Property Insurance Research Articles (Page 1)

As China enters an advanced stage of population aging, changes in demographic structure present new challenges and opportunities for the insurance industry. Building on a systematic review of literature concerning insurers participation in the eldercare industry, this paper uses city-level panel data to empirically test the effect of the degree of aging on insurance expenditures. The results indicate that population aging significantly drives growth in insurance demand, with a particularly pronounced effect on personal insurance products, while property insurance is relatively less affected. This finding suggests that insurers need to place greater emphasis on the development and innovation of eldercare-related products when responding to population aging. At the same time, diversified investment and service modelssuch as eldercare real estate and community- and home-based careoffer new avenues for insurer development. This study not only enriches the empirical evidence in the field of eldercare finance but also provides a reference for insurers strategic planning and policy formulation in the context of population aging.

The article is devoted to the issues of identifying and analyzing financial risks inherent in insurance companies of the People's Republic of China, as well as methods of managing them. The article provides a general characteristic of the insurance market of China, reveals the specifics of the activities of Chinese insurance companies, analyzes the most specific financial risks for them. On the example of one of the major insurance companies of China — Sunshine P&C (Sunshine Property And Casualty Insurance Company Limited), it was revealed that the most obvious financial risks are credit risk, investment risk and operational risk. The article also identifies the factors that affect the level of financial risk and proposes a number of measures to strengthen financial risk management, as well as makes suggestions for improving the financial risk management system in the company under study.

ABSTRACT Accounting estimates are crucial in financial reports, especially in the insurance industry, where actuaries’ estimates of future claims reserves directly affect corporate financial transparency and operational decisions. Existing research has revealed the uncertainty and subjectivity of traditional accounting estimation methods, and has recognized the positive role of machine learning, but pure machine learning faces the challenge of insufficient interpretability. Based on financial data of Chinese property insurance companies from 2014 to 2022, this paper selects five major insurance types, adopts three machine learning algorithms: random forest, gradient boosting machine, and NGBoost to predict future losses, and compares them with actuary predictions. The results show that the machine learning models are superior to actuary predictions in terms of indicators such as mean absolute error (MAE) and root mean square error (RMSE). After incorporating actuary prediction data into the model, accuracy and reliability are significantly improved. On this basis, this paper analyzes the interpretability of the optimal machine learning model through feature importance ranking, verifies the value of actuary predictions, and provides a feasible path for actuaries to transform into algorithm supervisors and auditors.

Substantial increases in homeowners' insurance costs since the late 2010s have raised homeowners' housing costs. Spreading reports about similar insurance cost increases for apartment buildings may prompt similar questions about the implications for renters' costs.

Amid accelerating climate change, climate-related hazards—such as floods, wildfires, hurricanes, and sea-level rise—increasingly drive land transformations and pose growing risks to housing markets by affecting property valuations, insurance availability, mortgage performance, and broader financial stability. This review synthesizes recent progress in two distinct domains and their linkage: (1) assessing climate-related financial risks in housing markets, and (2) applying AI-driven remote sensing for hazard detection and land transformation monitoring. While both areas have advanced significantly, important limitations remain. Existing housing finance studies often rely on static models and coarse spatial data, lacking integration with real-time environmental information, thereby reducing their predictive power and policy relevance. In parallel, remote sensing studies using AI primarily focus on detecting physical hazards and land surface changes, yet rarely connect these spatial transformations to financial outcomes. To address these gaps, this review proposes an integrative framework that combines AI-enhanced remote sensing technologies with financial econometric modeling to improve the accuracy, timeliness, and policy relevance of climate-related risk assessment in housing markets. By bridging environmental hazard data—including land-based indicators of exposure and damage—with financial indicators, the framework enables more granular, dynamic, and equitable assessments than conventional approaches. Nonetheless, its implementation faces technical and institutional barriers, including spatial and temporal mismatches between datasets, fragmented regulatory and behavioral inputs, and the limitations of current single-task AI models, which often lack transparency. Overcoming these challenges will require innovation in AI modeling, improved data-sharing infrastructures, and stronger cross-disciplinary collaboration.

Property insurance is a significant part of the non-life insurance sector, playing a vital role in providing financial security and stability for individuals and businesses. It is worth tracking the development of this part of the insurance sector particularly in light of the increasing number of extreme weather events observed worldwide. Previous research on the European non-life insurance market has mainly focused on an overview of the European Union’s Member States, with no exploration of potential common trends. This creates a research gap with regard to transition countries from Central and Eastern Europe (CEE). The article aims to compare and evaluate the development of the property insurance market in 16 transition countries from 2009 to 2023. The research methods involve descriptive statistics and statistical inference, and panel data models. Data was obtained from the XPRIMM database and reports. Main results lead to the conclusion that in several respects, a convergence phenomenon has been observed – the degree of differentiation of selected measures of market development is decreasing. However, the dynamics of changes cannot be described by a common panel trend. Keywords

In 1961, Los Angeles experienced the disastrous Bel Air fire, which swept through an affluent neighborhood situated in a hilly, WUI (wildland–urban interface) location. In January 2025, the city was devastated again by a nearly-simultaneous series of wildfires, the most severe of which took place close to the 1961 fire location. Disastrous WUI fires are, unfortunately, an anticipatable occurrence in many U.S. cities. A number of issues identified earlier remained the same. Some were largely solved, while other new ones have emerged. The paper examines the Palisades Fire of January, 2025 in this context. In the intervening decades, the population of the city grew substantially. But firefighting resources did not keep pace. Very likely, the single-most-important factor in causing the 2025 disasters is that the Los Angeles Fire Department operational vehicle count shrank to 1/5 of what it was in 1961 (per capita). This is likely why critical delays were experienced in the initial attack on the Palisades Fire, leading to a runaway conflagration. Two other crucial issues were the management of vegetation and the adequacy of water supplies. On both these issues, the Palisades Fire revealed serious problems. A problem which arose after 1961 involves the unintended consequences of environmental legislation. Communities will continue to be devastated by wildfires unless adequate vegetation management is accomplished. Yet, environmental regulations are focused on maintaining the status quo, often making vegetation management difficult or ineffective. House survival during a wildfire is strongly affected by whether good vegetation management practices and good building practices (“ignition-resistant” construction features) have been implemented. The latter have not been mandatory for housing built prior to 2008, and the vast majority of houses in the area predated such building code requirements. California has also suffered from a highly counterproductive stance on insurance regulation. This has resulted in some residents not having property insurance, due to the inhospitable operating conditions for insurance firms in the state. Because of the historical precedent, the details in this paper focus on the Palisades Fire; however, many of the lessons learned apply to managing fires in all WUI areas. Policy recommendations are offered, which could help to reduce the potential for future conflagrations.

In the last several years, disaster insurance programs around the world have experienced disruptions that many observers interpret to be a primary symptom of "climate crisis" (Bittle 2024). Governments have responded to these disruptions through disjointed and at times contradictory measures: they treat disasters, alternately, as "Acts of God" that should be a collective responsibility, or as the result of decisions that can be attributed to individual agency. This article argues that such shifts between mutualism and individualization in disaster insurance are symptoms of an "irrationalization" of disaster policy. The concept of irrationalization, derived from the Marxist state theory of Claus Offe (1973), describes the process of goal identification and policy formulation of contemporary states as they navigate simultaneously valid but ultimately contradictory principles of political morality and governmental rationality. Through case studies of two disaster insurance programs in the US-the National Flood Insurance Program and property insurance in California, which covers wildfires-the article shows that irrationalization processes are becoming more marked as disasters grow ever larger and costlier, fueled by climate change and other anthropogenic causes. It also suggests that the concept of irrationalization offers insight into the emerging forms of "climate crisis" that are unfolding in disaster policy and other domains. The concept of climate crisis is frequently invoked to designate the ruptural change that will follow from global warming, and to both summon and justify radical action to address problems that are attributed to a particular causal or moral agent. But in the context of the irrationalization of disaster policy, technical and moral attributions are uncertain and disputed. Disasters generate political conflict and crisis-driven reorganization rather than decisive courses of action.

ABSTRACT Excess burden refers to the economic cost of taxation beyond the revenue it raises. Past reviews of Australia’s tax system have ranked taxes by excess burden estimates at current revenue raising efforts. This paper extends that analysis by exploring the optimal tax mix, which necessarily involves departing from current revenue raising structures. We examine how the excess burdens of four Australian taxes change with varying revenue-to-GDP ratios. Using an economy-wide model with high levels of tax-specific detail, we show that transfer duties and insurance taxes remain highly inefficient even at low levels, strengthening the case for their replacement with more efficient taxes.

In Indonesia, there is unit-linked sharia insurance. The governance of Islamic financial instruments tends to be considered better than conventional instruments. The sharia principle is the principle of Islamic law in insurance activities based on fatwas issued by institutions that have authority in determining fatwas in the field of sharia. Based on Law Number 40 of 2014, sharia insurance is a collection of agreements, consisting of agreements between sharia insurance companies (tijarah or buying and selling contracts) and policyholders as well as agreements between policyholders (tabarru' or social funds), in the context of contribution management. The basic principle of Islamic finance is that the economy of the people involves the participation of as many people as possible. "In the midst of the Covid-19 pandemic, investors tend to choose instruments that are safe from turmoil". Sharia insurance is an insurance model that follows the principles of sharia law (Islam) also known as takaful, where customers as insurance participants contribute with the tabarru' contract to help each other among participants who face disasters, or it can be said to be a risk sharing concept among participants. Sharia insurance runs within the framework of sharia law, which prohibits transactions by charging interest (riba), engaging in speculative transactions (gharar), and investing in morally and socially detrimental businesses (haram). Types of sharia insurance, namely: (1). Life Insurance; (2). Health Insurance; (3). Property Insurance; (4). Motor Vehicle Insurance; (5). Travel Insurance; (6). Sharia health insurance. The concept of sharia insurance: (1). Help through the concept of ta'awun by forming a collective fund for the common good; (2). Protect each other through the concept of takaful by providing protection in the form of compensation, reimbursement, or payment for the occurrence of a certain risk. Sharia insurance can help manage risks, namely: (1). Anticipation of various risks should be identified in advance; (2). Participation in insurance should be in accordance with the needs and financial capabilities of each individual; (3). Forming an investment fund for future plans.

The article examines the state and trends of the Ukrainian insurance market in the context of unprecedented challenges caused by Russia's full-scale aggression and the economic crisis. The key indicators of market development for 2020-2024 are analyzed, and the main factors of their dynamics are identified. A significant reduction in the number of insurers and insureds interested in purchasing insurance products is noted. The downward trend is characterized by the share of insurance in Ukraine's GDP, which is associated with a decrease in insurance premiums relative to the country's total economic output and reflects both a decrease in the solvency of the population and business and an increase in risks that are difficult or impossible to insure in the current environment. An assessment of the current state of the Ukrainian insurance market shows that its largest segment, as in the pre-war period, remains risk insurance, which combines various types of insurance against potential risks, including liability insurance, accident insurance, health insurance, property insurance in various forms, etc. The main channel for selling insurance products in the Ukrainian market remained the agent network, although bancassurance, direct sales, and brokers are also significant for certain products. The author substantiates the need for a qualitative transformation of insurance activities through the introduction of innovations, development of distribution and adaptation of insurance products, in particular through the creation of an effective system of war risk insurance and the intensification of public-private partnerships to ensure economic stability and post-war reconstruction. It is determined that the ability of insurance companies to adapt to new realities and offer relevant insurance solutions is a key condition for further market development

The use of Snowflake as a cloud-native data warehouse has dramatically changed the management of analytics workload for Property and Casualty (P&C) insurers, while simultaneously presenting serious cost governance challenges. The heavy volume of searches, big data retention, and decentralized business intelligence operations are industry-standard procedures that tend to lead to uncontrolled credit usage and overspending on storage. This research introduces a modular five-layer optimization framework focused on property and casualty insurance data, combining workload segmentation, and compute sizing with Snowflake's account usage metadata. The framework is tested and validated using Kaggle’s Insurance Agency Data, representing real-world P&C operations across 17 states. Benchmark queries simulating core insurance workloads were designed using modified TPC-H logic, a standard decision support benchmark that enables realistic performance evaluation under analytical query conditions, achieving up to 82% cost reduction and a 64% reduction in execution time without compromising the results. These results highlight the efficiency of the framework to facilitate proactive and elastic cost control. Future studies can investigate AI-driven query forecasting, scalable warehouse dynamics, and real-time anomaly detection to further advance cloud-native data ecosystem governance.

Abstract Risk management is an important aspect of property insurance sales, underwriting, and rating that is covered lightly in standard texts or publishers' materials. This case is based on the risk management considerations of a fictitious insurer within an often competitive and always high‐stakes industry segment—the primary insurance market for property catastrophes. The case is primarily concerned with performance management and the inherent tension that the competitive business environment can add to the management of insurance sales, underwriting, and rating risk. This case engages students in risky decision making with the option to use a simulation game that can be restricted to one round and one class period or played over several rounds in or out of class time. The game simulates randomized loss occurrences (based on pseudo‐realistic probabilities) and competitive market dynamics (also containing an element of randomness). The case requires basic knowledge of commercial property insurance rating and underwriting, catastrophe risk, insurer performance analysis (namely, loss ratios and the premium‐to‐surplus ratio), and risk preferences. Decision making under uncertainty—with an appreciation of the linkages between sales, underwriting, loss costs, and risk capital—is a key learning of the case analysis. Group decision‐making is an element of the case, and as such a secondary learning is negotiation and conflict resolution. Usage of Excel for case analysis is necessary.

Property insurance is increasingly recognized as an enabler of not only post-disaster recovery, but also affordable housing, financial resilience, and the wider climate adaptation agenda for individuals and communities. In many cities and communities, however, climate change and other factors such as inflation and overdevelopment are increasing the risk of loss and damage from extreme weather hazards. In response, insurers are raising the premiums they charge for property insurance, or in some cases, withdrawing from vulnerable geographies completely—just at a time when insurance is becoming more important to cities as a tool in their climate adaptation toolkits. Through a conversation among three insurance experts from academia and industry, this article unpacks drivers of uninsurability and explores potential shifts in how responsibility for property risk is shared across an “ecosystem” of public institutions and private sector companies. It offers a practitioner’s look at the role of property insurance in urban climate adaptation and insight into how property insurance may evolve in an uncertain climate future.

Enterprise Content Management (ECM) systems have emerged as critical infrastructure within the insurance industry, fundamentally transforming how insurers manage documentation, ensure regulatory compliance, and deliver customer service across property, auto, and life insurance sectors. The integration of sophisticated ECM architectures enables seamless information flow between previously siloed departments while simultaneously addressing the stringent requirements of multi-jurisdictional compliance frameworks. Recent implementations by major insurers demonstrate significant improvements in claims processing efficiency, customer communication, and documentation accessibility through omnichannel delivery systems. Particularly noteworthy is the capacity of modern ECM systems to provide robust support during catastrophic events, allowing for rapid document processing and real-time communication when policyholders are most vulnerable. As insurers continue to navigate complex regulatory environments and evolving customer expectations, the strategic deployment of integrated content management solutions represents a decisive competitive advantage, balancing operational efficiency with enhanced service delivery and compliance assurance.

Purpose In this study, a standardized model was constructed to quantify and manage the disaster risks caused by natural hazards. Design/methodology/approach Hazard, exposure and vulnerability are the main components of the risk model used, entitled “Value at Disaster Risk” (VaDR). To decode these components, I combined Greek public hospitals’ financial data with data derived from the local property insurance market. Findings The units examined were found to have an overwhelming risk profile because of the increasing frequency and severity of climate change-driven disasters, particularly floods and fires, combined with internal vulnerability and asset underinsurance. Their finances imply that they lack liquidity to cover the potential loss caused by a natural disaster but are capable of partially sacrificing annual profitability for property insurance. Practical implications Similar to individuals and businesses, healthcare providers can become victims of natural disasters. Hence, managers should reconsider their tendency not to insure fundamental assets such as tangible fixed assets and inventories under the pretext of budget constraints. Concurrently, policymakers should reconsider making disaster insurance compulsory, at least for critical public healthcare infrastructure. These interventions and others that make assets more resilient are necessary to ensure continuity of care after a natural disaster. Originality/value This is the first study to investigate the financial implications of natural hazards on the Greek healthcare system. A major contribution of this study is the introduction of both the VaDR and disaster insurance concepts as economic arguments against the inaction approach.

In the property and casualty (P&C) insurance industry, reserves comprise most of a company’s liabilities. These reserves are the best estimates made by actuaries for future unpaid claims. Notably, reserves for different lines of business (LOBs) are related due to dependent events or claims. The actuarial industry and literature have extensively developed both parametric and nonparametric methods to model dependence in loss reserving. However, the use of machine learning tools to capture dependence between loss reserves from multiple LOBs and calculate the aggregated risk capital remains uncharted. This article introduces the use of the Deep Triangle (DT), a recurrent neural network, for multivariate loss reserving, incorporating an asymmetric loss function to combine incremental paid losses of multiple LOBs. The input and output to the DT are the vectors of sequences of incremental paid losses that account for the dependence between and within LOBs. In addition, we utilize generative adversarial networks (GANs) to generate synthetic loss triangles, enabling us to obtain the predictive distribution for reserves and calculate the risk capital. We call the combination of DT for multivariate loss reserving and GAN for risk capital analysis the Extended Deep Triangle (EDT). To speed up the training for DT in EDT, we leverage the DT trained on real data. To illustrate the EDT, we apply and calibrate these methods using data from multiple companies from the National Association of Insurance Commissioners (NAIC) database. To benchmark our method, we compare the EDT to the copula regression models and find that the EDT outperforms the copula regression models in predicting total loss reserve. Furthermore, with the obtained predictive distribution for reserves, we show that risk capitals calculated from the EDT are smaller than that of the copula regression models, suggesting a more considerable diversification benefit. Finally, these findings are also confirmed in a simulation study. Our analysis demonstrates the potential of the EDT in predicting loss reserves and conducting risk capital analysis in practice.

According to statistics from the National Highway Traffic Safety Administration (NHTSA) in the U.S., lane departures contribute to more than one-third (37.4%) of fatal crashes. Lane departure warning (LDW) systems have been widely installed and used, including in trucks. However, truck LDW false alarm rates have reached 33%–55%. Improper warning time is a key factor that affects LDW false alarms. Truck drivers’ response behavior affected the warning time of LDWs. Proposing an evaluation method that considers drivers’ response behavior is helpful to reduce false alarms for LDWs. This study extracted 777 lane departure events from ICARVISIONS’s truck LDW records of the China Pacific Property Insurance Company’s driving data. In-vehicle data collected during LDW activation were used to classify response behaviors by k-Shape clustering, which considered the changes over time in truck drivers’ response behaviors for six scenarios. Using the Responsibility-Sensitive Safety model to evaluate the LDWs, the parameters of safe lateral distance and safe longitudinal distance were calibrated for each scenario by the Non-Dominated Sorting Genetic Algorithm-II. The key findings include: 1) The difference in time-to-lane crossing under various scenarios needs to be considered in the truck LDW algorithm; 2) The safe longitudinal distance should not be ignored for truck LDWs. Analyzing driver response characteristics guides improvements in the adaptability of LDW algorithms. Evaluating the impact of safe longitudinal distance on false alarms confirms critical safety parameters in testing standards.

Property insurance bluelining as a social determinant of health.

The property and casualty insurance sector is experiencing profound technological transformation driven by digital innovations that fundamentally restructure traditional insurance models. This technical review explores how cloud-native architectures, artificial intelligence, telematics, and regulatory technologies are reshaping core insurance functions. Cloud platforms with microservices architectures enable superior scalability while significantly reducing operational costs across the insurance value chain. AI-driven algorithmic underwriting systems transcend traditional actuarial models through ensemble learning techniques and deep neural networks, delivering enhanced risk assessment accuracy and processing efficiency. Telematics infrastructure with multiple data acquisition mechanisms has revolutionized auto insurance risk assessment, while RegTech solutions have transformed regulatory compliance through automated monitoring systems and blockchain frameworks. Autonomous vehicle insurance requires fundamentally different technical approaches including Bayesian networks and digital twin simulations, while advanced analytics architectures incorporate sophisticated security implementations such as homomorphic encryption and federated learning. Despite implementation challenges related to legacy system integration, data standardization, and model explainability, emerging directions including quantum computing, edge AI deployment, and decentralized insurance platforms promise to further evolve the technological landscape of property and casualty insurance.