Housing Price Forecastability: A Factor Analysis

EFA 2012 Copenhagen Meetings Paper

33 Pages Posted: 24 May 2012 Last revised: 20 Mar 2016

Lasse Bork

Aalborg University - Department of Business and Management

Stig Vinther Møller

University of Aarhus - CREATES

Date Written: March 19, 2016

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.

Keywords: House prices, Forecasting, Partial least squares, Principal components, Macroeconomic factors

JEL Classification: C53, E3, G1

Suggested Citation

Bork, Lasse and Møller, Stig Vinther, Housing Price Forecastability: A Factor Analysis (March 19, 2016). EFA 2012 Copenhagen Meetings Paper. Available at SSRN: https://ssrn.com/abstract=2065343 or http://dx.doi.org/10.2139/ssrn.2065343

Lasse Bork

Aalborg University - Department of Business and Management ( email )

Aalborg, DK-9220
Denmark
+45 9940 2707 (Phone)

HOME PAGE: http://personprofil.aau.dk/profil/123645?lang=en

Stig Vinther Møller (Contact Author)

University of Aarhus - CREATES ( email )

Nordre Ringgade 1
Aarhus, DK-8000
Denmark

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