Applying Machine Learning and Time Series to Predict Real Estate Valuations

DOI

10.2991/978-94-6463-490-7_52How to use a DOI?

Keywords

machine learning; deep learning; real estate valuation; random forest; ARIMA model

Abstract

This paper focuses on the combined application of time series analysis models and machine learning models in real estate valuation forecasting. When dealing with real estate valuation data, we use deep learning models to extract key features to achieve high accuracy prediction. The performance of four machine learning models (multiple linear regression, random forest, gradient boosting, and support vector machine) is also compared, and the results show that the random forest model has the best performance on the test set, and it possesses high prediction performance. Finally, the prediction accuracy was improved by combining ARIMA model and random forest for regression analysis of residuals. This paper demonstrates the potential of machine learning and time series analysis in real estate market value assessment, which provides new perspectives and valuable references for market analysis, investment strategy development and policy decisions.

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  - Sijian Zhao
PY  - 2024
DA  - 2024/08/31
TI  - Applying Machine Learning and Time Series to Predict Real Estate Valuations
BT  - Proceedings of the 2024 3rd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2024)
PB  - Atlantis Press
SP  - 479
EP  - 486
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-490-7_52
DO  - 10.2991/978-94-6463-490-7_52
ID  - Zhao2024
ER  -