Author: Mark Bauer
This guide is intended for illustrative purposes only and was created to share methods I’ve found helpful when working with NFIP datasets, particularly within the open source community. FEMA does not endorse any of the products mentioned in this guide and makes no claim as to the assurance of their efficacy or to the security of the products. See below for the language suggested by OpenFEMA.
This analysis uses the Federal Emergency Management Agency’s OpenFEMA API, but is not endorsed by FEMA. The Federal Government or FEMA cannot vouch for the data or analyses derived from these data after the data have been retrieved from the Agency's website(s).
From OpenFEMA:
Respect the OpenFEMA API and content on this website. Use the Site in a lawful manner. Do not modify the Site or attempt to use it to publish or transmit malicious software or content. FEMA shall not be liable for any damages resulting from the use of this website, API services, or content. Do not attempt to reidentify the individuals whose data may be aggregated. We may suspend your access to this website if we feel you have not complied with these terms and conditions.
Read more about OpenFEMA's Terms and Conditions.
Last Data Refresh: 05-14-2025.
Table xx. Number and Amount Paid on NFIP Claims (both in millions). Total amount paid, in this analysis, is defined as the total amount paid for all Building, Contents, and Increased Cost of Compliance (ICC) claims. This table includes claims from 1978 through the most recent effective data refresh specified above.
| countClaimM | paidTotalClaimM | paidBuildingM | paidContentsM | paidICCM |
|---|---|---|---|---|
| 2.71 | 88,145.83 | 71,665.54 | 15,528.23 | 952.06 |
Table xx. Number of NFIP Policies in Force. Number of NFIP Policies in Force as of the effective data refresh specified above. Total Insurance Coverage, Premiums, and Policy Costs are in millions.
| policiesInForce | totalInsuranceCoverageM | totalInsurancePremiumOfThePolicyM | policyCostM |
|---|---|---|---|
| 4,469,069.00 | 1,229,228.66 | 3,874.69 | 5,047.91 |
Figure xx. Number of NFIP Claims and Policies (2009-2024).
Figure xx. Number of NFIP Claims and Policies by State (2009-2024).
Figure xx. Number of NFIP Claims by Year from 1978 to 2024.
Figure xx. Total Amount Paid on NFIP Claims by Year from 1978 to 2024 (Adjusted in 2024 Dollars). Total amount paid is defined as the total amount paid for all Building, Contents, and Increased Cost of Compliance (ICC) claims.
Table xx. Top 10 Costliest Flood Events by NFIP Claim Payments (in millions). Ranked by total amount paid, adjusted to 2025 dollars (i.e. paidTotalClaimM2025). Original payment amounts at the time of each event are shown in paidTotalClaimM.
| rank | yearOfLoss | floodEvent | countClaims | paidTotalClaimM | paidTotalClaimM2025 | averagePaidClaim2025 |
|---|---|---|---|---|---|---|
| 1 | 2005 | Hurricane Katrina | 208,348 | 16,261.70 | 27,088.99 | 130,017 |
| 2 | 2012 | Hurricane Sandy | 144,848 | 8,957.47 | 12,553.89 | 86,669 |
| 3 | 2017 | Hurricane Harvey | 92,398 | 9,055.71 | 11,846.27 | 128,209 |
| 4 | 2024 | Hurricane Helene | 57,843 | 6,027.67 | 6,208.53 | 107,334 |
| 5 | 2022 | Hurricane Ian | 48,754 | 4,838.68 | 5,467.26 | 112,139 |
| 6 | 2008 | Hurricane Ike | 58,126 | 2,702.51 | 4,067.22 | 69,972 |
| 7 | 2016 | Mid-summer severe storms | 30,018 | 2,533.53 | 3,397.11 | 113,169 |
| 8 | 2004 | Hurricane Ivan | 20,137 | 1,325.42 | 2,273.47 | 112,900 |
| 9 | 2001 | Tropical Storm Allison | 35,561 | 1,104.98 | 2,004.68 | 56,373 |
| 10 | 2011 | Hurricane Irene | 52,493 | 1,347.40 | 1,943.62 | 37,026 |
Figure xx. Number of NFIP Claims by State from 1978 to 2025.
Figure xx. Number of NFIP Claims Normalized by State Area from 1978 to 2025.
The National Flood Insurance Program (NFIP) is managed by FEMA and provides flood insurance to mitigate the socio-economic impacts of floods. In 2019, FEMA released two datasets on OpenFEMA related to the NFIP to promote transparency, reduce complexity for public data requests, and to improve how the agency’s stakeholders interact with and understand the NFIP:
- NFIP Redacted Policies: Provides details on NFIP policy transactions.
- NFIP Redacted Claims: Represents more than 2,000,000 NFIP claims transactions.
With over 69 million policies and 2.7 million claims transactions as of May 14, 2025, this is one of the largest openly available insurance datasets in the United States and possibly the world. This project examines both the NFIP Redacted Claims and Policies datasets, but more importantly, demonstrates how to query and manipulate the data with ease.
Due to its large file size, accessing the dataset can be a challenge, even for experienced analysts. Guidance from OpenFEMA:
In order to improve accessibility, we have one compressed file. Due to the file size we recommend using Access, SQL, or another programming/data management tool to visualize and manipulate the data, as Excel will not be able to process files this large without data loss.
OpenFEMA also provides a lot of great recommendations with their guide OpenFEMA Guide to Working with Large Data Sets:
Once data has been successfully downloaded, viewing, manipulating, and analyzing data can be a challenge. A spreadsheet program such as Microsoft Excel has a data size limit. Large data sets will exceed spreadsheet tool row limits and will not open without data loss. Common text editors such as Notepad++ or Sublime have 2 GB file limits, again preventing the opening, search, and editing capabilities from working. Using different tools, extracting subsets, and/or aggregating detailed data are good approaches for making analysis easier.
To address this, I designed a tutorial demonstrating how to analyze the datasets with my laptop locally. I utilized DuckDB, a lightweight, high-performance SQL OLAP database management system. DuckDB offers a smooth experience, is blazing-fast, includes a robust Python API, and is open-source. I used SQL via the Python Client API for data analysis and used GeoPandas for mapping.
The ultimate goal of this project is to promote these datasets for academic research and to assist communities in analyzing and downloading the data. This dataset is one of my favorites, and I hope you find these tutorials helpful in advancing the study and analysis of the NFIP.
To learn more about the NFIP:
- NFIP Website: NFIP information from FEMA.
- FloodSmart: For more information about what’s covered and to find a policy.
In the Additional Resources section, you’ll find some of my favorite reports and analyses related to the NFIP, particularly from the early days of the datasets' release. I highly recommend checking them out.
Personal identifiable information (PII) is redacted and data is anonymized to the census tract, reported zip code, and to one decimal point (.1) digit of latitude and longitude. Please see the official guidance at Frequently Asked Questions about NFIP Policies and Claims Data:
Q: How are you protecting policyholders’ privacy?
A: Personal identifiable information (PII) is redacted and data is anonymized to the census tract, reported zip code, and to one decimal point (.1) digit of latitude and longitude. If mapped, flood insurance policies and claims may appear to be clustered at a particular location due to this anonymization.
Q: Why can’t the National Flood Insurance Program provide address-level data?
A: FEMA has a responsibility to protect policyholder privacy pursuant to the Privacy Act of 1974. In the data published in June 2019, FEMA provided the most granular data possible to promote transparency while protecting customer privacy consistent with the Privacy Act of 1974 and the Freedom of Information Act (FOIA). This is consistent with additional FEMA programmatic datasets posted on OpenFEMA as well.
Geographic constraints:
Q: Why is there latitude and longitude in NFIP datasets, I thought the datasets were anonymized?
A: Latitude and longitude can be used to identify a specific location. In order to protect policy holder privacy, latitude and longitude are truncated to one decimal point. The more significant digits (decimals), the more precise a set of coordinates can be. The level of data that FEMA provides for users will find the coordinates accurate to within approximately six miles.
Q: Which geographic fields are best to use for aggregation?
A: Census tract and county fields are best to use for aggregation since they are derived from a policy or claim geocode (i.e. latitude and longitude are generated from provided address information). Therefore, we are confident that these values are reported with a relatively high degree of accuracy.
- Metadata: Examines the metadata for the NFIP Claims and Policies datasets. Here, we retrieve and save the data dictionaries.
- Download Data: Demonstrates how to download the NFIP Claims and Policies datasets as Parquet files from OpenFEMA.
- Claims Analysis: Explores NFIP Claims data with DuckDB.
- Policies Analysis: Explores NFIP Policies data with DuckDB.
- Generate Figures: Dedicated to generating figures and tables displayed on this page. For a more detailed analysis of the NFIP data, refer to the analysis notebooks.
OpenFEMA Datasets:
- FIMA NFIP Redacted Claims - v2. Retrieved from https://www.fema.gov/openfema-data-page/fima-nfip-redacted-claims-v2.
- FIMA NFIP Redacted Policies - v2. Retrieved from https://www.fema.gov/openfema-data-page/fima-nfip-redacted-policies-v2.
- NFIP:
- OpenFEMA:
- Wing, O.E.J., Pinter, N., Bates, P.D. et al. New insights into US flood vulnerability revealed from flood insurance big data. Nat Commun 11, 1444 (2020). https://doi.org/10.1038/s41467-020-15264-2
The Wharton School of the University of Pennsylvania
The Natural Resources Defense Council (NRDC)
- FEMA Flood Data: 2.4 Million Damage Claims and Counting (2019)
- FEMA Flood Data: What We Still Need to Know (2019)
- FEMA Puts New Data on the Map for Policymakers (2020)
Milliman
- Residential Flood Risk in the United States: Quantifying Flood Losses, Mortgage Risk and Sea Level Rise (2020)
- Insights into consumer demand for flood insurance: Trends in take-up (2021)
- Estimating undisclosed flood risk in real estate transactions (2025)
Verisk
- Modeling Fundamentals: Evaluating U.S. Flood Model Loss Output with Historical Loss Experience (2020
Insurance Information Institute
Norfolk Open Data
DHS/FEMA
Feel free to reach out.
- LinkedIn: markebauer
- Portfolio: mebauer.github.io
- GitHub: mebauer