Aquaculture Real Estate Price Prediction System: Machine Learning Approach for PropTech

The aquaculture industry is growing strongly, especially in facilities and technical infrastructure. Aquaculture real estate serves as an important component for these developments. Although the aquaculture real estate sector represents a dynamic market with substantial transaction volumes and high economic value, this sector has received less attention. This paper proposes an aquaculture real estate price prediction system that utilizes a machine learning approach. First, this paper focuses on collecting and analyzing data from aquaculture real estate trading websites and platforms. The dataset encompasses prices and features of aquaculture real estate from 2021 to the beginning of 2025, specifically in Southern Vietnam, obtained from various Vietnam real estate websites. We conduct experiments and assess six different models, including Random Forest, CatBoost, Hybrid, XGBoost, LightGBM and Linear Regression, to predict aquaculture real estate prices. Results from testing revealed that the Random Forest is the best model, due to the nature of aquaculture real estate data, which includes many categories associated with the location of land, the type of land, and the legal status, and has many complicated nonlinear relationships among its attributes. Following this, hyperparameter tuning using Optuna (50 trials, 5-fold cross-validation) can further enhance the model’s performance. The experimental results demonstrate that the Stacking Ensemble model achieved the best performance after hyperparameter tuning, with RMSE of 4394.77 million, MAE of 2114.19 million, and R2 of 0.5738, representing improvements of 3.3%, 6.0%, and 5.4%. The paper will provide a more comprehensive view of the aquaculture real estate market and support sustainable development for this industry.