Market research serves as the foundation for successful real estate investment and development, guiding strategic site selection and opportunity identification. This study focuses on analyzing the evolving landscape of real estate development in Houston through a multifaceted approach encompassing geographic information systems (GIS) visualization to explore the spatial dynamics and diversity of new developments, time series analysis to examine temporal trends of new development applications, and machine learning techniques to predict future urban growth trajectories. K-means clustering is employed to segment the market and reveal unique characteristics based on development distribution, while the impact of new developments on nearby home values is quantified through linear panel regression and tree-based models, offering insights into the complex relationships between development and property valuation. By integrating these complementary analyses, this research aims to provide a comprehensive understanding of Houston’s real estate development landscape, informing strategic decision-making processes and contributing to the interdisciplinary discourse on urban growth dynamics, market trends, and the interplay between development and valuation.