Thomas Chan
"I do research in REAL ESTATE".
I have two main research directions.
The first is empirical, mainly spatial spillovers, focus on DiD, statistical machine learning and conditional average treatment effects (CATE). The second direction is deep learning–oriented real estate finance, mainly leveraging ML to discover new asset pricing factors and capture “un-observables”, with the aim of forecasting future real estate prices and enhancing investment returns.
Always fascinate me are the insights that conventional statistics or numerical methods cannot capture.
Phone:
(+44) 7407798670
Email:
Language：
Cantonese Chinese - native
Mandarin Chinese - native
English - professional
Polish - A2
Key Words: Real Estate Finance; Machine Learning (ML); Asset Pricing; Urban (Spatial) Economics; Spatial Shock; Conditional Average Treatment Effect (CATE); Representation Learning
A Bit About Me
Undergraduate Education:
University of Warsaw (Econ, Top 1st GPA)
National Taiwan University, NTU (Econ, exchange, Top 5%)
University of Manchester (Urban, exchange, fisrt class)
Research Experiences:
Chinese University of Hong Kong (CUHK)
-(Center of Real Estate), with Prof. Desmond Tsang*
Kiel Institute for World Economy, Germany
-German Real Estate Index (GREIX) Team. (Machine-Learning Specialist)
Grant:
IV.2.2. Inclusion of Talented Young Scientists in Research Teams 2025
-By University of Warsaw, no. BP-015-0-414/2023 (Individual Research Grant, 2500 Euro)
Hong Kong Government Research Grants Committee (RGC) – General Research Fund (GRF) 2026/27 Candidate
-Co-author with Prof. Desmond Tsang (CUHK), under-review, amount: HK$800,000 (~£100,000)
◦ Project title: “The Starbucks Effect: Spatial Spillover on U.S. Commercial Real Estate”
Research Experience
Oct 2025 - Present
Aug 2025 - Present
9-11, July 2025
Melbourne, Australia
Berlin, Germany (remote)
Kiel Institute for World Economy (ifW, Kiel)
May 2025 - Aug 2025
Zurich, Switzerland
Hong Kong
Erasmus+ Exchange (Visiting reseacher)
University of Zurich (UZH), Departent of Finance
-
Utilised German real estate listing data to analyse housing price dynamics using Machine Learning techniques under the supervision of Prof. Francisco Amaral (UZH).
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Participated in the 1st Zurich-Oxford Syspotsium on Real Estate Market.
Assistant ML Researcher - Kiel Insitute for the World Economy
German Real Estate Index (GREIX) Team
-
Lead of Machine Learning (ML) project, apply deep learning architecture into GREIX time-seires forecasting and built a warning system.
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Utilized 54 Macro-level German economic data for index prediction (including GDP, unemployment, migration, financing conditions, and infrastructure indicators etc.). Heterogeneity Test applied for regional level.
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LSTM-Transformer-Attention architecture, KNN, Casual Forest, Ridge, XGBoost
Paper Presentation
AsRES International Conference 2025 (Asian Real Estate Society)
Research Assistant (RA)
Chinses Univeristy of Hong Kong (CUHK), Center for Real Estate research
-
responsible for financial data collection and analysis for mainland Chinese REITs, as well as 97 listed mainland Chinese real estate company.
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data collecting and machanism design for project “entertainment cost and Chinese real estate company‘s financing capabilities and financial performance", conducted by Prof. Desmond Tsang (CUHK) & Prof. Xiaoling Chu (Uni Macau), paper under-review by JREFE.
Ongoing Projects
"Empirics":
June 2025 - Present
July 2025 - Present
"Deep Learning":
Aug 2025 - Present
Sep 2025 - Present
The Starbucks Effect: Spatial Spillover on U.S. Commercial Real Estate
(with Desmond Tsang (CUHK), Erkan Yonder (Concordia), Wayne Wan (Monash U)
Causal Forest Application in Real Estate Pricing: Estimating Conditional Average Treatment Effects
(with Desmond Tsang, Erkan Yoner, Yagiz Karsli)
Forecasting German Real Estate Index (GREIX), A LSTM–Transformer–Attention with Incremental Learning Approach
(with Jonas Zdrzalek, Kiel Institute)
Semantic Embeddings: Do News Sentiment or Informational Content Drive Real Estate Prices?
(Ongoing)
“Born in the Guangdong-Hong Kong-Macau Greater Bay Area, exposed me to one of the highest real estate prices in the world.
Pursuing Undergraduate of Economics in Poland, I directly experienced the recent severe supply-demand imbalance in continental Europe’s housing market, caused by geopolitical tensions.
And Machines Learing bacame my way to interpret the real estate world"
