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

DOI

10.2991/978-94-6463-459-4_13How to use a DOI?

Keywords

Economy; house price prediction; machine learning

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.

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.

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TY  - CONF
AU  - Yujie Li
PY  - 2024
DA  - 2024/07/23
TI  - Analysis of USA National Home Prices Based on Different Machine Learning Models
BT  - Proceedings of the 2024 9th International Conference on Social Sciences and Economic Development (ICSSED 2024)
PB  - Atlantis Press
SP  - 100
EP  - 109
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-459-4_13
DO  - 10.2991/978-94-6463-459-4_13
ID  - Li2024
ER  -