Extreme weather and climate disaster events caused a total of **** billion U.S. dollars in damages across the United States in 2023. This was some ** billion U.S. dollars less than the previous year. In total, there were ** separate billion-dollar extreme weather and climate events in the United States in 2023. These included severe storms, wildfires, tropical cyclones, flooding, and drought.

In 2023, there was a total of *** natural disasters events recorded worldwide, down from *** recorded a year earlier. The Europe, Middle East and Africa region experienced the highest number of natural disasters that year. Deaths and costs of natural disasters Natural disasters affect almost every part of the world. In February 2023, Turkey and Syria were hit by earthquakes that resulted in the highest number of deaths due to natural disaster events that year. In terms of economic damage, Hurricane Katrina remains one of the most expensive natural disasters in the world, topped only by the earthquake/tsunami which hit Japan in 2011. Climate change and natural disasters Climate change has influenced the prevalence of natural disasters. Global warming can increase the risk of extreme weather, resulting in higher risk of droughts and stronger storms, such as tropical cyclones. For instance, higher levels of water vapor in the atmosphere give storms the power to emerge. Furthermore, the heat in the atmosphere and high ocean surface temperatures lead to increased wind speeds, which characterize tropical storms. Areas that are usually unaffected by the sea are becoming more vulnerable due to rising sea levels as waves and currents become stronger.

Natural Disasters Deaths

People killed in natural disasters by country by year

About this dataset

How much do natural disasters cost us? In lives, in dollars, in infrastructure? This dataset attempts to answer those questions, tracking the death toll and damage cost of major natural disasters since 1985. Disasters included are storms ( hurricanes, typhoons, and cyclones ), floods, earthquakes, droughts, wildfires, and extreme temperatures

How to use the dataset

This dataset contains information on natural disasters that have occurred around the world from 1900 to 2017. The data includes the date of the disaster, the location, the type of disaster, the number of people killed, and the estimated cost in US dollars

Research Ideas

• An all-in-one disaster map displaying all recorded natural disasters dating back to 1900.
• Natural disaster hotspots - where do natural disasters most commonly occur and kill the most people?
• A live map tracking current natural disasters around the world

Acknowledgements

License

See the dataset description for more information.

This report analyses the cost of natural disasters in New Zealand. This is defined as the value of total insured losses from all declared states of emergency. Natural disasters include earthquakes, landslips, floods, severe weather events, tsunamis, and volcanic and hydrothermal activity. The data for this report is sourced from the Insurance Council of New Zealand, and is expressed in millions of nominal dollars per calendar year.

In 2024, the economic losses due to natural disasters worldwide amounted to about *** billion U.S. dollars. Natural disasters occur as a result of natural processes on Earth. Many different types of natural disasters can occur, including floods, hurricanes, earthquakes, and tsunamis. Natural disasters in 2024 Tropical cyclones generated the highest amount of economic losses in 2024 with *** billion U.S. dollars worldwide. Hurricanes Helene and Milton were the most destructive events worldwide that year with over 100 billion U.S. dollars in economic losses. Flooding events ranked second in the costliest events in 2024, with flooding in Valencia, Spain, and South and Central China being the worst examples. Asia hardest hit by natural disasters A highly destructive force, Asia is one of the most susceptible regions to natural disasters. The repercussions of natural disasters are not only physical, but also economic. Costs may be high – depending on the severity – as areas affected by natural disasters might need to be rebuilt. Lower income countries are more likely to be affected by natural disasters for a multitude of reasons, including a lack of developed infrastructure, inadequate housing, and lack of back-resources.

Billion Dollar Weather Disasters

Financial and human impact of significant weather disasters in the United States

By Throwback Thursday [source]

About this dataset

Introduction:

Dataset Details: This dataset presents comprehensive information related to billion-dollar weather disasters that occurred in the United States. Each entry includes specific details about a particular disaster event:

1. Disaster: This column contains the name or title associated with each weather disaster.

2. Disaster Type: This column categorizes each disaster into specific types or categories such as hurricanes, floods, heatwaves, tornadoes, wildfires.

3. Beginning Date: The starting date when a particular weather disaster occurred.

4. Ending Date: The end date marking the conclusion of a given weather disaster.

5. Total CPI-Adjusted Cost (Millions of Dollars): This column provides an accurate representation of the total cost incurred by each disaster in millions of dollars while being adjusted for inflation using the Consumer Price Index (CPI).

6. Deaths: This numeric column records the number of deaths caused by each specific weather event.

7. Description: A brief yet informative summary describing key characteristics or impacts associated with a particular weather disaster.

By utilizing this rich dataset combined with advanced analytical tools and visualizations techniques; researchers can derive meaningful insights to support effective decision-making processes aimed at mitigating future damage caused by such destructive phenomena

How to use the dataset

Understanding the Columns

Before we delve into analyzing and visualizing the data, it's important to understand the meaning of each column:

• Disaster: The name or title of the weather disaster.
• Disaster Type: The type or category of the weather disaster.
• Total CPI-Adjusted Cost (Millions of Dollars): The total cost of each weather disaster in millions of dollars adjusted for inflation using the Consumer Price Index (CPI).
• Deaths: The number of deaths caused by each weather disaster.
• Description: A brief description or summary detailing each weather disaster.

Exploring Data Analysis Opportunities

Now that we have a clear understanding of what each column represents let's explore how you can use this dataset for analyzing billion-dollar weather disasters in more depth:

• Analyzing Financial Impact

  Utilize the Total CPI-Adjusted Cost column to analyze and compare the financial impact caused by different types or categoriesof billion-dollar disasters. You can plot graphs, compute averages, identify outliers or trends over time.

• Assessing HumanImpact

  Use data from Deaths column todeterminehow different typesorcategoriesofweatherdisastersvaryin theirhumanimpact.Visualizeandcomparethedeath tolls associated with various catastrophic events.

• Identifying Frequent Disaster Types

  Observe which types or categoriesofweatherdisastersoccurmore frequently than othersbyanalyzingtheDisaster Typecolumn.PlotagraptoshowthedistributionandfrequencyofthedisastertypesintheUnitedStates.

• Exploring Disaster Descriptions

  Dive deeper into the unique aspects of each weather disaster by studying the Description column. This will provide additional context and insight into the specific events.

Making Data Visualizations

Data visualizations can help you represent, summarize, and communicate patterns or insights hidden within the dataset. Here are a few ideas for creating impactful visualizations:

• Create a bar chart depicting the financial cost (Total CPI-Adjusted Cost) of different disaster types.

• Develop a line graph showing how deaths have varied over time for various weather disasters.

• Design a pie chart

Research Ideas

• Analyzing the financial impact of different types of weather disasters: This dataset provides information on the total cost of billion-dollar weather disasters, adjusted for inflation. By analyzing this data, one can gain insights into which types of weather events have the highest financial impact, helping to prioritize preparedness and mitigation efforts.
• Examining trends in weather disasters over time: With information on the beginning and ending dates of each event, this dataset can be used to analyze trends in the frequency and duration of billion-dollar weather disasters in the United States. This analysis could help identify if certain types of ...

Little is known about the fiscal costs of natural disasters, especially regarding social safety nets that do not specifically target extreme weather events. This paper shows that US hurricanes lead to substantial increases in non-disaster government transfers, such as unemployment insurance and public medical payments, in affected counties in the decade after a hurricane. The present value of this increase significantly exceeds that of direct disaster aid. This implies, among other things, that the fiscal costs of natural disasters have been significantly underestimated and that victims in developed countries are better insured against them than previously thought.

The United States experienced ** billion-dollar disaster events in 2023. The southern and midwestern drought and heatwave that hit the country from April to September was the costliest billion-dollar natural disaster event that year, incurring a total cost of nearly ** billion U.S. dollars.

Abstract: Natural disasters result in impacts on the population’s health, damage to healthcare establishments, and, in extreme situations, the health systems’ breakdown. National and global trends show an increase in the frequency of disasters associated with climate change. This article aims to analyze the impacts and economic costs of natural disasters for healthcare establishments, identifying the most frequent and costly types and distribution across the Brazilian territory, based on data recorded in Brazil’s Integrated Disaster Information System (S2ID) from 2000 to 2015. A total of 15,950 records were systematized and analyzed, of which only 29.4% of the events showed records of costs, totaling nearly BRL 4 billion. Climate disasters were the most frequent, but they did not account for the highest costs. In the cost per event ratio, the costs of hydrological disasters were 3.2 to 3.6 higher than for climate and geologic disasters. Pernambuco, Amazonas, and Santa Catarina were the states with highest total costs in millions of Brazilian reais. The North region, especially the state of Acre, had the highest cost per disaster. Despite the study’s limitations (involving the records’ quality), the data should be viewed as the tip of an iceberg, since the impacts go beyond the economic damages, impacting the infrastructure and resources that support services, compromising their capacity precisely when the population most needs health services.

As climate change increases the frequency and severity of natural disasters like floods, wildfires and hurricanes, funding supports the growing need for emergency relief services. Providers rely on government funding, mainly from FEMA under the US Department of Homeland Security, which surged until 2024. Private donations from individuals, corporations and foundations account for over two-thirds of total funds and are crucial. Strong growth in disposable income and corporate profit has supported these donations. With this diverse funding, industry revenue is expected to climb at a 3.0% CAGR, reaching $16 billion in 2025. However, a 0.6% drop is expected in 2025 as government and corporate funds weaken. In addition to funding, FEMA collaborates with Voluntary Agency Liaisons (VALs), Voluntary Organizations Active in Disaster (VOADs) and state and local emergency management agencies to organize and coordinate NGOs and other groups' services. This partnership ensures a unified, efficient and effective response to address disaster survivors' needs. Disaster relief organizations establish themselves strategically where disasters are prevalent for maximum efficiency. A result of the geographical spread of these incidents, the concentration of disaster relief services remains generally low. However, larger organizations can leverage their size to reap economies of scale, reinvesting profit into enhanced tools and service provision. Well-established nonprofits like the Red Cross might operate locally but benefit from a more expansive infrastructure. In the future, migration and its impact on population density, a location's infrastructure, mitigation efforts and the strength of social support networks will influence the total cost of a disaster event. New technologies will aid in reducing costs and improving the efficiency of service providers. Drones, mobile applications and AI tools to improve disaster relief by delivering essential supplies, optimizing resource management, enhancing communication, assisting in preparedness and reducing labor costs. Large and small relief organizations can leverage these technologies, although larger organizations may gain more from scale efficiencies. Local and state government investments may mitigate the impact of natural disasters, reducing demand for relief services. Still, potential reorganization of FEMA and cuts to NOAA and FEMA can disrupt services. With declines in corporate profit growth, slower local and state government investment growth and uncertainty in federal funding, industry revenue growth will slow to a CAGR of 1.2%, reaching $17.0 billion in 2030 with a slight profit decline.

The hurricanes in the U.S. and Mexico in September and October incurred an economic loss of about 110 billion U.S. dollars, the most of any natural disaster event in 2024. Three of the ten most expensive catastrophes in that year were hurricanes. Weather, climate, water related disaster The disasters that caused mortality in large numbers include droughts, storms, floods, and extreme temperatures. Hurricanes alone generated 35 percent of the total economic losses among the leading disasters over these 50 years. The global cost of natural disaster losses was primarily financial losses. Low-income countries are more affected by natural disasters when compared to the richer countries in the world. American Hurricanes Floods were the most common weather-related disasters recorded, yet storms had the highest human and economic losses. As the most common cause of damage, storms are the only disaster for which the attribution component grows. As of 2023, Hurricane Katrina was by far the most destructive hurricane in the United States. Officials confirmed more than 1,800 deaths, estimated damages of about 200 billion U.S. dollars, the destruction of approximately 350,000 homes, and displaced almost a million individuals.

The NOAA National Centers for Environmental Information ceased providing support for this product in May 2025 in response to an initiative to implement reductions within the U.S. federal government. This dataset contains U.S. disaster cost assessments of the total, direct losses ($) inflicted by: tropical cyclones, inland floods, drought & heat waves, severe local storms (i.e., tornado, hail, straight-line wind damage), wildfires, crop freeze events and winter storms. These assessments require input from a variety of public and private data sources including: the Insurance Services Office (ISO) Property Claim Services (PCS), Federal Emergency Management Agency (FEMA) National Flood Insurance Program (NFIP) and Presidential Disaster Declaration (PDD) assistance, and the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) & Risk Management Agency (RMA), the National Interagency Fire Center (NIFC) and state agency reporting, among others. Each of these data sources provides unique information as part of the overall disaster loss assessment.

Context

Extracted from this paper: Neumayer, E., & Plümper, T. (2007). The gendered nature of natural disasters: The impact of catastrophic events on the gender gap in life expectancy, 1981–2002. Annals of the Association of American Geographers, 97(3), 551-566. https://doi.org/10.1111/j.1467-8306.2007.00563.x

from the paper: "To be recorded in the database, an event must fulfill at least one of the following conditions: (a) ten or more people reported as killed; (b) 100 people reported as affected; (c) a state of emergency has been declared; or (d) the country has issued a call for international assistance."

"Most natural disasters cost few if any lives, but the three most severe disasters—the droughts in Ethiopia and Sudan in 1984 and the flood in Bangladesh in 1991—account for almost half of all fatalities in our sample."

Content of dataset

Types of natural disasters Number of events Number of death and people affected for each natural disaster

Inspiration

We still don't have any comprehensive picture of threats to human species. If you know any similar dataset, please leave a comment.

OverviewThe multiple hazard index for the United States Counties was designed to map natural hazard relating to exposure to multiple natural disasters. The index was created to provide communities and public health officials with an overview of the risks that are prominent in their county, and to facilitate the comparison of hazard level between counties. Most existing hazard maps focus on a single disaster type. By creating a measure that aggregates the hazard from individual disasters, the increased hazard that results from exposure to multiple natural disasters can be better understood. The multiple hazard index represents the aggregate of hazard from eleven individual disasters. Layers displaying the hazard from each individual disaster are also included.

The hazard index is displayed visually as a choropleth map, with the color blue representing areas with less hazard and red representing areas with higher hazard. Users can click on each county to view its hazard index value, and the level of hazard for each individual disaster. Layers describing the relative level of hazard from each individual disaster are also available as choropleth maps with red areas representing high, orange representing medium, and yellow representing low levels of hazard.Methodology and Data CitationsMultiple Hazard Index

The multiple hazard index was created by coding the individual hazard classifications and summing the coded values for each United States County. Each individual hazard is weighted equally in the multiple hazard index. Alaska and Hawaii were excluded from analysis because one third of individual hazard datasets only describe the coterminous United States.

Avalanche Hazard

University of South Carolina Hazards and Vulnerability Research Institute. “Spatial Hazard Events and Losses Database”. United States Counties. “Avalanches United States 2001-2009”. < http://hvri.geog.sc.edu/SHELDUS/

Downloaded 06/2016.

Classification

Avalanche hazard was classified by dividing counties based upon the number of avalanches they experienced over the nine year period in the dataset. Avalanche hazard was not normalized by total county area because it caused an over-emphasis on small counties, and because avalanches are a highly local hazard.

None = 0 AvalanchesLow = 1 AvalancheMedium = 2-5 AvalanchesHigh = 6-10 Avalanches

Earthquake Hazard

United States Geological Survey. “Earthquake Hazard Maps”. 1:2,000,000. “Peak Ground Acceleration 2% in 50 Years”. < http://earthquake.usgs.gov/hazards/products/conterminous/

. Downloaded 07/2016.

Classification

Peak ground acceleration (% gravity) with a 2% likelihood in 50 years was averaged by United States County, and the earthquake hazard of counties was classified based upon this average.

Low = 0 - 14.25 % gravity peak ground accelerationMedium = 14.26 - 47.5 % gravity peak ground accelerationHigh = 47.5+ % gravity peak ground acceleration

Flood Hazard

United States Federal Emergency Management Administration. “National Flood Hazard Layer”. 1:10,000. “0.2 Percent Annual Flood Area”. < https://data.femadata.com/FIMA/Risk_MAP/NFHL/

. Downloaded 07/2016.

Classification

The National Flood Hazard Layer 0.2 Percent Annual Flood Area was spatially intersected with the United States Counties layer, splitting flood areas by county and adding county information to flood areas. Flood area was aggregated by county, expressed as a fraction of the total county land area, and flood hazard was classified based upon percentage of land that is susceptible to flooding. National Flood Hazard Layer does not cover the entire United States; coverage is focused on populated areas. Areas not included in National Flood Hazard Layer were assigned flood risk of Low in order to include these areas in further analysis.

Low = 0-.001% area susceptibleMedium = .00101 % - .005 % area susceptibleHigh = .00501+ % area susceptible

Heat Wave Hazard

United States Center for Disease Control and Prevention. “National Climate Assessment”. Contiguous United States Counties. “Extreme Heat Events: Heat Wave Days in May - September for years 1981-2010”. Downloaded 06/2016.

Classification

Heat wave was classified by dividing counties based upon the number of heat wave days they experienced over the 30 year time period described in the dataset.

Low = 126 - 171 Heat wave DaysMedium = 172 – 187 Heat wave DaysHigh = 188 – 255 Heat wave Days

Hurricane Hazard

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Atlantic Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

National Oceanic and Atmospheric Administration. Coastal Services Center. “Historical North Pacific Tropical Cyclone Tracks, 1851-2004”. 1: 2,000,000. < https://catalog.data.gov/dataset/historical-north-atlantic-tropical-cyclone-tracks-1851-2004-direct-download

. Downloaded 06/2016.

Classification

Atlantic and Pacific datasets were merged. Tropical storm and disturbance tracks were filtered out leaving hurricane tracks. Each hurricane track was assigned the value of the category number that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as being more hazardous. Values describing each hurricane event were aggregated by United States County, normalized by total county area, and the hurricane hazard of counties was classified based upon the normalized value.

Landslide Hazard

United States Geological Survey. “Landslide Overview Map of the United States”. 1:4,000,000. “Landslide Incidence and Susceptibility in the Conterminous United States”. < https://catalog.data.gov/dataset/landslide-incidence-and-susceptibility-in-the-conterminous-united-states-direct-download

. Downloaded 07/2016.

Classification

The classifications of High, Moderate, and Low landslide susceptibility and incidence from the study were numerically coded, the average value was computed for each county, and the landslide hazard was classified based upon the average value.

Long-Term Drought Hazard

United States Drought Monitor, Drought Mitigation Center, United States Department of Agriculture, National Oceanic and Atmospheric Administration. “Drought Monitor Summary Map”. “Long-Term Drought Impact”. < http://droughtmonitor.unl.edu/MapsAndData/GISData.aspx >. Downloaded 06/2016.

Classification

Short-term drought areas were filtered from the data; leaving only long-term drought areas. United States Counties were assigned the average U.S. Drought Monitor Classification Scheme Drought Severity Classification value that characterizes the county area. County long-term drought hazard was classified based upon average Drought Severity Classification value.

Low = 1 – 1.75 average Drought Severity Classification valueMedium = 1.76 -3.0 average Drought Severity Classification valueHigh = 3.0+ average Drought Severity Classification value

Snowfall Hazard

United States National Oceanic and Atmospheric Administration. “1981-2010 U.S. Climate Normals”. 1: 2,000,000. “Annual Snow Normal”. < http://www1.ncdc.noaa.gov/pub/data/normals/1981-2010/products/precipitation/

. Downloaded 08/2016.

Classification

Average yearly snowfall was joined with point location of weather measurement stations, and stations without valid snowfall measurements were filtered out (leaving 6233 stations). Snowfall was interpolated using least squared distance interpolation to create a .05 degree raster describing an estimate of yearly snowfall for the United States. The average yearly snowfall raster was aggregated by county to yield the average yearly snowfall per United States County. The snowfall risk of counties was classified by average snowfall.

None = 0 inchesLow = .01- 10 inchesMedium = 10.01- 50 inchesHigh = 50.01+ inches

Tornado Hazard

United States National Oceanic and Atmospheric Administration Storm Prediction Center. “Severe Thunderstorm Database and Storm Data Publication”. 1: 2,000,000. “United States Tornado Touchdown Points 1950-2004”. < https://catalog.data.gov/dataset/united-states-tornado-touchdown-points-1950-2004-direct-download

. Downloaded 07/2016.

Classification

Each tornado touchdown point was assigned the value of the Fujita Scale that describes that event. Weighting each event by intensity ensures that areas with higher intensity events are characterized as more hazardous. Values describing each tornado event were aggregated by United States County, normalized by total county area, and the tornado hazard of counties was classified based upon the normalized value.

Volcano Hazard

Smithsonian Institution National Volcanism Program. “Volcanoes of the World”. “Holocene Volcanoes”. < http://volcano.si.edu/search_volcano.cfm

. Downloaded 07/2016.

Classification

Volcano coordinate locations from spreadsheet were mapped and aggregated by United States County. Volcano count was normalized by county area, and the volcano hazard of counties was classified based upon the number of volcanoes present per unit area.

None = 0 volcanoes/100 kilometersLow = 0.000915 - 0.007611 volcanoes / 100 kilometersMedium = 0.007612 - 0.018376 volcanoes / 100 kilometersHigh = 0.018377- 0.150538 volcanoes / 100 kilometers

Wildfire Hazard

United States Department of Agriculture, Forest Service, Fire, Fuel, and Smoke Science Program. “Classified 2014 Wildfire Hazard Potential”. 270 meters. < http://www.firelab.org/document/classified-2014-whp-gis-data-and-maps

. Downloaded 06/2016.

Classification

The classifications of Very High, High, Moderate, Low, Very Low, and Non-Burnable/Water wildfire hazard from the study were numerically coded, the average value was computed for each county, and the wildfire hazard was classified based upon the average value.

Catastrophe Modeling Software Market Outlook

According to our latest research, the catastrophe modeling software market size reached USD 2.71 billion globally in 2024, demonstrating robust growth driven by the increasing frequency and severity of natural disasters. The market is expected to expand at a CAGR of 10.4% from 2025 to 2033, reaching a projected value of USD 6.52 billion by 2033. This surge is primarily attributed to the growing need for advanced risk assessment tools among insurance, reinsurance, and government sectors, as well as heightened awareness regarding climate change adaptation and disaster management strategies. As per our latest research, technological advancements in predictive analytics and the integration of artificial intelligence are further catalyzing the adoption of catastrophe modeling solutions globally.

The primary growth driver for the catastrophe modeling software market is the increasing incidence of catastrophic events such as hurricanes, floods, wildfires, and earthquakes. These disasters have become more frequent and intense due to climate change, prompting organizations across sectors to invest in sophisticated modeling tools to quantify potential losses and enhance risk mitigation strategies. The insurance and reinsurance industries, in particular, have recognized the necessity of catastrophe modeling software to accurately price policies, manage reserves, and comply with regulatory requirements. Moreover, the rising costs associated with natural disasters, which have exceeded USD 300 billion annually in some years, underscore the urgent need for reliable risk assessment platforms, further fueling market demand.

Another significant factor propelling the catastrophe modeling software market is the rapid advancement in data analytics, artificial intelligence, and cloud computing technologies. Modern catastrophe modeling platforms leverage large datasets, including geospatial, meteorological, and socio-economic data, to deliver highly granular and dynamic risk assessments. The integration of AI and machine learning algorithms has enabled these solutions to improve predictive accuracy, automate scenario analysis, and facilitate real-time decision-making. Additionally, the shift towards cloud-based deployment models has democratized access to advanced modeling capabilities, allowing even small and medium-sized enterprises to benefit from scalable, cost-effective risk management tools. This technological evolution is expected to continue driving market expansion in the coming years.

A further catalyst for market growth is the increasing regulatory scrutiny on risk management practices, particularly in the financial and insurance sectors. Regulatory bodies worldwide are mandating more rigorous risk assessment and reporting standards, compelling organizations to adopt catastrophe modeling software to ensure compliance. Government agencies are also leveraging these tools to enhance disaster preparedness, response planning, and climate change adaptation strategies. The convergence of regulatory requirements, public sector initiatives, and private sector risk management needs is creating a fertile environment for the widespread adoption of catastrophe modeling solutions, positioning the market for sustained growth through 2033.

From a regional perspective, North America currently dominates the catastrophe modeling software market, accounting for more than 40% of the global revenue in 2024. This leadership is driven by the presence of major insurance and reinsurance companies, advanced technological infrastructure, and a high incidence of natural disasters in the United States and Canada. Europe follows closely, with significant investments in climate resilience and disaster management initiatives. The Asia Pacific region is expected to exhibit the fastest growth over the forecast period, fueled by increasing urbanization, rising insurance penetration, and heightened vulnerability to natural catastrophes. Latin America and the Middle East & Africa are also witnessing growing adoption of catastrophe modeling tools, albeit at a relatively nascent stage compared to other regions.

Component Analysis

The catastrophe modeling software market is segmented by component into software and services, each playing a critical role in the overall value proposition for end-users. The software segment encompasses a wide range of platforms and application

Weather and climate disasters are proving increasingly costly to humankind. In the United States, weather and climate disasters eclipsed $250 billion in damages 8 out of the last 10 years. In 2020 alone, the U.S. had a record 22 separate disasters of at least $1 billion each, breaking the old record of 16 such events. In an on-going effort to curb the cost to life and property, the U.S. Congress took action by passing PUBLIC LAW 115–123, FEB. 9, 2018, which states “Pursuant to section 703 of the Public Works and Economic Development Act (42 U.S.C. 3233), for an additional amount for ‘‘Economic Development Assistance Programs’’ for necessary expenses related to flood mitigation, disaster relief, long-term recovery, and restoration of infrastructure in areas that received a major disaster designation as a result of Hurricanes Harvey, Irma, and Maria, and of wildfires and other natural disasters occurring in calendar year 2017 for an amount for ‘‘Operations, Research, and Facilities’’ for necessary expenses related to the consequences of Hurricanes Harvey, Irma, and Maria, $120,904,000, to remain available until September 30, 2019, as follows:

In 2024, the United States experienced 29 natural disasters, which made it the most natural catastrophe-prone country in the world that year. Indonesia and China came second on that list, with 20 and 18 natural disasters occurring in the same year, respectively. Storms were the most common type of natural disaster in 2024. Types of natural disasters There are many different types of natural disasters that occur worldwide, including earthquakes, droughts, storms, floods, volcanic activity, extreme temperatures, landslides, and wildfires. Overall, there were 398 natural disasters registered all over the world in 2023. Costs of natural disasters Due to their destructive nature, natural disasters take a severe toll on populations and countries. Tropical cyclones have the biggest economic impact in the countries that they occur. In 2024, tropical cyclones caused damage estimated at more than 145 billion U.S. dollars. Meanwhile, the number of deaths due to natural disasters neared 18,100 that year. The Heat Wave in Saudi Arabia had the highest death toll, with 1,301 fatalities. Scientists predict that some natural disasters such as storms, floods, landslides, and wildfires will be more frequent and more intense in the future, creating both human and financial losses.

• The Ministry of the Interior and Safety publishes the 'Administrative Safety Statistical Yearbook' every year by compiling statistical data from the headquarters of the Ministry of the Interior and Safety and its affiliated organizations in accordance with the 'Ministry of the Interior and Safety Statistics Management Regulations'. - The statistical information by field included in the 'Administrative Safety Statistical Yearbook' is provided as an open API so that it can be used in various fields in both the public and private sectors. - The open API in question is the 'Status of Recovery Costs by City and Province for Natural Disasters by Cause' included in the 'Administrative Safety Statistical Yearbook', which provides statistical information on recovery costs by city and cause for natural disasters such as typhoons, heavy rain, heavy snow, lightning, earthquakes, and heat waves. - In addition, the 'Administrative Safety Statistical Yearbook' can be downloaded in PDF format from the Ministry of the Interior and Safety website at Policy Data > Statistics > Statistical Yearbook/Statistics by Subject.

According to Cognitive Market Research, the global natural catastrophes insurance market size was USD XX million in 2024. It will expand at a compound annual growth rate (CAGR) of 21.20% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.4% from 2024 to 2031.
Europe accounted for over 30% of the global USD XX million market size.
Asia Pacific held a market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.2% from 2024 to 2031.
The Latin America market will account for more than 5% of global revenue and was USD XX million in 2024, growing at a compound annual growth rate (CAGR) of 20.6% from 2024 to 2031.
The Middle East and Africa held the major markets, accounting for around 2% of the global revenue. The market was USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.9% from 2024 to 2031.
The brokers held the highest natural catastrophe insurance market revenue share in 2024.

Market Dynamics of Natural Catastrophes Insurance Market

Key Drivers of Natural Catastrophes Insurance Market

Technological Advancements in Risk Modeling and Assessment Drives Market Growth

Technological advancements are pivotal in driving growth within the natural catastrophes insurance market. With the advent of sophisticated risk modeling and assessment tools, insurers can better understand and quantify the potential impacts of natural disasters. These advancements enable insurers to assess risk levels more accurately, set appropriate premiums, and develop tailored insurance products to meet clients' evolving needs. Moreover, technology facilitates the integration of vast datasets, including historical weather patterns, geological data, and socio-economic factors, into risk models. This enhanced data analytics capability enables insurers to identify emerging trends and anticipate potential catastrophes more effectively. By leveraging advanced predictive analytics and machine learning algorithms, insurers can improve underwriting processes, streamline claims management, and enhance operational efficiency. As a result, technological innovation drives market growth and contributes to the resilience of communities and businesses in the face of natural disasters.

Regulatory Changes Affecting Insurance Requirements Propel Market Growth

Regulatory changes are pivotal in driving growth within the natural catastrophes insurance market. As governments worldwide implement stricter regulations regarding insurance coverage for natural disasters, insurers must adapt their offerings to meet these new requirements. These changes often involve mandates for increased coverage limits, expanded geographic coverage areas, or enhanced resilience measures, all contributing to a higher demand for insurance products. Moreover, regulatory frameworks frequently incentivize insurers to develop innovative solutions to mitigate the financial impact of natural catastrophes. This drive for innovation fosters the development of new risk assessment models, sophisticated underwriting techniques, and the creation of specialized insurance products tailored to address specific types of natural disasters. As a result, the natural catastrophe insurance market continues to evolve in response to regulatory pressures, ultimately driving growth and ensuring greater financial protection for individuals, businesses, and communities vulnerable to the impacts of such events.

Restraint Factors Of Natural Catastrophes Insurance Market

Limited Historical Data for Emerging Risks Hampers Market Growth

The natural catastrophes insurance market faces significant challenges due to limited historical data for emerging risks. As climate change introduces new and evolving hazards, insurers need robust historical data to accurately inform their model to assess and price these risks. This limitation inhibits the development of comprehensive risk management strategies and may lead to underestimation of potential losses, exposing insurers to greater financial volatility. Furthermore, the absence of historical data for emerging risks complicates insurers' ability to communicate risk to policyholders and regulators effectively. With a clear unders...

National Risk Index Version: March 2023 (1.19.0)A Hurricane is a tropical cyclone or localized, low-pressure weather system that has organized thunderstorms but no front (a boundary separating two air masses of different densities) and maximum sustained winds of at least 74 miles per hour (mph). Annualized frequency values for Hurricanes are in units of events per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.