rent-vs-buy-simulator 1.0.0

Real Estate vs. Equity Simulation Engine

Rent vs. Buy Simulator

Try the Live App

A financial simulation tool that compares three capital allocation strategies over time:

• Buy — Purchase property with a mortgage
• Rent & Invest — Rent and invest the down payment in equities
• Rent & Invest Savings — Rent and invest monthly savings (when mortgage > rent)

Features

• Mortgage amortization, property appreciation, equity growth, closing costs, property tax, insurance, and maintenance
• Tax benefit modeling: mortgage interest deduction, capital gains exclusion (Section 121), SALT cap
• Four interactive Plotly charts with breakeven analysis
• Quick presets for common scenarios (High Interest, Bull Market, Conservative, First-Time Buyer)
• Save, compare, and export up to 5 scenarios side-by-side
• PDF report export with charts and configuration summary
• Monte Carlo uncertainty analysis with stochastic simulation
• Vectorized NumPy engine — no Python loops

Installation

From PyPI

# As a CLI tool (recommended)
uv tool install rent-vs-buy-simulator
rent-vs-buy

# Or as a library
uv pip install rent-vs-buy-simulator

From Source

git clone https://github.com/palsagar/rent-vs-buy-simulator.git
cd rent-vs-buy-simulator
uv sync
streamlit run src/simulator/app.py

Docker

# Local development
docker compose up --build
# Open http://localhost:8501

# Production
docker build -t rent-vs-buy-simulator .
docker run -p 8501:8501 rent-vs-buy-simulator

Library Usage

from simulator import SimulationConfig, calculate_scenarios

config = SimulationConfig(
    duration_years=30,
    property_price=500000,
    down_payment_pct=20,
    mortgage_rate_annual=4.5,
    property_appreciation_annual=3.0,
    equity_growth_annual=7.0,
    monthly_rent=2000,
    rent_inflation_rate=0.03,
)

results = calculate_scenarios(config)
print(f"Buy: ${results.final_net_buy:,.0f}")
print(f"Rent & Invest: ${results.final_net_rent:,.0f}")
print(f"Difference: ${results.final_difference:,.0f}")

Contributing

Contributions welcome! Fork the repo, create a feature branch, and open a PR against main.

uv sync
uv run pytest tests/ --cov --cov-report=term   # 80% coverage minimum
uv run ruff check src/ tests/

See FORMULAS.md for the mathematical reference.

License

MIT License. See LICENSE for details.