Rolling-Time-Dummy House Price Indexes: Window Length, Linking and Options for Dealing with Low Transaction Volume

Robert Hill (), Scholz Michael (), Shimizu Chihiro () and Steurer Miriam ()
Additional contact information
Scholz Michael: Department of Economics, University of Klagenfurt, Universitätsstraße 65-67, 9020 Klagenfurt, Austria.
Shimizu Chihiro: University of Tokyo, Center for Spatial Information Science, 5-1-5, Kashiwanoha, Kashiwa, Chiba, 277-8568, Japan.
Steurer Miriam: Department of Economics, University of Graz, Universitätsstraße 15/F4, 8010 Graz, Austria

Journal of Official Statistics, 2022, vol. 38, issue 1, 127-151

Abstract: Rolling-time-dummy (RTD) is a hedonic method used by a number of countries to compute their official house price indexes (HPIs). The RTD method requires less data and is more adaptable than other hedonic methods, which makes it well suited for computing higher frequency HPIs (e.g., monthly or weekly). In this article, we address three key issues relating to RTD. First, we develop a method for determining the optimal length of the rolling window. Second, we consider variants on the standard way of linking the current period with earlier periods, and show how the optimal linking method can be determined. Third, we propose three ways of modifying the RTD method to make it more robust to periods of low transaction volume. These modifications could prove useful for countries using the RTD method in their official HPIs.

Keywords: House price index; hedonic quality adjustment; optimal window length; optimal chain linking; higher frequency indexes; low transaction volume (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://doi.org/10.2478/jos-2022-0007 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:38:y:2022:i:1:p:127-151:n:12

DOI: 10.2478/jos-2022-0007

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().