The impact of economic policies on housing prices. Approximations and predictions in the UK, the US, France, and Switzerland from the 1980s to today

TL;DR

This paper demonstrates that machine learning models using macroeconomic factors can effectively predict housing prices and detect market regime shifts across multiple countries, outperforming traditional indices in short-term forecasts.

Contribution

It introduces a machine learning approach with macroeconomic data for modeling and predicting housing prices, highlighting the role of central bank policies and market arbitrage opportunities.

Findings

Models can discriminate different market periods.

Some models predict next 4 quarters' prices better than indices.

Unconventional central bank policies influence real estate returns.

Abstract

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macro-economic 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. 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.