Real Estate Comping Skill

Perform appraiser-grade property valuations using the Two-Bucket method for disclosure states or the Triangulation method for non-disclosure states. This skill is strictly about comparable sales analysis and ARV determination — it does not estimate rehab costs, renovation budgets, or scope of work. If the user needs rehab cost estimation, direct them to the appropriate skill for that.

Workflow Overview

Identify property location → Determine state from address
Route to correct methodology → Disclosure or Non-Disclosure framework
Execute 9-step analysis → Follow the appropriate prompt framework
Generate deliverables → PDF summary report + Excel breakdown + comps table

State Detection & Routing

Determine state type from property address:

Non-Disclosure States (sold prices not publicly recorded): TX, UT, WY, NM, ID, MT, ND, AK, KS, MS, LA, MO
Disclosure States: All other US states

Routing:

Non-disclosure state → Read references/non-disclosure-prompt.md
Disclosure state → Read references/disclosure-prompt.md

Quick Reference

Framework	States	Key Method	Price Source
Disclosure	Most US states	Two-Bucket (Unrenovated vs Renovated PPSF)	MLS sold prices
Non-Disclosure	TX, UT, WY, NM, ID, MT, ND, AK, KS, MS, LA, MO	Triangulation (LLP + DOM, Deed of Trust, Tax Ratio)	Derived estimates

Core Comping Rules (Both Frameworks)

These rules apply regardless of disclosure status:

Comp Selection Filters

Age: ≤90 days preferred, 6 months max
Subdivision: Same micro-pocket, do not cross major roads
GLA: ±100 sqft ideal, ±250 sqft outer bound
Property type: Match elevation style (ranch↔ranch, 2-story↔2-story)
Build year: ±10 years

Feature Adjustments (by price tier)

See references/adjustment-cheatsheet.md for complete adjustment values.

Quick reference:

Feature	<$500k Tier	>$500k Tier
Bedroom	+$10k	+$25k
Bathroom	±$10k	±$10k
Garage	+$10-25k	+$10-25k
Traffic (backing)	-$10k	-10-15%
Traffic (fronting)	-$20k	-20%

Basement/ADU Rules

Basements: Not counted as GLA; value at ~50% of above-grade PPSF if finished
ADUs: 50% value if not separately deeded; 100% if separately titled

Required Deliverables

Every comp analysis MUST produce these two outputs:

1. Excel Breakdown Workbook

Comprehensive multi-sheet workbook with:

Executive Summary sheet: Quick-view of key findings
Subject Property sheet: All property details
Comparable Sales sheet: Full comps table with bucket analysis
Adjustments Detail sheet: Line-by-line adjustment breakdown
Market Analysis sheet: Market metrics and trends
ARV Calculation sheet: Step-by-step ARV math
Sources & Notes sheet: Data sources, parameters, recommendations

Generate using: scripts/generate_excel_report.py

2. In-Context Analysis

The detailed analysis text with tables shown directly in the conversation, including:

Step-by-Step ARV Breakdown (Base PPSF → Adjustments → Final ARV)
Comps Summary Table (Address, Sale Date, Price, GLA, Beds/Baths, Year, Condition, Adjustments, Final Adjusted Value)
Market Overview (Median price, PPSF, DOM, sale-to-list ratio, market phase)
Sources & Assumptions (Data sources, time window, radius constraints)
Recommendations & Caveats (Verification steps, risk factors, disclaimer)

Output Generation Instructions

Data Structure for Report Generation

Prepare analysis data as JSON with this structure:

{
    "subject_property": {
        "address": "123 Main St",
        "city": "Austin",
        "state": "TX",
        "zip": "78701",
        "county": "Travis",
        "subdivision": "Downtown",
        "property_type": "Single Family",
        "gla": 1850,
        "lot_size": 6500,
        "beds": 3,
        "baths": 2,
        "year_built": 1985,
        "condition": "Dated"
    },
    "comps": [
        {
            "address": "456 Oak Ave",
            "sale_date": "2025-12-15",
            "sale_price": 485000,
            "gla": 1780,
            "ppsf": 272.47,
            "beds": 3,
            "baths": 2,
            "year_built": 1982,
            "condition": "Renovated",
            "distance": 0.3,
            "total_adjustments": -5000,
            "adjusted_value": 480000
        }
    ],
    "bucket_analysis": {
        "unrenovated": { "count": 2, "median_ppsf": 235.20, "avg_ppsf": 235.20 },
        "renovated": { "count": 2, "median_ppsf": 257.33, "avg_ppsf": 257.33 },
        "market_premium_pct": 9.4
    },
    "market_overview": {
        "market_phase": "Balanced",
        "median_price": 455000,
        "median_ppsf": 248.50,
        "avg_dom": 28,
        "sale_to_list_ratio": 0.98,
        "active_count": 45,
        "pending_count": 22,
        "notes": ["Market observations..."]
    },
    "arv_calculation": {
        "base_ppsf": 235.20,
        "market_premium_pct": 9.4,
        "renovated_ppsf": 257.33,
        "subject_gla": 1850,
        "base_arv": 476061,
        "feature_adjustments": -5000,
        "final_arv": 471000,
        "confidence_level": "Moderate",
        "confidence_band_pct": 5.0,
        "arv_low": 447450,
        "arv_high": 494550
    },
    "adjustments_applied": [
        {
            "comp_number": 1,
            "comp_address": "456 Oak Ave",
            "adjustment_type": "GLA",
            "reason": "Subject 70 sqft larger",
            "amount": -5000
        }
    ],
    "sources": ["MLS", "County Records", "Zillow"],
    "search_parameters": {
        "time_window": "90 days",
        "radius": "0.5 miles",
        "gla_range": "1600-2100 sqft"
    },
    "recommendations": ["Verification steps..."],
    "caveats": ["Disclaimers..."]
}

Generate Reports

Save the analysis data to a JSON file

Run the Excel generator:

python scripts/generate_excel_report.py output_report.xlsx data.json

Execution Instructions

Gather property address from user
Identify state and determine disclosure status
Load appropriate framework:
- Disclosure: references/disclosure-prompt.md
- Non-Disclosure: references/non-disclosure-prompt.md
Add property context (if provided): current condition, seller notes, known issues
Optional boundary drawing: For block-by-block markets, use Zillow boundary tool
Execute analysis following the 9-step framework
Verify results: Cross-reference with market knowledge
Prepare data structure: Compile all analysis into JSON format
Generate deliverables:
- Run generate_excel_report.py for Excel breakdown
Deliver all outputs to user: Excel and in-context analysis

Special Considerations

Non-Disclosure State Caveats

Wider confidence bands (±5-7% vs ±2-5%)
Must derive sold prices using triangulation methods
Recommend "Option Period Verification" once under contract

Market Sentiment Adjustments

Hot market (fast pendings, >50% over list): +5-7%
Balanced market: +3-5%
Cool market (60+ DOM, price cuts): 0-2% or negative

Two-Bucket Spread Sanity Check

The spread between unrenovated and renovated bucket PPSF is a market-derived metric (not a rehab cost estimate). It reflects what buyers in that market are willing to pay for updated finishes.

Typical spread: 10-30%
<5% or >30%: Re-examine comp validity — comps may be miscategorized or the market may have unusual dynamics

real-estate-comping

SKILL.md.claude/skills/real-estate-comping/SKILL.md