A Comparative Study of Machine Learning Algorithms for Property Price Forecasting

Abstract

As the housing market grows, it's important to estimate pricing for both businesses and individuals. Nonetheless, a number of variables influence changes in home prices. Bangladesh is an overpopulated country, therefore a number of interrelated factors affect the price at which real estate is sold.The property's amenities, location, and size are crucial factors that could affect the cost.The objective of this investigation is to predict the prices of property in Bangladesh by employing a variety of machine learning algorithms. In order to guarantee precise predictions and robust model training, we assembled a comprehensive dataset of 18,835 property listings from Bproperty & Bikroy.com. Random Forest, Support Vector Machine (SVR), Decision Tree, XGBoost, CatBoost, and LightGBM are among the algorithms implemented in this investigation. The performance of these models was assessed using a variety of metrics, including R-squared, Mean Absolute Error (MAE), Root Mean Absolute Error (RMAC), and Mean Squared Error (MSE). The CatBoost and XGBoost models achieved the highest R-squared value of 91%, indicating superior accuracy, but XGBoost performed slightly better with less RMSE, MAE, MSE value. While the DecisionTrees model yielded the lowest R-squared value of 82%, indicating relatively poorer performance. The value of our findings and the potential for future research in property market analytics are underscored by the effectiveness of advanced machine learning models, particularly CatBoost, in predicting property prices within the Bangladeshi market. This information is valuable for stakeholders.