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The #1 Real Estate Course From PhDs & Finance Experts

Become a Real Estate Data Scientist in 11 Weeks.


PropertyQuants is the only Masters-level Real Estate Course that gives you the hard skills to find market-beating real estate opportunities & take action before the rest of the market. Live online classes taught by PhD instructors, using data science and machine learning that’s proven to work.

Data Science Fundamentals

Geographic Information Systems

Data Science Methods for Real Estate

Statistical Analysis & Forecasting Using Large Datasets

What You Will Learn

Data Science Fundamentals

including Python, Pandas, and Scikit-Learn.

Geographic Information Systems

for skills like visualizing commute times by location, school quality, and population demographics, as well as understanding spatial relationship and patterns.

Data Science Methods for Real Estate

such as index construction, automated valuation, cluster analysis, and time series forecasting.

Statistical Analysis & Forecasting Using Large Datasets

for skills like determining fair transaction prices and forecasting future returns.

Course Overview

We are proud to offer one of the world’s first data science, machine learning, and GIS courses dedicated to analyzing, investing in, and forecasting property globally.

Through a series of interactive online lectures, hands-on learning and the completion of a key capstone project, you will gain the knowledge and expertise to construct indexes, automate valuations, analyze clusters and forecast time series.

You will gain the necessary skills to utilize large datasets to determine fair transaction prices, forecast future returns and how to analyze locations with geographic information systems (GIS).

Course Segments & Time Commitment
The program is made up of three main segments, Boot Camp, Real Estate and GIS. Segments can be taken individually, pending your interest and level of expertise.
Total Course Time Commitment: ~60 hours - 5 hours/week

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Course Modules

Python We cover everything you need (including how to install Python!) to successfully analyze real estate with our techniques and produce a great capstone project.

Pandas allows you to quickly and easily organize and work with large datasets, including time series data.

Scikit-Learn Learning this package will enable you to effectively use the large majority of popular machine learning algorithms in existence today.

We discuss the opportunities unlocked by applying data science to real estate. We also present an hands-on lesson in programmatically gathering data from a wide variety of web sources.

We have all seen real estate price indices, but what do they really represent and how exactly are they computed?

Have you tried the Zillow Zestimate? We’ll show you how to build a statistical model that uses large data sets to predict a fair transaction price for a property. Never overpay for a house again!

Where are markets headed? Learn to use statistical models to predict future trends and real estate performance.

Different real estate sub-markets sometimes diverge in investment performance. How can we identify groups of properties that are likely to perform similarly, so that we can price and accurately forecast?

Our course culminates in a capstone project. Participants will choose a real-world dataset and apply some of the techniques in the course to produce analyses.

Visualize commute times by location, school quality, population demographics, and understand spatial relationship and patterns using GIS. We teach you to use Quantum GIS in this two-part series. The second session is fully hands-on, so you’ll be able to unlock the power of public data sets for your own analysis in the future.

PropertyQuants Achievements

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Who is this course for?

  • Participants with a basic foundation in mathematics / statistics at a high school level (GCE ‘A’ level, International Baccalaurate, or equivalent) or higher.
  • Participants without a background in Python, Pandas, and Sci-kit Learn are required to participate in the bootcamp prior to the course.
  • The topics we cover are novel and constitute an extension to typical data science courses. Experienced data scientists will gain significant value from participating in all sections of the program.
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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.

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