A big data–based geographically weighted regression model for public housing prices: A case study in Singapore

• Main Authors: CAO, Kai, DIAO, Mi, WU, Bo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: big data; GWR; Housing Development Board (HDB); hedonic pricing model; Singapore; Asian Studies; Databases and Information Systems; Urban Studies and Planning
• Online Access: https://ink.library.smu.edu.sg/sis_research/5460; https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6463&context=sis_research
• Institution: Singapore Management University

In this research, three hedonic pricing models, including an ordinary least squares (OLS) model, a Euclidean distance–based (ED-based) geographically weighted regression (GWR) model, and a travel time–based GWR model supported by a big data set of millions of smartcard transactions, have been develo...