Integrated covariate correlative and geographically weighted model for house price prediction

  • 陳 柏宏

Student thesis: Doctoral Thesis


House prices are affected by numerous reasons Government policies economic growth and land use may cause regional housing price fluctuations Thus the housing price estimation problem will be challenging This study aims to provide simple and efficient ways to estimate house prices The normal regression model may not the best choice for estimation due to different house prices and heterogeneities in space This paper proposed a covariate weighted regression (CWR) house price estimation model which is an extended geographically weighted regression (GWR) under the conditions of various districts and house types The GWR and the machine learning models are simultaneously used to verify the model performance and evaluate practical applications of datasets from actual selling prices of real estate and house price information websites Results show that the proposed model has better performance than machine learning models in most of cases Compared with the proposed model in which only house age and building floor area are considered the RMSE of machine learning and GWR models can be improved by 8 2% and 4 5% respectively Therefore CWR can effectively reduce estimation errors from traditional spatial regression models and provide novel and feasible models for house price estimation
Date of Award2019
Original languageEnglish
SupervisorHone-Jay Chu (Supervisor)

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