House price prediction using a hybrid GRU-MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning.

Overview

Authors: Sarah M Alhammad¹, Yasser Fouad², Amira A Mahmoud³, Ahmed M Osman⁴, Hazem M El-Bakry⁵, Ahmed M Elshewey²

ORCID iDs: Ahmed M Osman, Ahmed M Elshewey

Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, 11671 Riyadh, Saudi Arabia
Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt
Department of Computer Science, Future Higher Institute for Specialized Technological Studies, Cairo, Egypt
Department of Information Systems, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt
Department of Information Systems, Faculty of Computers and Information, Mansoura University, P.O. Box:35516, Mansoura, Egypt

Institutions: Princess Nourah bint Abdulrahman University (Saudi Arabia); Suez University (Egypt); Mansoura University (Egypt)

Journal: Scientific reports, volume 16, issue 1, article 23225

Dates: received 13 May 2026; accepted 14 July 2026; published online 25 July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41598-026-62854-z · PMID 42502102 · PMCID PMC13401595 · OpenAlex W7171097652

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: other (organism)

Methods: Machine learning

Keywords: Hybrid deep learning, GRU–MLP model, House price prediction, Metaheuristic optimization, Binary whale optimization algorithm (BWOA), Ant colony optimization (ACO), Hyperparameter tuning, Regression modeling, Engineering, Mathematics and computing

MeSH: Commerce*, Algorithms, Animals, Multilayer Perceptrons, Prediction Algorithms, Predictive Learning Models, Soft Computing (* major topic)

Topic: Housing Market and Economics (Economics and Econometrics, Economics, Econometrics and Finance), according to OpenAlex

Funding: Princess Nourah Bint Abdulrahman University (PNURSP2026R442)

Citations: not cited yet (Europe PMC); 31 references in the paper

Abstract

Accurate house price prediction is essential for real estate valuation, investment planning, and intelligent property decision-support systems. This study proposes an optimized hybrid deep learning framework that integrates a Gated Recurrent Unit and Multilayer Perceptron model with the Binary Whale Optimization Algorithm for feature selection and Ant Colony Optimization for hyperparameter tuning. The proposed framework was evaluated using a publicly available Kaggle house price regression dataset containing 500 housing records with structural, locational, and amenity-related attributes. The dataset was divided into training, validation, and testing subsets using a 70:20:10 ratio, and leakage-free normalization was applied using only the training data. Experimental results show that the proposed BWOA–ACO–GRU–MLP model outperformed standalone GRU, MLP, CNN, LSTM, and BiLSTM models. It achieved an MSE of 0.0146, MAE of 0.1051, RMSE of 0.1208, MAPE of 0.0112, MedAE of 0.0969, and an R2 of 99.04%. These results demonstrate that combining feature selection, hyperparameter optimization, and hybrid neural regression improves prediction accuracy and model stability for house price estimation. The proposed framework provides a reliable data-driven approach for smart real estate valuation applications.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

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Data

Datasets cited

kaggle.com/datasets/denkuznetz, at Kaggle; found in “Data availability”

Data availability

The data that support the findings of this study are available at https://www.kaggle.com/datasets/denkuznetz/housing-prices-regression

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 10 keywords, 7 MeSH terms, 1 funder, 2 references.

Cite

This paper

Alhammad, S. M., Fouad, Y., Mahmoud, A. A., Osman, A. M., El-Bakry, H. M., & Elshewey, A. M. (2026). House price prediction using a hybrid GRU-MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning. Scientific reports, 16(1), 23225. https://doi.org/10.1038/s41598-026-62854-z

BibTeX

@article{alhammad2026house, author = {Alhammad, Sarah M and Fouad, Yasser and Mahmoud, Amira A and Osman, Ahmed M and El-Bakry, Hazem M and Elshewey, Ahmed M}, title = {{House price prediction using a hybrid GRU-MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning}}, journal = {Scientific reports}, year = {2026}, month = jul, volume = {16}, number = {1}, pages = {23225}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-62854-z}, url = {https://doi.org/10.1038/s41598-026-62854-z}, pmid = {42502102}, pmcid = {PMC13401595} }

RIS

TY - JOUR AU - Alhammad, Sarah M AU - Fouad, Yasser AU - Mahmoud, Amira A AU - Osman, Ahmed M AU - El-Bakry, Hazem M AU - Elshewey, Ahmed M TI - House price prediction using a hybrid GRU-MLP based on binary whale optimization algorithm and ant colony optimization for hyperparameter tuning T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/07/25 VL - 16 IS - 1 SP - 23225 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-62854-z UR - https://doi.org/10.1038/s41598-026-62854-z LA - en ER -

CSL-JSON

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