Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines
||Hill, Robert; Michael Scholz
||Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines
||23rd Annual European Real Estate Society Conference in Regensburg, Germany
||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.
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||Residential Real Estate
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