Real Deal – Cash-Flow Property Scanner

A simple tool for investors to find cash-flowing properties in Canada. Scans target cities, underwrites with a conservative cash-flow model, and ranks deals by margin of safety.

Features

• Data sources: Realtor.ca (RapidAPI) or Redfin Canada (RapidAPI) – switch via config.yaml or --source; use both to merge and dedupe
• Property types: Duplex, triplex, multi-unit, single-family with secondary suites (house-hack plays)
• Underwriting: Base case + stress test, margin-of-safety score, pass/fail thresholds
• Outputs: CSV, JSON, DuckDB (listings_raw, deals_underwritten)

Quick Start

# Install
pip install -r requirements.txt

# Set RAPIDAPI_KEY in .env (or export RAPIDAPI_KEY)

# Run full pipeline (fetch from API → underwrite → report)
python -m real_deal.cli run

# Or step by step
python -m real_deal.cli fetch
python -m real_deal.cli underwrite
python -m real_deal.cli report

CLI Commands

Command	Description
python -m real_deal.cli fetch	Fetch listings from API and store raw data
python -m real_deal.cli fetch --source redfin	Use Redfin Canada instead of Realtor.ca
python -m real_deal.cli fetch --source both	Fetch from both APIs, merge and dedupe by ID
python -m real_deal.cli underwrite	Underwrite stored listings and save results
python -m real_deal.cli report	Display ranked deals table
python -m real_deal.cli run	End-to-end: fetch → underwrite → report

Configuration

Edit config.yaml:

• data_source.connector: realtor, redfin, or both (default) – which API(s) to use (both merges and dedupes by listing ID)
• max_price: 550000
• cities: Tier 1–3 Ontario cities
• keyword_filters: include/exclude for duplex, triplex, secondary suite, etc.
• underwriting: vacancy, management, maintenance, capex, insurance, utilities, closing costs, down payment, interest rate, amortization, property tax
• stress_test: rent haircut, interest rate bump, vacancy bump
• pass_fail: min cashflow, min DSCR, min cash-on-cash
• rent_estimation: tiered formula by city tier (base + per_bedroom × bedrooms); overridden by explicit rent in listing description

Underwriting Method & Cash Flow Prediction

The engine uses a conservative income-property model: base-case cash flow plus a stress test, then ranks deals by a margin-of-safety score and applies pass/fail thresholds.

Rent estimation

• Explicit rent: If the listing description contains a rent amount (e.g. $2000/mo, Rent: $2400), that value is used.
• Fallback (tiered): Otherwise rent is estimated as base + (per_bedroom × bedrooms) from config.yaml, using the city’s tier. Tiers: tier_1 (higher markets, e.g. Hamilton), tier_2, tier_3, bruce_county (smaller markets). Example: Hanover 3-bed in bruce_county uses base $1,000 + $500/bed = $2,500/mo.

Base-case cash flow

1. NOI (Net Operating Income, annual)

   • Gross potential income = monthly rent × 12, then reduced by vacancy (configurable, e.g. 6%).
   • Operating expenses (all annual): management %, maintenance %, capex reserve %, property tax (price × rate), insurance, utilities, snow/lawn.
   • NOI = GPI − vacancy − all op ex.

2. PITI (monthly)

   • Principal + interest from a standard amortization (down payment %, interest rate, amortization years).
   • Plus monthly property tax and monthly insurance.

3. Monthly cash flow

   • Cash flow = (NOI ÷ 12) − PITI.

4. Metrics

   • Cap rate = NOI ÷ purchase price.
   • Cash-on-cash = annual cash flow ÷ (down payment + closing costs).
   • DSCR = NOI ÷ annual debt service (lender-style coverage ratio).

Stress test

A worse-case scenario is run with:

• Rent haircut (e.g. 7% lower rent)
• Higher vacancy (e.g. +2%)
• Higher interest rate (e.g. +1%)

Stress NOI and PITI are recomputed with these inputs; stress cash flow = (stress NOI ÷ 12) − stress PITI. A deal is expected to remain viable (or at least not deeply negative) under this scenario.

Margin of safety (0–100)

The score starts at 50 and adds points for:

• Stress cash flow > 0 (+25)
• Stress cash flow ≥ min threshold (+15)
• Cash-on-cash ≥ threshold (+5)
• DSCR ≥ threshold (+5)

Higher score = more cushion against rent, vacancy, or rate shocks.

Pass/fail

A listing passes only if all of the following hold (thresholds in config.yaml):

• Base monthly cash flow ≥ min (e.g. $150)
• Stress cash flow ≥ 0
• Cash-on-cash ≥ min (e.g. 8%)
• DSCR ≥ min (e.g. 1.15)

The report shows reason_flags for each metric (PASS/FAIL and value).

Data Source Setup

All listing data comes from the API. No mock or hardcoded data.

1. Sign up at RapidAPI
2. Subscribe to either:
   • Realtor.ca Scraper API (baqo271) – residential listings by bounding box
   • Redfin Canada API (Apidojo) – more listings per city, search by region
3. Add your API key to .env:

   RAPIDAPI_KEY=your_key_here
   

4. Choose connector in config.yaml: data_source.connector: "realtor" or "redfin"
5. Run:

   python -m real_deal.cli run

   Or step by step: fetch → underwrite → report

The connector is designed so you can add an Apify HouseSigma actor or another source by implementing the ListingConnector interface.

Project Structure

RealDeal/
├── config.yaml           # Config (cities, underwriting, thresholds)
├── requirements.txt
├── src/real_deal/
│   ├── cli.py            # Typer CLI
│   ├── config.py         # Config loader
│   ├── models.py         # Listing, UnderwritingResult, etc.
│   ├── filters.py        # Keyword + price filters
│   ├── connectors/
│   │   ├── base.py       # ListingConnector interface
│   │   ├── rapidapi_realtor.py
│   │   └── rapidapi_redfin.py
│   ├── underwriting/
│   │   ├── engine.py     # UnderwritingEngine
│   │   └── rent.py       # Rent estimation + parse from description
│   └── storage/
│       ├── db.py         # DuckDB (listings_raw, deals_underwritten)
│       └── export.py     # CSV, JSON export
├── tests/
└── output/               # CSV, JSON, DuckDB, raw payloads

Output Metrics

• cashflow_monthly: Base-case monthly cash flow
• stress_cashflow_monthly: Stress-case (rent haircut, higher vacancy, higher rate)
• cap_rate: NOI / purchase price
• cash_on_cash: Annual cash flow / total cash in (down + closing)
• DSCR: Debt service coverage ratio
• margin_of_safety_score: 0–100 (higher = more cushion)
• reason_flags: Pass/fail reasons for each threshold

Tests

pytest tests/ -v -p no:anchorpy

Example Output

All data comes from the API. Example report from python -m real_deal.cli run:

                        Best Deals (Run YYYYMMDD_HHMMSS)
┏━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━┳━━━━━┳━━━━━━┓
┃ Rank ┃ Address┃ City   ┃  Price ┃ CF/mo ┃ Stress ┃   CoC ┃ DSCR ┃ MoS ┃ Pass ┃
┡━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━╇━━━━━╇━━━━━━┩
│ 1    │ ...    │ Windsor│ $399,000│  $xxx │   $xxx │  x.x% │ x.xx │ xx  │  ✓   │
│ 2    │ ...    │ London │ $475,000│  $xxx │   $xxx │  x.x% │ x.xx │ xx  │  ✗   │
└──────┴────────┴────────┴────────┴───────┴────────┴───────┴──────┴─────┴──────┘

Output files:

• output/deals_<run_id>.csv – Ranked deals
• output/deals_<run_id>.json – Full underwriting details
• output/real_deal.duckdb – DuckDB database
• output/raw/listings_<run_id>.json – Raw API payloads (for debugging)

License

MIT