The Role of Artificial Intelligence-Powered Property Valuation Models: A Case Study of Housing Estates in Abuja, FCT
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- NGN 5000
1.1 Background of the Study
Property valuation is an essential component of real estate transactions, ensuring accurate assessments for buyers, sellers, and investors. Traditional property valuation relies heavily on human expertise and subjective assessments, which can be influenced by biases and inaccuracies. AI-powered property valuation models, however, can analyze vast datasets to provide objective, data-driven property assessments. These models use machine learning algorithms to consider various factors, such as location, property features, historical sales data, and market trends, to estimate a property's value with greater precision.
In Abuja, the Federal Capital Territory (FCT), real estate is a key sector in the economy, with significant investments in housing estates. AI-powered valuation models have been adopted by developers, real estate agents, and financial institutions to enhance property assessments and ensure fair pricing (Ibrahim & Mohammed, 2025). This study investigates the role of AI-powered property valuation models in improving accuracy and efficiency in housing estate transactions in Abuja.
1.2 Statement of the Problem
The traditional methods of property valuation in Abuja often lead to inconsistent and inaccurate appraisals, which can result in financial losses for buyers, sellers, and investors. The lack of real-time data integration and reliance on outdated valuation methods further exacerbates these issues. AI-powered property valuation models have the potential to address these challenges but have seen limited adoption due to high costs and lack of awareness.
1.3 Objectives of the Study
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To evaluate the role of AI-powered property valuation models in Abuja’s housing estate sector.
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To assess the accuracy and efficiency of AI-based property valuations compared to traditional methods.
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To identify the barriers to the widespread adoption of AI-powered property valuation models in Abuja.
1.4 Research Questions
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How do AI-powered property valuation models compare to traditional methods in Abuja's housing estates?
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What impact has AI had on the accuracy and efficiency of property valuations in Abuja?
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What barriers limit the implementation of AI-powered property valuation models in the real estate sector?
1.5 Research Hypothesis
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AI-powered property valuation models significantly improve the accuracy of property assessments.
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AI valuation tools increase the efficiency of the property valuation process in Abuja’s real estate sector.
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Cost and lack of expertise are the main challenges hindering AI adoption in property valuation.
1.6 Significance of the Study
The findings of this study will provide important insights into how AI can transform property valuation practices, benefiting the real estate sector by improving accuracy and enabling fair pricing of properties.
1.7 Scope and Limitations of the Study
The study focuses on AI-powered property valuation models used in housing estates in Abuja, FCT. Limitations include limited access to proprietary valuation algorithms and varying levels of AI adoption among real estate agencies.
1.8 Operational Definition of Terms
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Property Valuation: The process of determining the market value of a property.
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Artificial Intelligence (AI): Machine learning algorithms used to analyze and process property data for valuations.
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Machine Learning: A subset of AI where systems learn from data to improve over time.
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Real Estate Transactions: The buying, selling, and leasing of properties in the real estate market.
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Data-Driven Valuation: The use of real-time, comprehensive data to assess property value.
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