Policy portfolio risk review — the line-of-business read.

Aggregate revenue is the first number a buyer sees; line-of-business mix is the first number that actually matters. Every line carries a distinct profile along four dimensions — average policy size, commission rate, retention rate, and volatility — so a book dominated by personal auto (short terms, modest commissions, high retention, low volatility) and a book dominated by commercial property (long relationships, higher commissions, moderate retention, higher volatility) can produce the same revenue and EBITDA while being fundamentally different assets. And the mix radiates into everything else: carrier concentration is line-dependent because carriers specialize, producer productivity is line-dependent because commercial and personal producers operate at different scales, and service infrastructure is line-dependent because commercial accounts consume more servicing hours. The mix is the description of what the agency actually is.

§ 01 · The benchmark distributionsBy agency type.

The standard categories — personal lines, small commercial, middle-market commercial, specialty commercial, life and health, specialty personal — are consistent enough to benchmark against. When a target's distribution falls materially outside the modal range for its type, the anomaly deserves explicit explanation: sometimes it reflects deliberate strategic positioning, and sometimes it reflects drift the seller hasn't recognized. The benchmark isn't a verdict; it's the prompt for the right diligence question.

§ 02 · Margin and retention varianceThe mechanism behind the multiple.

Mix matters for valuation because neither margin nor retention is uniform across lines. On commission rate, personal lines runs 10–15%, small commercial 12–18%, middle-market 10–15% on larger premiums, and specialty commercial 15–25% or higher; life and health is its own analysis, with substantial first-year commissions and much smaller renewals that have to be separated. On retention, personal homeowners runs 90–95% and auto 85–95%, small commercial 80–90%, middle-market 85–95%, and specialty commercial varies widely (cyber churns fast). A commercial-concentrated book typically produces higher EBITDA margins than a comparable personal-heavy book — but the advantage can be offset by lower retention and higher producer cost, which is exactly why the two have to be modeled line by line rather than blended.

§ 03 · The carrier-LOB matrixWhere concentration really lives.

The matrix also surfaces alignment quality. A book where personal lines sits with personal-lines specialists and commercial with commercial specialists shows deliberate carrier alignment; misalignment — personal lines parked with a commercial-focused carrier — flags either an under-served line or a legacy placement never optimized. And it exposes the post-close upside: some carriers offer broader line access at higher tiers, so the tier upgrades from consolidation can unlock lines the target couldn't previously place. The discipline that ties it together is also the discipline that exposes cyclicality — hard-market tailwinds hit lines differently, so a book riding commercial-property rate increases should be valued on normalized, mid-cycle revenue rather than recent peaks.

§ 04 · Using the analysisPrice by line, both sides.

For buyers, line-of-business analysis is the organizing framework for diligence, not a supplementary check: build a valuation model that applies line-specific multiples by margin, retention, and volatility; benchmark the target's lines against your own portfolio to find the expansions worth a premium and the overlaps that need carve-outs; stress-test on cyclicality so a hard-market tailwind prices on mid-cycle economics; and model post-close line evolution because some lines grow faster in your hands than others. For sellers, it's a pre-listing tool — document the mix by revenue, policy count, and retention; explain any anomaly as strategy rather than drift; address under-served lines with pre-sale investment because buyers pay more for balanced portfolios; and clean the data so the management system and financials classify lines consistently, since internal inconsistency is a diligence friction point that slows the whole process.

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