Abstract:
This article considers the ability of large-scale (involving 145 fundamental
variables) time-series models, estimated by dynamic factor analysis and
Bayesian shrinkage, to forecast real house price growth rates of the four US
census regions and the aggregate US economy. Besides the standard Minnesota
prior, we also use additional priors that constrain the sum of coefficients of the
VAR models. We compare 1- to 24-months-ahead forecasts of the large-scale
models over an out-of-sample horizon of 1995:01–2009:03, based on an insample
of 1968:02–1994:12, relative to a random walk model, a small-scale
VAR model comprising just the five real house price growth rates and a medium-
scale VAR model containing 36 of the 145 fundamental variables besides the
five real house price growth rates. In addition to the forecast comparison exercise
across small-, medium- and large-scale models, we also look at the ability of the
‘optimal’ model (i.e. the model that produces the minimum average mean squared
forecast error) for a specific region in predicting ex ante real house prices (in
levels) over the period of 2009:04 till 2012:02. Factor-based models (classical or
Bayesian) perform the best for the North East, Mid-West, West census regions
and the aggregate US economy and equally well to a small-scale VAR for the
South region. The ‘optimal’ factor models also tend to predict the downward
trend in the data when we conduct an ex ante forecasting exercise. Our results
highlight the importance of information content in large number of fundamentals
in predicting house prices accurately.
