What does this research mean for the field?

XGBoost outperforms Multiple Linear Regression, Support Vector Regression, and Feed Forward Neural Networks in accurately predicting house prices by better capturing complex nonlinear relationships between house attributes. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate and compare the predictive accuracy of four distinct machine learning algorithms for residential property price estimation.

September 10, 2026Science Journal of Business and ManagementOpen Access

A Comparative Study of Machine Learning-Based Predictive Models for House Price Prediction

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Authors

PSPraveen Singh RJRachna Jain SSShikha Sharma

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Overview

Comparative modeling study demonstrates superior accuracy of extreme gradient boosting for real estate valuation, highlighting its ability to capture complex nonlinear housing market relationships.

Key Points

To evaluate and compare the predictive accuracy of four distinct machine learning algorithms for residential property price estimation.
Trained and evaluated Multiple Linear Regression (MLR), Support Vector Regression (SVR), Feed Forward Neural Network (FFNN), and Extreme Gradient Boosting (XGBoost) on the Boston Housing dataset.
Assessed model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R²).
XGBoost achieved the best predictive performance among all tested models, yielding an R² of 0.7910, RMSE of 3.9147, MAE of 2.8454, and MSE of 15.3252.
Support Vector Regression achieved the second-highest accuracy, followed by the Feed Forward Neural Network, while Multiple Linear Regression was the least accurate.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6aa27ab158559d80afc7366c https://doi.org/10.11648/j.sjbm.20261403.15

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September 10, 2026Science Journal of Business and ManagementOpen Access

A Comparative Study of Machine Learning-Based Predictive Models for House Price Prediction

View Full Paper

Ask AI

Bookmark

Authors

PSPraveen Singh RJRachna Jain SSShikha Sharma

Discussion

Member takes

Overview

Comparative modeling study demonstrates superior accuracy of extreme gradient boosting for real estate valuation, highlighting its ability to capture complex nonlinear housing market relationships.

Key Points

To evaluate and compare the predictive accuracy of four distinct machine learning algorithms for residential property price estimation.
Trained and evaluated Multiple Linear Regression (MLR), Support Vector Regression (SVR), Feed Forward Neural Network (FFNN), and Extreme Gradient Boosting (XGBoost) on the Boston Housing dataset.
Assessed model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Coefficient of Determination (R²).
XGBoost achieved the best predictive performance among all tested models, yielding an R² of 0.7910, RMSE of 3.9147, MAE of 2.8454, and MSE of 15.3252.
Support Vector Regression achieved the second-highest accuracy, followed by the Feed Forward Neural Network, while Multiple Linear Regression was the least accurate.

Cite This Study

Singh et al. (2026) studied this question.

synapsesocial.com/papers/6aa27ab158559d80afc7366c https://doi.org/10.11648/j.sjbm.20261403.15

View Full Paper

Ask AI

Bookmark