vehicle-appraiser

Vehicle Appraiser — Comparable-Backed Valuations With Transaction Evidence

Appraiser Profile (Load First — Optional)

Load the marketcheck-profile.md project memory file if exists. Extract: zip/postcode, specialization, radius, country, min_comp_count. If missing, ask for ZIP and radius. US: all tools (decode, predict, search, history); UK: search_uk_active_cars/search_uk_recent_cars only (no decode — ask YMMT, use comp median). Confirm profile.

CPO Detection & Valuation

When appraising a vehicle, determine if it is Certified Pre-Owned (CPO):

1. From user input: If the user states the vehicle is certified/CPO, or the "Certified pre-owned status" field (already collected) is yes.
2. From listing data: If the vehicle's listing has is_certified=true.
3. From VIN history: If get_car_history shows the vehicle currently listed as certified.

When the vehicle IS CPO, the Full Comparable Appraisal workflow adds these steps:

• CPO predicted value: Call predict_price_with_comparables with is_certified=true to get the certified market value.
• Non-CPO predicted value: Call predict_price_with_comparables WITHOUT is_certified to get the standard market value.
• CPO retail comps: Call search_active_cars with the YMMT filters PLUS is_certified=true to find certified-only comparables.
• CPO premium calculation: CPO Premium = CPO Predicted Value - Non-CPO Predicted Value

In the Valuation Summary output, add:

Measure	Value
CPO Predicted Retail Value	$XX,XXX
Non-CPO Predicted Retail Value	$XX,XXX
CPO Premium	+$X,XXX (+X.X%)
Active CPO Comps	N within radius
Active Non-CPO Comps	N within radius

For the Trade-In Quick Appraisal: if CPO, note the premium but keep the quick format. Show: "CPO Value: $XX,XXX | Standard Value: $XX,XXX | Premium: +$X,XXX"

User Context

User is an appraiser needing a defensible, comparable-backed valuation with cited comparables, methodology notes, and confidence assessment.

Required	Field	Source
Yes	VIN or YMMT	Ask
Yes	Odometer reading	Ask
Auto/Ask	ZIP, radius	Profile or ask
Recommended	Condition (Clean/Average/Rough), purpose	Ask
Optional	CPO status	Ask

VIN provided → decode first (US only). Assumed trims lose credibility.

Workflow: Full Comparable Appraisal

Use this for formal appraisals, insurance claims, estate valuations, or any situation where the valuation must be supported by cited comparables.

1. Decode VIN — Call mcp__marketcheck__decode_vin_neovin with vin. → Extract only: year, make, model, trim, body_type, drivetrain, engine, transmission. Discard full response.

2. Predict price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise, is_certified if applicable. → Extract only: predicted_price, comparable VINs with prices and miles. Discard full response.

3. Pull active comps — Call mcp__marketcheck__search_active_cars with YMMT from step 1, zip, radius=75, miles_range=<odo-15k>-<odo+15k>, car_type=used, sort_by=price, sort_order=asc, rows=20. → Extract only: per listing — VIN, price, miles, dealer_name, distance, dom. Discard full response.

4. Pull sold transactions — Call mcp__marketcheck__search_past_90_days with same YMMT + location filters, sold=true. → Extract only: per listing — VIN, sold_price, miles, dealer_name, sale_date. Discard full response.

5. Synthesize the valuation — Combine all three data sources:

   • Algorithmic predicted price from step 2 (central estimate)
   • Active comparable range from step 3 (current retail context)
   • Sold transaction range from step 4 (transaction evidence)
   • Calculate a recommended value range (low / mid / high) using the overlap of all three.
   • Adjust for condition if the user provided it (rough = low end of range, clean = high end).

6. Present the appraisal report — Deliver a structured report with the valuation, every cited comparable (VIN, price, miles, dealer, distance), methodology notes, and confidence assessment. The report must be defensible — include enough detail for a third party to verify the conclusion.

Workflow: Trade-In Quick Appraisal

Use this when speed matters — a quick but credible estimate is needed in under 60 seconds.

1. Predict price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise. → Extract only: predicted_price, top comparable VINs with prices and miles. Discard full response.

2. Pull tight comps — Call mcp__marketcheck__search_active_cars with YMMT, zip, radius=75, car_type=used, sort_by=price, sort_order=asc, rows=5. → Extract only: per listing — price, miles, dealer_name, distance. Discard full response.

3. Deliver the quick value — Present:

   • Predicted retail value: from step 1
   • Estimated trade-in value: predicted retail minus a typical retail-to-wholesale spread (usually 15-22% below retail, adjusted by vehicle age and demand)
   • Top 5 retail comps: brief table showing the market context
   • Confidence note: indicate whether the comparable count supports a high-confidence or low-confidence estimate (reference min_comp_count from profile, default 10)

Workflow: Regional Price Variance

Use this when the user needs to understand how values differ across geographies, common for fleet valuations, multi-state insurance claims, or relocation decisions.

1. Primary market stats — Call mcp__marketcheck__search_active_cars with year, make, model, zip, radius=100, stats=price,miles, rows=0, car_type=used. → Extract only: mean, median, min, max, count for price and miles. Discard full response.

2. Comparison market stats — Repeat step 1 for each additional ZIP. → Extract only: mean, median, count per market. Discard full response.

3. Sold summary by state — Call mcp__marketcheck__get_sold_summary with make, model, inventory_type=Used, summary_by=state, ranking_measure=average_sale_price, ranking_order=desc, top_n=10. → Extract only: per state — average_sale_price, sold_count. Discard full response.

4. Calculate regional variance — Build a comparison table: market, median price, mean price, sample size, and delta from the lowest market. Identify arbitrage opportunities where the same vehicle sells for significantly more in one region.

5. Present the regional map — Show the price variance table and highlight any market where the price delta exceeds 5% — these represent real geographic value differences relevant to the appraisal.

Workflow: Wholesale vs Retail Spread

Use this when the user needs to understand the gap between wholesale and retail values, critical for trade-in offers, insurance claim valuations, and auction buying decisions.

1. Predict franchise (retail) price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise. → Extract only: predicted_price. Discard full response.

2. Predict independent (wholesale-proxy) price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=independent. → Extract only: predicted_price. Discard full response.

3. Pull franchise listings — Call mcp__marketcheck__search_active_cars with YMMT, zip, radius=75, dealer_type=franchise, car_type=used, sort_by=price, sort_order=asc, rows=10. → Extract only: per listing — price, miles, dealer_name; plus median. Discard full response.

4. Pull independent listings — Call mcp__marketcheck__search_active_cars with same filters, dealer_type=independent, rows=10. → Extract only: per listing — price, miles, dealer_name; plus median. Discard full response.

5. Calculate the spread — Present:

   • Franchise median price vs Independent median price
   • Spread in dollars and percentage
   • Predicted retail value vs predicted wholesale-proxy value
   • Recommended value positioning based on appraisal purpose (insurance claims typically use retail; trade-in offers use wholesale-proxy; estate valuations may use fair market midpoint)

Note: Always show both franchise and independent prices. The appraiser selects the appropriate benchmark based on the purpose of the appraisal (insurance = retail, trade-in = wholesale, estate = midpoint).

Workflow: Historical Value Trajectory

Use this when the user asks "what has this VIN been listed at over time" or needs to understand depreciation patterns for a specific unit.

1. Pull listing history — Call mcp__marketcheck__get_car_history with vin, sort_order=asc. → Extract only: per event — date, dealer_name, price, dom. Discard full response.

2. Decode VIN — Call mcp__marketcheck__decode_vin_neovin with vin. → Extract only: year, make, model, trim, MSRP. Discard full response.

3. Build the trajectory — From the history, extract each listing event: date, dealer, asking price, and DOM at that dealer. Calculate:

   • Total days on market across all listings
   • Total price depreciation from first listing to most recent
   • Average price drop per listing hop
   • Number of unique dealers

4. Current market context — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip. → Extract only: predicted_price. Discard full response.

5. Present the timeline — Show a chronological table of all listings with price, dealer, and DOM. Highlight any unusual patterns (rapid dealer hops, price increases between dealers suggesting reconditioning, or steep drops suggesting undisclosed issues). These patterns are critical context for an appraiser assessing the vehicle's market history.

Output

Present: vehicle identification summary, valuation table (predicted/comp range/sold range/recommended value/confidence), comparable data tables (active retail + sold transactions with VIN/price/miles/dealer), and methodology notes with condition adjustments and caveats.