Construction and Analysis of House Price Forecasting Model Based on Machine Learning and Deep Learning

DOI

10.2991/978-94-6463-823-3_24How to use a DOI?

Keywords

House price forecasting; Machine learning; Deep learning

Abstract

The main application scenarios of house price forecast include real estate transaction, investment decision, financial risk assessment and so on. Machine learning model prediction has the advantages of being able to handle complex data, automatically learning features, strong generalization ability, being able to quickly handle a large number of data and continuously optimize and improve performance. In this paper, economic factors such as income are combined with traditional factors, and various models are used to explore the performance of different models. After comparison, the best model is selected and optimized to achieve more accurate prediction. In this experiment, the random forest model and the neural network in deep learning have higher accuracy than other models. This does not mean that other models are at a disadvantage in forecasting. Therefore, this paper makes a model comparison experiment and reveals the applicable boundaries of different algorithms, thus providing more optimistic results for housing price forecasting of different data sets.

Copyright

© 2025 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  - Xuanshu Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Construction and Analysis of House Price Forecasting Model Based on Machine Learning and Deep Learning
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 254
EP  - 262
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_24
DO  - 10.2991/978-94-6463-823-3_24
ID  - Zhang2025
ER  -