In the United States, homeowner insurance claims due to lightning losses made insurance companies pay out a total of approximately ******* million U.S. dollars in 2024. In 2020, the insurance claims due to lightning losses amounted to over *** billion U.S. dollars.

Since 2013, the total amount of net property insurance claims paid in the United Kingdom (UK) has increased overall, exceeding 20 billion British pounds in 2023. During that year, most of the money paid to the claims with the underwriting year 2022, amounting to almost nine billion British pounds.

Sweden Non Life Insurnace: Claims Paid: Householders and Homeowners Insurance data was reported at 4,450.321 SEK mn in Jun 2018. This records an increase from the previous number of 2,185.190 SEK mn for Mar 2018. Sweden Non Life Insurnace: Claims Paid: Householders and Homeowners Insurance data is updated quarterly, averaging 4,555.500 SEK mn from Dec 1996 (Median) to Jun 2018, with 74 observations. The data reached an all-time high of 10,238.553 SEK mn in Dec 2014 and a record low of 1,396.000 SEK mn in Mar 2001. Sweden Non Life Insurnace: Claims Paid: Householders and Homeowners Insurance data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.RG006: Non Life Insurance: Claims Paid.

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Homeowners Insurance Market Size 2024-2028

The homeowners insurance market size is forecast to increase by USD 65.9 billion at a CAGR of 4.6% between 2023 and 2028.

The market is experiencing significant growth due to several key factors. The increasing number of natural disasters and man-made hazards has led to a higher demand for comprehensive insurance coverage. New technological developments In the home insurance industry, such as the use of drones for property inspections and smart home systems for risk mitigation, are transforming the market. Additionally, the vulnerability to cybercrimes, including identity theft and hacking, is driving insurers to offer cyber insurance policies as part of their homeowners packages. These trends are shaping the future of the market and are expected to continue influencing its growth.

What will be the Size of the Homeowners Insurance Market During the Forecast Period?

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The market is a significant segment of the global casualty insurance sector, providing financial protection for homeowners against various risks. Homeowners, as key asset holders, rely on insurance companies to safeguard their financial security against potential losses from incidents such as natural disasters, theft, and property damage. Insurers employ advanced risk assessment tools to evaluate and price policies based on factors like location, property values, and historical claims data. Recent market trends include increasing concerns over catastrophic risks, driven by both natural disasters and pandemic-related losses. The low-interest-rate environment has also influenced the market, affecting loss reserves and policyholder surplus.
Moreover, insurance companies continue to navigate the challenges posed by financial market losses and the legal responsibility to policyholders for covered damages. Asset prices and loss reserves remain crucial indicators of market stability, with property insurance and household/private property insurance being the primary types of coverage sought by homeowners.

How is this Homeowners Insurance Industry segmented and which is the largest segment?

The homeowners insurance industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Fire and theft
  House damage
  Floods and earthquake
  Others


Source

  Captive
  Independent agent
  Direct response


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Type Insights

The fire and theft segment is estimated to witness significant growth during the forecast period.

The market growth is driven by the increasing prevalence of natural disasters and theft incidents, leading homeowners to seek additional coverage beyond standard property insurance policies. Fire insurance, a significant segment, protects against losses caused by fire, with many homeowners opting for additional coverage to offset costs exceeding their base policy limits. Policies exclude certain perils, such as war and nuclear risks. Theft insurance, another essential component, safeguards against financial losses resulting from theft or vandalism. Advanced risk assessment tools enable insurance firms to customize policies based on clients' risk profiles and underwriting guidelines, offering premium payment flexibility and virtual interactions.

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The fire and theft segment was valued at USD 80.90 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 40% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American market will experience steady growth due to the high frequency of natural disasters, leading to an increased demand for reinsurance policies. Catastrophic events, such as hurricanes, tornados, and tsunamis, can cause significant damage to residential properties, resulting in substantial insurance claims. Reinsurers provide capital to primary insurers when the number of claims is high, ensuring financial security for policyholders. Despite the challenges, reinsurance firms have managed to maintain consistent revenue streams. Property values, homeowners, assets, and liability coverage are integral components of homeowners insurance policies. Insurance providers offer customized policies for various risks, including natural disasters, theft, an

Graph and download economic data for Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Homeowner's Insurance (PCU9241269241262) from Jun 1998 to Sep 2025 about property-casualty, premium, insurance, housing, PPI, industry, inflation, price index, indexes, price, and USA.

In 2022, the net claims incurred by the personal property insurance sector in Canada amounted to nearly *** billion Canadian dollars. This is around *** billion Canadian dollars higher than the previous year,and over four times higher than the value recorded in 1990.

Recent three-year property insurance market accident insurance claims rate statistics - (annual system) (Insurance Bureau)

Dataset Description: Insurance Claims Prediction

Introduction: In the insurance industry, accurately predicting the likelihood of claims is essential for risk assessment and policy pricing. However, insurance claims datasets frequently suffer from class imbalance, where the number of non-claims instances far exceeds that of actual claims. This class imbalance poses challenges for predictive modeling, often leading to biased models favoring the majority class, resulting in subpar performance for the minority class, which is typically of greater interest.

Dataset Overview: The dataset utilized in this project comprises historical data on insurance claims, encompassing a variety of information about the policyholders, their demographics, past claim history, and other pertinent features. The dataset is structured to facilitate predictive modeling tasks aimed at accurately identifying the likelihood of future insurance claims.

Key Features: 1. Policyholder Information: This includes demographic details such as age, gender, occupation, marital status, and geographical location. 2. Claim History: Information regarding past insurance claims, including claim amounts, types of claims (e.g., medical, automobile), frequency of claims, and claim durations. 3. Policy Details: Details about the insurance policies held by the policyholders, such as coverage type, policy duration, premium amount, and deductibles. 4. Risk Factors: Variables indicating potential risk factors associated with policyholders, such as credit score, driving record (for automobile insurance), health status (for medical insurance), and property characteristics (for home insurance). 5. External Factors: Factors external to the policyholders that may influence claim likelihood, such as economic indicators, weather conditions, and regulatory changes.

Objective: The primary objective of utilizing this dataset is to develop robust predictive models capable of accurately assessing the likelihood of insurance claims. By leveraging advanced machine learning techniques, such as classification algorithms and ensemble methods, the aim is to mitigate the effects of class imbalance and produce models that demonstrate high predictive performance across both majority and minority classes.

Application Areas: 1. Risk Assessment: Assessing the risk associated with insuring a particular policyholder based on their characteristics and historical claim behavior. 2. Policy Pricing: Determining appropriate premium amounts for insurance policies by estimating the expected claim frequency and severity. 3. Fraud Detection: Identifying fraudulent insurance claims by detecting anomalous patterns in claim submissions and policyholder behavior. 4. Customer Segmentation: Segmenting policyholders into distinct groups based on their risk profiles and insurance needs to tailor marketing strategies and policy offerings.

Conclusion: The insurance claims dataset serves as a valuable resource for developing predictive models aimed at enhancing risk management, policy pricing, and overall operational efficiency within the insurance industry. By addressing the challenges posed by class imbalance and leveraging the rich array of features available, organizations can gain valuable insights into insurance claim likelihood and make informed decisions to mitigate risk and optimize business outcomes.

Description

The dataset, named "insurance_claims.csv", is a comprehensive collection of insurance claim records. Each row represents an individual claim, and the columns represent various features associated with that claim.

The dataset is, highlighting features like 'months_as_customer', 'age', policy_number, ...etc. The main focus is the 'fraud_reported' variable, which indicates claim legitimacy.

Claims data were sourced from various insurance providers, encompassing a diverse array of insurance types including vehicular, property, and personal injury. Each claim's record provides an in-depth look into the individual's background, claim specifics, associated documentation, and feedback from insurance professionals.

The dataset further includes specific indicators and parameters that were considered during the claim's assessment, offering a granular look into the complexities of each claim.

For privacy reasons, and in agreement with the participating insurance providers, certain personal details and specific identifiers have been anonymized. Instead of names or direct identifiers, each entry is associated with a unique ID, ensuring data privacy while retaining data integrity.

The insurance claims were subjected to rigorous examination, encompassing both manual assessments and automated checks. The end result of this examination, specifically whether a claim was deemed fraudulent or not, is clearly indicated for each record.

Citation

AQQAD, ABDELRAHIM (2023), “insurance_claims ”, Mendeley Data, V2, doi: 10.17632/992mh7dk9y.2

Acknowledgement

Foto von Scott Graham auf Unsplash

The recent two years' statistics of property and casualty insurance claims rates - (annual) - by company (insurance development center)

United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data was reported at 93.700 % in 2021. This records a decrease from the previous number of 100.400 % for 2020. United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data is updated yearly, averaging 97.600 % from Dec 2009 (Median) to 2021, with 13 observations. The data reached an all-time high of 103.400 % in 2016 and a record low of 88.100 % in 2014. United States Property & Casualty Insurance: Combined Ratio: Other Liability - Claims-Made data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG013: Property & Casualty Insurance: Combined Ratio by Lines of Business.

Home insurance, often a cornerstone of financial stability for families, saw significant shifts in 2024. Rising natural disasters, an unpredictable economic landscape, and evolving demographics are changing the way insurers approach policies, coverage, and premiums. New trends emerged, highlighting the importance of affordable yet comprehensive coverage for homeowners. This article...

Property Insurance Complaints Statistics (Insurance Industry Development Center)

Kazakhstan Insurance Market: Year to Date: Claims Payments: Voluntary Property Insurance data was reported at 6,983.377 KZT mn in Jun 2018. This records an increase from the previous number of 6,240.388 KZT mn for May 2018. Kazakhstan Insurance Market: Year to Date: Claims Payments: Voluntary Property Insurance data is updated monthly, averaging 2,961.755 KZT mn from Jan 2002 (Median) to Jun 2018, with 198 observations. The data reached an all-time high of 39,536.381 KZT mn in Dec 2007 and a record low of 54.000 KZT mn in Jan 2002. Kazakhstan Insurance Market: Year to Date: Claims Payments: Voluntary Property Insurance data remains active status in CEIC and is reported by Committee for Control and Supervision of the Financial Market and Financial Organizations. The data is categorized under Global Database’s Kazakhstan – Table KZ.Z009: Insurance Market Statistics.

Forecast: Property Insurance Claims in Germany 2024 - 2028 Discover more data with ReportLinker!

Recent three years of property insurance market statistics for motor vehicle insurance claims ratio - (annual)(CIRC)

Germany Property & Casualty Insurance (P&C): Number of Claims data was reported at 23,885.000 Unit th in 2023. This records an increase from the previous number of 22,676.000 Unit th for 2022. Germany Property & Casualty Insurance (P&C): Number of Claims data is updated yearly, averaging 22,720.000 Unit th from Dec 1985 (Median) to 2023, with 39 observations. The data reached an all-time high of 25,255.000 Unit th in 2002 and a record low of 18,552.000 Unit th in 1985. Germany Property & Casualty Insurance (P&C): Number of Claims data remains active status in CEIC and is reported by German Insurance Association. The data is categorized under Global Database’s Germany – Table DE.RG014: Non Life Insurance: Property & Casualty: Number of Claims.

Home insurance rates in France will continue to rise due to an estimated increase in claims of *** percent to * percent in 2020. The lockdown linked to the COVID-19 had a moderate and favorable impact on claims, resulting in a drop in the frequency of theft and fire, respectively ** percent and * percent. On the other hand, the climactic events of the first quarter, with notably heavy rainfall, have strongly deteriorated the claims experience for hail and snowstorms as well as for water damage, which accounted for the main cause of claims.

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 Aug 2025 about property-casualty, premium, insurance, vehicles, commercial, PPI, industry, inflation, price index, indexes, price, and USA.

The recent three-year property insurance market aviation insurance claims rate statistics - (underwriting year system) (Insurance Development Center)