Optimizing Real Estate Investment Strategies through Big Data-Driven Spatial Analysis: A Focus on Commercial Districts

Purpose: Over the last several years, commercial districts have become increasingly popular with heavy consumer traffic, location near infrastructure, and any possible capital gain. This study explores how big data-driven spatial analyses can optimize commercial real estate investment by focusing on changeable urban environments. Research design, data and methodology: This study uses a systematic literature review using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. PRISMA provides an effective outline to enhance transparency and reproducibility of systematic reviews and could be applied to synthesize interdisciplinary topics including big data, GIS, and real estate analytics. Results: The outcomes of this research support the advancement in the impact of big data technologies, spatial analytics, and AI in current state-of-the-art strategies in real estate investment. As an investor and urban planner, I believe this indicates a change in plan to evidence-based adoption of decisions based on real-time and spatially rich data. Conclusions: This study concludes that future developments can be made in either improving the AI-based investment models to be explainable or extending the spatial data, where open-data partnerships can help. There should also be a study to implement cross-sector integration strategies to correlate real estate technology with the transportation, energy, and public infrastructure systems.