Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

Depreciation Tracker — Residual Value Signals for OEM & Lending Stocks

User Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: tracked_tickers, tracked_makes, tracked_states, benchmark_period_months, focus, country. If missing, ask for make/model/segment focus. US-only. Confirm profile.

User Context

Financial analyst evaluating residual value trends as investment signals for OEM stocks (pricing power, incentive spend), auto lending/leasing stocks (collateral erosion, residual exposure), and dealer group stocks (used car margins, inventory valuation). Every metric includes BULLISH/BEARISH/NEUTRAL/CAUTION signal tied to specific tickers.

Built-in Ticker → Makes Mapping

OEM TICKERS:
F     → Ford, Lincoln
GM    → Chevrolet, GMC, Buick, Cadillac
TM    → Toyota, Lexus
HMC   → Honda, Acura
STLA  → Chrysler, Dodge, Jeep, Ram, Fiat, Alfa Romeo, Maserati
TSLA  → Tesla
RIVN  → Rivian
LCID  → Lucid
HYMTF → Hyundai, Kia, Genesis
NSANY → Nissan, Infiniti
MBGAF → Mercedes-Benz
BMWYY → BMW, MINI, Rolls-Royce
VWAGY → Volkswagen, Audi, Porsche, Lamborghini, Bentley

Workflow: Make/Model Depreciation Curve (Investment Signal)

Use this when a user asks "how fast is the RAV4 losing value" or "depreciation signal for Ford trucks."

1. Get current period sold data — Call get_sold_summary with make, model, inventory_type=Used, date_from (first of prior month), date_to (end of prior month). Include state if specified. → Extract only: average_sale_price, sold_count. Discard full response.

2. Get historical sold data at multiple intervals — Make separate calls to get_sold_summary for each lookback period:

   • 60 days ago
   • 90 days ago
   • 6 months ago
   • 1 year ago Record average_sale_price at each point. Adjust dates based on today's date. → Extract only: average_sale_price per interval. Discard full response.

3. Get current active market asking price — Call search_active_cars with year, make, model, car_type=used, stats=price, rows=0. Include zip/state if available. → Extract only: mean, median, min, max price stats. Discard full response.

4. Get original MSRP baseline — Call search_active_cars with same YMMT, rows=1, sort_by=price, sort_order=desc. Decode the VIN for MSRP. Fallback: highest 1-year-ago sold price. → Extract only: msrp from decode. Discard full response.

5. Build the depreciation curve with investment signal — Calculate at each time interval:

   • Retention % = (average_sale_price at interval / original MSRP) × 100
   • Monthly depreciation rate = (price change between consecutive intervals) / (months between intervals)
   • Annualized depreciation rate = monthly rate × 12
   • Signal:
     • BEARISH for OEM ticker: Monthly rate > 1.5% — "Accelerating depreciation suggests residual erosion; OEM may need production cuts or increased incentive spend"
     • CAUTION: Monthly rate 0.8-1.5% — "Moderate depreciation; watch for acceleration"
     • NEUTRAL: Monthly rate 0.3-0.8% — "Normal depreciation within historical range"
     • BULLISH for OEM ticker: Monthly rate < 0.3% or appreciating — "Strong residual retention signals pricing power and brand desirability"

Workflow: Segment Value Trends (Sector Signal)

Use this when a user asks "are SUVs holding value better than sedans" or "EV depreciation vs ICE — investment implications."

1. Get current period segment data — Call get_sold_summary with ranking_dimensions=body_type, ranking_measure=average_sale_price, date_from (first of prior month), date_to (end of prior month), inventory_type=Used, top_n=10. → Extract only: per body_type — average_sale_price, sold_count. Discard full response.

2. Get prior period segment data — Same call with dates shifted back 3 months (or user's chosen comparison window). → Extract only: per body_type — average_sale_price, sold_count. Discard full response.

3. Get fuel type comparison — Call get_sold_summary with fuel_type_category=EV, current period dates, inventory_type=Used. Repeat with fuel_type_category=ICE. Repeat both for prior period. → Extract only: average_sale_price, sold_count per fuel_type per period. Discard full response.

4. Calculate segment trends with investment signals — For each body type and fuel type:

   • Period-over-period price change with signal
   • Volume change with demand signal
   • Flag segments where price declined more than 3% as "ACCELERATING DEPRECIATION — BEARISH for OEMs heavy in this segment"
   • Map each segment to most-exposed tickers (e.g., Pickup depreciation → F, GM; EV depreciation → TSLA, RIVN, LCID)

5. Deliver the segment comparison — Present a ranked table from strongest retention to weakest. Highlight the EV vs ICE gap specifically with lending stock implications.

Workflow: Brand Residual Ranking (OEM Stock Signal)

Use this when a user asks "which brands hold value best" or "rank OEMs by residual strength."

1. Get current period brand prices — Call get_sold_summary with ranking_dimensions=make, ranking_measure=average_sale_price, ranking_order=desc, date_from (first of prior month), date_to (end of prior month), inventory_type=Used, top_n=25. → Extract only: per make — average_sale_price. Discard full response.

2. Get prior period brand prices — Same call with dates shifted back 6 months. → Extract only: per make — average_sale_price. Discard full response.

3. Get volume context — Call get_sold_summary with ranking_dimensions=make, ranking_measure=sold_count, ranking_order=desc, current period dates, inventory_type=Used, top_n=25. → Extract only: per make — sold_count. Discard full response.

4. Calculate brand retention scores with ticker mapping — For each make:

   • Retention % = current average_sale_price / prior average_sale_price × 100
   • Map to ticker
   • Rank by retention % descending
   • Tier classification:
     • Tier 1 (>98% retention) — BULLISH: Strong residual retention = pricing power
     • Tier 2 (95-98%) — NEUTRAL: Normal depreciation
     • Tier 3 (90-95%) — CAUTION: Accelerating, watch for incentive spend
     • Tier 4 (<90%) — BEARISH: Significant residual erosion

5. Present the brand ranking with investment thesis per tier.

Workflow: MSRP Parity Tracker (Pricing Power Signal)

Use this when a user asks "which new cars are selling over sticker" or "pricing power by OEM."

1. Get current MSRP parity data — Call get_sold_summary with inventory_type=New, ranking_dimensions=make,model, ranking_measure=price_over_msrp_percentage, ranking_order=desc, date_from (first of prior month), date_to (end of prior month), top_n=30. → Extract only: per make/model — price_over_msrp_percentage. Discard full response.

2. Get prior period parity data — Same call with dates shifted back 3 months. → Extract only: per make/model — price_over_msrp_percentage. Discard full response.

3. Classify parity status with investment signals — For each make/model:

   • Above MSRP — BULLISH: demand exceeds supply, pricing power intact, positive for OEM margins
   • At MSRP (±1%) — NEUTRAL: balanced market
   • Below MSRP — BEARISH: incentive-dependent, margin pressure, signals OEM may need to increase incentive spend
   • Map to ticker and aggregate by OEM

4. Present the parity report with ticker-level summaries.

Output

Present: investment signal headline with ticker and direction, depreciation curve/trend data table with signals, ticker impact summary (OEM, lending/leasing, dealer group implications), and key BULLISH/BEARISH/NEUTRAL/CAUTION signals per finding.

Important Notes

• US-only: All data requires US sold transaction data.
• Always map depreciation findings back to stock tickers. An analyst needs to know "Ford (F) trucks depreciating at X%/month" not just "F-150 prices are falling."
• For EV vs ICE comparisons, explicitly call out implications for TSLA, RIVN, LCID as well as legacy OEMs with growing EV portfolios.
• Depreciation acceleration is a LEADING indicator — it precedes incentive spend increases (margin pressure), production cuts (revenue impact), and residual restatements (balance sheet hit). Frame accordingly.
• If a model is transitioning from above-MSRP to below-MSRP, this is a key inflection point — flag it prominently with the affected ticker.