Planning approval rates: how to read council statistics

Ask what is being counted

On PlanningTrack, an authority’s approval percentage is approved records divided by approved plus refused records in the collected data. Pending, withdrawn and unknown outcomes are left out. This describes the records available here, not every application the authority has handled.

For example, 18 approvals and two refusals produce a 90% approval rate. If the same dataset has 80 pending applications, the percentage is still 90%. The calculation has not become wrong; it is answering a narrower question than “what happened to all 100 applications?”. These numbers are illustrative.

Compare similar application types

A set of applications can include householder proposals, larger developments and applications dealing with details of existing permissions. If you want to research extensions, a combined percentage across those types may not describe comparable projects.

Open individual records and look for comparable proposals. Note the application type, site context, decision and relevant documents. Our status guide explains why a broad outcome label needs context.

Check date ranges and missing records

An earliest and latest received date do not prove that every date between them is covered. One old record and a batch of recent records can produce a wide range with a large gap in the middle.

Check collection coverage, record counts and the last source checks before comparing authorities. A difference between two percentages may reflect which records have been collected. A recently imported record may itself describe an old decision.

Use the authority page’s methodology alongside the numbers. If the dataset cannot support your question, say so in your notes rather than making the chart look more certain.

Use statistics to find comparable proposals

Statistics are useful for spotting a group of records worth examining. Build a small set of similar proposals and read the reasons in the reports and decision notices. Keep refusals in that set; they can reveal issues that a list of approvals misses.

For your own scheme, bring the actual site and proposal to a qualified adviser. They will need to assess the site, drawings and relevant conditions individually.

Browse the authority hubs for the underlying applications. The application reading guide gives you a repeatable way to inspect them, and the open data page explains why UK coverage takes work.

Define which outcomes you want to compare

An approval rate can describe several different things depending on the records and calculation used. It may concern one application type, a particular period or a mixed collection of decisions. Before comparing numbers, write down the question you want answered. “What proportion of these collected decisions were approvals?” is different from “what chance does my proposed extension have?”.

The first question can be answered with a defined dataset and calculation. The second requires assessment of a particular site and proposal, and cannot be settled by a general percentage. Treat the statistic as context for research, not as a probability attached to your property. A precise-looking number does not become a project assessment because it is displayed beside a council name.

Decide which unit you are counting. Applications, decisions, properties and physical developments are not interchangeable. Several applications can relate to one project, while one application can cover multiple elements. If you are counting records, describe them as records. Avoid turning the number into homes, construction starts or completed buildings without evidence that supports that conversion.

Keep the definition with the chart or table. A reader should not need to search a separate report to discover what the denominator means. Include the source, date range where known and the main exclusions. That small amount of context makes the statistic much more useful and reduces the temptation to interpret it as a universal performance score.

Worked example: two correct percentages, two different questions

Consider a fictional collection containing 18 approved applications, two refused applications, 20 withdrawn applications and 60 pending applications. Approved divided by approved plus refused gives 18 divided by 20, or 90 per cent. Approved divided by all 100 records gives 18 per cent. Both calculations can be performed correctly, but they answer different questions.

The 90 per cent figure describes the balance of approved and refused outcomes in that collection. It excludes records whose status is pending or withdrawn. The 18 per cent figure describes how many records in the whole collection currently show approval. It should not be described as the eventual success rate, because many of the pending records do not yet have a recorded decision.

Now imagine that ten pending applications are decided next month. The percentage may change even though no earlier outcome was altered. A dataset is a snapshot, and the population of completed decisions can grow. Keep the date checked with the calculation so readers understand why a later figure may differ.

The interactive example in this guide lets you change the counts and see the denominator explicitly. Its numbers are illustrative, not council data. Use it to understand the arithmetic before applying the same reasoning to an authority page. The key habit is to name what is included and excluded rather than assuming that “approval rate” has only one possible meaning.

Small samples can make large-looking differences

Suppose one collection contains nine approvals and one refusal, while another contains 900 approvals and 100 refusals. Both show 90 per cent. The equal percentages conceal very different sample sizes. Always read the count beside the rate, particularly when a newly collected authority has only a handful of records available.

In the smaller collection, one additional refusal changes the rate substantially. In the larger collection, one additional refusal has a much smaller effect. This does not establish which authority is more consistent or which future proposal will succeed. It demonstrates why a percentage without its denominator can create an exaggerated impression of stability.

Avoid ranking authorities to one decimal place when the underlying data is sparse or incomplete. Extra decimal places can imply precision that the collection does not support. A rounded figure with counts and a coverage explanation is often more informative than a tightly ordered league table built from incomparable samples.

If you are preparing a report, make the sample size visible in the main table rather than burying it in a footnote. Add a clear note where coverage is limited. The purpose of the comparison should determine how much analysis is appropriate; not every small dataset needs a complicated model to reveal that the evidence is insufficient for a strong conclusion.

Build a group of comparable applications

A combined dataset can include householder proposals, larger developments, certificates and applications addressing details of existing permissions. Their outcomes do not all answer the same question. If your research concerns extensions, begin by identifying records that actually concern comparable extension proposals rather than relying on an authority-wide headline.

Use application type and proposal text together. A keyword such as “extension” can appear in a condition submission or a historical reference within another record. Read the actual case before placing it in your comparison set. A small number of carefully checked examples may be more useful than a large automated group assembled from ambiguous descriptions.

Record exclusions and borderline cases. If you leave out applications with uncertain types or missing documents, say so. Those decisions affect the population you are describing. Avoid quietly removing inconvenient cases simply because they make the percentage less neat or do not match the story you hoped to tell.

For a professional comparison, consider what other factors make cases meaningfully similar and seek appropriate analytical or planning advice where needed. This guide does not provide a model for predicting decisions. It helps you avoid obvious category mistakes and identify the records worth reading in detail.

Received dates and decision dates describe different cohorts

A group of applications received in a year is not the same as a group decided in that year. Some received applications may remain pending, and some decisions may concern applications submitted earlier. State which date field defines your group. Otherwise two apparently conflicting statistics may simply concern different populations.

If you calculate outcomes for a received-date group, be clear about how many records are still pending at the time of the analysis. A recent group can have a different mix of completed cases from an older one. Do not treat incomplete outcomes as if they were final or compare groups without acknowledging that difference.

An earliest and latest date in a collection do not prove continuous coverage between them. One historical record and a batch of recent records can produce a wide span with substantial gaps. Check how the data was collected and what coverage evidence is available before presenting the range as a complete archive.

Keep source collection dates separate from application dates. A record imported this month may describe a decision made years ago. If you are measuring recent planning activity, using import dates can create a misleading surge. PlanningTrack’s coverage page explains the state of collection so you can judge whether the available data fits your question.

Identify missing outcomes and incomplete records

An unknown outcome is not automatically a refusal, approval or pending case. It may mean the source did not publish enough information or that its wording could not be classified confidently. Keep unknown records visible in your quality checks, even if your chosen calculation excludes them. Their number helps readers understand how much of the collection supports the result.

Check whether missing data is concentrated in particular periods, types or sources. A random-looking gap and a whole missing historical period have different implications for comparison. You do not need to solve every collection problem to recognise that a statistic should be qualified. State what is known and which questions the data cannot currently answer.

Avoid filling gaps by guessing from proposal wording or nearby records. An application described as modest or similar to an approval does not inherit that outcome. If you manually review a record, keep the evidence and classification rule with the correction. A data table should remain traceable to the source that supports each outcome.

When reporting percentages, include a short quality note explaining exclusions and collection limits. This is part of describing the measure, not an optional warning added after the conclusion. A useful figure tells readers what it represents before inviting them to compare or act on it.

Explain how you count withdrawals and appeals

A withdrawn application may have no published explanation of why it was withdrawn. Do not classify every withdrawal as a disguised refusal or a likely approval. If you want to analyse withdrawals, treat them as a distinct outcome and read relevant evidence before making claims about reasons.

Appeals can add another outcome to a case’s history. Decide whether your measure concerns the original council decision or a later final position, and preserve that distinction in the data. Silently replacing one with the other can make a historical comparison difficult to interpret. A reader should know which decision stage the statistic describes.

Related submissions can also create double-counting if your intended unit is a physical project. A main application and later approval of details may both appear as records, but counting them as two separate building projects would answer a different question. Keep references and relationships available when moving from a record count to any broader interpretation.

For research on individual cases, open the source and follow the sequence. The application status guide explains common labels and the documents that add context. Statistics can help you select a group, but reading the records is what reveals why apparently similar outcomes may concern very different matters.

Write a comparison that another person can reproduce

Document the source, extraction or check date, filters, included outcomes and calculation. Keep the underlying list of references where appropriate. Another researcher should be able to understand how you moved from records to a percentage, even if later collection updates mean they obtain a slightly different snapshot.

Use plain descriptions in the chart title. “Approved share of collected approved and refused records” is less catchy than “best council for planning”, but it tells readers what the number measures. If you want to make a stronger interpretation, explain the additional evidence and reasoning rather than allowing the title to do unsupported work.

Present counts beside percentages and keep comparable columns in the same order. If one authority has materially different coverage, make that visible in the table. Do not rely on a colour scale alone to communicate quality or outcome; labels and notes are needed for accessibility and accurate reading.

Finish with a bounded conclusion. You may have identified a pattern worth exploring, a group of comparable records or a limitation that prevents a fair comparison. Those are legitimate research findings. A statistic does not need to become a prediction or league table to be useful.

Find comparable records and assess your site separately

For a homeowner, the most productive next step is often to inspect a small set of relevant applications and read their reports and decision notices. Note similarities and differences in site, design and the issues discussed. Keep refusals in the set so that you can understand concerns that a list of approvals would omit.

Bring those examples to your adviser as context, not as proof that the council must decide your proposal the same way. Explain what you think is comparable and what differs. A useful discussion starts with the actual site and scheme rather than a percentage you hope will substitute for an assessment.

For a business researching activity, distinguish planning records from confirmed demand for services. An approval does not prove work will proceed, when it will begin or who will undertake it. Use the data as one source of research and verify the facts relevant to any commercial decision through appropriate channels.

Browse authority hubs to inspect the records behind the figures. Use the application reading guide to follow the evidence consistently. The purpose of a good metric is to help you ask a better next question, while keeping the answer it already provides clear and limited to the data.

• State the unit, period, source and denominator beside every approval rate.
• Show approved and refused counts as well as the percentage.
• Keep pending, withdrawn and unknown outcomes visible in the methodology.
• Compare similar records and acknowledge coverage differences.
• Do not present a collected approval rate as the probability of an individual proposal succeeding.

Can two authority pages be compared fairly?

Only after checking whether their figures describe comparable records. Look at application mix, date fields, available counts, missing outcomes and collection coverage. If one page contains a small recent sample while another spans a much larger archive, a direct ranking can imply a comparison the data does not support. State the difference before interpreting the percentages.

You may still be able to make a narrower comparison by defining a common group and reviewing the underlying records. Document the selection and retain exclusions so another person can understand the result. If the available data cannot support that group, say so instead of using a more elaborate chart to hide the gap. The most useful outcome may be a list of records for further research or a clear explanation of what additional coverage is needed, rather than a league table of councils.