Housing Price Forecastability: A Factor Analysis
Lasse Bork and
Stig V. Møller
Real Estate Economics, 2018, vol. 46, issue 3, 582-611
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.
References: Add references at CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
Working Paper: Housing price forecastability: A factor analysis (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:reesec:v:46:y:2018:i:3:p:582-611
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1080-8620
Access Statistics for this article
Real Estate Economics is currently edited by Crocker Liu, N. Edward Coulson and Walter Torous
More articles in Real Estate Economics from American Real Estate and Urban Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().