# Flood Risk Engine

*/Opportunities/Flood_Risk_Engine*

## Opportunity Overview

**Wedge**: Target coastal Florida and Gulf Coast managing general agents writing mid-market commercial real estate policies. This niche experiences the most acute pain from outdated municipal maps and frequent convective storms, offering rapid proof of value through immediate loss-ratio improvements. Expansion proceeds geographically to inland river basins, then horizontally into adjacent climate perils like wildfire and wind modeling.
**Timing**: High-resolution satellite topography data and the compute required for granular fluid dynamics simulations are now cheap enough to run property-level inference on demand. Simultaneously, an increasing frequency of unmapped flash floods drives record underwriting losses, forcing carriers to adopt dynamic modeling alternatives to survive.
**Why This I C P**: Commercial property underwriters face immediate, existential financial penalties for mispriced catastrophe risk. They hold the direct mandate and budget to purchase specialized risk-scoring data layers to protect their loss ratios.
**Size Of Prize**: Approximately 3,000 property and casualty insurance carriers and managing general agents in the US and Europe spend an average of $150,000 annually on specialized catastrophe modeling data, yielding a $450M addressable market.
**Gap Narrative**: Legacy flood maps rely on static, backward-looking elevation data and lack property-level granularity, leaving commercial underwriters blind to flash flood and climate-shifted risks. Commercial property insurers require dynamic, high-resolution risk scoring that incorporates real-time hydrological data and localized drainage capacities to price policies accurately.
**Defensibility**: Defensibility compounds through a proprietary feedback loop of localized claims data ingested from early customers, which continuously calibrates the underlying hydrological models. As model accuracy increases, switching costs for underwriters rise because reverting to legacy maps mathematically guarantees higher loss ratios. The raw weather and elevation data is a commodity; the moat relies entirely on the tuned algorithmic accuracy over time.
**Why This Thesis**: An API-delivered data software approach matches how underwriters currently consume risk factors during the quoting process. They do not adopt new standalone workflow tools; they ingest higher-fidelity probabilistic scores directly into their existing automated rating engines.

## Opportunity Linked Thesis

**Thesis**: [Software](/Theses/Software)

## Opportunity Linked I C P

**Icp**: [Property Insurance Carrier](/CompanyTypes/Property_Insurance_Carrier)

## Opportunity Market Sizing

_Illustrative — target and order-of-magnitude estimate figures, not an achieved track record (this Thing is concept-stage)._

**S A M**: ~$300-400M US regional property carriers and coastal specialty MGAs
**S O M**: ~$15-40M
**T A M**: ~5,000 property insurance carriers and MGAs globally × ~$150k-250k/yr data licensing and modeling spend ≈ ~$750M-1.25B
**Growth Rate**: ~12-18%/yr, driven by increasing frequency of unmapped secondary perils and tightening reinsurance capital requirements
**Paid Comparable Spend**: ~$150k-500k/yr spent on legacy catastrophe model licenses, raw GIS data subscriptions, and manual underwriter review time

## Opportunity Incumbents

- [FEMA Flood Maps](/Products/FEMA_Flood_Maps) — Tool
- [First Street Risk Factor](/Products/First_Street_Risk_Factor) — Tool
- [CoreLogic Flood Services](/Products/CoreLogic_Flood_Services) — Service
- [RMS Flood Models](/Products/RMS_Flood_Models) — Tool
- [Internal Actuarial Models](/Products/Internal_Actuarial_Models) — Spreadsheet
- [JBA Risk Management](/Products/JBA_Risk_Management) — Service

## Opportunity Win Conditions

**Kill Thresholds**:
- Underwriter override rate exceeds 30 percent after 60 days of deployment
- Fewer than 3 successful portfolio backtests completed in the first 90 days
- Average sales cycle exceeds 180 days without a paid pilot commitment
- Annual contract value offers stall below 100k USD per carrier
**Leading Metrics**:
- API calls per active underwriter per week
- Straight-through processing rate for evaluated coastal properties
- Underwriter manual override rate on automated flood scores
- Days to complete initial retroactive portfolio backtest
- Percentage of total quotes bound using engine risk multipliers
**What Proves Right**: Regional property carriers and coastal specialty MGAs integrate the Flood Risk Engine via API to price individual policies. Actuaries replace manual GIS data reviews with automated property-level flood scores, achieving an 80 percent straight-through processing rate for coastal renewals. Customers convert from 30-day retroactive portfolio backtests to paid 150k USD annual data licensing contracts.
**What Proves Wrong**: Underwriters override the automated flood scores more than 40 percent of the time due to perceived inaccuracy or lack of trust. Carriers relegate the tool to an advisory dashboard instead of embedding it into their core rating algorithms. Actuarial teams refuse to adopt the output because the scoring lacks transparency or conflicts irreconcilably with incumbent catastrophe models.

## Opportunity Build Profile

**Hardest Part**: Accurately modeling micro-topography and municipal drainage infrastructure at the parcel level, as a two-foot elevation difference determines whether a specific property floods or stays dry.
**Min Viable Scope**: Deliver a coastal storm surge risk score and expected annual loss specifically for single-family residential properties in Florida. Deliberately leave out inland rainfall flooding, commercial infrastructure, and real-time active storm tracking.
**Cold Start Problem**: The predictive model lacks credibility without localized ground-truth validation data to prove it outperforms legacy FEMA maps. Break this by running historical backtests on a single high-risk county using public property records and partnering with a regional insurer to score their past loss history.
**Time To First Value**: Minutes via API to score a single address, or 1-2 weeks to backtest an entire property portfolio
**Data Moat Available**: true
**Technical Difficulty**: High

## Neighborhood

### Where the gap lives

- [Geography](/Knowledge/Geography) — latent gap · Knowledge

### Applies thesis

- [Property Insurance Carrier](/CompanyTypes/Property_Insurance_Carrier) — applies thesis · CompanyTypes

### Incumbent in

- [CoreLogic Flood Services](/Products/CoreLogic_Flood_Services) — incumbent in · Products
- [FEMA Flood Maps](/Products/FEMA_Flood_Maps) — incumbent in · Products
- [First Street Risk Factor](/Products/First_Street_Risk_Factor) — incumbent in · Products
- [Internal Actuarial Models](/Products/Internal_Actuarial_Models) — incumbent in · Products
- [JBA Risk Management](/Products/JBA_Risk_Management) — incumbent in · Products
- [RMS Flood Models](/Products/RMS_Flood_Models) — incumbent in · Products

### Embodies

- [Software](/Theses/Software) — embodies · Theses

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