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The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today

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  • Nicolas Houlié

    (Institute of Geophysics, Seismology and Geodynamics, ETH Zurich, Sonnegstrasse 5, 8002 Zurich, Switzerland)

Abstract

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties.

Suggested Citation

  • Nicolas Houlié, 2025. "The Impact of Economic Policies on Housing Prices: Approximations and Predictions in the UK, the US, France, and Switzerland from the 1980s to Today," Risks, MDPI, vol. 13(5), pages 1-50, April.
    • Handle: RePEc:gam:jrisks:v:13:y:2025:i:5:p:81-:d:1640812
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    References listed on IDEAS

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    1. Archana Singh & Apoorva Sharma & Gaurav Dubey, 2020. "Big data analytics predicting real estate prices," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 208-219, July.
    2. Zhenguo Lin & Kerry D. Vandell, 2007. "Illiquidity and Pricing Biases in the Real Estate Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(3), pages 291-330, September.
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Rosenberg, Signe, 2019. "The effects of conventional and unconventional monetary policy on house prices in the Scandinavian countries," Journal of Housing Economics, Elsevier, vol. 46(C).
    5. Farlow, Andrew, 2013. "Crash and Beyond: Causes and Consequences of the Global Financial Crisis," OUP Catalogue, Oxford University Press, number 9780199578016, Decembrie.
    6. Nneji, Ogonna & Brooks, Chris & Ward, Charles W.R., 2013. "House price dynamics and their reaction to macroeconomic changes," Economic Modelling, Elsevier, vol. 32(C), pages 172-178.
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