ev-transition-monitor

name: ev-transition-monitor description: > EV lending risk and portfolio exposure analysis. Triggers: "EV market update", "EV adoption rate", "EV vs ICE pricing", "Tesla market position", "EV lending risk", "electric vehicle trends", "EV depreciation", "EV price parity", "hybrid adoption", "electrification progress", "EV days supply", "which OEMs are winning EV", "EV penetration by state", "EV residual risk", "EV portfolio exposure", "EV collateral risk", tracking electric vehicle market dynamics for lending risk assessment and portfolio management. version: 0.1.0

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.

get_sold_summary parameter safety:

• Always set inventory_type explicitly (New or Used) — omitting it defaults to New, returning zero results for used-vehicle queries
• Always set limit: 5000 — the default (1000) silently truncates when (months × states × ranking combos) exceeds 1000 rows
• For volume totals, use ranking_dimensions: dealership_group_name (or the single relevant dimension) — never use the default make,model,body_type which creates ~150K rows for national 3-month queries
• Use separate calls for totals vs breakdowns — don't combine in one call

EV Transition Monitor — Electric Vehicle Lending Risk Intelligence

Lender Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: portfolio_focus, country, state, tracked_segments, risk_ltv_threshold, high_risk_ltv_threshold. If missing, ask for EV focus area and geography. US-only. Confirm profile.

User Context

Lender (residual analyst, portfolio risk manager, auto finance director) assessing EV residual risk, portfolio concentration exposure, and lending opportunity signals. Also serves lease residual committees and floor plan providers. Each metric includes an explicit lending risk signal.

Workflow: EV Market Scorecard

The comprehensive EV market analysis. Use for "EV market update" or "EV lending risk assessment."

Step 1 — EV penetration rate

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• state: from profile or user input (omit for national)
• date_from / date_to: current month
• inventory_type: New (or Used if user specifies)
• ranking_dimensions: fuel_type_category
• ranking_measure: sold_count
• top_n: 5
• limit: 5000

Repeat for total market (no fuel_type_category filter). Also repeat for prior month and 3 months ago. → Extract only: sold_count per fuel_type_category per period. Discard full response.

Calculate:

• EV Penetration % = EV sold / total sold x 100
• Hybrid Penetration % = Hybrid sold / total sold x 100 (if available)
• Combined Electrified % = (EV + Hybrid) / total x 100
• MoM Change (bps) = (current % - prior %) x 100
• 3-Month Trend (bps) = (current % - 3mo %) x 100
• Lending Signal: GROWING OPPORTUNITY if MoM > +20 bps AND 3mo > +50 bps (increasing origination volume); STABLE MARKET if stable +/-10 bps; CONTRACTING if MoM < -10 bps (review EV lending appetite)

Step 2 — EV vs ICE pricing parity

Call mcp__marketcheck__get_sold_summary for each fuel type with inventory_type=Used (or New if user specifies), limit=5000:

• fuel_type_category: EV → get average_sale_price
• No filter (or fuel_type_category: Gas) → get average_sale_price for ICE

Repeat for prior periods. → Extract only: average_sale_price per fuel type per period. Discard full response.

Calculate:

• EV Avg Price vs ICE Avg Price
• Price Gap $ = EV - ICE
• Price Gap % = (EV - ICE) / ICE x 100
• Gap Trend = is the gap narrowing or widening?
• Lending Signal: APPROACHING PARITY if gap < 15% and narrowing (prepare for EV volume increase); STALLED if gap stable; DIVERGING if gap widening (EV remains niche — limited origination volume)

Break down by segment if possible:

• SUV: EV vs ICE price
• Sedan: EV vs ICE price
• Pickup: EV vs ICE price

Step 3 — EV depreciation vs ICE (CRITICAL FOR LENDERS)

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• inventory_type: Used
• ranking_dimensions: make,model
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 15
• limit: 5000
• Current month AND 3 months ago

Repeat without fuel_type filter for ICE comparison (with inventory_type=Used, limit=5000). → Extract only: per make/model — average_sale_price per period. Discard full response.

Calculate:

• EV Monthly Depreciation % = (3mo_price - current_price) / 3mo_price / 3 x 100
• ICE Monthly Depreciation % = same for ICE
• Depreciation Ratio = EV rate / ICE rate (e.g., 2.1x means EVs depreciate 2.1x faster)
• Residual Risk Signal: HIGH RISK if EV depreciation > 2x ICE (set EV residuals 8-12% lower than ICE equivalents); ELEVATED if 1.3x-2x (set EV residuals 5-8% lower); NORMALIZING if < 1.3x (EV residuals approaching ICE parity)

Step 4 — EV days supply

Call mcp__marketcheck__search_active_cars with:

• fuel_type: Electric, car_type: new, stats: price,dom, rows: 0

Plus sold data from Step 1 for volume. → Extract only: num_found, stats.dom.mean. Discard full response.

Calculate:

• EV New Days Supply = active EV new / monthly EV new sold x 30
• EV Used Days Supply = active EV used / monthly EV used sold x 30
• Compare to ICE equivalents
• Lending Signal: DEMAND > SUPPLY if < 30 days (residuals supported); BALANCED if 30-60; SUPPLY BUILDING if > 60 (residual pressure ahead); GLUT if > 90 (residual risk HIGH — tighten advance rates on EV originations)

Step 5 — Brand-level EV share

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• inventory_type: New (or Used if user specifies)
• ranking_dimensions: make
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 15
• limit: 5000
• Current month AND prior month → Extract only: per make — sold_count per period. Discard full response.

Calculate:

• Brand EV Share % = brand EV sold / total EV sold x 100
• MoM Share Change (bps)
• Highlight Tesla vs legacy OEMs for portfolio concentration assessment
• Signal per brand: GAINING / LOSING / STABLE

Step 6 — Regional EV adoption (optional)

Call mcp__marketcheck__get_sold_summary with:

• fuel_type_category: EV
• inventory_type: New (or Used if user specifies)
• summary_by: state
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 15
• limit: 5000 → Extract only: per state — sold_count. Discard full response.

Calculate state-level EV penetration rate by also pulling total sold by state.

Identify: highest adoption states, fastest growing states, lowest adoption states. Map to lending implications — high-adoption states have better EV resale infrastructure and lower residual risk.

Output

Present: EV lending risk headline, adoption/parity/depreciation/supply data tables with lending signals, brand EV share for portfolio concentration risk, and actionable lending policy recommendations (residual setting, advance rates, GAP requirements).

Important Notes

• US-only: All data requires US get_sold_summary.
• Tesla dominates EV share nationally (~50-60%) — always contextualize portfolio concentration risk. A portfolio heavy in Tesla-collateralized loans has single-brand risk even within the EV segment.
• EV depreciation patterns differ significantly by brand: Tesla tends to hold value better than Nissan Leaf or Chevy Bolt. Break down by model when possible for accurate residual setting.
• The EV-to-ICE depreciation ratio is the single most important metric for EV lending risk. When the ratio drops below 1.3x, EV residuals are approaching ICE parity — this is the signal to normalize EV advance rates.
• EV battery degradation adds a non-linear depreciation component not captured in market price trends alone. For vehicles with >50K miles or >3 years old, consider an additional 2-3% residual haircut for battery risk.