Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines

Robert Hill () and Michael Scholz

ERES from European Real Estate Society (ERES)

Abstract: Determining how and when to use geospatial data (i.e., longitudes and latitudes for each house) is probably the most pressing open question in the house price index literature. This issue is particularly timely for national statistical offices in the European Union who are now required by Eurostat to produce official house price indexes. Our solution combines the hedonic imputation method with a flexible hedonic model that captures geospatial data using a nonparametric spline surface. For Sydney, Australia, we find that the extra precision provided by geospatial data as compared with postcode dummies has only a marginal impact on the resulting hedonic index. This is good news for resource-stretched statistical offices. We nevertheless observe a slight downward bias when postcodes are used (which gets much larger when postcodes are replaced by bigger Residex regions). This bias can be attributed to a gradual shift of sold houses towards worse locations within each postcode (Residex region) during our sample period.

JEL-codes: R3 (search for similar items in EconPapers)
Date: 2016-01-01
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://eres.architexturez.net/doc/oai-eres-id-eres2016-146 (text/html)

Related works:
Journal Article: Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines (2018)
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:arz:wpaper:eres2016_146

Access Statistics for this paper

More papers in ERES from European Real Estate Society (ERES) Contact information at EDIRC.
Bibliographic data for series maintained by Architexturez Imprints ().