Market Trends Reporter
Competitive landscape deep dives — depreciation rankings, EV-ICE price parity, regional pricing, and markup trends
Purpose
OEM brand managers need data-driven narratives, not just dashboards. A product planner preparing a competitive response brief needs to know: which competitor models are gaining MSRP premiums, where your brand's residuals are diverging, and what the EV-ICE price gap looks like in your key segments — all cited with specific numbers that hold up in a strategic review.
Market Trends Reporter generates defensible competitive intelligence analyses across four lenses: fastest and slowest depreciating models with your brands highlighted, EV vs ICE price parity by segment with year-over-year gap trends, regional price variance by state showing where your brand commands premiums, and new car markup rankings showing who has pricing power and who is discounting. Each output is framed with strategic recommendations for production, incentives, and competitive response.
How It Works
Execution flow. MCP tool calls are shown inline on each step.
↔ Parallel Execution
get_sold_summaryCalls get_sold_summary ranked by make,model with ranking_measure=average_sale_price for used vehicles, current month.
get_sold_summarySame call for same month one year ago. Calculates YoY depreciation rate per model. Identifies fastest and slowest depreciators with your brand models highlighted.
get_sold_summaryCalls get_sold_summary by body_type with fuel_type_category=EV and then without filter for ICE, for SUV, Sedan, Pickup, Hatchback. Calculates EV-ICE gap $ and % with YoY change trend per segment.
get_sold_summaryCalls get_sold_summary with summary_by=state for your brand and each competitor to build state-by-state price comparison table and identify premium vs discount markets.
↔ Parallel Execution
get_sold_summaryCalls get_sold_summary ranked by price_over_msrp_percentage descending for new vehicles to identify models commanding the largest premiums.
get_sold_summarySame call ascending to identify models with deepest discounts. Generates competitive narrative on pricing power vs discounting across your models and competitors.
MCP Tool Calls
| Tool | Calls | Purpose |
|---|---|---|
get_sold_summary | 4–8 | Depreciation rankings, EV-ICE parity by segment, regional variance, MSRP markup/discount analysis |
search_active_cars | 0–3 | Active listings spot-check for fastest-depreciating own-brand models (optional) |
Example Output
MARKET TRENDS REPORTER — Toyota | February 2026 ════════════════════════════════════════════════ FASTEST DEPRECIATING MODELS (YoY, Used Vehicles) Rank Make/Model Current Avg 1yr Ago Avg Depr Rate Volume ──── ────────────────────── ─────────── ─────────── ───────── ────── 1 Chevrolet Bolt EUV $18,200 $28,400 -35.9% 4,122 2 Nissan Leaf $14,800 $22,100 -33.0% 2,847 3 Hyundai Ioniq 5 $29,400 $42,100 -30.2% 3,218 4 Ford Mustang Mach-E $32,100 $45,300 -29.1% 4,901 ★ Toyota bZ4X $28,900 $38,700 -25.3% 1,847 Toyota bZ4X is depreciating at -25.3% YoY — 5th steepest in EV segment. Consider strengthening CPO residual support and extending warranty coverage. EV-TO-ICE PRICE PARITY TRACKER Segment EV Avg ICE Avg Gap $ Gap % YoY Gap Δ Trend ──────── ──────── ──────── ─────── ────── ────────── ────────────── SUV $47,200 $38,100 $9,100 +23.9% -4.2 pts Narrowing ↓ Sedan $41,800 $31,400 $10,400 +33.1% -0.8 pts Stable → Pickup $62,400 $48,700 $13,700 +28.1% -2.1 pts Narrowing ↓ SUV approaching parity — plan production ramp for RAV4 EV successor NEW CAR MARKUP LEADERS #1 Toyota Tacoma TRD Pro +7.2% above MSRP #2 Toyota 4Runner TRD +4.8% above MSRP #3 BMW M3 Competition +9.1% above MSRP (competitor) YOUR BRAND DISCOUNTING: Sienna XLE at -2.7% — review allocation
Cost Estimate
25 analyses/month ≈ $1–3
Limitations
- US market only.
- Depreciation rankings require models to have 100+ sold units in both periods to be statistically reliable — low-volume models are excluded.
- EV-ICE parity analysis covers 4 segments; additional body types multiply API call count proportionally.
- Regional price variance is most meaningful for high-volume models; lower-volume models may have sparse state-level data.