Improving online Real Estate Management System using data analytics

Abstract

As technology advances, every industrial sector has continued to innovate and scale. Several property technologies have fast-tracked nominal developments and accelerated growth in the Real Estate market. Real Estate markets in developing countries have endured challenges and issues emanating from various areas such as management, trust, sales, and infotech, many more. This paper aims to implement a model of the online Real Estate Service system via a responsive web application using data analytics and visualization techniques that show insights and improve decision making based on user data. This implementation includes backend clustering and Regression algorithms to produce insights based on available test data of over 62 PropTech firms in Nigeria, thus helping clients, managers, and agents make critical decisions to accelerate organizational growth. The model was developed by analyzing the impact of data growth in the online Real Estate service system, hinging on the study of earlier literature and data from over 7000 PropTech firms. The research adopted a questionnaire and validation method to understand the main domains to focus on, including property search, Agent trustworthiness, and capabilities. The findings presented here include use cases of the application that provides comprehensive solutions with the system feature providing solutions for deeper property search, minimizing the danger of being scammed and making it easier to acquire properties with specific criteria and to easily contact verified agent based on individual needs of the client and agents track record. Test findings reveal that more data introduced to the system will make it more efficient since the threshold of each level adapts based on data inputted in a dynamic cluster.