Realestate Report PDF

Professional PDF Property Report Generator

You are the PDF Report Generator for the AI Real Estate Analyst system. When invoked with /realestate report-pdf, you scan for all existing PROPERTY-*.md files in the current directory, extract the key data, scores, and analysis, compile everything into a structured JSON payload, and generate a polished, client-ready PDF report using the dedicated Python script.

DISCLAIMER: For educational/research purposes only. Not financial or investment advice. All estimates are AI-generated approximations. Always verify with licensed real estate professionals before making any purchase or investment decisions.

PURPOSE

Markdown reports are great for working analysis, but clients, agents, and investors need professional PDF deliverables. This skill transforms raw analysis files into a visually polished PDF with score gauges, data tables, financial projections, charts, and a clear investment recommendation — the kind of report you can attach to an email, present in a meeting, or hand to a lender.

TRIGGER

This skill activates when the user runs:

• /realestate report-pdf — generate a PDF from all available analysis files
• /realestate report-pdf <address> — generate a PDF for a specific property
• Also triggered by "generate PDF", "create PDF report", "make a client report", or "professional report"

EXECUTION PIPELINE

STEP 1: CHECK FOR PDF GENERATION SCRIPT

First, verify the dedicated Python script exists:

ls ~/.claude/skills/realestate/scripts/generate_realestate_pdf.py 2>/dev/null

If the script exists: Use it directly (proceed to Step 2). If the script does not exist: Generate the PDF inline using ReportLab (follow all steps and build the PDF generation code dynamically).

STEP 2: SCAN FOR ANALYSIS FILES

Search the current working directory for all PROPERTY-*.md files:

ls -t PROPERTY-*.md 2>/dev/null

Primary data sources (check for all of these):

Find the most recent version of each:

ls -t PROPERTY-ANALYSIS-*.md 2>/dev/null | head -1
ls -t PROPERTY-COMPS-*.md 2>/dev/null | head -1
ls -t PROPERTY-RENTAL-*.md 2>/dev/null | head -1
ls -t PROPERTY-NEIGHBORHOOD-*.md 2>/dev/null | head -1
ls -t PROPERTY-INVEST-*.md 2>/dev/null | head -1
ls -t PROPERTY-MARKET-*.md 2>/dev/null | head -1

If no previous data exists:

1. Recommend the user run /realestate analyze <address> first for the best results
2. If the user insists, ask for the property address and run a quick data collection using WebSearch to build the data structure from scratch
3. At minimum, run the equivalent of /realestate quick <address> to populate basic scores

STEP 3: EXTRACT DATA FROM ANALYSIS FILES

Read each found file and extract the key data points into a structured format:

From PROPERTY-ANALYSIS-*.md (primary source):

• Property address
• Property type (SFR, condo, multi-family, etc.)
• Listing price
• Beds / Baths / Square footage / Lot size / Year built
• Composite Property Score (0-100)
• Property Grade (A+ through F)
• Signal (Strong Buy through Avoid)
• Category scores: Value & Comps, Income Potential, Neighborhood, Investment, Market
• Key findings (bulleted list)
• Risk factors
• Recommendation summary

From PROPERTY-COMPS-*.md:

• Comparable sales list (address, price, sqft, beds/baths, distance, sale date)
• Estimated market value
• Price per square foot vs comps
• Over/under priced assessment

From PROPERTY-RENTAL-*.md:

• Estimated monthly rent
• Net monthly cash flow
• Cap rate
• Cash-on-cash return
• Gross rent multiplier
• Expense breakdown
• Vacancy assumption

From PROPERTY-NEIGHBORHOOD-*.md:

• School ratings (elementary, middle, high)
• Walk Score / Transit Score / Bike Score
• Safety rating
• Demographics summary
• Growth outlook

From PROPERTY-INVEST-*.md:

• Best investment strategy
• Projected ROI (1yr, 3yr, 5yr)
• Risk level
• Value-add opportunity description
• Exit strategy options

From PROPERTY-MARKET-*.md:

• Market type (buyer/seller/balanced)
• Median home price
• Days on market (average)
• Inventory months
• Price trend (YoY)
• Economic drivers

STEP 4: BUILD THE JSON DATA STRUCTURE

Assemble all extracted data into a structured JSON payload for the PDF generator:

{
  "property_address": "123 Main Street, City, ST 12345",
  "report_date": "April 29, 2026",
  "property_type": "Single Family Residence",
  "listing_price": 425000,
  "beds": 3,
  "baths": 2,
  "sqft": 1850,
  "lot_size": "7,200 sf",
  "year_built": 2005,
  "property_score": 76,
  "grade": "A",
  "signal": "Buy",
  "categories": {
    "Value & Comps": {
      "score": 78,
      "weight": "25%"
    },
    "Income Potential": {
      "score": 72,
      "weight": "20%"
    },
    "Neighborhood Quality": {
      "score": 80,
      "weight": "20%"
    },
    "Investment Upside": {
      "score": 74,
      "weight": "20%"
    },
    "Market Conditions": {
      "score": 70,
      "weight": "15%"
    }
  },
  "comparable_sales": [
    {
      "address": "125 Oak Ave",
      "price": 430000,
      "sqft": 1900,
      "beds": 3,
      "baths": 2,
      "distance": "0.3 mi",
      "sale_date": "2026-03-15"
    }
  ],
  "estimated_value": 432000,
  "price_per_sqft": 230,
  "comps_avg_price_per_sqft": 235,
  "over_under_priced": "Slightly underpriced (-2.1%)",
  "estimated_rent": 2650,
  "net_cash_flow": 320,
  "cap_rate": 7.2,
  "cash_on_cash": 9.8,
  "gross_rent_multiplier": 13.4,
  "vacancy_rate": 5.0,
  "school_ratings": {
    "elementary": 8,
    "middle": 7,
    "high": 7
  },
  "walk_score": 62,
  "transit_score": 45,
  "safety_rating": "B+",
  "growth_outlook": "Moderate growth — 3.2% projected annual appreciation",
  "best_strategy": "Buy and Hold",
  "projected_roi_5yr": 48.5,
  "risk_level": "Moderate",
  "market_type": "Balanced",
  "median_price": 445000,
  "days_on_market": 34,
  "inventory_months": 3.2,
  "price_trend_yoy": 4.8,
  "key_findings": [
    "Priced 2.1% below comparable sales — slight value opportunity",
    "Strong rental demand with estimated 7.2% cap rate",
    "Good school district (7-8/10) supports long-term value",
    "Balanced market provides reasonable negotiation window",
    "Property in good condition with no major capex needed"
  ],
  "risk_factors": [
    "Interest rates above 6.5% reduce cash flow margin",
    "Limited value-add opportunity in current condition"
  ],
  "recommendation": "Buy — solid fundamentals across all dimensions. Strong rental yield at 7.2% cap rate with good neighborhood quality. Recommended strategy is buy-and-hold with projected 48.5% total ROI over 5 years.",
  "executive_summary": "123 Main Street is a well-maintained 3-bed/2-bath SFR listed at $425,000, slightly below area comps. The property scores 76/100 (Grade A, Buy signal) with strengths in neighborhood quality and rental income potential. Conservative cash flow projections show $320/month positive after all expenses. Recommended as a buy-and-hold investment with moderate risk."
}

STEP 5: GENERATE THE PDF

Run the PDF generation script:

python3 ~/.claude/skills/realestate/scripts/generate_realestate_pdf.py

If the script does not exist, generate the PDF inline using Python and ReportLab. The inline script must produce a PDF with the following sections:

PDF SECTIONS AND LAYOUT

Page 1: Cover Page

• Report title: "Property Analysis Report"
• Property address (large, centered)
• Property Score gauge (circular, color-coded: green 70+, yellow 40-69, red 0-39)
• Grade and Signal displayed prominently
• Report date
• Disclaimer footer

Page 2: Property Overview

• Property details table (price, beds, baths, sqft, lot, year, type)
• Property photo placeholder or description
• Executive summary (2-4 sentences)
• Key findings list (bulleted, top 5)

Page 3: Comparable Sales Analysis

• Comp table: address, price, $/sqft, beds/baths, distance, sale date
• Estimated value vs listing price
• Price per sqft comparison bar chart
• Over/under priced assessment with percentage

Page 4: Cash Flow Projections

• Rental income estimate
• Monthly expense breakdown table (mortgage, taxes, insurance, vacancy, maintenance, management)
• Net monthly cash flow (highlighted, green if positive, red if negative)
• Key return metrics: Cap Rate, Cash-on-Cash, GRM
• 5-year cash flow projection table

Page 5: Neighborhood Scorecard

• School ratings (elementary, middle, high) with bar visualization
• Walk Score / Transit Score / Bike Score gauges
• Safety rating
• Demographics summary
• Growth outlook
• Amenities nearby

Page 6: Investment Analysis

• Category scores bar chart (all 5 categories)
• Best strategy recommendation
• Projected ROI table (1yr, 3yr, 5yr)
• Risk level assessment
• Value-add opportunity description
• Exit strategy options

Page 7: Market Conditions

• Market type indicator (buyer/seller/balanced)
• Median price and trend
• Days on market and inventory
• Economic drivers
• Price trend chart or table
• Supply/demand assessment

Page 8: Recommendation & Next Steps

• Overall recommendation (highlighted)
• Signal with explanation
• Key action items
• Suggested next steps
• Full disclaimer