Skill: Evaluate Real Estate Investment

Domain

real_estate

Description

Analyzes commercial real estate investment opportunities using proprietary valuation models, cap rate analysis, and risk-adjusted return calculations.

Tags

real-estate, investment-analysis, valuation, cap-rate, property-assessment

Use Cases

• •Acquisition underwriting
• •Portfolio performance review
• •Disposition analysis
• •Development feasibility

Proprietary Business Rules

Rule 1: Cap Rate Benchmarking

Property-type and market-specific cap rate comparisons against proprietary benchmarks.

Rule 2: NOI Projection Methodology

Standardized NOI projection with market-specific vacancy and expense assumptions.

Rule 3: Risk-Adjusted Returns

IRR calculations with property and market risk premiums.

Rule 4: Debt Service Coverage

Minimum DSCR requirements by property type and lender profile.

Input Parameters

• •property_id (string): Property identifier
• •property_type (string): Office, retail, industrial, multifamily
• •market (string): MSA or market identifier
• •asking_price (float): Purchase price
• •noi (float): Current net operating income
• •square_feet (int): Rentable square feet
• •occupancy (float): Current occupancy rate
• •lease_terms (dict): Lease structure details
• •capex_needed (float): Capital expenditure requirements

Output

• •investment_rating (string): Buy, hold, pass
• •cap_rate (float): Going-in cap rate
• •projected_irr (float): Projected internal rate of return
• •risk_score (int): Investment risk score
• •valuation_range (dict): Value range estimate

Implementation

The analysis logic is implemented in investment_analyzer.py and references market data from CSV files:

• •markets.csv - Reference data
• •property_types.csv - Reference data
• •financing_assumptions.csv - Reference data
• •parameters.csv - Reference data.

Usage Example

from investment_analyzer import evaluate_investment

result = evaluate_investment(
    property_id="PROP-2024-001",
    property_type="office",
    market="NYC",
    asking_price=50000000,
    noi=3000000,
    square_feet=100000,
    occupancy=0.92,
    lease_terms={"walt": 5.2, "escalations": 0.03},
    capex_needed=2000000
)

print(f"Rating: {result['investment_rating']}")
print(f"IRR: {result['projected_irr']:.1%}")

Test Execution

from investment_analyzer import evaluate_investment

result = evaluate_investment(
    property_id=input_data.get('property_id'),
    property_type=input_data.get('property_type'),
    market=input_data.get('market'),
    asking_price=input_data.get('asking_price'),
    noi=input_data.get('noi'),
    square_feet=input_data.get('square_feet'),
    occupancy=input_data.get('occupancy'),
    lease_terms=input_data.get('lease_terms', {}),
    capex_needed=input_data.get('capex_needed', 0)
)