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AI-Based House Price Prediction
Abstract
The housing market is one of the most dynamic and significant sectors of any economy, influencing both individual wealth and broader economic stability. Buyers, sellers, investors, and policymakers all rely on accurate housing price predictions. With the advent of artificial intelligence (AI) technologies, particularly machine learning algorithms, the task of house price prediction has seen remarkable advancements. This study provides a detailed overview of AI-based techniques for house price prediction. Firstly, the paper outlines the traditional methods employed in house price prediction, highlighting their limitations in handling complex patterns and large datasets. Subsequently, it delves into the transformative impact of AI techniques, such as neural networks, support vector machines, decision trees, and ensemble methods, in enhancing prediction accuracy and robustness. Moreover, it discusses the significance of feature selection, data preprocessing, and model evaluation techniques in optimizing AI-based prediction models. Furthermore, the paper examines the various data sources utilized in house price prediction, including structured housing datasets, geospatial information, socio-economic indicators, and unstructured textual data from online listings and social media. It explores the integration of diverse data sources to enhance model performance and capture nuanced market dynamics. The review also addresses challenges and ethical considerations associated with AI-based house price prediction, such as algorithmic bias, data privacy concerns, and transparency in model interpretation. Strategies for addressing these issues and guaranteeing responsible AI implementation in the housing sector are presented. Finally, the paper concludes with future directions and emerging trends in AI-based house price prediction, including the integration of deep learning techniques, explainable AI methodologies, and the adoption of blockchain technology for transparent and secure property transactions.
Keywords
References
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