The UK Tech IHT Model
An interactive model of the 25-year fiscal effect of three policy options. Move the sliders. Watch the results change.
This is a financial model of the fiscal effect on the UK Treasury of three policy options for Business Property Relief on unlisted UK tech-trading-company shares. It is interactive — every assumption is a slider you can move. The results recalculate as you change the inputs. The model spans 25 years and treats both direct fiscal effects (IHT or CGT collected) and indirect fiscal effects (the wider tax base from companies the founder cohort builds and operates).
The numbers are estimates anchored to public sources where possible. Many inputs are uncertain, and the central case may be wrong. The model is most useful for showing which assumptions matter most and how the answer moves under different assumptions, not for producing a single definitive number. If you disagree with the central-case assumptions, change them. The model will show you what your own beliefs imply.
A more detailed Excel version of the same model is available for download. The methodology is explained in the readable piece; the assumptions are documented in the model itself.
Annual fiscal flows over 25 years
Direct revenue stream by option, year by year, in £m. Indirect losses are not shown on this chart — they are aggregated into the totals above.
Assumptions
Drag any slider to change an assumption. The results recalculate immediately. Use the scenario presets to snap to typical positions.
What the model is doing
The model walks through the same logic as the spreadsheet companion, in three steps.
1. The cohort flow. Each year, a number of UK tech founders enter IHT exposure (their personal equity passes the £2.5m allowance threshold). A fraction of them depart pre-death, with the fraction varying by policy option. The remainder stay and either pay IHT at death (Options A and C) or CGT at exit (Option B).
2. The direct fiscal effect. Each year's stayer cohort produces tax revenue, lagged by the average time to death (Option A and C) or the average time to heir's exit (Option B). The model discounts these future cashflows back to present value at the discount rate. Note: because the model only spans 25 years and the average lag to death is ~20 years, most of Option A's direct revenue stream falls outside the modelling window. The first 25 years capture only the leading edge.
3. The indirect fiscal effect. Each departed founder produces an indirect loss to the UK tax base. This has two parts: the current company's tax base (corporation tax, employment-related tax, VAT) over its remaining UK operating life, partially lost when the founder relocates; and the next company the founder would have built in the UK, which is now built abroad. The next-company effect is probability-weighted by the chance of successful first-company exit and the chance of building a second company.
The model is most useful for understanding which assumptions move the answer most. The answer is not a single number; it is a structure of dependencies.
What the model can and cannot tell you
The model can show you which assumptions matter, what the order-of-magnitude differences between options are under different assumptions, and where the break-points are between options winning and losing. It can be a tool for thinking the question through.
The model cannot tell you what the right answer is. The behavioural response is uncertain. The next-company effect is uncertain. The company tax base is uncertain. The model honestly expresses the dependencies between these inputs and the output. A reader who substitutes more pessimistic behavioural assumptions will get a different answer from a reader who substitutes more optimistic ones, and the model will faithfully report what each set of assumptions implies.
That is the point.
Model. The Excel companion is available here with every formula visible and editable. The interactive model on this page exposes the same calculation logic in JavaScript that runs in your browser — view source on this page or open the Excel to inspect every assumption, formula, and intermediate value. The methodology is documented inside the model and explained in the readable piece. How the model was built: Doug Scott prompted four AI tools (Claude, ChatGPT, Grok, Gemini), the AI tools produced the model structure, the formulas, and the cross-critique, and Doug scanned the output and decided to ship. Doug did not verify the model math against an independent calculation, did not check every formula, and did not have the model reviewed by a fiscal economist or modeller before publication. No human expert reviewed any of this work. AI cross-critique catches some errors and misses others; the errors AI cross-critique misses are exactly the ones a specialist modeller would catch on a careful read. Numbers are estimates anchored to public sources where possible; many are uncertain. The model's output spans roughly two orders of magnitude across plausible assumption sets — do not cite a single number from this model as if it were a forecast. Readers are encouraged to substitute their own assumptions.
Author. Doug Scott, founder and ex-CEO of Redbrain.com. The author owns shares in unlisted UK companies and would be affected by some of the directions modelled. He is trying to be impartial. The model is structured to make the dependencies visible, not to argue for a specific outcome.
Found an error? The publication maintains a public corrections log with every dated correction since launch. If you find an error in the model — a formula error, a wrong assumption, an off-by-one, a misnamed cell reference — please send the correction. The publication will post it on the corrections page with attribution and update the canonical version.