Demystifying Quantitative Methods in Real Estate Credit Investment

Tingting Zhang, Ph.D., Founder and CEO, The TerraCotta Group

While quantitative data analysis has long been adopted as a tool in public market investment, it still raises eyebrows when we explain to investors how TerraCotta applies regression analysis to property valuation. On one hand, we understand the long-held belief that success in real estate credit investment, as in other private market investments, is driven by experience, knowledge and market intel that cannot be replaced by data and algorithms. On the other, it is high time to recognize that the traditional approach to real estate credit investment has been hit-or-miss. Historically, credit performance has been highly co-related with the state of the economy. Credit performs well during the good times and poorly in bad times. Reliance on third-party appraisals and the gut-instincts of grey-haired industry experts has proven inadequate to address market risks inherent in real estate credit strategies. This white paper aims to shed light on decades of scientific progress in the application of Quantitative Methods in solving complex real-world problems. It will examine the fallacy of the common bias against a data-driven methodology in real estate credit investment, and highlight how TerraCotta has been successfully using data and a quantitative methodology since the firm’s inception in 2004. It is my hope that this paper will spur further debate in the human vs. machine dialogue in the broader context of private market investment, and provide food for thought about the need for intellectual diversity in our industry of investment management.