What is sensitivity analysis?
Sensitivity analysis tests how a development's financial viability changes when key assumptions are varied. It answers the question every lender asks: "What happens if things don't go to plan?" By modelling downside scenarios, sensitivity analysis demonstrates the resilience of a deal — or exposes hidden fragility.
The three core stress tests
Most development finance lenders want to see sensitivity analysis on three variables:
- GDV reduction — what happens if sale prices fall by 5%, 10%, 15%? This tests resilience to market downturns.
- Build cost increase — what if construction costs rise by 5%, 10%, 15%? This tests resilience to contractor claims, material price inflation, or design changes.
- Programme extension — what if the build takes 3, 6, or 9 months longer? This tests the impact of additional finance costs and delayed exit.
How to present sensitivity analysis
The most effective format is a matrix showing how profit on cost and LTGDV change under different scenarios. For example:
- Base case: GDV £5.0m, costs £3.8m → profit on cost 31.6%, LTGDV 62%
- GDV -10%: GDV £4.5m, costs £3.8m → profit on cost 18.4%, LTGDV 69%
- Costs +10%: GDV £5.0m, costs £4.18m → profit on cost 19.6%, LTGDV 62%
- Both stressed: GDV £4.5m, costs £4.18m → profit on cost 7.7%, LTGDV 69%
The key question is: under which scenarios does the lender still get repaid in full? And how much buffer exists before the lender is in a loss position?
What lenders want to see
- The deal should remain viable (positive profit) under individual stress scenarios (GDV -10% OR costs +10%).
- Even under combined stress (GDV -10% AND costs +10%), the lender should still be covered — LTGDV should remain below 100% with reasonable margin.
- Programme extension should be modelled with realistic additional finance costs, not just assumed at zero.
Why sensitivity analysis matters for AI and automated lending
As development finance increasingly uses technology for deal assessment, sensitivity analysis becomes even more important. Automated platforms like Assesr generate sensitivity matrices as part of every credit paper, ensuring lenders receive stress-tested analysis without manual modelling. This speeds up credit decisions and improves the quality of submissions.