Bio

Real estate finance researcher using AI, machine learning, computer vision, and long-run data to study property markets

Thies Lindenthal is the Grosvenor Professor of Real Estate Finance at the Department of Land Economy, University of Cambridge and a Professorial Fellow at Pembroke College.

His research sits at the intersection of real estate finance, AI, machine learning, computer vision, and long-run historical data. He studies how unconventional data and modern AI methods can improve our understanding of property markets, investment behaviour, valuation, and risk.

One strand of his work examines real estate markets over the very long run, reconstructing rents, prices, and returns across centuries. Another applies machine learning, semantic embeddings, image analysis, and “Big(ish)” data to questions that traditional empirical methods struggle to address: how aesthetics shape property values, how investors forecast returns, how households reveal preferences, and how market participants make imperfectly rational decisions.

Recent work explores whether AI can help generate and map research ideas in real estate, how machine learning can improve REIT return forecasts, and how missing data can be used rather than discarded in property-market prediction. His broader aim is to combine economic theory, historical evidence, and AI-based methods to produce better measures of real estate risk, value, and behaviour.

Before joining the University of Cambridge, Thies was a postdoctoral researcher at MIT’s Center for Real Estate, where he worked on the market for virtual locations such as Internet domain names. He received his PhD from Maastricht University.

Professor Lindenthal has served as a board member of the American Real Estate and Urban Economics Association (AREUEA), as a JM Keynes Fellow in Financial Economics, and as an expert witness on internet domain names in US courts.