshness
Timing is a tactical edge, not strategic justification. Use alongside the constraint check + pattern fingerprint to make the case strong on substance, then time it to land well.
- planning_applications.received_date (updated Daily ingest)
25,848 dated decisions where both received_date and decision_type are known. Approval = approved/granted/permit. Sample size gating: months with fewer than 8 decisions excluded from best/worst recommendation. - Day-of-week patterns
Reflects when applicants choose to submit, which may correlate with applicant type (Monday = professional consultants; Friday = end-of-week DIY submissions). Causality is correlative not causal. - Decision-time outliers
Months showing 200+ day averages are flagged — they reflect older PINS-appeal-derived rows where determinations stretched over many months. Read the Jan/Feb/Nov/Dec numbers (largest samples) as the reliable benchmark.