Homeowners insurance premiums in the United States have increased in cost over the past two decades and reached a peak in 2024. The average premiums for homeowners insurance increased from ***** U.S. dollars in 2007 to ***** U.S. dollars in 2024.

Graph and download economic data for Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Primary Services (PCU924126924126P) from Jun 1998 to Jun 2025 about property-casualty, premium, primary, insurance, services, PPI, industry, inflation, price index, indexes, price, and USA.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 2023, Corsica (Corse) was the most expensive French region for house insurance. In that year, the average annual cost of a home insurance premium to insure a house in the Île-de-France region was around *** euros, compared to *** euros for an apartment. Meanwhile, it cost *** euros for a house and *** euros for an apartment on Corsica.

This statistic shows the value of home insurance rate in the United States in 2018, by value of house coverage amount. In that year, the homeowner insurance rate for houses worth ******* U.S. dollars amounted to ***** U.S. dollars.

Graph and download economic data for Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Commercial Auto Insurance (PCU9241269241263) from Jun 1998 to May 2025 about property-casualty, premium, insurance, vehicles, commercial, PPI, industry, inflation, price index, indexes, price, and USA.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Tenants' and Household Insurance in U.S. City Average (CUUR0000SEHD) from Dec 1997 to Jun 2025 about insurance, urban, households, consumer, CPI, inflation, price index, indexes, price, and USA.

Homeowner peril insurance premiums in the United States peaked in 2023 after increasing by 14.5 billion U.S. dollars since 2022. Net premiums written in 2023 amounted to approximately 129.65 billion U.S. dollars, up from 115.2 billion U.S. dollars in the previous year.

Between 2014 and 2023, the value of the average premium for comprehensive home insurance contracts increased in France. In 2014, the average multi-risk home insurance premium amounted to *** euros, compared with *** euros nine years later. During this period, the average premium for comprehensive home insurance increased by about four euros per year.

Homeowners' insurance protects households against property damage from natural causes or theft and liability from inflicting bodily injury or property damage on others. Demand for homeowners' insurance is typically stable regardless of fluctuations in macroeconomic factors. Homeowners' insurance is typically considered a requisite expense for protection against the inherent risks of homeownership. To this end, well over three-quarters of all US households have homeowners' insurance. Although, the industry's supply of homeowners' insurance and underwriting results vary significantly by geographic region because of differences in local claims costs, profitability and competitive market conditions. Over the past five years, revenue has been growing at a CAGR of 2.3% to $144.0 billion, including an expected 0.8% increase in 2024 alone. Profit is also expected to climb to 12.6% of revenue in 2024 from 12.5% in 2019. An increase in housing starts over the past five years, combined with a rising number of households, has increased demand for homeowners' insurance and boosted revenue from premiums. Also, revenue growth has been accelerated by elevated fixed-income yields in the latter part of the period, specifically in yields of the 10-year Treasury note. However, in 2024 the Federal Reserve cut interest rates and is anticipated to cut rates further. Over the next five years, declines in the homeownership rate will limit insurance premiums. Also, investment income will be pressured because of falling fixed-income yields. To service greater demand for homeowners' insurance, new enterprises are expected to enter the industry. Due to new entrants and the expansion of incumbents, industry participation and employment are projected to swell. The rising frequency and intensity of natural disasters will continue to limit growth in profitability because of increases in claims payments. Overall, revenue is forecast to grow at a CAGR of 1.9% to $158.1 billion over the five years to 2029.

Version 2: I added a CSV with the state rows stripped out and added to each county so counties can be disambiguated. The code for this operation is included.

"This new report is the culmination of an investigation the Senate Budget Committee launched last fall. Over the course of the probe, the Committee obtained national county-level non-renewal data from nearly two dozen companies that collectively account for about 65 percent of the national homeowners’ insurance market. The data cover 249 million total insurance policies over the six-year period beginning in 2018 and ending in 2023. While sky-rocketing insurance premiums have been well documented, comprehensive non-renewal data of this scope and magnitude has never been publicly available—until now."

From the report: "The Committee ultimately obtained national, county-level non-renewal data from 23 of the 41 companies from which it requested this data. The data cover the years 2018 through 2023, and the companies responding collectively account for approximately 65 percent of the homeowners’ insurance market nationwide. The data released with this Report demonstrate climate change beginning to upend insurance markets around the country."

I am uploading this data here in February 2025 out of an abundance of caution given that climate change related data is currently being scrubbed from U.S. federal websites. In addition, I created a CSV of the data that is in a more usable format, in my opinion. The CSV adds a column for the year instead of separating data on different worksheet tabs, making longitudinal analysis more difficult.

Note that the formatting of the data is still terrible. County data is duplicated in state totals. "AK" is not a U.S. county, it is the state of Alaska, and it is the sum of the next several rows, which are for its counties. If you sum a column, you receive double the "grand total" listed at the bottom due to this duplication. Without states to disambiguoate, counties/districts are not uniquely identified and there are many duplicate county names. One should be able to move states/territories out and use them to disambiguate counties.

To the best of my knowledge, this dataset is in the public domain, as are most U.S. federal government publications.

Global commercial property insurance prices rose by *** percent in the fourth quarter of 2023. Prices have been steadily climbing since the second quarter of 2018, until the second quarter of 2024 when prices did not increase. Upon reaching the third quarter of 2024, prices decreased by *** percent.

Homeowners insurance premiums vary significantly among U.S. states. In 2018, the residents of the state of Louisiana had to pay on average ***** U.S. dollars for homeowner insurance - the highest of any U.S. state in that year.

Total insurance premium income statistics for health insurance in the property and casualty insurance market in the last five years (Insurance Regulatory and Development Authority of India)

Statista estimates that the overall average cost of online premiums in the property insurance industry in Germany will increase by ** euros between 2019 and 2024, reaching *** euros in 2024. In 2019, the B2C property insurance market was worth over ** billion euros.

United States - Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Non-Auto Liability Insurance was 133.35300 Index Jun 1998=100 in June of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Non-Auto Liability Insurance reached a record high of 133.35300 in June of 2025 and a record low of 99.70000 in November of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Non-Auto Liability Insurance - last updated from the United States Federal Reserve on July of 2025.

United States Property & Casualty Insurance: Premium Pricing: Rate Changes by Line of Business: Commercial Property data was reported at 6.000 % in Dec 2024. This records a decrease from the previous number of 7.900 % for Sep 2024. United States Property & Casualty Insurance: Premium Pricing: Rate Changes by Line of Business: Commercial Property data is updated quarterly, averaging 5.900 % from Dec 2011 (Median) to Dec 2024, with 53 observations. The data reached an all-time high of 20.400 % in Mar 2023 and a record low of -6.000 % in Jun 2016. United States Property & Casualty Insurance: Premium Pricing: Rate Changes by Line of Business: Commercial Property data remains active status in CEIC and is reported by The Council of Insurance Agents & Brokers. The data is categorized under Global Database’s United States – Table US.RG015: Property & Casualty Insurance: Premium Pricing: Quarter on Quarter Rate Changes.

In 2020, the United Kingdom (UK) was the largest property insurance market with over 25 billion euros in insurance premiums. Germany and France completed the top three and together with the UK made up close to 60 percent of them European property insurance market.

Dataset contains information on premiums written, losses paid and taxes paid for property casualty insurance companies licensed to operate in the State of Iowa by year and line of insurance. The data listed in this table comes from amounts reflected in each company's annual statement. The taxes paid column may not represent amounts actually collected by the State of Iowa.

Insurance Premium: Year to Date: Property: Hebei data was reported at 17,404.000 RMB mn in Mar 2025. This records an increase from the previous number of 10,114.000 RMB mn for Feb 2025. Insurance Premium: Year to Date: Property: Hebei data is updated monthly, averaging 15,801.007 RMB mn from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 64,961.000 RMB mn in Dec 2024 and a record low of 563.021 RMB mn in Jan 2006. Insurance Premium: Year to Date: Property: Hebei data remains active status in CEIC and is reported by National Financial Regulatory Administration. The data is categorized under Global Database’s China – Table CN.RGD: Insurance Premium: Monthly Summary by Region: Property Insurance.