Application of Transformer Model in Predicting Housing Price in Selected Vietnamese Provinces

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 Transformers superior performance. We automatically collected raw data from Vietnams 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 types unique characteristics, improving the accuracy and efficiency of machine learning for real estate price prediction.