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Analysis of USA National Home Prices Based on Different Machine Learning Models

In: Proceedings of the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024)

Author

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  • Yujie Li

    (Sun Yat-Sen University, Sun Yat-Sen University Cancer Center)

Abstract

Numerous nations rely heavily on the real estate industry, and changes in home prices have a big impact on people’s quality of life. On this basis, house price prediction plays an important role in the economic field, e.g., making economic policy. Affected by thousands of potential factors, it is complicated to estimate the house price accurately. This study uses several machine learning models to build the relationship between 5 different factors of macro perspective with house prices in the US and managed to predict the real estate price. Among these models, the Decision tree model, KNN model, and Neural network model all perform high fitting effects and stable generalization activity. The SVR model is also suitable for this case. The article also indicates that the MLR model shows the worst fitting effect because of being limited in capturing the non-linear characters in datasets. Overall, these results provide accurate house price prediction models, which may be very valuable in real property sectors.

Suggested Citation

  • Yujie Li, 2024. "Analysis of USA National Home Prices Based on Different Machine Learning Models," Advances in Economics, Business and Management Research, in: Radulescu Magdalena & Bootheina Majoul & Satya Narayan Singh & Abdul Rauf (ed.), Proceedings of the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024), pages 100-109, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-459-4_13
    DOI: 10.2991/978-94-6463-459-4_13
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