DLDecision Lab
GO_REDUCED · Proceed at reduced size

Score

0.80

Kelly

35.6%

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Buying a first home

Property / major purchase · 120-month horizon

Free

Score 0.80 · worth doing, but scale the stake to your risk budget (GO_REDUCED)

With the resources you committed ($54,020 of equivalent capital) over a 120-month horizon, the prior win rate is 64.3%, the payoff ratio is b=1.24, giving an expected value of EV=$23,925 and a risk-adjusted return of RAROC=1.27. The expected value is positive, so the arithmetic supports acting. The Kelly-optimal stake of 35.6% exceeds the 20% risk budget for your Working professional profile, so the platform caps its recommendation at that ceiling.

A 10% first-time-buyer down payment plus closing costs on the median existing home, held ten years. The win rate is the share of ten-year windows with positive *real* appreciation; the nominal figure would read above 90%. Mortgage interest, ownership costs and forgone rent are all excluded.

GO_REDUCED · Proceed at reduced size
Profile: Working professional

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The three numbers

Win rate, payoff ratio and expected value decide most of the recommendation; Kelly, RAROC and score turn them into an action.

Free

Win rate p

64.3%

Prior probability of success

Payoff ratio b

1.24 ×

b = E[win] / E[loss]

Expected value

$23,925

EV = capital × (p × b − (1 − p))

Kelly

35.6%

(b·p − (1 − p)) / b

RAROC

1.27

EV / (capital × σ)

Score

0.80

score = p × b

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What you are committing

Equivalent capital $54,020, converted from five kinds of resource.

Free

This opportunity carries $54,020 of equivalent capital. The largest single component is cash, at 90% of the total ($48,700). Cash has a clear floor, but keep 30% in reserve. Never put every liquid resource behind one decision.

How non-cash resources are priced

One hour of your own time is priced at $38 and one person-month of hired labour at $8,080. These are market shadow prices, not what you personally earn; changing them changes only the scale, not the ranking.

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How this compares with the category

Your assumptions against the Beta(α, β) prior for this kind of decision.

Free

Category mean win rate

63.3%

Beta(α=19, β=11)

Category payoff P50

1.25 ×

P90 2.50×

Your win rate of 64.3% is in line with the category baseline of 63.3%. The payoff ratio of 1.24× is close to the baseline median of 1.25×. Baseline source: Case-Shiller national index, real 10-year windows (US-RE-10..15) + NAR buyer profile (US-RE-01..05).

Baseline source: Case-Shiller national index, real 10-year windows (US-RE-10..15) + NAR buyer profile (US-RE-01..05)

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Action plan

Sized to the risk budget of your profile: Working professional.

Free

Recommendation: GO_REDUCED — the expected value is positive and this is worth doing, but the Kelly-optimal stake exceeds your risk budget. Scale the commitment down to the risk_budget ceiling shown below.

  • Kelly's stake clearly exceeds your risk budget, which means the opportunity is good but large relative to your resources today. Fund it to the risk-budget ceiling and no further.
  • Replace one all-in commitment with staged funding: run a pilot or MVP first, then add capital once the metrics hold.
  • Consider whether a partner can carry half the cash or time. That halves your effective position without halving the upside.
Caution: A high Kelly number is not a reason to ignore the risk budget. Money committed beyond it is the ammunition you will need for the next opportunity.

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Sensitivity analysis

How much the expected value moves when each variable is swung ±20%, ranked by impact. The top two are the ones worth researching before you commit.

Full PDF
  • · The tornado analysis identifies Win rate (p) as the variable the conclusion is most sensitive to: a ±20% swing moves the expected value by $31,178. Spend your research budget here first.
  • · The second most sensitive variable is Payoff ratio (b) (expected value swings $17,284); the remaining inputs matter markedly less.
  • · A quick test for whether to keep researching: buy the information only if it costs less than one fifth of the expected-value swing it would resolve.

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Monte Carlo distribution

10,000 simulated outcomes, seed demo-tier1-property — rerunning with the same inputs gives the same distribution.

Full PDF

P5

-$64,794

Median (P50)

$51,822

P95

$110,759

Probability of a positive outcome

64.5%

Across 10,000 Monte Carlo runs: P5 = -$64,794, P50 = $51,822, P95 = $110,759, and the probability of a positive outcome is 64.5%. The distribution is roughly symmetric — gains and losses are balanced.

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Scenarios

Bull, base and bear, moving win rate and payoff ratio together by ±15%.

Full PDF

Scenario analysis (win rate and payoff ratio moved together by ±15%): bull = $43,071, base = $23,925, bear = $6,724. The $36,347 gap between bull and bear measures how fragile your assumptions are — the wider it is, the more due diligence pays for itself.

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Counterfactual matrix

What the expected value becomes at other combinations of win rate and payoff ratio — how wrong your assumptions can be before the answer changes.

Full PDF
b=0.5b=1.0b=1.5b=2.0b=3.0b=4.0b=6.0b=8.0p=10%-$45.9K-$43.2K-$40.5K-$37.8K-$32.4K-$27K-$16.2K-$5.4Kp=20%-$37.8K-$32.4K-$27K-$21.6K-$10.8K$0$21.6K$43.2Kp=30%-$29.7K-$21.6K-$13.5K-$5.4K$10.8K$27K$59.4K$91.8Kp=40%-$21.6K-$10.8K$0$10.8K$32.4K$54K$97.2K$140.5Kp=50%-$13.5K$0$13.5K$27K$54K$81K$135.1K$189.1Kp=60%-$5.4K$10.8K$27K$43.2K$75.6K$108K$172.9K$237.7Kp=70%$2.7K$21.6K$40.5K$59.4K$97.2K$135.1K$210.7K$286.3Kp=80%$10.8K$32.4K$54K$75.6K$118.8K$162.1K$248.5K$334.9Kp=90%$18.9K$43.2K$67.5K$91.8K$140.5K$189.1K$286.3K$383.5K

The counterfactual grid shows that expected value turns non-negative once the win rate reaches 20%, or once the payoff ratio reaches 0.5×. Treat those two lines as the minimum bar any further evidence has to clear.

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Assumptions

Every input behind the numbers above, in one place.

Full PDF
Win rate (p)64.3%
Payoff ratio (b)1.24 ×
Volatility (σ)0.35
Horizon120 months
Cash$48,700
Your own time140 hours
Hired labour0.0 person-months
Physical assets$0
Opportunity cost$0
Profile risk_budget (Working professional)20%

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Reproducibility appendix

Everything needed to recompute this report yourself.

Full PDF

Core formulas

EV = capital × (p × b − (1 − p))      f* = (b·p − (1 − p)) / b      RAROC = EV / (capital × σ)

Python

from models.feasibility_model import expected_value, kelly_fraction, raroc, go_no_go

p, b, sigma, capital = 0.643, 1.244, 0.35, 54020
ev    = expected_value(p, b, capital)
kelly = kelly_fraction(p, b)
rr    = raroc(ev, capital, sigma)
print(ev, kelly, rr, go_no_go(ev, kelly, 0.20, rr))

TypeScript

import { expectedValue, kellyFraction, raroc, goNoGo } from "decision-lab/lib/feasibility";

const p = 0.643, b = 1.244, sigma = 0.35, capital = 54020;
const ev    = expectedValue(p, b, capital);
const kelly = kellyFraction(p, b);
const rr    = raroc(ev, capital, sigma);
console.log(ev, kelly, rr, goNoGo(ev, kelly, 0.20, rr));

Both implementations are in the repository and are kept in agreement by golden-parity tests: models/feasibility_model.py and lib/feasibility/.

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Sources and disclaimer

Full PDF
  • · Kelly J.L. (1956). A New Interpretation of Information Rate. Bell System Technical Journal.
  • · Markowitz H. (1952). Portfolio Selection. The Journal of Finance.
  • · Sharpe W.F. (1966). Mutual Fund Performance. The Journal of Business.
  • · Kahneman D. & Tversky A. (1979). Prospect Theory. Econometrica.
  • · Industry baseline: Case-Shiller national index, real 10-year windows (US-RE-10..15) + NAR buyer profile (US-RE-01..05)
  • · Decision Lab methodology
  • · Full business plan (PDF)
Disclaimer. This report is decision support for information purposes only. It is not financial, legal, medical or professional advice. Verify the underlying data, make your own decision, and accept responsibility for the outcome. For anything material to your money, health or legal position, consult a qualified professional.