Appropriate Rental Price Prediction for Condominiums in Pattaya, Thailand Using Artificial Neural Network Approach

There is a high demand for condominiums in Pattaya, Thailand, the tourist destination and business hub. It is an economic and strategic location within the Eastern Economic Corridor (EEC). An accurate rental price estimation is crucial for investors, tenants, real estate developers, and policymakers. The traditional methods such as regression analysis have limitations in terms of requiring linear relationships and capturing the complex data. This study applied Artificial Neural Network (ANN) to predict the condominium rental prices in Pattaya by using factors of distance to the beach, property size, building age, number of bedrooms and bathrooms, floor level, room type, and sea view. A dataset of 983 rental lists was used for ANN model training, validation, and prediction optimization. The study compares prediction from ANN and stepwise multiple regression analysis. The results identify that ANN provides the superior prediction accuracy over the multiple regression analysis, while the regression model offers the relative influence of each factor. This study identifies the effectiveness of ANN in condominium rental price prediction and highlights the importance of combining ANN method and traditional method to enhance the prediction accuracy and performance in Thai real estate market.