Housing Price Forecastability: A Factor Analysis

Lasse Bork, Stig Vinther Møller

Research output: Contribution to journalJournal articleResearchpeer-review

16 Citations (Scopus)
375 Downloads (Pure)


We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets.
Original languageEnglish
JournalReal Estate Economics
Issue number3
Pages (from-to)582-611
Number of pages30
Publication statusPublished - 1 Sept 2018


  • House prices
  • Forecasting
  • Factor model
  • Principal components
  • Partial Least Squares


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