House-price Prediction Based on OLS Linear Regression and Random Forest

Wang, Yige

doi:10.1145/3456126.3456139

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Azimlu et al [23] ANN, GP, Lasso, Ridge, Linear, Polynomial, SVR Not performed Wang [24] OLS Linear Regression, Random Forest Not performed Ridge Linear Regression, Lasso Linear Regression, Random Fan et al [25] Forest, Support Vector Regressor (Linear Kernel and Gaussian Kernel), XGBoost GridSearchCV Linear Regression, Random Forest, LightGBM, XGBoost, Auto-klearn, Regression Layer Keras(Hyperas)…”

Section: Author(s) Methods Hyperparametermentioning

confidence: 99%

An Optimal House Price Prediction Algorithm: XGBoost

Sharma,

Harsora,

Ogunleye

2024

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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.

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Section: Author(s) Methods Hyperparametermentioning

confidence: 99%