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Paper eres2008_111:
PREDICTING HOUSE PRICES WITH SPATIAL DEPENDENCE: IMPACTS OF ALTERNATIVE SUBMARKET DEFINITIONS

id eres2008_111
authors Bourassa, Steven C.; Eva Cantoni; Martin Hoesli
year 2008
title PREDICTING HOUSE PRICES WITH SPATIAL DEPENDENCE: IMPACTS OF ALTERNATIVE SUBMARKET DEFINITIONS
source Book of Abstracts: 15th Annual European Real Estate Society Conference in Kraków, Poland
summary We analyze the impacts of alternative submarket definitions when predicting house prices in a mass appraisal context, using both ordinary least squares (OLS) and geostatistical techniques. For this purpose, we use over 13,000 housing transactions for Louisville, Kentucky. We use districts defined by the local property tax assessment office as well as a classification of census tracts generated by principal components and cluster analysis. We also experiment with varying numbers of census tract groupings. Our results indicate that somewhat better results are obtained with more homogeneous submarkets. Also, the accuracy of house price predictions increases as the number of submarkets is increased, but then quickly levels off. Adding submarket variables to the OLS model yields price predictions that are as accurate as when geostatistical methods are used to account for spatial dependence in the error terms. However, using both dummy variables for subm arkets and geostatistical methods leads to significant increases in accuracy.
series ERES:conference
email martin.hoesli@hec.unige.ch
discussion No discussions. Post discussion ...
ratings
session G5
last changed 2008/11/26 14:05
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