# UrbanStat

> UrbanStat is a geospatial analytics and AI platform for property insurance underwriting. It helps insurers manage portfolio exposure, select better risks, score wildfire risk, find new business and enforce moratoriums, with over 300 data points per property across more than 35 countries.

Founded in 2014 in Istanbul; in the US since 2017 as Urbanstat, Inc. Full text of the reference pages: https://www.urbanstat.com/llms-full.txt

Every page on this site has a Markdown copy at the same address with index.md added; the links below point to those copies. Remove index.md for the HTML page. Figures about UrbanStat are the company's own published claims.

## Products

- [Products](https://www.urbanstat.com/products/index.md): UrbanStat products for property underwriting: Exposure Management, Risk Selection, Wildfires, Find Better Risks and Moratorium Management.
- [Exposure Management](https://www.urbanstat.com/products/exposure-management/index.md): Track catastrophic events and your exposure in real time. Identify dangerous accumulations before they become losses, and take action in minutes, not days.
- [Risk Selection](https://www.urbanstat.com/products/risk-selection/index.md): Improve your loss ratio by up to 7 points and spend up to 20% less time on underwriting with our risk selection tools.
- [Wildfires](https://www.urbanstat.com/products/wildfires/index.md): Wildfires cost billions every year. Traditional models use 2–4 variables. UrbanStat uses more than 30 — achieving 80.5% accuracy vs. 54.8% for US Forestry's map.
- [Find Better Risks](https://www.urbanstat.com/products/find-better-risks/index.md): AI-powered opportunity identification that helps you focus on submissions that matter and find the risks your competitors miss.
- [Moratorium Management](https://www.urbanstat.com/products/moratorium-management/index.md): Insurance companies have hundreds of underwriting guidelines and moratoriums. Sometimes hard to set up, harder to review, impossible to manage. Until now.

## Guides and reference

- [What Is Exposure Management in Property Insurance?](https://www.urbanstat.com/guides/exposure-management/index.md): Exposure management is how a property insurer measures and limits the insured value it has in one place or under one peril. Definition, metrics and methods.
- [What Is Risk Selection in Property Underwriting?](https://www.urbanstat.com/guides/risk-selection/index.md): Risk selection is how an underwriter decides which submissions to accept, at what terms, and in what order to work them. Definition, steps, data and results.
- [How a Wildfire Risk Score Works](https://www.urbanstat.com/guides/wildfire-risk-score/index.md): A wildfire risk score rates how likely a location is to burn. What goes into one, how to read a 0 to 100 score, and how to judge whether a model is any good.
- [What Is an Underwriting Moratorium?](https://www.urbanstat.com/guides/underwriting-moratoriums/index.md): An underwriting moratorium is a temporary restriction on writing policies in a defined area. When insurers use them and what blanket moratoriums cost.
- [ZIP Codes vs. Census Tracts vs. Property-Level Risk](https://www.urbanstat.com/guides/zip-codes-vs-property-level-risk/index.md): Why ZIP codes mislead insurance underwriting, how census tracts and boundary-free risk maps differ, and a worked example from Santa Clarita, California.
- [Five Cognitive Biases in Insurance Underwriting](https://www.urbanstat.com/guides/cognitive-biases-in-underwriting/index.md): Status quo bias, loss aversion, anchoring, availability bias and the endowment effect: what each is, how it shows up in underwriting, and what counters it.
- [Wildfire Model Performance: The Record So Far](https://www.urbanstat.com/wildfire-model-performance/index.md): Published results of UrbanStat’s Wildfire Risk Map against real fire seasons from 2019 to 2021, and against the US Forestry hazard map, with sources.
- [Data Coverage and Integrations](https://www.urbanstat.com/data-coverage/index.md): Where UrbanStat has data and how carriers connect to it: 35+ countries, 300+ data points per property, APIs, Duck Creek and OneShield integrations.
- [Property Underwriting Glossary](https://www.urbanstat.com/glossary/index.md): 40 terms from property insurance underwriting, exposure management and geospatial analytics, defined in plain language.
- [Frequently Asked Questions](https://www.urbanstat.com/faq/index.md): Straight answers about UrbanStat: what it does, where it has data, how accurate the wildfire model is, and how long integration takes.

## Company

- [Why UrbanStat](https://www.urbanstat.com/why-urbanstat/index.md): Traditional underwriting relies on outdated data and manual processes. UrbanStat replaces guesswork with geospatial intelligence.
- [About Us](https://www.urbanstat.com/about/index.md): From Istanbul to the world. Building the geospatial platform modern insurers deserve.
- [Request a Demo](https://www.urbanstat.com/demo/index.md): Book a 30-minute walkthrough tailored to your portfolio and perils of interest.

## Insights

- [Insights](https://www.urbanstat.com/insights/index.md): Research, analysis, and lessons learned from building wildfire models, underwriting AI, and geospatial analytics.
- [Why Should Insurers Look at La Niña Differently?](https://www.urbanstat.com/insights/why-should-insurers-look-at-la-nina-differently/index.md): Between 1955 and 2019, over 350,000 hail events occurred in the US. La Niña increases hail frequency by ~50%. We dug into the geographic and temporal patterns insurers need to understand.
- [UrbanStat Classified 80% of Structures Burned in Dixie Fire as High or Worse Risk](https://www.urbanstat.com/insights/dixie-fire-structures-80-percent-classified/index.md): Dixie Fire, the 2nd largest wildfire in California history, burned almost 1 million acres and destroyed ~1,300 structures. UrbanStat's AI model classified 80% of affected structures as High or worse risk.
- [UrbanStat Classified Over 95% of Areas Burned in Dixie Fire as High or Worse Risk](https://www.urbanstat.com/insights/dixie-fire-95-percent-areas-classified/index.md): As California's Dixie Fire exploded in size, we analyzed our model's performance. Over 95% of burned areas were classified as High or worse risk by UrbanStat's pre-season wildfire risk map.
- [UrbanStat Adds Parcel and Permit Data to Its Analytics Platform](https://www.urbanstat.com/insights/parcel-and-permit-data/index.md): We've added parcel and permit data covering all 50 states — over 30 new variables including construction date, building value, and permits. UrbanStat now has hundreds of data points per address.
- [UrbanStat Expands Wildfire Risk Coverage to New Geographies](https://www.urbanstat.com/insights/wildfire-coverage-new-geographies/index.md): Our proprietary wildfire risk map now covers California, Nevada, Arizona, Washington, and Oregon. The model has predicted over 90% of areas burned by the most destructive fires as High or worse risk.
- [Using ZIP Codes Costs You, and Here Is Why](https://www.urbanstat.com/insights/using-zip-codes-costs-you/index.md): ZIP codes were invented to deliver mail, not to underwrite insurance. We analyzed the opportunity cost of using ZIP codes for moratoriums and found insurers are leaving significant premium on the table.
- [UrbanStat Predicted Over 85% of Burned Areas in 2020 Before Wildfire Season Started](https://www.urbanstat.com/insights/predicted-85-percent-burned-areas-2020/index.md): 2020 was a record-breaking year with over 4 million acres burned. Our model classified over 85% of burned areas as High or worse risk — and over 90% for the top 10 most destructive fires.
- [Is Machine Learning the Silver Bullet in Underwriting?](https://www.urbanstat.com/insights/is-machine-learning-the-silver-bullet/index.md): Machine learning for insurance underwriting isn't straightforward. From imbalanced datasets to regulatory constraints, here's what we've learned building ML models for property insurance over 3 years.
- [The Wildfires Won't Stop Miraculously, What's Next?](https://www.urbanstat.com/insights/wildfires-wont-stop-whats-next/index.md): Over 30 wildfires sparked in California within days, burning 3.1 million acres in 2020. As an industry, we need better risk models that focus on causation, not just correlation.
- [UrbanStat Adds Over 30 Countries in Europe to Its Data Collection](https://www.urbanstat.com/insights/30-countries-europe-data-expansion/index.md): UrbanStat expanded data coverage from 3 to over 35 countries. European carriers can now access 25+ data points per address including earthquake, storm, flood, wildfire, and crime data.
- [Four Major Lessons We Learned About Wildfires in California](https://www.urbanstat.com/insights/four-lessons-wildfires-california/index.md): After 2 years of wildfire research, we found that commonly used variables like fuel and slope aren't enough. Power infrastructure, climate patterns, and human factors matter far more than the industry realizes.

## Behavioral economics and insurance

- [Behavioral Economics & Insurance](https://www.urbanstat.com/resources/index.md): Five cognitive biases that silently erode profitability across the P&C industry, and how data-driven underwriting neutralizes each one.
- [The Status Quo Bias Is Costing Insurers Millions](https://www.urbanstat.com/resources/status-quo-bias-underwriting/index.md): Insurers default to legacy models not because they're better, but because switching feels risky. Behavioral economics explains why, and what it's costing the industry.
- [How Loss Aversion Creates Self-Defeating Moratoriums](https://www.urbanstat.com/resources/loss-aversion-moratoriums/index.md): After every major wildfire, carriers rush to impose blanket moratoriums. Loss aversion drives the decision, but the data shows it destroys more value than it protects.
- [The Anchoring Effect: Why Your First Data Point Ruins Everything](https://www.urbanstat.com/resources/anchoring-effect-risk-assessment/index.md): The first number an underwriter sees becomes an anchor that distorts every subsequent judgment. Understanding this bias is the key to building better risk selection processes.
- [Availability Bias: Why Insurers Overreact to the Last Catastrophe](https://www.urbanstat.com/resources/availability-bias-catastrophes/index.md): After every major catastrophe, the entire industry pivots. But data shows this reactive pattern, driven by availability bias, creates portfolio volatility that destroys long-term profitability.
- [The Endowment Effect: Why You Overvalue Your Existing Book](https://www.urbanstat.com/resources/endowment-effect-portfolio-management/index.md): Carriers consistently overvalue their existing book of business and undervalue new opportunities. The endowment effect explains why, and reveals a path to better portfolio construction.