An Analysis of Sustainable Strategies for Property Insurance
Introduction
In this paper, I developed an LSTM model with an accuracy of 85% to predict future natural disasters using natural disaster data in Florida and California of the past 30 years. Applied neural network, linear regression, and deep learning models to assess the relationship between natural disasters and property damage.
Summary
In recent years, homeowners and insurance companies have faced significant crises, necessitating the development of comprehensive solutions to meet the needs of all stakeholders involved in the insurance industry. This paper presents an innovative approach to property insurance by introducing an insurance company’s property allocation model based on deep learning and LSTM and a market investment model utilizing regression analysis, as well as a community conservation building model employing grey correlation analysis. These models provide valuable insights and correlation analyses for the property-casualty insurance sector, promoting a more sustainable industry.
Task 1: Natural Disaster Prediction and Property Damage Assessment Model
We conducted a comprehensive time series analysis of natural disasters and related data over the past 30 years in Florida and California. Subsequently, we developed an LSTM model to forecast future natural disasters. We then utilized neural network, linear regression, and deep learning models to assess the relationship between natural disasters and property damage, ultimately selecting the most effective deep learning model as the prediction model. Our next step involved predicting future disasters in California and Florida using the LSTM model, and leveraging the outcomes to further anticipate potential property damage through the deep learning model. Finally, by integrating the prediction results with the time value calculation of insurance theory, we formulated a suitable index system to aid insurance companies in determining underwriting decisions.
Task 2: Real Estate Investment Decision Support Model
This task focuses on establishing a real estate investment model to support decision-making in construction and development. Through comprehensive regression analysis, including linear regression, ridge regression, and Lasso regression, we considered factors such as population density, income levels, employment opportunities, and willingness to purchase data to calculate housing market demand. Additionally, we predicted the impact of extreme weather events and property losses. By integrating income, willingness to purchase, and disaster-related losses, we accurately calculated the minimum investment cost. The model was successfully applied in Nanjing, China, providing accurate predictions and informing investment decisions.
Task 3: Community Insurance Challenge Assessment Model
To empower community leaders in addressing insurance-related challenges, we designed a grey correlation analysis model. This model assisted in evaluating cultural, historical, economic, and other factors. By analyzing the problem’s context, delineating data characteristics, and outlining the implementation environment, we applied the grey correlation analysis model, which yielded precise results.
Task 4: Multi-Site Model Validation and Conservation Strategy Development
We scrutinized the models established in Task 2 and 3 across five sites in Nanjing, incorporating indicators such as resident population and GDP per capita. Through this analysis, we determined conservation priorities. Based on the outcomes of the model, we submitted an explanatory report to the community, detailing conservation priorities and strategies.
Keywords
- Deep Learning
- LSTM (Long Short-Term Memory)
- Regression Analysis
- Grey Correlation Analysis
- Time Value
- Property Insurance
- Natural Disaster Prediction
- Investment Decision Support
Research Significance
This research integrates multiple advanced data analysis technologies to provide innovative solutions for the property insurance industry, contributing to:
- Improved accuracy in natural disaster prediction
- Optimization of insurance companies’ underwriting decisions
- Support for real estate investment decisions
- Facilitation of community conservation strategy development
- Promotion of sustainable development in the insurance industry
Technical Approach
Deep Learning & LSTM
- Time series analysis of 30-year disaster data
- Neural network models for damage assessment
- Predictive modeling for future disasters
Regression Analysis
- Linear, Ridge, and Lasso regression models
- Market demand calculation
- Investment cost optimization
Grey Correlation Analysis
- Multi-factor evaluation framework
- Cultural and economic factor analysis
- Community decision support
Applications
- Florida and California: Natural disaster prediction and insurance assessment
- Nanjing, China: Real estate investment and community conservation
- Insurance Industry: Underwriting decision support and risk management
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An Analysis of Sustainable Strategies for Property Insurance