Research on Housing Prices Forecasts Based on A Multiple Linear Regression Model

Yiwen Wang

2024

Abstract

House prices have always been a hotly debated topic. However, the factors affecting them and the extent of their influence have changed over time, so this paper aims to find a simple method of predicting house prices that best fits the recent past. This paper collects a sample of 545 independent samples just updated this quarter. By preprocessing the data and analyzing the multiple linear regression, accurate multiple linear regression equations are obtained for prediction. Meanwhile, the diagnostic illustrates that the samples are independent, there is no multicollinearity between the variables, and the residuals follow a normal distribution. 12 independent variables (Area, Bedroom, Bathroom, Story, Parking, Furnishing status, Guestroom, Basement, Hot water, Air-conditioner, Main road, Preferred area) correspond to a significant positive effect on the variable (Housing prices), with Area, Bathroom’s number, and Air-conditioner’s number being the top this paper.

Paper Citation

in Harvard Style

Wang Y. (2024). Research on Housing Prices Forecasts Based on A Multiple Linear Regression Model. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 53-59. DOI: 10.5220/0012991000004601

in Bibtex Style

@conference{iampa24,
author={Yiwen Wang},
title={Research on Housing Prices Forecasts Based on A Multiple Linear Regression Model},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={53-59},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012991000004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Research on Housing Prices Forecasts Based on A Multiple Linear Regression Model
SN - 978-989-758-722-1
AU - Wang Y.
PY - 2024
SP - 53
EP - 59
DO - 10.5220/0012991000004601
PB - SciTePress