House price prediction using machine learning for Ames, Iowa

Qiongwei Ye

doi:10.54254/2755-2721/55/20241483

Research Article

Open access

Published on 25 April 2024

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Ye,Q. (2024). House price prediction using machine learning for Ames, Iowa. Applied and Computational Engineering,55,44-54.

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House price prediction using machine learning for Ames, Iowa

Qiongwei Ye *^,1,

¹ Georgia Institute of Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/55/20241483

Abstract

The real estate sector is pivotal to economic growth and plays a substantial role in the global GDP. In this technologically advanced age, the adoption of machine learning for accurate house price prediction is crucial. These models optimize decision-making for homeowners, sellers, and investors alike. This study represents a comprehensive exploration into the field of house price prediction within the context of Ames, Iowa, United States. The primary objective of this research is to construct a reliable and highly accurate predictive model, empowering individuals to estimate property values with unprecedented precision. The research encompasses three different machine learning algorithms: linear regression, random forest, and XGBoost. The use of the dataset from the reputed website helps improve the reliability of the result. Furthermore, the investigation extends to a detailed examination of the multifaceted determinants exerting a profound influence on house prices in the dynamic Ames real estate landscape, and determined that the factor that will influence the house price most is the total area of the house. Among all models, XGBoost produces the best result, which achieved an R-square of 0.8803. Moreover, the importance of each feature is also analyized using the feature ranking algorithm in random forest, showing that the overall quantity of the house, the living area of the house, and the total area of the basement are the top three factors that influence the house price most.

Keywords

Machine learning, house price prediction, linear regression model, random forest, XGBoost

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References

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Cite this article

Ye,Q. (2024). House price prediction using machine learning for Ames, Iowa. Applied and Computational Engineering,55,44-54.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning

Conference website: https://www.confspml.org/

ISBN：978-1-83558-355-5(Print) / 978-1-83558-356-2(Online)

Conference date: 15 January 2024

Editor：Marwan Omar

Series: Applied and Computational Engineering

Volume number: Vol.55

ISSN：2755-2721(Print) / 2755-273X(Online)

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