real-estate-comping

name

real-estate-comping

description

Perform AI-powered property valuation and comparable sales analysis for real estate wholesaling. Use when the user needs to comp a property, determine ARV, analyze comparable sales, or perform property valuation. Automatically detects disclosure vs non-disclosure states and applies the appropriate methodology (standard comping for disclosure states, triangulation method for non-disclosure states like Texas). This skill focuses purely on comping — determining market value through comparable sales analysis. It does NOT estimate rehab costs or renovation budgets; those are handled separately.

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

1. Identify property location → Determine state from address
2. Route to correct methodology → Disclosure or Non-Disclosure framework
3. Execute 9-step analysis → Follow the appropriate prompt framework
4. 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" 
        
             
             
             
             
             
             
             
             
             
             
             
             
        
    
     
                
                
         
    
     
         
         
         
         
         
         
         
         
    
     
         
         
         
         
         
         
         
         
         
         
         
    
     
        
             
             
             
             
             
        
    
       
     
         
         
         
    
     
     

Generate Reports

1. Save the analysis data to a JSON file
2. Run the Excel generator:

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

Execution Instructions

1. Gather property address from user
2. Identify state and determine disclosure status
3. Load appropriate framework:
   • Disclosure: references/disclosure-prompt.md
   • Non-Disclosure: references/non-disclosure-prompt.md
4. Add property context (if provided): current condition, seller notes, known issues
5. Optional boundary drawing: For block-by-block markets, use Zillow boundary tool
6. Execute analysis following the 9-step framework
7. Verify results: Cross-reference with market knowledge
8. Prepare data structure: Compile all analysis into JSON format
9. Generate deliverables:
   • Run generate_excel_report.py for Excel breakdown
10. 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