An Optimal House Price Prediction Algorithm: XGBoost

SHARMA, Hemlata, HARSORA, Hitesh and OGUNLEYE, Bayode (2024). An Optimal House Price Prediction Algorithm: XGBoost. Analytics, 3 (1), 30-45.

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Abstract

An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is significantly influenced by its surrounding neighborhood. Meeting the diverse housing needs of individuals while balancing budget constraints is a primary concern for real estate developers. To this end, we addressed the house price prediction problem as a regression task and thus employed various machine learning (ML) techniques capable of expressing the significance of independent variables. We made use of the housing dataset of Ames City in Iowa, USA to compare XGBoost, support vector regressor, random forest regressor, multilayer perceptron, and multiple linear regression algorithms for house price prediction. Afterwards, we identified the key factors that influence housing costs. Our results show that XGBoost is the best performing model for house price prediction. Our findings present valuable insights and tools for stakeholders, facilitating more accurate property price estimates and, in turn, enabling more informed decision making to meet the housing needs of diverse populations while considering budget constraints.

Item Type:	Article
Additional Information:	** Article version: VoR ** From MDPI via Jisc Publications Router ** Licence for VoR version of this article: https://creativecommons.org/licenses/by/4.0/ ** Peer reviewed: TRUE **Journal IDs: eissn 2813-2203 **Article IDs: publisher-id: analytics-03-00003 **History: collection 03-2024; published_online 02-01-2024; accepted 29-12-2023; rev-recd 20-12-2023; submitted 21-11-2023
Uncontrolled Keywords:	feature engineering, hyperparameter tuning, house price prediction, machine learning, regression modeling, XGBoost, feature importance
Identification Number:	https://doi.org/10.3390/analytics3010003
Page Range:	30-45
SWORD Depositor:	Colin Knott
Depositing User:	Colin Knott
Date Deposited:	09 Feb 2024 15:39
Last Modified:	09 Feb 2024 15:45
URI:	https://shura.shu.ac.uk/id/eprint/33154

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