Proceedings of the 2020 3rd International Conference on Humanities Education and Social Sciences (ICHESS 2020)

A Geographic Feature Integrated Multivariate Linear Regression Method for House Price Prediction

Authors
Yuhang Mao, Ruili Yao
Corresponding Author
Yuhang Mao
Available Online 16 December 2020.
DOI
10.2991/assehr.k.201214.522How to use a DOI?
Keywords
House price prediction, Multiple linear regression models, Geographic feature integrated multivariate linear regression method, Real-world case
Abstract

Housing price prediction is of great significance in financial real estate investment and urban construction planning. Multiple linear regression models are commonly used for housing price prediction. However, traditional methods are mostly focused on the characteristics of the houses themselves, without or little considering the features of the surroundings. The features of the surroundings are also important for house price prediction. Motivated by these, we propose a geographic feature integrated multivariate linear regression method for house price prediction. Especially, the Zip Code is chosen as the additional geographic feature for its convenience to obtain. Then the integrated features are used to learn the multivariate linear regression model. We conduct an extensive experiment on the real-world case of the King County area and compare our method linear regressions. The results verified the effectiveness and superiority of our model.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2020 3rd International Conference on Humanities Education and Social Sciences (ICHESS 2020)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
16 December 2020
ISBN
10.2991/assehr.k.201214.522
ISSN
2352-5398
DOI
10.2991/assehr.k.201214.522How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yuhang Mao
AU  - Ruili Yao
PY  - 2020
DA  - 2020/12/16
TI  - A Geographic Feature Integrated Multivariate Linear Regression Method for House Price Prediction
BT  - Proceedings of the 2020 3rd International Conference on Humanities Education and Social Sciences (ICHESS 2020)
PB  - Atlantis Press
SP  - 347
EP  - 351
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201214.522
DO  - 10.2991/assehr.k.201214.522
ID  - Mao2020
ER  -