Abstract:
Accurate forecasts of home sales can provide valuable information for not only, policy
makers, but also financial institutions and real estate professionals. Given this, our analysis
compares the ability of two different versions of Singular Spectrum Analysis (SSA) meth-
ods, namely Recurrent SSA (RSSA) and Vector SSA (VSSA), in univariate and multivariate
frameworks, in forecasting seasonally unadjusted home sales for the aggregate US economy
and its four census regions (Northeast, Midwest, South and West). We compare the perfor-
mance of the SSA-based models with classical and Bayesian variants of the autoregressive
and vector autoregressive models. Using an out-of-sample period of 1979:8-2014:6, given an
in-sample period of 1973:1-1979:7, we find that the univariate VSSA is the best performing
model for the aggregate US home sales, while the multivariate versions of the RSSA is the
outright favorite in forecasting home sales for all the four census regions. Our results high-
light the superiority of the nonparametric approach of the SSA, which in turn, allows us to
handle any statistical process: linear or nonlinear, stationary or non-stationary, Gaussian or
non-Gaussian.
