TY - GEN
T1 - Novel methods to demarcate urban house submarket-cluster analysis with spatially varying relationships between house value and attributes
AU - Yu, Danlin
AU - Yin, Jingyuan
AU - Ye, Feiyue
PY - 2011
Y1 - 2011
N2 - In urban house market studies, urban housing market can be divided into a series of submarkets. Usually, submarkets are identified with either geographic locations or housing structural characteristics, or some combination of both. In this study, we propose an alternative to identify urban housing submarkets. Instead of using house characteristics or locations, we use the relationships obtained through a geographically weighted hedonic regression (GWHR) model. In particular, we apply a K-means classification on the coefficients obtained via GWHR to identify different submarkets. Data from the City of Milwaukee are used to test the model and procedure. Comparison of a regular cluster analysis using housing structural and neighborhood socioeconomic information and the proposed procedure is conducted in terms of prediction accuracy. The analytical results suggest that hedonic regression on demarcated submarkets is better than a uniform market, and our proposed method yields more reasonable result than the ones using raw data.
AB - In urban house market studies, urban housing market can be divided into a series of submarkets. Usually, submarkets are identified with either geographic locations or housing structural characteristics, or some combination of both. In this study, we propose an alternative to identify urban housing submarkets. Instead of using house characteristics or locations, we use the relationships obtained through a geographically weighted hedonic regression (GWHR) model. In particular, we apply a K-means classification on the coefficients obtained via GWHR to identify different submarkets. Data from the City of Milwaukee are used to test the model and procedure. Comparison of a regular cluster analysis using housing structural and neighborhood socioeconomic information and the proposed procedure is conducted in terms of prediction accuracy. The analytical results suggest that hedonic regression on demarcated submarkets is better than a uniform market, and our proposed method yields more reasonable result than the ones using raw data.
KW - Cluster analysis
KW - Geographically weighted hedonic regression
KW - House submarket
KW - Milwaukee
UR - http://www.scopus.com/inward/record.url?scp=84856272161&partnerID=8YFLogxK
U2 - 10.1049/cp.2011.0288
DO - 10.1049/cp.2011.0288
M3 - Conference contribution
AN - SCOPUS:84856272161
SN - 9781849193269
T3 - IET Conference Publications
SP - 41
BT - IET International Conference on Smart and Sustainable City, ICSSC 2011
T2 - IET International Conference on Smart and Sustainable City, ICSSC 2011
Y2 - 6 July 2011 through 8 July 2011
ER -