Thies Lindenthal

Thies Lindenthal

Research and teaching, mostly real estate finance, a bit of Big Data, ML, domain names, and a careful dose of proptech. University of Cambridge, Department of Land Economy.

Work in Progress

  • Leow, K and T. Lindenthal. “Enhancing Real Estate Investment Trust (REIT) Return Forecasts via Machine Learning”
    • We add to the emerging literature on machine learning empirical asset pricing by analyzing a comprehensive set of return prediction factors on REITs. We find that REIT in vestors experience significant economic gains when using machine learning forecasts. Comparing to the stock market (Gu et al. 2020), we show that REITs are more predictable than stocks, and that the superiority of non-linear machine algorithms is robust to both large and small predictor sets for REITs while stocks tend to suffer from smaller predictor sets. Risk-based signals such as secured debt emerge as the most dominant group of predictive characteristics for REITs, as opposed to price trends for stocks. For macroeconomic predictors, aggregate market variance (a risk-based factor) is the most important time series predictor for REITs, as opposed to aggregate book-to-market ratio (a fundamentals-based signal) for stocks.
  • Eichholtz, P.; M. Korevaar and T. Lindenthal. “Growth and Predictability of Urban Housing Rents”
    • This paper studies urban rental prices for half a millennium (1500–2020) and seven cities: Amsterdam, Antwerp, Bruges, Brussels, Ghent, London, and Paris. Based on a dataset of 436,000 rental cash flow observations, we build continuous annual indices of housing rents, which we employ to study the long-term developments in rental cash flows, as well as their predictability. We find that real rent growth has been limited, but with large differences across cities: average annual growth rates range between 0.12 percent for the Belgian cities to 0.30 percent for Paris. At the market level, we show that sluggish supply adjustment implies that past population growth negatively predicts current rental growth. At the individual asset level, we find that past excess rental growth rates are predictive of future rent revisions, and that increasing steepness of the term structure of contract rents is predictive for future rent levels.
  • Eichholtz, P.; M. Korevaar and T. Lindenthal. “The Housing Affordability Revolution”
    • This paper provides the first long-term overview of developments in urban housing affordability, quality and inequality, focusing on seven European cities from 1500 to the present. Based on the rental indices developed by EKL (2022), we create new indices of housing quality and inequality, and relate these to changes in wages and population. Before 1900, markets were unregulated and rent prices and wages rose in tandem when cities grew while housing quality and inequality increased. We document a housing affordability revolution between the 1910s and the 1970s when housing affordability and quality improved dramatically while housing consumption inequality declined. We show that part of the short-term affordability improvement in this period was attributable to rent controls and housing supply expansions. Most of the surge in housing expenditure that did occur over time is due to increasing housing quality rather than rising rent.
  • T. Lindenthal and C. Schmidt. “The Odd One Out: Asset Uniqueness and Price Precision”
  • Lindenthal T. and P. Eichholtz. “That’s What We Paid for It: The Spell of the Home Purchase Price through the Centuries”

Published work

My publications can be found here.