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    A dynamic property valuation decision support system

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    Wentzel MJ 24999369.pdf (2.016Mb)
    Date
    2021
    Author
    Wentzel, Matthys Johannes
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    Abstract
    A decision support system (DSS) is an information system that provides assistance towards solving problems that usually involve multiple courses of action to a specific problem. An expert system incorporates expert knowledge and inference procedures to solve more unconventional or complex problems in a certain field. The property valuation process involves performing certain procedures through the use of expert knowledge to determine the value of a property. The most common property valuation technique is known as the sales comparison method that involves identifying the most optimal property which closely resembles the property under consideration. Existing methods to execute the valuation process quickly and efficiently are known as automated valuation models or mass-appraisal. This study was conducted to determine if valuation mathematics used by expert valuers can be effectively integrated into a DSS that compensates for dynamic fluctuations in the economy in the South African residential property market. The aim was achieved through the development of a computerised demonstrator implementing the property valuation process through methods that were identified during the knowledge engineering process. The expert DSS was successfully applied to test scenarios of real-world residential properties. The system consists of a user interface for subject property attribute inputs, a decision-making module that incorporates the sales comparison method, a database of real-world properties and a knowledge base which uses property valuation mathematics to valuate the subject property. The expert DSS successfully calculates the value by selecting the most comparable property from the database according to an algorithm and compensating for attribute differences according to a mathematical model.
    URI
    https://orcid.org/0000-0002-3088-2353
    http://hdl.handle.net/10394/38021
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    • Natural and Agricultural Sciences [2757]

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