Optimizing Real Estate Portfolios: The Role of Generative AI in Geographic Diversification

This study investigates the data analysis capabilities of GPT-4o in real estate portfolio selection by integrating predictive modeling, model evaluation, and investment decision-making into a fully autonomous AI-driven framework. Unlike the earliest large language models (LLMs) that primarily process textual data or recent LLMs such as OpenAI’s o1 and DeepSeek’s R1, which are designed for complex reasoning, GPT-4o actively executes code and conducts quantitative analysis using the Code Interpreter tool. Leveraging a dataset of Zillow home price data and several predictive factors, the AI-generated portfolios consistently outperform various benchmarks in our out-of-sample backtest. Further, we find that data obfuscation—removing city names, states, and dates—reduces geographic diversification and produces lower Sharpe ratios than the unobfuscated portfolios. Overall, our findings highlight the potential of generative AI in advancing data-driven portfolio management.