# RECORD 6ae7a44769e1c434ed0b69f49a62bca7496a80ac
# TITLE: Higher frequency hedonic property price indices: a state-space approach
# YEAR: 2020.0  VENUE: Empirical Economics  DOI: 10.1007/s00181-020-01862-y
# AUTHORS: R. Hill; A. Rambaldi; Michael Scholz
# PAGES: 25  CANDIDATE TABLE PAGES (1-based): []
# ABSTRACT: The hedonic imputation method allows characteristic shadow prices to evolve over time. These shadow prices are used to construct matched samples of predicted prices, which are inserted into standard price index formulas. We use a spatio-temporal model to improve the method’s effectiveness on housing data at higher frequencies. The problem is that at higher frequencies, there may not be enough observations per period to reliably estimate the characteristic shadow prices. In such cases, the reliability of the hedonic imputation method is improved by using a state-space formulation which yields estimates of the shadow prices that are weighted sums of previous periods’ information. In addition, the state-space representation of the model includes a geospatial spline surface which significantly reduces the number of parameters to be estimated when compared to the standard practice of including postcode dummies in the model. Empirically, using a novel criterion, we show that in higher frequency comparisons, our hedonic method outperforms competing alternatives.


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Empirical Economics (2021) 61:417–441
https://doi.org/10.1007/s00181-020-01862-y
Higher frequency hedonic property price indices:
a state-space approach
Robert J. Hill1 · Alicia N. Rambaldi2 · Michael Scholz1
Received: 16 June 2019 / Accepted: 17 March 2020 / Published online: 11 April 2020
© The Author(s) 2020
Abstract
The hedonic imputation method allows characteristic shadow prices to evolve over
time. These shadow prices are used to construct matched samples of predicted prices,
which are inserted into standard price index formulas. We use a spatio-temporal model
to improve the method’s effectiveness on housing data at higher frequencies. The prob-
lem is that at higher frequencies, there may not be enough observations per period to
reliably estimate the characteristic shadow prices. In such cases, the reliability of
the hedonic imputation method is improved by using a state-space formulation which
yields estimates of the shadow prices that are weighted sums of previous periods’ infor-
mation. In addition, the state-space representation of the model includes a geospatial
spline surface which significantly reduces the number of parameters to be estimated
when compared to the standard practice of including postcode dummies in the model.
Empirically, using a novel criterion, we show that in higher frequency comparisons,
our hedonic method outperforms competing alternatives.
Keywords Housing market · Hedonic imputation · State-space model · Geospatial
data · Spline · Quality adjustment and matched sample
JEL C33 · C43 · R31
We acknowledge financial support for this project from the Austrian Research Promotion Agency (FFG),
Grant #10991131, and we thank Australian Property Monitors for supplying the data. We are very thankful
for the detailed and excellent comments provided by an anonymous referee and the handling editor.
B Michael Scholz
michael.scholz@uni-graz.at
Robert J. Hill
robert.hill@uni-graz.at
Alicia N. Rambaldi
a.rambaldi@uq.edu.au
1
Department of Economics, University of Graz, Universitätsstr. 15/F4, 8010 Graz, Austria
2
School of Economics, The University of Queensland, St. Lucia, QLD 4072, Australia
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