THE ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATING MAINTENANCE REQUESTS IN REAL ESTATE: A CASE STUDY OF HOUSING ESTATES IN SOKOTO STATE
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- NGN 5000
CHAPTER ONE
1.1 Background of the Study
Property maintenance is a cornerstone of real estate management, ensuring that housing estates remain habitable, safe, and attractive to tenants. In Sokoto State, where housing estates cater to a diverse population, the traditional maintenance request system often involves manual logging, tracking, and resolution of issues. This approach is prone to delays, miscommunication, and inefficiencies, leading to tenant dissatisfaction and higher operational costs.
Artificial Intelligence (AI) has emerged as a game-changer in automating maintenance requests, offering solutions that streamline the reporting and resolution process. AI-powered systems enable tenants to submit maintenance requests through intuitive platforms, which prioritize tasks, assign them to appropriate personnel, and provide real-time updates. By leveraging natural language processing (NLP), chatbots, and predictive maintenance algorithms, these systems reduce response times, improve task allocation, and prevent recurring issues.
In Sokoto State, the adoption of AI for maintenance automation remains limited, with many estate managers unaware of its potential benefits. Concerns around implementation costs, data security, and system reliability further hinder its uptake. This study seeks to explore the role of AI in automating maintenance requests within housing estates in Sokoto State, providing insights into its effectiveness and challenges.
1.2 Statement of the Problem
Traditional maintenance request systems in Sokoto State housing estates are inefficient, leading to delays, increased costs, and tenant dissatisfaction. While Artificial Intelligence offers a solution, its adoption in Sokoto is minimal, with limited research on its impact and feasibility. This study addresses this gap by evaluating the role of AI in automating maintenance requests in Sokoto State housing estates.
1.3 Aim and Objectives of the Study
The aim of this study is to assess the role of Artificial Intelligence in automating maintenance requests within housing estates in Sokoto State. The specific objectives are:
To evaluate the efficiency of AI systems in automating maintenance request processes.
To identify challenges faced in implementing AI-powered maintenance solutions.
To examine the impact of AI on tenant satisfaction and operational costs.
1.4 Research Questions
- How effective are AI systems in automating maintenance request processes in Sokoto State housing estates?
- What challenges do estate managers face in implementing AI-powered maintenance solutions?
1.5 Research Hypotheses
- AI systems significantly improve the efficiency of maintenance request automation.
- The use of AI reduces operational costs for housing estate managers.
- Tenants in AI-enabled housing estates report higher satisfaction levels compared to those using traditional systems.
1.6 Significance of the Study
This study underscores the potential of AI in revolutionizing property maintenance processes, providing evidence for its adoption in Sokoto State housing estates. It highlights benefits for both tenants and managers, promoting efficiency, cost-effectiveness, and enhanced tenant satisfaction.
1.7 Scope and Limitation of the Study
The study focuses on housing estates in Sokoto State and examines the role of AI in automating maintenance requests. It does not cover other applications of AI in real estate or explore non-residential properties. Limitations include restricted access to proprietary AI systems and tenant feedback data.
1.8 Definition of Terms
- Artificial Intelligence (AI): Technology enabling machines to perform tasks typically requiring human intelligence, such as decision-making and problem-solving.
- Maintenance Requests: Reports submitted by tenants or property managers to address repair or maintenance issues.
- Automation: The use of technology to perform tasks with minimal human intervention.
- Housing Estates: Residential communities comprising multiple housing units, managed collectively.
- Sokoto State: A state in northwestern Nigeria, serving as the geographical focus of this study.
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