AI-Driven Housing Market Analytics: Predictive Models for Sustainable Urban Development

As cities around the world continue to expand and evolve, understanding the dynamics of the housing market becomes increasingly critical for urban planners, policymakers, and developers. The housing market plays a pivotal role in shaping urban environments, influencing socioeconomic structures, and contributing to problems such as housing affordability, spatial inequalities, and urban sprawl. The application of cutting-edge Artificial Intelligence (AI) techniques, such as Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs), offers transformative potential for analyzing and predicting housing market trends. These advanced technologies allow for the processing and interpretation of complex housing data, providing insights that can guide decision-making for more equitable, efficient, and sustainable urban development. AI can help housing delivery by improving planning, predicting demand, optimizing construction, reducing costs, and supporting better housing policies.This research collection aims to explore the intersection of artificial intelligence (AI) and housing market analysis, with particular emphasis on how machine learning (ML), deep learning (DL), and large language models (LLMs) can address the persistent housing challenges, such as housing affordability, demand-supply forecasting, and spatial inequalities. Despite significant advances in data science, housing markets remain highly complex systems shaped by economic, social, and spatial dynamics that are difficult to model using traditional analytical approaches. AI-driven techniques offer new opportunities to enhance housing market forecasting through advanced predictive models capable of identifying hidden patterns, anticipating market shifts, and supporting evidence-based decision-making. The goal of this collection is therefore to highlight innovative applications of AI that improve housing market analysis and inform policies and planning strategies for more resilient, equitable, and sustainable cities.We invite contributions that address, but are not limited to, the following themes:• AI-driven predictive models for forecasting housing demand, pricing, and market trends• Machine learning and deep learning techniques for spatial analysis and identifying housing market inequalities• Applications of AI in optimizing affordable housing solutions and resource allocation• Use of AI in understanding urban growth and its impact on housing markets• Predictive analytics for long-term housing planning and urban development strategies• Data-driven approaches for addressing socioeconomic disparities in housing accessibility Topic editors: Jun Wang, Hui Zhao, AbdulLateef Olanrewaju Source: https://www.frontiersin.org/research-topics/79491/ai-driven-housing-market-analytics-predictive-models-for-sustainable-urban-development