Housing price forecastability: A factor analysis

Lasse Bork and Stig V. Møller ()
Additional contact information
Stig V. Møller: Aarhus University and CREATES, Postal: Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We examine US housing price forecastability using a common factor approach based on a large panel of 122 economic time series. We find that a simple three-factor model generates an explanatory power of about 50% in one-quarter ahead in-sample forecasting regressions. The predictive power of the model stays high at longer horizons. The estimated factors are strongly statistically signi?cant according to a bootstrap resampling method which takes into account that the factors are estimated regressors. The simple three-factor model also contains substantial out-of-sample predictive power and performs remarkably well compared to both autoregressive benchmarks and computational intensive forecast combination models.

Keywords: House prices; Forecasting; Factor model; Principal components; Macroeconomic factors; Factor forecast combination; Bootstrap (search for similar items in EconPapers)
JEL-codes: C53 E3 G1 (search for similar items in EconPapers)
Pages: 44
Date: 2012-05-25
New Economics Papers: this item is included in nep-for and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
https://repec.econ.au.dk/repec/creates/rp/12/rp12_27.pdf (application/pdf)

Related works:
Journal Article: Housing Price Forecastability: A Factor Analysis (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2012-27

Access Statistics for this paper

More papers in CREATES Research Papers from Department of Economics and Business Economics, Aarhus University
Bibliographic data for series maintained by ().