In this session, we’ll use quantitative approaches applied in Python to construct optimal portfolio allocations from a set of real estate investment opportunities. This hands on session will enable participants to maximize returns and minimize risk. We’ll cover:

Risk and return metrics
Modern Portfolio Theory
Risk Parity
Real estate specific considerations

Format: 3h online lecture

Learn more: https://propertyquants.com/workshops

Prerequisites: A working knowledge of Python and Pandas. Knowledge of cluster analysis (e.g., in Scikit-Learn) is recommended. (PropertyQuants offers a bootcamp in Python, Pandas, and Scikit-Learn. Learn more here.)

Meet your instructor:

Nelson Lau, PhD, CFA is the CEO of PropertyQuants Pte. Ltd., a PropTech startup bringing quantitative methods to global real estate. He has a PhD in Decision Sciences from INSEAD, is a CFA Charterholder, and completed his undergraduate work at Columbia University, double majoring in Economics and Mathematics-Statistics.

He has published papers in Management Science, Decision Support Systems, and Decision Analysis, one of which received a special recognition award. Nelson started his career as a trader/researcher at R G Niederhoffer Capital Management, an award-winning US hedge fund deploying systematic data-driven medium and low frequency strategies to global markets, and also spent significant time as lead trader at KCG, a leading global high frequency algorithmic trading firm.

He was also a Quantitative Macro Strategist at GIC and Managing Director at a proprietary trading firm (Acceletrade Technologies). Nelson has been investing in international residential real estate in a personal capacity for 10 years, and has a deep interest in bringing more systematic, quantitative, and data-driven approaches to real estate practice.

Course Terms & Conditions

[PQ REDS Series] Portfolio Construction and Optimization in Real Estate

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