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Enter an address. DealScope estimates the most you can pay for a rental property so rent covers mortgage, tax and insurance. For information only.
FinancePython · HTMLGitHub @learnyourmindOnlineOwner unconfirmed
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Real estate deal scope app
Translation pending
Taken from the repository README
Loading the full README.
Read the full README ↗bash run.sh
Then open: http://localhost:5000
Double-click run.bat
Then open: http://localhost:5000
python3 -m venv .venv && .venv/bin/pip install -r requirements.txt
.venv/bin/python app.py
Type an address; get the price a rental can support.
RENTCAST_API_KEY to identify the home's bedrooms, baths, size and type and use its own rent estimate (one RentCast request per analysis; the free plan allows 50 a month). Without it, or if a lookup fails, the result is for a typical home in the ZIP and says why.intent.md, spec.md, plan.md.POST /analyze with Content-Type: application/json (all assumptions are optional):
{
"address": "1100 Congress Ave, Austin, TX 78701",
"assumptions": {"down_payment_pct": 20, "annual_interest_rate_pct": 7.0, "loan_term_years": 30,
"min_monthly_cash_flow": 100, "rent_adjustment_pct": 0,
"property_tax_rate_pct": 1.63, "insurance_rate_pct": 1.55, "hoa_monthly": 0}
}
Success: {"success": true, "analysis": {location, market, assumptions, home, qualifier, limitations, sources}}; the single home object has label, configuration (status not_identified until a source exists), rent, prices {piti_breakeven, allin_breakeven, target}, monthly_at_price, and at_target. Every value carries kind (retrieved, user, estimate, unavailable) and its source or basis. Failures: {"success": false, "error": {code, message, retryable, request_id}} — 400 INVALID_REQUEST/OUT_OF_RANGE, 404 PROPERTY_NOT_FOUND, 422 AMBIGUOUS_MATCH/INSUFFICIENT_DATA, 429, 502/503/504 PROVIDER_*, 500. Bounds and formulas: spec.md §5.
make install # one-time: .venv + dependencies
make check # lint, format check, secret scan, tests with coverage gate
make hooks # enable the pre-commit secret scan for this clone
make run # dev server on http://localhost:5000
Contributing with an AI agent? Read docs/ai-sdlc.md (task loop, human gates, guardrails, mapping to Anthropic's Claude Code guidance) and AGENTS.md. Hooks and skills you install yourself are in docs/claude-config/.
Deployed on Vercel (Flask app served as a Python function; see ADR-0004). CI (.github/workflows/ci.yml) runs on every pull request. Setup, release, and rollback steps are in docs/runbook.md; configuration variables are in .env.example. A Dockerfile (built in CI) keeps the app portable to any container host; Procfile platforms run gunicorn app:app. Do not expose the Flask development server.
data/zip_market.json, rebuilt monthly with python scripts/update_market_data.py.(income − HOA − goal) ÷ cost-per-dollar-of-price. Prices are rounded down to $1,000.RENTCAST_API_KEY is set); nothing is stored