lender-benchmarking

Lender ESG Benchmarking

Produce underwriting-grade ESG screening for capital-markets audiences: lenders sizing a loan, CMBS underwriters assembling disclosure, insurers pricing risk. The output must be auditable — every number carries its source and retrieval date.

Design principle: live data first, bundled data as fallback

This skill deliberately ships no benchmarking engine of its own. Peer statistics come from MCP servers that already exist. Only if none are connected may you fall back to the bundled approximate table, and the output must then say so.

Priority order for every benchmark number:

1. BPD MCP (bpd) — LBNL Building Performance Database, 1M+ real US buildings. Query the peer group (building type + state or climate zone + vintage band) and read the EUI distribution (median, 25th/75th percentile). Compute the subject's percentile position within that distribution.
2. ENERGY STAR Portfolio Manager MCP (energy-star-pm) — the ENERGY STAR score (1–100) is a national peer percentile, adjusted for weather and use. Report it as such.
3. Bundled fallback — references/national-median-eui.md (approximate national medians). Flag clearly: source: approximate national median — connect BPD for peer-group percentile.

Workflow

1. Assemble asset facts

Collect for each asset (from the platform asset record, ESPM, utility data, or the user):

• Property type (map to BPD/ENERGY STAR property-type vocabulary), gross floor area
• Location (city, state → climate zone), year built / last major renovation
• 12-month site EUI (kBtu/ft²·yr) and fuel mix; weather-normalized if available
• ENERGY STAR score if benchmarked in ESPM
• Carbon intensity (kgCO₂e/ft²·yr) — use eGRID subregion factors (Arcadia MCP) or NREL Cambium for forward years

Missing EUI is a blocker for percentile work — say what's missing and how to get it (connect utility data via Arcadia/Nectar, or share the ESPM property). Never estimate a subject building's EUI to fill the gap.

2. Peer benchmark

• Define the peer group explicitly and state it in the output: e.g. "Multifamily, Colorado + Mountain census division, built 1990–2010, n=412 (BPD)".
• Report: subject EUI, peer median, subject percentile (lower EUI = better = lower percentile; always label the direction), ENERGY STAR score if available.
• If peer count n < 30, widen the geography one step (state → census division → national) and note the widening.

3. Lender ESG Card (one page per asset)

Structured card with these sections — omit a section only if no data source is connected, and list omissions at the bottom:

Section	Content	Source
Identity	address, type, GFA, vintage	platform
Energy	site EUI, peer percentile + peer group, ENERGY STAR score	BPD / ESPM
Carbon	kgCO₂e/ft²·yr, grid subregion, CRREM stranding year if available	utility + eGRID / CRREM
Regulatory	applicable BPS laws, current + projected penalty exposure	bps-compliance skill
Physical risk	hazard ratings if a risk MCP is connected	Moody's RMS / physrisk
Green financing	program eligibility summary (see step 4)	references + asset data
Flags	material ESG risks with one-line rationale each	derived

Verdict line at top: Green / Neutral / Flagged with the single most decision-relevant sentence. Render as markdown by default; offer the soapbox-report template for a branded PDF when the user wants a client-facing deliverable.

4. Green financing eligibility

Check references/green-financing-programs.md for program criteria (Fannie Mae Green Rewards, Freddie Mac Green Advantage, C-PACE availability, green bond / CMBS green tranche framing). For each program output: eligible / not eligible / data gap, and the requirements gap — what improvement or documentation would qualify the asset. Program terms change; when a deal decision hinges on eligibility, verify current program terms with a web search and cite the date.

5. Portfolio roll-up (collateral pools, loan books)

For multiple assets, produce a ranked screening table: asset, type, GFA, EUI, percentile, ENERGY STAR, carbon intensity, BPS exposure ($), flags. Sort worst-percentile first. Summarize: distribution of percentiles, total projected BPS penalty exposure, count of green-financing-eligible assets, and the 3 assets that most drag the pool.

Rules

• Every number carries source + date. No source, no number.
• Never present the bundled fallback table as a peer percentile — it yields only "above/below approximate national median".
• Percentile direction is stated explicitly every time ("22nd percentile — uses less energy than 78% of peers").
• This is screening, not an energy audit: recommend rapid-sustainability-risk for acquisition-grade analysis and retrofit-advisor for measure-level CapEx planning.
• Run the verifier (if connected) on any card leaving for a client.