CHAPTER ONE
1.1 Background of the Study
Efficient property management is a critical aspect of real estate, ensuring tenant satisfaction and the long-term sustainability of housing estates. Maintenance requests, ranging from plumbing repairs to electrical fixes, are a recurring aspect of property management. However, traditional methods of handling these requests often involve manual logging, communication delays, and inefficient resource allocation, leading to tenant dissatisfaction and increased operational costs.
Artificial Intelligence (AI) offers solutions to these challenges through automation. AI-driven systems can streamline maintenance request processes by enabling tenants to report issues through smart platforms, which then prioritize, assign, and monitor the tasks automatically. These systems also use predictive analytics to identify potential maintenance issues before they escalate, ensuring proactive management.
In Sokoto State, housing estates face challenges in managing maintenance efficiently due to limited resources and outdated systems. This study explores the role of AI in automating maintenance requests, with a focus on improving efficiency, reducing costs, and enhancing tenant satisfaction in housing estates in Sokoto State.
1.2 Statement of the Problem
Housing estates in Sokoto State struggle with inefficient maintenance management systems, resulting in delays, high costs, and tenant dissatisfaction. Manual processes are prone to errors and resource wastage. This study investigates how AI technologies can address these challenges by automating maintenance requests and enabling predictive maintenance.
1.3 Aim and Objectives of the Study
The aim of this study is to assess the role of AI in automating maintenance requests in real estate management in Sokoto State. The specific objectives are:
1.4 Research Questions
1.5 Research Hypotheses
1.6 Significance of the Study
This study highlights the potential of AI in transforming property management practices, offering practical recommendations for real estate managers and policymakers. It contributes to the understanding of how technology can enhance operational efficiency and tenant experiences in Nigerian housing estates.
1.7 Scope and Limitation of the Study
The study focuses on housing estates in Sokoto State and the use of AI to automate maintenance requests. It excludes other aspects of property management, such as lease management, and regions outside Sokoto State. Limitations include access to data on existing maintenance systems and the level of AI adoption.
1.8 Definition of Terms
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