Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis

Fengyuan Tian

2024

Abstract

The prediction of housing prices has received widespread attention from researchers due to its importance. This study offers a comprehensive analysis of house price forecasting, employing both traditional linear regression models and advanced deep learning techniques to enhance prediction accuracy. Through meticulous comparison and experimentation, deep learning methods, particularly feedforward neural networks, emerged as significantly superior in capturing complex nonlinear relationships and high-dimensional data patterns compared to linear regression models. In order to improve prediction performance, the research integrates data preparation, feature selection, and model evaluation as it methodically investigates different aspects of the dynamics of the housing market. Results highlight the potential of deep learning techniques to offer substantial improvements over conventional models, particularly in recognizing spatial and temporal trends in house pricing data. Future research aims to integrate external factors like economic indicators and urban development parameters to refine and expand predictive capabilities. It is anticipated that this strategic approach will improve the model's accuracy and usefulness in real-world real estate market analysis, enabling better informed decision-making processes.

Paper Citation

in Harvard Style

Tian F. (2024). Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 794-798. DOI: 10.5220/0012973500004508

in Bibtex Style

@conference{emiti24,
author={Fengyuan Tian},
title={Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={794-798},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012973500004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Advancing House Price Forecasting: Linear Regression and Deep Learning Models Analysis
SN - 978-989-758-713-9
AU - Tian F.
PY - 2024
SP - 794
EP - 798
DO - 10.5220/0012973500004508
PB - SciTePress