Stochastic Modeling

Spreadsheet and appraisal tools underwrite on point estimates and expected-value blends. CFDL models are natively stochastic: the same model file gives you the deterministic number and the distribution around it — deterministically seeded and byte-reproducible.

Declaring uncertainty

Any assumption can be a distribution instead of a constant:

assume discount_rate = 0.10
assume rent_growth ~ Normal(mean=0.03, stdev=0.01, clip=[-0.02, 0.08])

Supported distributions: Normal(mean, stdev, clip?), LogNormal(mu, sigma, clip?), Uniform(min, max), Triangular(min, mode, max). Expressions reference stochastic values the same way as constants, via inputs.<name>.

Running Monte Carlo

run monte_carlo trials 20000 seed 42

Every Monte Carlo run declares an explicit seed. Each assumption gets its own deterministic draw stream, so adding a new assumption never reshuffles another assumption's draws — results are reproducible byte-for-byte across machines and runs. Run-config JSON can override or add distributions without touching the model.

Scenario-consistent branching

Because draws are ordinary values, expressions can branch on them — producing coherent, binary outcomes per trial rather than expected-value blends:

// Per trial: either the tenant renews (renewal rent, no downtime)
// or the space rolls (market rent after downtime and re-lease costs).
amount = if(inputs.renewal_draw < 0.70, renewal_rent, market_rent)

An expected-value blend hides the bimodal shape of outcomes like lease rollover; per-trial branching preserves it.

What results carry

Monte Carlo results summarize each metric with mean, stdev, min/max, and percentiles (p01 through p99, including p05/p25/p50/p75/p95) — see the Results schema. The Python SDK exposes them via results.monte_carlo().