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

ArXiv ID: 2505.09620 “View on arXiv”

Authors: Unknown

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.

Keywords: Real Estate Prices, Machine Learning, k-Nearest Neighbors (kNN), Macro-Economic Factors, Central Bank Policy

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper uses accessible machine learning models (kNN, tree-bagging) with no advanced mathematical derivations, but implements sensitivity tests and compares predictions to existing indices using macroeconomic data from multiple countries over decades, making it practical and data-heavy.

  flowchart TD
    A["Research Goal: Model Real Estate Prices via Macro-Economic Factors"] --> B["Data Collection & Feature Selection"]
    B --> C["Machine Learning Modeling"]
    C --> D["Sensitivity & Uncertainty Analysis"]
    D --> E["Key Findings & Outcomes"]

    subgraph B ["Inputs"]
        B1["MEFs: GDP, CPI, 10-yr Treasury"]
        B2["Central Bank Assets: ECB, FED"]
    end

    subgraph C ["Models"]
        C1["kNN"]
        C2["Tree-Bagging"]
        C3["Period Discrimination"]
    end

    subgraph E ["Outcomes"]
        E1["Predict 4-Quarter Price Trends"]
        E2["Identify Arbitrage in Unconventional Policy Era"]
        E3["Outperform Index Uncertainties"]
    end

    B1 & B2 --> C
    C1 & C2 & C3 --> D
    D --> E1 & E2 & E3