TY - JOUR
T1 - Housing Price Forecastability
T2 - A Factor Analysis
AU - Bork, Lasse
AU - Møller, Stig Vinther
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - House prices
KW - Forecasting
KW - Factor model
KW - Principal components
KW - Partial Least Squares
UR - http://www.scopus.com/inward/record.url?scp=85008259838&partnerID=8YFLogxK
U2 - 10.1111/1540-6229.12185
DO - 10.1111/1540-6229.12185
M3 - Journal article
SN - 1080-8620
VL - 46
SP - 582
EP - 611
JO - Real Estate Economics
JF - Real Estate Economics
IS - 3
ER -