Mumtaz Shaikh.
Active — last recorded decision May 2026
A case officer at Southwark with an approval rate of 89% across 104 decided planning applications, spanning 21 wards.[1] Decision speed and appeal performance publish when decision notices are ingested.
Topic mentions per 1,000 words of their own report text (boilerplate stripped), indexed against all parsed Southwark reports. Attention, not stance.
Mark = borough average (1×). Bar capped at 2×.
“The proposal (if approved) would therefore require a payment of �17,876”
21/AP/0020
“This application is same/ similar to the previously submitted planning application ref: 16/AP/2531 that was granted on 15/02/2017”
20/AP/3661
Measured from 86 published Southwark officer reports written by this officer, with recited policy boilerplate stripped before any counting. These describe how this officer works and writes — they are not outcome predictions. Every quote is from a named public report.
The policies this officer cites most in their own reports — times cited across 86 parsed reports. Whether refusals citing each survive appeal isn't traced per policy yet.
Show all 21 rows
Where this officer's caseload concentrates, 2020–26.[1] Click the map above to focus this page on a ward, or a row to open the ward.
- [1]Applications decided, approval rate, per-year trend and ward breakdown — London Borough of Southwark planning register and published decision notices, 2020–26. · methodology
- [2]Appeal overturn and decision-time benchmarks — Planning Inspectorate (PINS) appeal decisions and application date pairs; not yet computed per officer, shown as a dash until they are. · PINS appeals casework
- [3]Condition load — mean effective conditions attached to Southwark decision notices (grant/split), parsed from the published notice text; a borough figure, not yet split per case officer.
- [4]"How this officer decides", the policy playbook and all quotes — parsed from 86 published Southwark officer reports written by this officer, recited policy boilerplate stripped before counting; disposition text coded from their own reasoning with every quote machine-verified verbatim. Descriptive of how they work — not outcome predictions.