# Honeycomb Financial Model Digital-first NeoInsurer offering multi-family property insurance via proprietary AI underwriting and a 100% online platform. - Canonical: https://finamodel.com/startups/honeycomb - Excel download: https://finamodel.com/startup-models/honeycomb.xlsx - Category: InsurTech - Model type: Insurance GWP - Funding round: Series A - Funding: $15.4M - Founded: 2022 - Geography: United States. - Customer: B2B ## About the company Honeycomb is a digital-first insurer for multi-family property, offering building and general-liability cover through online quoting and binding. Its computer-vision underwriting evaluates property photos to identify risk signals before a policy is issued. The business earns property-insurance premium through direct digital and broker channels. Its underwriting technology is intended to improve risk selection and loss ratios, rather than simply make insurance distribution more efficient. The model is P&C GWP and combined ratio. Policies, average premium, property mix, loss ratio, reinsurance, broker commission, and expense ratio determine underwriting income. Quote conversion, AI underwriting performance, and catastrophe exposure are the key sensitivities. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition - Covers multi-family residential properties: building (fire, water damage, wind/hail) and general liability for common areas. - Does NOT cover tenant personal property/liability (distinct product segment). - 100% digital quoting and binding platform launched June 2021. - Proprietary AI/computer-vision underwriting: ingests photos of the property and uses ML to flag risk signals (mold, fire hazard, trip hazards, egress obstructions) in real time at near-zero variable cost. - Customers can customize coverage and select from tiered proposals; customizable deductibles ($5,000 shown). - Three-sided value proposition - customer (right-priced, quick, customized), broker (time-saving, capacity access, retention), Honeycomb (better loss ratio, proprietary data, scalability). ## Market - TAM: $26B annual premium - US multi-family property insurance market. - Demand driver: purchase mandated by law or by lenders. - NPS of top 5 incumbents: 38% (negative sentiment signal, not a positive NPS). - Average age of top 5 incumbents: 144 years - used to argue low innovation and low competitive bar. ## Revenue model - Primary revenue: Gross Written Premium (GWP) / earned premium on multi-family property policies. - Indicative premium pricing (from product UI mockup): ~$2,627–$2,879/year per policy; property limit $1.8M, general liability limit $2M shown as illustrative coverage. - Distribution: direct digital (own platform) + independent broker channel. - Additional income sources for brokers mentioned but not specified (implies potential ancillary fee/service revenue). - Loss ratio management via proprietary underwriting as core profitability lever (better risk selection → lower loss ratio). - Claims handling advantage cited as a differentiator. ## Traction & metrics - Launch date: June 2021. - No historical financial data or traction metrics (ARR, GWP, bound policies) disclosed in the deck. ## Unit economics - Average annual premium per policy: ~$2,627 (most affordable tier, 35% of customers select) and ~$2,879 (best value tier, 50% select). - Qualitative claim: improved profitability and scalability vs. incumbents via AI underwriting at "$0 variable cost." ## Competition / moat - Incumbent competitors: large, old (avg 144 yrs), offline, manual underwriting, superficial pricing. - Moat sources: proprietary AI/computer-vision underwriting platform; 1st-party data from property photos accumulates with each policy written; real-time automated risk evaluation at near-zero marginal cost. - No named competitors cited in deck. ## Team & funding ask / use of funds - Itai Ben-Zaken (CEO): ran $100M online insurance business (Insurance.com, CarInsurance.com); repeat entrepreneur; BCG, Wharton. - Dr. Nimrod Sadot (CTO): repeat entrepreneur; Stanford PhD; Intel, Applied Materials. - Adam Cherubini (CRO): 30-year insurance industry veteran (underwriting, sales, marketing, BD); Insurance SVP at QuinStreet; Willis Towers Watson, InSweb. - Ben Piening (CUO): 17 years habitational underwriting; Midwest region VP at Seneca; CPCU, CRM, CIC, ASLI, AU credentials. --- ## Recommended financial model - **Archetype + why:** Insurance GWP / P&C underwriting model. Honeycomb is a regulated insurance carrier (or MGA fronted by a carrier) - the core financial logic is Gross Written Premium → earned premium → loss ratio → combined ratio → underwriting income. This is distinct from a SaaS or marketplace model; the key profitability lever is the loss ratio, not gross margin on a service. If Honeycomb operates as an MGA (managing general agent) rather than a full carrier, a commission-income model sits on top (ceding premium to reinsurers, retaining a % commission). - **Forecast horizon & granularity:** 5 years (Year 1–5); Year 1–2 monthly (ramp period), Year 3–5 annual. Monthly granularity needed to track policy count growth, premium in-force, and cash/surplus adequacy. - **Key drivers & assumptions:** - Policies in force - starting count; growth rate - Average annual premium per policy - Gross Written Premium = policies in force × avg premium - Earned premium ratio - Gross loss ratio - Ceding ratio / reinsurance - Net loss ratio - Expense ratio (commission + G&A + tech) - Combined ratio = loss ratio + expense ratio [target <100% for underwriting profit] - CAC by channel - direct digital vs. broker - Policy retention / renewal rate - Headcount and OpEx - **Scenarios (Base / Bull / Bear - which variables flex):** - Bear: loss ratio stays elevated (65–70%, AI underwriting takes longer to prove out); policy growth 20% YoY; reinsurer demands higher cede %. - Base: loss ratio improves to 55% by Year 3; 35–40% policy growth; combined ratio below 100% by Year 3. - Bull: loss ratio 48–52% by Year 3 (AI moat proves out fast); 50%+ policy growth; ability to reduce reinsurance cede % and retain more premium; ancillary revenue from broker services. - **Required sheets / outputs:** 1. Assumptions - all driver inputs with toggle for scenario 2. Policy volume build (new policies, renewals, lapses, total in-force by month/year) 3. GWP bridge (new business GWP + renewal GWP = total GWP) 4. P&C income statement (GWP → ceded premium → net written premium → earned premium → losses → LAE → underwriting expenses → underwriting income) 5. Reinsurance / ceding schedule 6. Combined ratio waterfall chart 7. Operating P&L (underwriting income + investment income − corporate G&A − tech opex = net income) 8. Surplus / capital adequacy tracker (statutory surplus, premium-to-surplus ratio; regulatory minimum) 9. Cash flow statement 10. KPI dashboard (GWP, policies in force, loss ratio, combined ratio, net retention %) ## Frequently asked questions ### Is the Honeycomb financial model free? Yes. The Honeycomb model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.