John Carrafiell, co-CEO of BGO, a global real estate investment manager with $89 billion in assets under management, has transformed his firm’s investment strategy by leveraging artificial intelligence and deep data analysis. With nearly four decades in real estate, Carrafiell grew frustrated with outdated research methods that relied on the same data sources and produced predictable, often identical conclusions. His question became simple: How do we truly outperform? The answer came from looking inward. BGO analyzed two decades of its own investment history using a computer model that removed human bias and focused solely on data. The results were clear: investment success was driven almost entirely by local market fundamentals—not national trends or property pricing. This reinforced the classic real estate adage—location, location, location—but with a modern, data-driven twist. Traditional market rankings often felt arbitrary to BGO, so the firm built its own predictive model using thousands of data points, including government statistics, telecom data, demographic shifts, and supply trends unique to each region. AI dramatically increased the model’s ability to process vast amounts of data quickly and identify patterns invisible to conventional analysis. One standout example was a decision to invest in an industrial development in Las Vegas alongside partner Northpoint Development. While other models predicted only average returns, BGO’s system flagged the site as a high-potential opportunity. The model identified that rising costs in the Inland Empire of California were pushing logistics companies to seek alternatives. Las Vegas offered significantly lower rents, taxes, and labor costs—despite a two-hour longer drive. The savings on total operating costs were substantial, around 60%, making it an attractive hub for serving broader regional markets. The outcome validated the model: BGO secured rents of $9 per square foot, far exceeding the original underwritten $5.88. “That does not happen by luck,” Carrafiell said. Similar data-driven insights led to strong returns in Florida and the Rust Belt, where the model identified undervalued markets overlooked by traditional investors. Carrafiell credits the AI-powered system with materially improving BGO’s performance. Still, he acknowledges limitations. Unpredictable events—like a major company relocating or a sudden policy shift—can disrupt even the most advanced models. “Boeing moving out of Seattle? That’s not something the model can foresee,” he noted. While the investment team uses AI to maximize upside, the lending team applies it to stress-test downside risks, ensuring robust risk management. Future upgrades will incorporate asset allocation recommendations across commercial real estate sectors, aiming to optimize portfolio mix. For Carrafiell, AI isn’t a replacement for human expertise—it’s a powerful tool. “It’s like having a six-person data science team sitting next to your CEO and your acquisitions team,” he said. “AI enhances and accelerates what we do, but it’s still rooted in data science.”