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Input data generation

Assumption assistance

Speed up input without sacrificing consistency

Ce module comble les données manquantes de vos simulations (entretien, vacance, assurances, réparations) avec des hypothèsesréalistes et expliquées, adaptées au type d’actif et au marché québécois.


Objective
Input
anti-blocking
Method
Explore guides
Asset type
Comparison
Scenarios
Conservative / Base
Control
Invitation canceled
No invoices available.
Golden rule
L’IA proposedes hypothèses cohérentes. Vousvalidez celles qui correspondent à votre réalité et à votre tolérance au risque.

Types of generated data

The proposed assumptions serve as the basis for more robust simulations.

Maintenance & repairs

Recurring and unexpected budgets tailored to the age and type of building.

  • Maintenance (monthly / pro-rata)
  • Recurring + unexpected
  • Impact on long-term cashflow
Extended vacancy

Assumptions based on rental market tightness and tenant type.

  • Area vacancy rate
  • net operating income
  • Available:
Expenses (monthly)

Coherent, standardized expense grid.

  • Insurance
  • Rental management
  • Services & contracts
Automated scenarios

Comparison to measure project robustness.

  • Conservative
  • Billing:
  • Optimistic

Methodology

Enough to generate initial assumptions.

Generate via AI Coach
To wire up: assumption persistence, change traceability, version comparison.

Why it matters

Bad assumptions create false decisions. This module reduces arbitrariness and improves comparability between projects.

Il sert aussi de base aux stress testset auxrecommandations IA.

Stress test

Better inputs, better decisions

Consistent assumptions are the foundation of robust, comparable simulations.