What question did this study set out to answer?

This study aims to compare various regression techniques for accurately predicting housing prices.

January 22, 2026Science Journal of Applied Mathematics and StatisticsOpen Access

Unveiling the Impact of Nonlinear Modeling in Housing Price Prediction: An Empirical Comparative Study

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This study aims to compare various regression techniques for accurately predicting housing prices.
Conducted empirical comparison of five regression techniques: OLS, Ridge, Lasso, Elastic Net, and Polynomial regression.
Applied techniques to the Boston and California Housing datasets.
Utilized a multi-dimensional evaluation framework with metrics like Mean Squared Error and residual analysis.
Polynomial regression outperformed other techniques in prediction accuracy across both datasets.
Ridge, Lasso, and Elastic Net showed comparable performance but were better suited for addressing multicollinearity.
OLS provided baseline results but struggled with real-world nonlinearities.

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Implication

Empirical comparison of five regression techniques demonstrates superior accuracy in housing price predictions, indicating effective methods for practitioners.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6971bd90642b1836717e2478 https://doi.org/10.11648/j.sjams.20261401.12

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Implication

Empirical comparison of five regression techniques demonstrates superior accuracy in housing price predictions, indicating effective methods for practitioners.

Unveiling the Impact of Nonlinear Modeling in Housing Price Prediction: An Empirical Comparative Study

Key Points

Discussion

Implication

Cite This Study

Unveiling the Impact of Nonlinear Modeling in Housing Price Prediction: An Empirical Comparative Study

Key Points

Discussion

Implication

Cite This Study

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