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Assessment of real estate attributes for catastrophe insurance (property estimate) using geospatial techniques

Student Name: Mr. Nishant
Guide: Prof. P K Joshi
Year of completion: 2012


Evaluation and estimation of real estate insurance is a complex process and involves assessments that are subject to socio-economic uncertainties. This requires an understanding of the principles underlying risk management, valuation and assessment. Classic insurance modeling uses paper maps, field inventory datasets, and modeling CRESTA (Catastrophe Risk Evaluation and Standardizing Target Accumulations) for loss and vulnerability assessment. These tools have limited functionality due to lack of spatial database, location intelligence and geospatial modeling for actuarial decision-making.

This study uses oblique imagery to determine height and position of buildings in dense residential areas as opposed to stereo imagery. The method is tested on three selected sites of Delhi resulting high accuracy (~70%) of building information extraction. This would assist risk managers and modelers with cost-effective and up to date building level database for risk models thereby facilitating better decision making. Risk assessment is s a multidisciplinary issue that involves assessment of complex factors and uncertainties. For this multi-criteria decision analysis (MCDA) of seismic hazard and vulnerability parameters are carried, to support risk profiling that involves procedural and declarative knowledge. MapXtreme, SQL server, and .NET framework are used to manage the datasets. The integrated knowledge-based system shell (GIS, RDBMS spatial analysis, and expert knowledge, within an integral system), are used to develop a Spatial Decision Support System (SDSS) named as Insurance Profiler (InsPro).

InsPro is developed as a suite of actuarial analysis tools with geocoding, multi-criteria evaluation, and mapping abilities to deal with complex problems of property insurance. The system is designed to evaluate property risk assessment from seismic hazard in a dense urbanized city of Delhi, India. The developed SDSS aims to assist insurer with four basic functions viz. (i) locate and visualize insured property and risk zones (ii) evaluate risk potential of property under consideration (iii) report generation and (iv) portfolio management, for actuarial risk assessment. This prototype SDSS can be replicated for other disciplines involving similar spatial decision making.

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