| 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
- 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
Comp Data Acquisition (before any analysis)
Primary path: the Zillow /search API pull. If an OpenWeb Ninja Real-Time Zillow Data key is available (OPENWEBNINJA_API_KEY env var), pull the comp universe programmatically instead of browsing listing sites. The companion comp-package skill ships the full contract and a self-contained puller script. The load-bearing facts:
/search with home_status=RECENTLY_SOLD (exact enum; other casings return 400) for solds, FOR_SALE for actives- Every search caps at 41 rows (about 5 weeks of sales in an active zip): partition by
min_price/max_price bands and recursively split any band returning 41 (recovers 12-24 months per zip)
price_min/price_max are silently ignored; confirm every filter against the echoed parameters objectdateSold is epoch milliseconds; use unformattedPrice, not the soldPrice display string- Clip to the target pocket with a lat/lon bounding box AND a street-name whitelist; verify LOT-type or missing-sqft surprises against the county card (often teardown sales or new builds)
- The API is MLS-only: auction, wholesale, and off-market transfers come from county records
Fallback path: no API key, or the user prefers manual comping: browse Zillow/Redfin/MLS exactly as the framework references describe. The methodology below is identical either way; only data acquisition changes.
Bedroom-band rule (both paths): when the subject's bedroom count is below the comp set (especially 2-bed vs 3-bed), run a dual-track ARV: the base case is valued ONLY against same-bed comps, clamped to that band's median price (extra sqft does not escape the band); reconfig to more bedrooms is a labeled upside credited only after a walkthrough verifies the layout converts. Underwrite off the base track, and project future value on the same-bed curve.
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:
GLA Definition
GLA = Gross Living Area = above-grade finished living space.
GLA is the single most important measurement in comparable sales analysis. Misidentifying GLA leads to incorrect PPSF calculations and unreliable ARVs.
Included in GLA:
- All finished rooms above grade (living rooms, bedrooms, kitchens, bathrooms, hallways)
- Second/third stories above grade
- Finished basement with separate exterior entrance at grade level (walkout basement)
Excluded from GLA:
- Unfinished basement (any portion below grade)
- Finished basement without separate exterior entrance (value separately, see Basement rules)
- Garage (attached or detached)
- Covered porches, screened porches, patios
- Attic space (unless finished with permanent stairs and standard ceiling height)
- Carports, breezeways, storage sheds
Basement valuation (not GLA but has value):
- Finished to same quality as main floor: value at ~50% of above-grade PPSF
- Finished with drop ceilings/inferior finishes: value at ~35-40% of above-grade PPSF
- Partially finished: value at ~25-35% of above-grade PPSF
- Unfinished: value at ~10-15% of above-grade PPSF (storage/utility value only)
Discrepancy protocol: When public records GLA differs from MLS GLA by >5%, flag it. Public records often include non-GLA space. Use MLS GLA as primary, cross-reference with county assessor measurements.
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 to ranch, 2-story to 2-story)
- Build year: ±10 years
Outlier Detection
After gathering comps, screen for PPSF outliers before bucket analysis:
- Calculate median PPSF of all comps in the set
- Calculate standard deviation of comp PPSF values
- Flag any comp >2 standard deviations from the median PPSF
- Apply exclusion rules:
- If 5+ comps total and one is an outlier: exclude it and note the exclusion with reason
- If only 3-4 comps total and one is an outlier: keep it but flag it with notation in the analysis (too few comps to discard)
- If 2+ outliers in a small set: re-examine comp selection criteria — the set may be too heterogeneous
Common outlier causes to document:
- Estate/distress sale (below market)
- REO/bank-owned (typically 10-15% below market)
- Major unreported renovation (above market)
- Seller concessions not reflected in price
- Related-party transaction (non-arm's-length)
Market Sentiment Assessment
Assess market conditions using quantitative indicators before applying sentiment adjustments:
| Indicator | Hot Market | Balanced Market | Cool Market |
|---|
| DOM trend | Declining month-over-month | Stable | Increasing month-over-month |
| Inventory | <3 months supply | 3-6 months supply | >6 months supply |
| Price trend (YoY) | Rising 5%+ | Flat to +3% | Flat to declining |
| Sale-to-list ratio | >100% | 97-100% | <97% |
| Sentiment adjustment | +5-7% | +3-5% | 0-2% |
Data sources for market sentiment:
- Redfin market tracker (monthly updates, metro and zip level)
- Zillow Home Value Index (ZHVI) for YoY price trends
- Local MLS statistics (DOM, inventory, sale-to-list)
- County assessor recent sale volumes
Apply the sentiment adjustment in Step 9 (Final ARV Assembly), not during comp selection.
Recent Renovation Handling (Bucket Classification)
When classifying comps into Bucket A (unrenovated) or Bucket B (renovated), apply these rules for properties with known renovation history:
| Renovation Timing | Classification Rule |
|---|
| Within 2 years of sale | Bucket B regardless of original condition |
| Within 5 years of sale | Bucket B if renovation was substantial (see definition below) |
| More than 5 years before sale | Classify based on current condition vs comps |
Key indicator: A significant price jump between the last two sales of the same property (e.g., bought for $150K, sold 18 months later for $260K) strongly suggests a flip/renovation. Cross-reference with permit history and listing photos.
Definition of "substantial renovation":
- Kitchen + at least 1 full bathroom remodel (minimum ~$15K combined spend)
- OR full kitchen gut (cabinets + countertops + appliances replaced)
- Cosmetic-only (paint + flooring only) does NOT qualify — classify as Bucket A
- Paint + flooring + fixture updates = borderline — classify as Bucket A unless listing explicitly says "renovated" or "remodeled"
If renovation scope is unknown: Default to listing photos and remarks. "Updated," "remodeled," or "new kitchen/bath" language = Bucket B. "Original," "vintage," "needs TLC" = Bucket A.
Feature Adjustments (by price tier)
See references/adjustment-cheatsheet.md for complete adjustment values.
Quick reference (aligned with comp_analyzer.py source):
| Feature | <$500k Tier | >$500k Tier |
|---|
| $/sqft (GLA) | $85/sqft | $85/sqft |
| Bedroom | +$5,000 | +$10,000 |
| Bathroom (full) | +/-$7,500 | +/-$10,000 |
| Bathroom (half) | +/-$3,750 | +/-$5,000 |
| Garage | +$8,000/stall | +$10,000-$25,000/stall |
| Age (year built) | $500/year | $500/year |
| Market condition (time) | 0.3%/month | 0.3%/month |
Unusual Feature Adjustments
| Feature | Adjustment | Notes |
|---|
| Pool (warm climate: TX, AZ, FL, NV) | +$15,000-$25,000 | Year-round use markets |
| Pool (seasonal climate: TN, NC, VA) | +$5,000-$15,000 | 4-6 month use season |
| Pool (cold climate: MN, WI, MI) | $0 to -$5,000 | Maintenance liability can be negative |
| Water/mountain views | +5-10% of home value | Compare only on same grade/elevation |
| Easements (utility, access) | -5-10% depending on impact | Worse if easement crosses buildable area |
| Corner lot | -3-5% | Less privacy, more road frontage/maintenance |
| Cul-de-sac | +3-5% | More privacy, less traffic, child-friendly |
| Backing to commercial/industrial | -10-15% | Noise, traffic, visual impact |
| Backing to multifamily | -5-10% | Less impact than commercial |
| Power lines/cell tower proximity | -5-10% | Within 300 ft is the impact zone |
Pool condition matters:
- Updated pool with modern finishes: full value
- Dated pool needing resurface: 50% of value
- Pool needing major repairs: may be net negative (removal cost $10,000-$15,000)
Basement/ADU Rules
- Basements: Not counted as GLA; value at ~50% of above-grade PPSF if finished (see GLA Definition section for full breakdown)
- ADUs: 50% value if not separately deeded; 100% if separately titled
Two-Bucket Methodology (Core of This Skill)
The Two-Bucket method separates comps into unrenovated (Bucket A) and renovated (Bucket B) to derive the market premium that renovation commands. This is a market-derived metric, not a rehab cost estimate.
Bucket Spread Sanity Check
The spread between Bucket A and Bucket B PPSF values tells you what the market pays for renovation in that micro-market.
- 5-30% spread: Normal range. Proceed with analysis.
- <5% spread: Suspect — unrenovated comps may be mispriced, or the market doesn't reward renovation strongly. Re-examine Bucket A comp conditions.
- >30% spread: Suspect — Bucket A comps may include distressed sales, or Bucket B comps may be ultra-luxury renovations beyond typical flip quality. Re-examine both buckets.
Worked Example: Two-Bucket Spread Analysis
Subject property: 1,500 SF ranch, 3 bed/2 bath, built 1978, dated condition, Knoxville TN
Bucket A (as-is/dated) — 5 comps:
| Comp | GLA | Sale Price | PPSF |
|---|
| A1 | 1,480 | $175,000 | $118.24 |
| A2 | 1,520 | $184,000 | $121.05 |
| A3 | 1,450 | $172,500 | $118.97 |
| A4 | 1,540 | $186,000 | $120.78 |
| A5 | 1,510 | $181,500 | $120.20 |
Median PPSF_A: $120.20
Bucket B (renovated) — 5 comps:
| Comp | GLA | Sale Price | PPSF |
|---|
| B1 | 1,470 | $240,000 | $163.27 |
| B2 | 1,530 | $256,000 | $167.32 |
| B3 | 1,490 | $243,500 | $163.42 |
| B4 | 1,510 | $250,000 | $165.56 |
| B5 | 1,460 | $244,000 | $167.12 |
Median PPSF_B: $165.56
Spread calculation:
Market Premium = ($165.56 - $120.20) / $120.20 x 100% = 37.7%
Sanity check: 37.7% exceeds the normal 5-30% range. Investigation needed:
- Check if Bucket A comps include any distress/estate sales pulling the average down
- Verify Bucket B comps are standard flips, not ultra-luxury renovations
- Check if subject micro-market has high renovation demand (near university, hospital, etc.)
If comps check out, proceed but note the elevated spread and widen confidence band to +/-5%.
Value calculations:
- As-is value: $120.20 x 1,500 SF = $180,300
- ARV (after renovation): $165.56 x 1,500 SF = $248,340
- Market premium from renovation: $248,340 - $180,300 = $68,040
Rehab budget sanity check: Rehab budget should be 40-70% of the market premium to maintain deal economics:
- Rehab range: $27,216 to $47,628
- If rehab estimate exceeds $47,628, the deal margin is thin — re-evaluate
Maximum Allowable Offer (MAO) check:
- MAO = ARV x 70% - Rehab = $248,340 x 0.70 - $35,000 (est. rehab) = $138,838
- Compare MAO to as-is value ($180,300) — if as-is value > MAO, the deal likely does not work as a wholesale flip
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)
- Outlier Screening Results (any comps flagged or excluded, with reasons)
- Market Overview (Median price, PPSF, DOM, sale-to-list ratio, market phase, sentiment assessment)
- 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
- 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
- Screen for outliers after gathering comps, before bucket classification
- Assess market sentiment using quantitative indicators
- Classify recent renovations properly into buckets
- 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
Source Code Alignment (comp_analyzer.py)
The following adjustment values are calibrated for the Knoxville, TN regional market and are used in the automated comp analyzer:
| Parameter | Value | Notes |
|---|
| $/sqft (GLA) | $85.00 | Per sqft of GLA difference |
| Bedroom | $5,000 | Per bedroom difference |
| Bathroom | $7,500 | Per bathroom difference |
| Year built | $500 | Per year of age difference |
| Lot size | $2.00/sqft | Capped at $15,000 max |
| Garage | $8,000 | Per stall difference |
| Market condition | 0.3%/month | Applied to comps >1 month old |
These are conservative Knoxville-calibrated values. For higher-value markets (>$500K), use the scaled values in the adjustment cheatsheet.
