Real Estate Data Science Workshops
We regularly run in-depth, hands on, online classes teaching specific topics in the application of data science to real estate. Check out the upcoming sessions below.


[V1] Visualizing Data in Python

Understand and communicate the story living in your data with graphical representations that summarize large amounts of information to quickly highlight relationships, patterns, trends, and outliers. In this session, we’ll discuss:

  • Types of charts and when to use each
  • Charting with Matplotlib and Seaborn (including advanced features such as multiple plots on the same chart, outputting to pdf, and more)
  • Parallels between Tableau, Qlikview, PowerBI and Python capabilities
  • Other packages (Altair, ggplot, Plotly, D3.js)

While examples are drawn heavily from real estate, these methods are highly adaptable across industries.

Prerequisites: A working knowledge of Python and Pandas. Scikit-Learn is helpful but not required.

Format: 3h online lecture

Next Courses:
December 8, 2020 - 8 AM GMT

January 11, 2021 - 8 PM EST

[P1] Portfolio Construction and Optimization in Real Estate

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

Prerequisites: A working knowledge of Python and Pandas. Knowledge of cluster analysis (e.g., in Scikit-Learn).

Format: 3h online lecture

Next Courses:
January 29, 2021 - 8 AM GMT

February 4, 2021 - 8 PM EST

[E1] Introduction to Databases and SQL for Real Estate

Harness the potential of truly large datasets to find market-beating insights and opportunities. In this hands-on session, we’ll explain what databases are, how they are set up, and work through numerous exercises using Structured Query Language (SQL) to efficiently find and extract the specific information you need. Topics include:

  • Why use databases, types of databases, and use cases
  • Best practices in data architecture and datatable design
  • Inserting and retrieving data (including SQL queries)
  • Accessing databases across different environments (including Python)
  • Spatial database extensions (PostGIS, SpatiaLite)

While examples are drawn heavily from real estate, these methods are highly adaptable across industries.

Prerequisites: A working knowledge of Python.

Format: 3h live online lecture

Next Courses:
January 21, 2021 - 8 AM GMT

January 26, 2021 - 8 PM EST

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Meet Your Instructors

Nelson Lau, PhD, CFA

Nelson 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.

Xingzhi Cheng, PhD

Xingzhi is CTO of PropertyQuants and has a PhD in Statistical Physics from the National University of Singapore (NUS) and a B.S. in Computer Science from Peking University, with papers published in Physical Review Letters and elsewhere.

He was a postdoctoral research fellow at the Santa Fe Institute and NUS before moving to quantitative trading, where he has 5 years of experience as a researcher, trader, and quantitative developer.

Xingzhi enjoys architecting and developing software and frameworks for systematic and automated research. He’s also developed mobile apps and several different websites in his free time, one of which focused on tracking SGX-listed REITs, and another which analyzed which properties were best to buy or rent for parents in Singapore looking to maximize primary school admission priority for their children. He’s currently excited about building the PropertyQuants platform enabling quantitative and systematic approaches to be applied to real estate investing globally.

Keith Tan

Keith Tan is a Data Scientist at PropertyQuants. He has a Bachelor of Science (Economics) from Singapore Management University, with a double major in Finance.

He was in the pioneer batch of Management Associates at Singapore Economic Development Board, and eventually graduated to lead the investment facilitation for technology MNCs looking to anchor and expand operations in Singapore. He also graduated from General Assembly with a certification in Data Science.