Housing Price Forecastability: A Factor Analysis

Lasse Bork, Stig Vinther Møller

Research output: Contribution to journalJournal articleResearchpeer-review

3 Citations (Scopus)

Abstract

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
Volume46
Issue number3
Pages (from-to)582-611
Number of pages30
ISSN1080-8620
DOIs
Publication statusPublished - 1 Sep 2018

Fingerprint

Factor analysis
Housing prices
Partial least squares
Predictive power
Principal component analysis
Economics
Regression model
Benchmark
Macroeconomic fundamentals
Rent

Keywords

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

Cite this

Bork, Lasse ; Møller, Stig Vinther. / Housing Price Forecastability : A Factor Analysis. In: Real Estate Economics. 2018 ; Vol. 46, No. 3. pp. 582-611.
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Housing Price Forecastability : A Factor Analysis. / Bork, Lasse; Møller, Stig Vinther.

In: Real Estate Economics, Vol. 46, No. 3, 01.09.2018, p. 582-611.

Research output: Contribution to journalJournal articleResearchpeer-review

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