Borough Report

Southwark

281 determined applications · 23 wards · 10 areas · 54 conservation areas · 2023–2025

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Headline findings

£179k
6-unit sweet spot
Highest per-unit residual land value
Verified
−40%
10th unit trap
Residual collapses at AH threshold
Verified
35%
AH on ALL schemes
P1 applies from 1 unit — unique to Southwark
Verified
30%
Schemes compressed
Avg 2.4 units cut during determination
Verified

Key findings

Verified The 6-unit sweet spot: £179k/unit residual, delegated, zero committee exposure ›
Six units delivers the highest per-unit residual land value with delegated determination and zero committee threshold exposure. Per-unit residual drops at 7 units (committee finance cost added) and collapses at 10 units (full affordable housing obligation). n≥30 applications.
Verified 10th unit trap: adding one unit from 9 to 10 destroys 40% of per-unit residual ›
At 10 units, full affordable housing obligations trigger a step-change in scheme economics. The viability model shows 9-unit schemes consistently outperform 10–11 unit schemes on per-unit residual land value. This is the single most important number in every land bid.
Verified P1 requires 35% affordable housing on ALL schemes — even 1–9 units ›
Unlike most London boroughs, Southwark requires 35% affordable housing on ALL schemes including sub-threshold 1–9 unit developments via in-lieu financial contribution. Viability appraisals are mandatory and published. This is a critical differentiator from other boroughs and must be factored into every land bid from the outset.
Verified 29.8% of schemes compressed — propose N+2 to protect your appraisal ›
Nearly 1 in 3 schemes is compressed during determination. Average loss is 2.4 units, median 2 units. This is significantly higher than comparable boroughs. Strategy: propose two more units than your target density with design flexibility for removal. n=84 approved applications.
Verified Pre-app lifts approval from 49% to 66% (p=0.030) ›
Pre-application engagement lifts approval rate by 17 percentage points (65.9% vs 48.5%). Determination takes longer (median 92 vs 47 days), but trades speed for certainty. Essential for Southwark’s complex policy environment. n=156 applications with pre-app data.
Verified Space standards non-compliance is the strongest refusal predictor (p=0.0001) ›
Space standards is the single strongest predictor of refusal in Southwark. Applications where space standards are flagged as an issue face 48.1% refusal vs 15.9% when compliant. This outranks density, conservation status, and every other factor tested. Rigorous space standards compliance is non-negotiable. n=281.
Verified Density above P75 increases refusal risk (p=0.0045) — but general density does not ›
Borough-wide, proposing more units does NOT increase refusal risk (p=0.32). However, exceeding the 75th percentile density for your site type does: 45.1% refusal above P75 vs 23.0% at or below. The practical takeaway: propose aggressively up to the P75 benchmark, but don’t exceed it. n=281.

Plus additional findings covering conservation area effects, ward-level patterns, officer tendencies, amenity impact, and determination time. Each graded Robust, Moderate, Indicative, or Anecdotal.

What you get

The full report

  • 40+ page report (HTML & PDF) with 21+ evidence-graded findings
  • Density matrix by site type with P20–P75 ranges
  • PTAL-adjusted density lookup table
  • Viability model with cliff chart and sensitivity analysis
  • Ward-level and area-level approval rate maps
  • V2 refusal predictor analysis with p-values
  • Conservation area risk assessment (54 CAs)
  • Case officer patterns across 44 officers

Southwark-specific insights

  • P1 affordable housing impact on ALL schemes (1–9 units)
  • Space standards as refusal predictor (p=0.0001)
  • Density >P75 as refusal predictor (p=0.0045)
  • 3-zone CIL analysis: £50–£400/m² borough + £60 MCIL2
  • 29.8% compression rate — N+2 strategy recommendations

Decision tools

  • Before-you-bid and before-you-submit checklists
  • One-page Market Snapshot for quick reference
  • Underlying data in Excel for your own analysis

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