In 2021, there were about 338,000 home structure fires reported in the United States. This is a decrease from the previous year, when there were 356,500 home structure fires reported across the country.

Incident-based fire statistics, by type of fire incident, Canada, Nova Scotia, New Brunswick, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, Yukon, Canadian Armed Forces, 2005 to 2021.

There were an estimated 486,500 structure fires reported to U.S. fire departments in 2021. Of these, about 361,000 occurred in residential structures. 81,500 fires occurred in apartments in 2021, and another 256,500 fires occurred in one- and two-family homes.

Incident-based fire statistics, by type of casualty, age group of casualty, status of casualty and type of structure, Canada, Nova Scotia, New Brunswick, Ontario, Manitoba, Saskatchewan, Alberta, British Columbia, Yukon, Canadian Armed Forces, 2005 to 2021.

In 2019, there were 339,500 home structure fires reported in the United States, which caused 12,200 civilian injuries. This was a slight increase from the previous year, where 11,600 civilians were injured from home structure fires.

In 2019, about 339,500 home structure fires were reported in the United States, which caused property damage worth of about 7.7 billion U.S. dollars. This is a small decrease from the previous year, where home structure fires reported 8.3 billion U.S. dollars worth of damage.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The global fire fighting material market, valued at $2967.2 million in 2025, is poised for significant growth. While a precise Compound Annual Growth Rate (CAGR) isn't provided, considering the increasing frequency and severity of wildfires globally, coupled with stringent safety regulations and rising industrialization, a conservative estimate of a 5% CAGR for the forecast period (2025-2033) seems plausible. This would project the market to surpass $4,700 million by 2033. Key growth drivers include escalating urbanization leading to a higher concentration of assets at risk, the increasing demand for advanced fire suppression technologies, and a growing awareness of fire safety among both businesses and individuals. Market segmentation reveals a strong demand across various applications, including wildland fires (driven by climate change and increased drought conditions), structural fires (residential and commercial buildings), and industrial fires (petrochemical, manufacturing plants). Foam-based materials currently hold a substantial market share due to their effective extinguishing capabilities, but liquid-based and other specialized materials are experiencing growth due to their efficacy in specific applications. The market's growth trajectory isn't without challenges. Restraints include the high initial investment costs associated with advanced firefighting equipment and materials, fluctuating raw material prices, and the potential for technological obsolescence. However, continuous innovation in material science, the development of eco-friendly alternatives, and government initiatives promoting fire safety are expected to mitigate these challenges. The geographic distribution shows strong growth potential across emerging economies in Asia-Pacific and the Middle East & Africa due to rapid infrastructure development and industrialization. North America and Europe, while mature markets, will continue to see moderate growth driven by technological advancements and replacement of aging infrastructure. Competitive landscape analysis highlights a mix of large multinational corporations and regional players, indicating opportunities for both established brands and new entrants in specialized niches. This in-depth report provides a comprehensive analysis of the global fire fighting material market, valued at approximately $15 billion in 2023 and projected to reach $22 billion by 2028, exhibiting a robust CAGR. The report delves into market segmentation, key trends, competitive landscape, and future growth prospects, offering invaluable insights for industry stakeholders. Keywords: Fire fighting foam, fire suppression systems, firefighting chemicals, fire retardants, wildland fire suppression, industrial fire protection, structural fire protection, fire safety equipment.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place. Related datasets representing components of risk across the entire landscape are available in a separate data publication (Scott et al. 2020, https://doi.org/10.2737/RDS-2020-0016). Likewise, transmitted risk to housing units from the source locations where damaging fires originate will be also be delivered in a separate publication.

Vegetation and wildland fuels data from LANDFIRE 2014 (version 1.4.0) form the foundation for wildfire hazard and risk data included in the Wildfire Risk to Communities datasets. As such, the data presented here reflect wildfire hazard from landscape conditions as of the end of 2014. National wildfire hazard datasets of annual burn probability and fire intensity were generated from the LANDFIRE 2014 data by the USDA Forest Service, Rocky Mountain Research Station (Short et al. 2020) using the large fire simulation system (FSim). These national datasets produced with FSim have a relatively coarse cell size of 270 meters (m). To bring these datasets down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30-m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability and intensity into developed areas represented in LANDFIRE fuels data as non-burnable. Additional methodology documentation is provided with the data publication download.

The data products in this publication that represent where people live reflect 2018 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Microsoft (version 1.1), LandScan 2018 where building footprint data were unavailable, USGS building coverage data, and land cover data from LANDFIRE.

The specific raster datasets included in this publication include:

Housing Unit Density (HUDen): HUDen is a nationwide raster of housing-unit density measured in housing units per square kilometer. The HUDen raster was generated using population and housing-unit count and data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate housing unit locations because Microsoft data were not available across the whole state.

Population Density (PopDen): PopDen is a nationwide raster of residential population density measured in persons per square kilometer. The PopDen raster was generated using population count data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. In Alaska, LandScan 2018 data were used to identify approximate population locations because Microsoft data were not available across the whole state.

Building Coverage (BuildingCover): BuildingCover is a raster of building density measured as the percent cover of buildings within an approximately 5 acre area around each pixel. It includes all buildings and can be used to complement the HUDen raster, which just reflects residential buildings. Building coverage was generated using building footprint data from Microsoft (v1.1), building coverage data from USGS, and land cover data from LANDFIRE. Building Coverage is not available in Alaska because source data were not available across the whole state.

Building Exposure Type (BuildingExposure): Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. The BuildingExposure layer delineates whether buildings at each pixel are directly exposed to wildfire from adjacent wildland vegetation (pixel value of 1), indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition (pixel values between 0 and 1), or not exposed to wildfire due to distance from direct and indirect ignition sources (pixel value of 0). It is similar to Exposure Type in the companion data publication, RDS-2020-0016, but just where HUDen > 0 or BuildingCover > 0. Pixels where both HUDen and BuildingCover rasters are zero are NoData in the BuildingExposure raster.

Housing Unit Exposure (HUExposure): HUExposure is the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year. It is calculated as the product of wildfire likelihood and housing unit count. Pixels where the HUDen raster is zero are NoData in the HUExposure raster.

Housing Unit Impact (HUImpact): HUImpact is an index that represents the relative potential impact of fire to housing units at any pixel, if a fire occurs there. It incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire. HUImpact does not include the likelihood of fire occurring, and it does not reflect mitigations done to individual structures that would influence susceptibility. It is conceptually similar to Conditional Risk to Potential Structures in the companion data publication, RDS-2020-0016, but also incorporates housing unit count and exposure type. Pixels where the HUDen raster is zero are NoData in the HUImpact raster.

Housing Unit Risk (HURisk): HURisk is an index that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density > 0. It is conceptually similar to Risk to Potential Structures (i.e., Risk to Homes) in the companion data publication, RDS-2020-0016, but also incorporates housing unit count. Pixels where the HUDen raster is zero are NoData in the HURisk raster.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. These data represent the first time wildfire risk to communities has been mapped nationally with consistent methodology. They provide foundational information for comparing the relative wildfire risk among populated communities in the United States.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information. The suite of seven raster layers included in this publication are downloadable as zip files by U.S. state. Population Density, Building Coverage, Housing Unit Density, Housing Unit Impact, and Housing Unit Risk are also downloadable as national datasets. National datasets of Housing Unit Exposure and Building Exposure Type are too large for download, but users can request them through the point of contact listed in this metadata document.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.

National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2020 estimates of housing units and 2021 estimates of population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.

The specific raster datasets included in this publication include:

Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.

Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).

Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.

Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.

Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).

Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.

Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).

Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.

Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.

Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.The geospatial data products described and distributed here are part of the Wildfire Risk to Communities project. This project was directed by Congress in the 2018 Consolidated Appropriations Act (i.e., 2018 Omnibus Act, H.R. 1625, Section 210: Wildfire Hazard Severity Mapping) to help U.S. communities understand components of their relative wildfire risk profile, the nature and effects of wildfire risk, and actions communities can take to mitigate risk. The first edition of these data represented the first time wildfire risk to communities had been mapped nationally with consistent methodology. They provided foundational information for comparing the relative wildfire risk among populated communities in the United States. In this version, the 2nd edition, we use improved modeling and mapping methodology and updated input data to generate the current suite of products.See the Wildfire Risk to Communities website at https://www.wildfirerisk.org for complete project information and an interactive web application for exploring some of the datasets published here. We deliver the data here as zip files by U.S. state (including AK and HI), and for the full extent of the continental U.S.

This data publication is a second edition and represents an update to any previous versions of Wildfire Risk to Communities risk datasets published by the USDA Forest Service. This second edition was originally published on 06/03/2024. On 09/10/2024, a minor correction was made to the abstract in this overall metadata document as well as the individual metadata documents associated with each raster dataset. The supplemental file containing data product descriptions was also updated. In addition, we separated the large CONUS download into a series of smaller zip files (one for each layer).

There are two companion data publications that are part of the WRC 2.0 data update: one that characterizes landscape-wide wildfire hazard and risk for the nation (Scott et al. 2024, https://doi.org/10.2737/RDS-2020-0016-2), and one that delineates wildfire risk reduction zones and provides tabular summaries of wildfire hazard and risk raster datasets (Dillon et al. 2024, https://doi.org/10.2737/RDS-2024-0030).

In order to have a better understanding of statistical distribution of firebrands' mass, size (projected area), and traveling distance, full-scale firebrand generation experiments were conducted. Full-scale structural components (fence, corner, and roof) and their assemblies were built from typical residential building construction materials. The samples were ignited and exposed to realistic gusty wind traces in a wind tunnel facility in the Insurance Institute for Business & Home Safety (IBHS) Research Center located in Richburg, South Carolina. Water pans were placed downwind to quench the flying firebrands immediately after landing. The distance between the center of the water pans in which the firebrands' landed and the front location of the burning sample was defined as the traveling distance. The firebrands were collected from the water pans and placed in an oven to reach zero percent moisture content. Dried firebrands were scattered on a white sheet. High-resolution pictures were captures of each sheet using a digital camera (Nikon D5600). Following that, an automated image processing algorithm using MATLAB was developed and employed to measure firebrand projected area. Using a digital balance (Sartorius H51, resolution of ±0.0001 gram), firebrand mass was measured. Experiments and raw data collection for this study were conducted from 2016-2017. The result was 50,571 firebrands collected and measured, with 24,149 from structural components and 26,422 from structural assemblies.The collected firebrands from previous firebrand production experiments using full-scale building components and their assemblies varied between 50 and 1000 firebrands. The sample size of this study is significantly larger than any existing firebrand data sets. This work was based on a statistics-based framework for the sampling and measurement processes in firebrand generation experiments so that the obtained firebrand data can achieve the desired level of statistical reliability. These firebrand data sets are useful in understanding the characteristics and distribution of firebrands generated from various structural fuels. They can be used for developing and training predictive models for the firebrand phenomenon (generation, transport, and ignition), models to predict fire spread in the wildland and wildland-urban interface, and models to estimate risks from wildfire. They are also useful for wildfire mitigation strategies or guidelines to minimize threat and damage from firebrand attacks.Original metadata was published on 05/20/2020. On 09/01/2021 the data embargo was lifted and the data for this publication became available.

In 2021, property loss in apartment structures due to fire amounted to about 1.73 billion U.S. dollars. A further 6.97 billion U.S. dollars of property damage from structure fires occurred in one- and two-family homes.

The global home fire sprinkler market, valued at $1275.9 million in 2025, is projected to experience robust growth, driven by increasing awareness of fire safety, stringent building codes mandating sprinkler systems in new constructions, and rising disposable incomes fueling home improvement projects. The 7.1% CAGR (Compound Annual Growth Rate) signifies a considerable expansion over the forecast period (2025-2033). Key market segments include residential and commercial building applications, with further categorization into wet, dry, preaction, and deluge systems. The residential segment is expected to dominate, fueled by increasing homeowner preferences for enhanced fire protection and insurance incentives. Technological advancements, such as the development of more efficient and cost-effective sprinkler systems, are also contributing to market growth. However, high initial installation costs and potential concerns regarding water damage act as restraints, though the long-term safety benefits often outweigh these concerns. North America and Europe currently hold significant market shares, benefiting from established building codes and a higher adoption rate of fire safety measures. However, Asia-Pacific is poised for substantial growth, driven by rapid urbanization and increasing construction activity in developing economies like India and China. Leading market players, including Watts Water Technologies, APi Group, and Tyco International, are focusing on strategic partnerships, product innovation, and geographic expansion to capitalize on this growth. The competitive landscape is characterized by both established players and emerging companies. Established players leverage their extensive distribution networks and brand recognition to maintain their market positions. Emerging companies are focusing on innovative product offerings and cost-effective solutions to gain market share. Future growth will depend on addressing the challenges associated with installation costs and promoting the long-term cost benefits of fire sprinkler systems. Government initiatives promoting fire safety, along with effective marketing strategies emphasizing the life-saving capabilities of home fire sprinklers, are crucial in accelerating market expansion globally. The market's future trajectory reflects a continuous trend towards prioritizing fire safety in residential and commercial settings.

Fire Perimeters were compiled from the CalFire FRAP database (https://frap.fire.ca.gov/mapping/gis-data/) and spatially joined with the administrative boundary of the five ecological provinces within the Pacific Southwest Region (Northern, Sierra Cascade, Central Sierra, Southern Sierra, Southern California). To create a feature layer which could be used in a dashboard setting, the fires were also intersected with the entire region. This means that there are multiple fire boundaries in this datasets for each fire (one for each province where the fire burned, and one that represents the region). Version InformationFirep22_1 was released in April 2023. Three hundred five fires from the 2022 fire season were added to the database (1 from BIA, 8 from BLM, 176 from CAL FIRE, 49 from Contract Counties, 14 from LRA, 8 from NPS, 38 from USFS, and 11 from USFW). The 2021 Dotta (part of Beckwourth Complex), Greenhorn, and Hartman fire perimeters were added. Another 45 fires were added by USFW from 2015-2021. The 1988 Hessel fire was added in LNU. The 2019 Cave fire was replaced with a more detailed perimeter submitted by Santa Barbara County. The 2017 Hudson, 2017 Lake, 2017 Jones, 2017 "37", 2019 Tucker, and 2019 Refuge perimeters were replaced with imagery digitized perimeters from USFW. Attributes were updated for 32 records. One hundred ten perimeters were removed due to duplication or being completely contained outside of California state borders. The field IRWINID was added to provide a unique ID; fires before 2022 are lacking this attribution (with the exception of those added in this publication where possible). The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: 2022 Cable (CAL FIRE, AEU), 2022 All American (BIA, CRA).If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a comprehensive fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres. _Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage ~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Redbook Fire data criteria1979 - Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981 - 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992 - 1981 criteria plus 1500 acres ag products, or destroys three residence or one commercial structure or does $300,000 damage1993 - 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2008 - simply 300 acres and larger

The global residential fire extinguisher market is experiencing steady growth, with a market size of $653.4 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 5.0% from 2025 to 2033. This growth is driven by increasing awareness of fire safety, stringent building codes mandating fire safety equipment in residential properties, and rising disposable incomes in developing economies leading to higher adoption rates. The market is segmented by extinguisher size (less than 5kg, 5kg-10kg, more than 10kg) and application location (kitchen, courtyard, other). The kitchen segment currently dominates due to the higher risk of cooking-related fires. However, growing awareness of fire safety in other areas like courtyards and garages is fueling growth in these segments. Key players like UTC, Tyco Fire Protection, Minimax, and Amerex are driving innovation through the introduction of technologically advanced extinguishers with enhanced features like improved ease of use and longer lifespan. Competitive pricing strategies and strategic partnerships are also impacting market dynamics. Geographic expansion, particularly in rapidly developing regions like Asia-Pacific, presents a significant opportunity for market players. While regulatory changes and economic fluctuations pose potential restraints, the overall market outlook remains positive, driven by sustained demand and product innovation. Growth is expected to be driven by factors such as increasing urbanization, rising construction activity globally, and growing awareness of fire safety regulations, especially in emerging economies. Furthermore, advancements in extinguisher technology, leading to lighter, more user-friendly, and environmentally friendly products, are expected to boost demand. However, factors such as high initial investment costs and the presence of substitute products might restrain market growth to some extent. Nevertheless, the long-term outlook for the residential fire extinguisher market remains positive, propelled by a sustained focus on fire safety and the increasing prevalence of residential buildings globally. The market is witnessing a shift towards technologically advanced extinguishers with features like automatic fire suppression systems, integrated smoke detectors, and remote monitoring capabilities, further fueling the market's growth trajectory.

Version InformationFirep22_1 was released in April 2023. Three hundred five fires from the 2022 fire season were added to the database (1 from BIA, 8 from BLM, 176 from CAL FIRE, 49 from Contract Counties, 14 from LRA, 8 from NPS, 38 from USFS, and 11 from USFW). The 2021 Dotta (part of Beckwourth Complex), Greenhorn, and Hartman fire perimeters were added. Another 45 fires were added by USFW from 2015-2021. The 1988 Hessel fire was added in LNU. The 2019 Cave fire was replaced with a more detailed perimeter submitted by Santa Barbara County. The 2017 Hudson, 2017 Lake, 2017 Jones, 2017 "37", 2019 Tucker, and 2019 Refuge perimeters were replaced with imagery digitized perimeters from USFW. Attributes were updated for 32 records. One hundred ten perimeters were removed due to duplication or being completely contained outside of California state borders. The field IRWINID was added to provide a unique ID; fires before 2022 are lacking this attribution (with the exception of those added in this publication where possible). The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: 2022 Cable (CAL FIRE, AEU), 2022 All American (BIA, CRA).If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a comprehensive fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres. _Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage ~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Redbook Fire data criteria1979 - Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981 - 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992 - 1981 criteria plus 1500 acres ag products, or destroys three residence or one commercial structure or does $300,000 damage1993 - 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2008 - simply 300 acres and larger--------------------------------Year and Number of missing Large Damaging Fires for that yearYear# of Missing “Redbook” Fires19792219801319811519826198331984201985521986121987561988231989819909199121992161993171994221995919961519979199810199972000420015200216200352004220051200611200732008432009320102201102012420132201472015102016220171120186Total483---------------------------------Enumeration of fires in the Redbook that are missing from Fire Perimeter data. Three letter unit code follows fire name.1979-Sylvandale (HUU), Kiefer (AEU), Taylor(TUU), Parker#2(TCU), PGE#10, Crocker(SLU), Silver Spur (SLU), Parkhill (SLU), Tar Springs #2 (SLU), Langdon (SCU), Truelson (RRU), Bautista (RRU), Crocker (SLU), Spanish Ranch (SLU), Parkhill (SLU), Oak Springs(BDU), Ruddell (BDF), Santa Ana (BDU), Asst. #61 (MVU), Bernardo (MVU), Otay #20 1980– Lightning series (SKU), Lavida (RRU), Mission Creek (RRU), Horse (RRU), Providence (RRU), Almond (BDU), Dam (BDU), Jones (BDU), Sycamore (BDU), Lightning (MVU), Assist 73, 85, 138 (MVU)1981– Basalt (LNU), Lightning #25(LMU), Likely (MNF), USFS#5 (SNF), Round Valley (TUU), St. Elmo (KRN), Buchanan (TCU), Murietta (RRU), Goetz (RRU), Morongo #29 (RRU), Rancho (RRU), Euclid (BDU), Oat Mt. (LAC & VNC), Outside Origin #1 (MVU), Moreno (MVU)1982- Duzen (SRF), Rave (LMU), Sheep’s trail (KRN), Jury (KRN), Village (RRU), Yuma (BDF)1983- Lightning #4 (FKU), Kern Co. #13, #18 (KRN)1984-Bidwell (BTU), BLM D 284,337, PNF #115, Mill Creek (TGU), China hat (MMU), fey ranch, Kern Co #10, 25,26,27, Woodrow (KRN), Salt springs, Quartz (TCU), Bonanza (BEU), Pasquel (SBC), Orco asst. (ORC), Canel (local), Rattlesnake (BDF)1985- Hidden Valley, Magic (LNU), Bald Mt. (LNU), Iron Peak (MEU), Murrer (LMU), Rock Creek (BTU), USFS #29, 33, Bluenose, Amador, 8 mile (AEU), Backbone, Panoche, Los Gatos series, Panoche (FKU), Stan #7, Falls #2 (MMU), USFS #5 (TUU), Grizzley, Gann (TCU), Bumb, Piney Creek, HUNTER LIGGETT ASST#2, Pine, Lowes, Seco, Gorda-rat, Cherry (BEU), Las pilitas, Hwy 58 #2 (SLO), Lexington, Finley (SCU), Onions, Owens (BDU), Cabazon, Gavalin, Orco, Skinner, Shell, Pala (RRU), South Mt., Wheeler, Black Mt., Ferndale, (VNC), Archibald, Parsons, Pioneer (BDU), Decker, Gleason(LAC), Gopher, Roblar, Assist #38 (MVU)1986– Knopki (SRF), USFS #10 (NEU), Galvin (RRU), Powerline (RRU), Scout, Inscription (BDU), Intake (BDF), Assist #42 (MVU), Lightning series (FKU), Yosemite #1 (YNP), USFS Asst. (BEU), Dutch Kern #30 (KRN)1987- Peach (RRU), Ave 32 (TUU), Conover (RRU), Eagle #1 (LNU), State 767 aka Bull (RRU), Denny (TUU), Dog Bar (NEU), Crank (LMU), White Deer (FKU), Briceburg (LMU), Post (RRU), Antelope (RRU), Cougar-I (SKU), Pilitas (SLU) Freaner (SHU), Fouts Complex (LNU), Slides (TGU), French (BTU), Clark (PNF), Fay/Top (SQF), Under, Flume, Bear Wallow, Gulch, Bear-1, Trinity, Jessie, friendly, Cold, Tule, Strause, China/Chance, Bear, Backbone, Doe, (SHF) Travis Complex, Blake, Longwood (SRF), River-II, Jarrell, Stanislaus Complex 14k (STF), Big, Palmer, Indian (TNF) Branham (BLM), Paul, Snag (NPS), Sycamore, Trail, Stallion Spring, Middle (KRN), SLU-864 1988- Hwy 175 (LNU), Rumsey (LNU), Shell Creek (MEU), PG&E #19 (LNU), Fields (BTU), BLM 4516, 417 (LMU), Campbell (LNF), Burney (SHF), USFS #41 (SHF), Trinity (USFS #32), State #837 (RRU), State (RRU), State (350 acres), RRU), State #1807, Orange Co. Asst (RRU), State #1825 (RRU), State #2025, Spoor (BDU), State (MVU), Tonzi (AEU), Kern co #7,9 (KRN), Stent (TCU), 1989– Rock (Plumas), Feather (LMU), Olivas (BDU), State 1116 (RRU), Concorida (RRU), Prado (RRU), Black Mt. (MVU), Vail (CNF)1990– Shipman (HUU), Lightning 379 (LMU), Mud, Dye (TGU), State 914 (RRU), Shultz (Yorba) (BDU), Bingo Rincon #3 (MVU), Dehesa #2 (MVU), SLU 1626 (SLU)1991- Church (HUU), Kutras (SHF) 1992– Lincoln, Fawn (NEU), Clover, fountain (SHU), state, state 891, state, state (RRU), Aberdeen (BDU), Wildcat, Rincon (MVU), Cleveland (AEU), Dry Creek (MMU), Arroyo Seco, Slick Rock (BEU), STF #135 (TCU)1993– Hoisington (HUU), PG&E #27 (with an undetermined cause, lol), Hall (TGU), state, assist, local (RRU), Stoddard, Opal Mt., Mill Creek (BDU), Otay #18, Assist/ Old coach (MVU), Eagle (CNF), Chevron USA, Sycamore (FKU), Guerrero, Duck1994– Schindel Escape (SHU), blank (PNF), lightning #58 (LMU), Bridge (NEU), Barkley (BTU), Lightning #66 (LMU), Local (RRU), Assist #22 & #79 (SLU), Branch (SLO), Piute (BDU), Assist/ Opal#2 (BDU), Local, State, State (RRU), Gilman fire 7/24 (RRU), Highway #74 (RRU), San Felipe, Assist #42, Scissors #2 (MVU), Assist/ Opal#2 (BDU), Complex (BDF), Spanish (SBC)1995-State 1983 acres, Lost Lake, State # 1030, State (1335 acres), State (5000 acres), Jenny, City (BDU), Marron #4, Asist #51 (SLO/VNC)1996- Modoc NF 707 (Ambrose), Borrego (MVU), Assist #16 (SLU), Deep Creek (BDU), Weber (BDU), State (Wesley) 500 acres (RRU), Weaver (MMU), Wasioja (SBC/LPF), Gale (FKU), FKU 15832 (FKU), State (Wesley) 500 acres, Cabazon (RRU), State Assist (aka Bee) (RRU), Borrego, Otay #269 (MVU), Slaughter house (MVU), Oak Flat (TUU)1997- Lightning #70 (LMU), Jackrabbit (RRU), Fernandez (TUU), Assist 84 (Military AFV) (SLU), Metz #4 (BEU), Copperhead (BEU), Millstream, Correia (MMU), Fernandez (TUU)1998- Worden, Swift, PG&E 39 (MMU), Chariot, Featherstone, Wildcat, Emery, Deluz (MVU), Cajalco Santiago (RRU)1999- Musty #2,3 (BTU), Border # 95 (MVU), Andrews, Roadside 9323 (MMU), Lacy (BDU), Range (SCU)2000- Latrobe (AEU), Shell (SLU), Happy Camp (Inyo), Golden Fire (BDU)2001- Pacheco (MMU), Orosco (CNF/MVU), Observation (LNF), Modoc Complex (LMU), Happy Camp Complex (SKU)2002- Nicholas (MMU), Aliso Assist #73 (MVU), Assist, Leona, Williams (BDU), BLM D596, horse complex (LMU), KNF Assist #15 (SKU), Cajalco Evening State 925 (RRU), Airport, Bouquet, Copper, Inyo Complex (BDU)2003- F.K.U. 7076 (LOC) 15k, Local (2) 12k 2k (RRU), MNF 964 Assist (LNU) 3+k2004- F.K.U. 7654, NOD BBT42005- Pine

The global house fire insurance market is anticipated to grow at a CAGR of XX% during the forecast period of 2025-2033. This growth is attributed to a surge in the number of residential fires, rising awareness about the importance of fire insurance, and increasing disposable income. In 2025, the market size was valued at XXX million. North America and Europe are expected to remain the dominant regions in the house fire insurance market due to their high levels of urbanization and homeownership. Key drivers for the growth of the house fire insurance market include increasing awareness about the importance of fire insurance, rising disposable income, and the growing number of residential fires. The rising frequency and severity of natural disasters, such as wildfires, is also contributing to the demand for house fire insurance. Moreover, the increasing number of home renovations and upgrades is further driving the demand for house fire insurance. This comprehensive report provides an in-depth analysis of the global house fire insurance market, highlighting key trends, drivers, challenges, and opportunities.

This map feeds into a web app that allows a user to examine the known status of structures damaged by the wildfire. If a structure point does not appear on the map it may still have been impacted by the fire. Specific addresses can be searched for in the search bar. Use the imagery and topographic basemaps and photos to positively identify a structure. Photos may only be available for damaged and destroyed structures.For more information about the wildfire response efforts, visit the CAL FIRE incident page.