Can Big Data Help Forecast House Prices?
- By Sam van de Schootbrugge
- 4 min read
We now generate more data in two days than we did from the dawn of time until 2003. The finance profession is harnessing that information in increasingly innovative ways. A new Swiss Finance Institute research paper analyses over 70 mixed-frequency macroeconomic and financial variables to assess their ability to backcast, nowcast and forecast US house prices. Specifically, they ask whether investors can better predict today’s residential housing market returns than the two-month-lagged Case-Shiller index. They find:
Financial variables improve house price predictability, especially at higher frequencies. The best performers are the S&P 500, REITs, and the construction industry portfolio returns.
Relative to financial predictors, macroeconomic variables offer limited improvement, with housing permits a notable exception.
During periods of economic stress and market volatility, financial predictors offer a far better indication of current house prices than the Case-Shiller index.
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