Forecast Analysis of Urban Housing Prices in China Based on Multiple Models

Zelin Qu

2024

Abstract

Social attention has long been focused on the cost of housing in China. Rising urbanization and quick economic growth have made housing costs crucial to social stability and the standard of living for locals. This study compared the performance of four machine learning models - eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), and Long Short-Term Memory network (LSTM) - in detail in order to increase the accuracy of housing price prediction. The models were then combined with nominal GDP data to forecast housing price. The experimental findings demonstrate that the XGBoost model, which is used to forecast future home prices, performs well across a range of evaluation indices. In addition, this study predicts that housing prices in Chinese cities will show a slight upward trend in 2024-2025. This study fills the gap of the existing research in comparing and integrating multiple models and provides a reference for the government to make more accurate real estate policies and investors' decisions.

Paper Citation

in Harvard Style

Qu Z. (2024). Forecast Analysis of Urban Housing Prices in China Based on Multiple Models. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI; ISBN 978-989-758-726-9, SciTePress, pages 257-262. DOI: 10.5220/0013214400004568

in Bibtex Style

@conference{ecai24,
author={Zelin Qu},
title={Forecast Analysis of Urban Housing Prices in China Based on Multiple Models},
booktitle={Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI},
year={2024},
pages={257-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013214400004568},
isbn={978-989-758-726-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence - Volume 1: ECAI
TI - Forecast Analysis of Urban Housing Prices in China Based on Multiple Models
SN - 978-989-758-726-9
AU - Qu Z.
PY - 2024
SP - 257
EP - 262
DO - 10.5220/0013214400004568
PB - SciTePress