Application of Transformer Model in Predicting Housing Price in Selected Vietnamese Provinces
Phan Thi Ha1, Tran Thi Thuy Ha2
1Dr. Phan Thi Ha, Lecturer, Faculty of Information Technology, Posts and Telecommunications Institute of Technology (PTIT), Ha Noi, Vietnam, and Computing Fundamentals Department, FPT University, Hanoi, Vietnam.
2Dr. Tran Thi Thuy Ha, Lecturer at the Faculty of Electronics Engineering at Posts and Telecommunications Institute of Technology (PTIT) in Ha Noi, Vietnam.
Manuscript received on 22 September 2026 | First Revised Manuscript received on 30 September 2026 | Second Revised Manuscript received on 06 October 2026 | Manuscript Accepted on 15 October 2026 | Manuscript published on 30 October 2026 | PP: 6-11 | Volume-15 Issue-11, October 2026 | Retrieval Number: 100.1/ijitee.L131115121126 | DOI: 10.35940/ijitee.L1311.15121126
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper selects an appropriate machine learning model to forecast real estate and apartment prices across several major provinces and cities in Vietnam. While we primarily deploy the Transformer, we also experimented with other machine learning algorithms, such as Linear Regression, Decision Tree, and Random Forest, to demonstrate the Transformer’s superior performance. We automatically collected raw data from Vietnam’s largest real estate website and then preprocessed, analysed, and evaluated it. The analysis revealed that building a prediction model on a single dataset yields fewer effective results than splitting the data into two distinct sets, one for landed properties and the other for apartments, and developing separate models for each product category. Building two independent prediction models allows effective use of each product type’s unique characteristics, improving the accuracy and efficiency of machine learning for real estate price prediction.
Keywords: Linear Regression, Decision Tree, Random Forest, Transformer, Classification, Prediction, Forecasting, Apartments, Houses, Real Estate.
Scope of the Article: Computer Science and Engineering