2022-09-262022-09-262020https://repository.tcu.edu/handle/116099117/55769House Price Index (HPI) is commonly used to estimate the changes in housing price. Since housing price is strongly correlated to other factors such as location, area, population, it requires other information apart from HPI to predict individual housing price. There has been a considerably large number of papers adopting traditional machine learning approaches to predict housing prices accurately, but they rarely concern about the performance of individual models and neglect the less popular yet complex models. As a result, to explore various impacts of features on prediction methods, this paper will apply both traditional and advanced machine learning approaches to investigate the difference among several advanced models. This paper will also comprehensively validate multiple techniques in model implementation on regression and provide an optimistic result for housing price prediction. © 2020 The Authors. Published by Elsevier B.V.https://creativecommons.org/licenses/by-nc-nd/4.0/Housing Price PredictionHybrid RegressionMachine LearningStacked GeneralizationConference ProceedingCC BY-NC-ND 4.0https://doi.org/10.1016/j.procs.2020.06.111