Abstract
We construct daily house price indexes for ten major U.S. metropolitan areas. Our
calculations are based on a comprehensive database of several million residential
property transactions and a standard repeat-sales method that closely mimics the procedure
used in the construction of the popular monthly Case-Shiller house price indexes.
Our new daily house price indexes exhibit similar characteristics to other daily asset
prices, with mild autocorrelation and strong conditional heteroskedasticity, which
are well described by a relatively simple multivariate GARCH type model. The sample
and model-implied correlations across house price index returns are low at the daily
frequency, but rise monotonically with the return horizon, and are all commensurate
with existing empirical evidence for the existing monthly and quarterly house price
series. A simple model of daily house price index returns produces forecasts of monthly
house price changes that are superior to various alternative forecast procedures based
on lower frequency data, underscoring the informational advantages of our new more
finely sampled daily price series.
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