Scenario Analysis Software

Scenario analysis software models multiple possible outcomes from a shared set of assumptions. It helps users compare a baseline with cases such as rate shocks, stagflation, changing occupancy, or different investment allocations. The supplied examples use DSCR, break-even occupancy, cumulative cash flow, return, volatility, drawdown, and correlation. The category is useful for finance, property, investment, operations, and infrastructure decisions. Its central value is making assumptions and trade-offs easier to inspect before committing resources.

This directory lists three supplied examples. They cover a 10-year rental-property cash-flow stress test, a €40,000 ETF portfolio dashboard, and a campground build feasibility analysis. The examples represent property finance, investment allocation, and infrastructure sequencing. Each one includes the available dates, metrics, description, primary use case, source dashboard, and relevant tags. No additional tools or unsupported performance data have been added to the collection.

Scenario analysis compares coordinated sets of assumptions, such as a rate shock or stagflation case. Sensitivity analysis usually changes one input or a focused group of inputs to show how strongly an output responds. A scenario can therefore tell a broader business story, while sensitivity analysis can isolate a specific driver. The supplied rental dashboard is scenario-oriented because it compares named operating environments across multiple measures. Both approaches can be used together when a decision requires both a coherent case and a detailed driver check.

The supplied dashboards include dates such as June 29, 2026, but they do not prescribe a universal update schedule. Update frequency should follow the pace at which material assumptions change in the underlying project or portfolio. Rate-sensitive models may need attention when financing terms change, while construction models may need review when input inflation or scope changes. Investment dashboards may need refreshed source metrics when allocations or market inputs change. The important practice is to preserve the source trail and distinguish refreshed figures from earlier snapshots.

Yes, the supplied Energent Audit description explicitly says that it audits other AI’s work. It operates as an independent second agent rather than the agent that produced the original deliverable. It recomputes numbers, traces them to the exact source file, row, and field, and provides a pass/fail verdict with evidence. The stated purpose is to catch hallucinations and failures before delivery. This makes it relevant when scenario outputs need a reviewable and reproducible quality-control layer.