Simulator Primitive: Cost of Space

Objective

To provide the AI with the logic to simulate real-world physical costs by hooking into live real estate data.

Data Hooks

• Primary Source: Real estate portals (Scraping logic for Housing.com, 99acres).
• Secondary Source: Industrial Development Corporation (SIDC) fee schedules.

Simulation Variables

1. Rent & Lease

• Logic: Base Rent * Area (sq ft) + Common Area Maintenance (CAM).
• Dynamic Input: Average rent for “Commercial” or “Industrial” categories in the selected Pin Code.
• Scenario Impact: Moving from a Tier 1 city (Mumbai) to a Tier 2 city (Pune) triggers a 40% reduction in this variable.

2. Security Deposit

• Logic: Usually 3 to 10 months of rent (varies by state).
• Dynamic Input: Regional standard (e.g., Bangalore is famously high-deposit).
• Scenario Impact: Massive impact on “Day 0” capital requirements.

3. Fit-out & Interior Capex

• Logic: Cost per sq ft * Area.
• Archetype Triggers:
  • Chai Walla: Basic stall (₹500/sq ft) vs. Premium Cafe (₹2,500/sq ft).
  • Electronics Factory: Cleanroom requirements (₹5,000/sq ft) vs. Basic Warehouse (₹800/sq ft).

4. Utility Load

• Logic: Calculated based on the machinery/equipment blocks selected in Phase 6.
• Dynamic Input: State-specific industrial power tariffs.

Scenario Modeling: “The Real Estate Pivot”

The Simulator should allow the user to pivot their entire business universe based on space costs:

• “The Urban Premium” Scenario:
  • Location: Koramangala, Bangalore.
  • Rent: High.
  • Footfall Projection: High.
  • Outcome: Fast growth, thin margins, high risk.
• “The Cluster Advantage” Scenario:
  • Location: Peenya Industrial Estate.
  • Rent: Low (per sq ft).
  • Footfall Projection: Zero (B2B focus).
  • Outcome: Slow start, stable margins, industrial scale.

Implementation Note for AI Services

When a user provides a location, the AI must fetch the current median rent for that category in that area to populate these primitives.