Academic profile


Dr. Philip Kalikman is University Assistant Professor of Real Estate, Finance, and Economics at the University of Cambridge, Department of Land Economy. Prior to joining Cambridge, he was an Assistant Professor of Finance and Real Estate at the Sy Syms School of Business at Yeshiva University in New York City. Dr. Kalikman researches mortgage default and prepayment, housing market dynamics, effects of climate change, macro policy, financial crises, and structural, heterogeneous, computational, and AI/ML models.

Before completing his PhD, Dr. Kalikman worked as a quantitative researcher at a real estate focused hedge fund, where he led a team of programmers and data scientists developing quantitative models of mortgage performance and housing markets. He served as a consulting economic policy advisor to Secretary of State Hillary Clinton and to members of the U.S. Senate, and subsequently as lead economist for a fintech venture capital firm led by a former U.S. Under Secretary of the Treasury and a former U.S. Comptroller of the Currency.

Dr. Kalikman serves as Treasurer on the boards of Students for Educational Justice and the New York Festival of Song. He received his MA, MPhil, and PhD in Economics from Yale University and his BA in Mathematics from the University of Chicago.







Research interests


My research is focused on policy-relevant questions related to housing and equality and opportunity in the wider economy. I am interested in pushing policy towards better understanding of distributional consequences and towards embracing targeted tools to manage those consequences. I deploy big data and high-performance computational models when these tools can help avoid making sweeping simplifying assumptions. In such cases these tools can thereby help to settle debates that would otherwise be intractable. I favor structural models for their interpretability and robustness to changing underlying circumstances, and I am curious to explore what insights may arise from combining structural modeling with AI/ML.




Real Estate Finance