House Price Modeling in Semarang and Surabaya City using Component Regression with Frequentist and Bayesian Approach

DOI

10.2991/978-94-6463-566-9_22How to use a DOI?

Keywords

Bayesian; Frequentist; House Price; Entrepreneurship; Principal Component Regression

Abstract

House prices are getting more and more expensive. Entrepreneurs compete to collect assets. Therefore, this research aims to determine the right house price based on several considerations for that price. Considerations can come from the location of the house or the environment. To find out the relationship, we can use regression analysis. In a regression analysis, predictor variables often found that are interconnected. This results in multicollinearity between these variables. But often these predictor variables should theoretically be significant, and not worth removing. To overcome this, a regression analysis was compared by including all predictor variables, by including predictor variables that were highly correlated with response variables, by including the best subset selection variable, and then the principal component regression. Estimates for the models already mentioned are approached through the frequentist and Bayesian estimation methods. Based on the analysis, it was found that Bayesian analysis is better when compared to frequentist estimates. Then the principal component of the regression turns out to be better used if there is multicollinearity between variables. Thus, it means that we assume all the predictor variables are significant in the model.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Dwi Rantini
AU  - Arip Ramadan
AU  - Alhassan Sesay
AU  - Mochammad Fahd Ali Hillaby
PY  - 2024
DA  - 2024/11/01
TI  - House Price Modeling in Semarang and Surabaya City using Component Regression with Frequentist and Bayesian Approach
BT  - Proceedings of the  International Conference on Advanced Technology and Multidiscipline (ICATAM 2024)
PB  - Atlantis Press
SP  - 330
EP  - 353
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-566-9_22
DO  - 10.2991/978-94-6463-566-9_22
ID  - Rantini2024
ER  -