PropertyQuants is founded by a pair of quant traders with a track record of successfully deploying automated trading models in global financial markets.
We're bringing the quantitative revolution to real estate investing. Property investing today is often driven by emotion, incomplete and inadequate information, and untested assumptions and hypotheses. It can and should be evidence-based, data-driven, and systematic.
Today we are a truly unique team, with significant experience across the fields of data science, quantitative investing, software and mobile app development, research, communications, and real estate fund management. We are leading the way forward by using real estate data science to help investors beat the market.
Why choose us
Our technology automatically keeps track of a wide range of relevant real estate (and related) data series around the world, all cleaned, standardized, and stored in our proprietary data warehouse. Our team researches and understands laws, taxes, costs, financing, and procedures for global real estate investment, as well as relevant locality intelligence, all of which are coded into our research systems.
We’ve designed a customized analysis platform that combines cloud-based database technologies, geographic information systems, machine learning / artificial intelligence / data science tools, and quantitative / systematic investment methodologies. This allows us to study global real estate markets, visualize relationships across our database, and help clients generate specific and actionable data-driven insights. We are able to empirically identify factors that influence real estate investment performance, helping market participants move from anecdotal and unverified heuristics to evidence-based and systematic decision making.
We are launching private real estate investment funds.
Powered by our global data warehouse, proprietary back-tested algorithms, and portfolio optimization system, these will be truly quantitative, ML/AI-driven property funds.
We’re keen to help real estate businesses implement data-driven decision making approaches and produce tangible and quantifiable results through bespoke consulting engagements. Or, if your business has a property-related data science project you need help with, we’d love to help you find actionable insights and business improvements.
We’re experts in Real Estate Data Science.
Sign up for our flagship course and learn all you need to be a Real Data Scientist. This course will equip participants with all the pre-requisites to be a Data Scientist (in any field), plus real estate specific techniques and knowledge.
Or, sign up for one of our short or medium courses to get a useful understanding of a specific topic e.g., Geographic Information Systems, Python, etc.
Nelson Lau 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 started his career as a trader/researcher at an award-winning hedge fund deploying medium and low frequency strategies to global markets, and also spent significant time as lead trader at a leading global high frequency trading firm focusing on Asian markets.
Xingzhi Cheng has a PhD in Statistical Physics from the National University of Singapore (NUS) and a B.S. in Computer Science from Peking University. 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.
Rayson Yeong is a CFA Charterholder and completed his undergraduate work at Nanyang Technological University Singapore, majoring in Financial Analysis. He has more than 15 years in real estate, transacting over a billion dollar of real estate projects across Asia in all asset classes. He was a Fund manager in various institutional investors and fund management companies and had a successful track record of real estate investing in his career.
Pauline Lim read International Relations with NTU and English, Sociology with NUS. In 2015, she interned with the American Chamber of Commerce in Singapore and worked on the SG50 report. She has an interest in current affairs and sees value in data analytics. Currently, Pauline is learning data science with a local institution and is happy for the opportunity to work on real estate data.
Ziteng Wang has a Bachelor’s degree in Information Systems from Singapore Management University(SMU). He has completed several IT projects for different companies, including a mobile app for MSD, an e-learning platform for Epitrain and a POC data analysis project for Johnson & Johnson.
If you are a real estate institution, broker, developer, agent, fund, data provider, individual investor, PropTech startup, or if you'd simply like to find out more about what we're doing, drop us an e-mail:
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