Application of Artificial Intelligence for Predicting Real Estate Prices: The Case of Saudi Arabia

Abstract

The housing market is a crucial economic indicator to which the government must pay special attention because of its impact on the lives of freshly minted city inhabitants. As a guide for government regulation, individual property purchases, third-party evaluation, and understanding how housing prices are distributed geographically may be of great practical use. Therefore, much research has been conducted on how to arrive at a more accurate and efficient way of calculating housing prices in the current market. The goal of this study was to use the artificial neural network (ANN) technique to correctly identify real estate prices. The novelty of the proposed research is to build a prediction model based on ANN for predicting future house prices in Saudi Arabia. The dataset was collected from Aqar in four main Saudi Arabian cities: Riyadh, Jeddah, Dammam, and Al-Khobar. The results showed that the experimental and predicted values were very close. The results of the proposed system were compared with different existing prediction systems, and the developed model achieved high performance. This forecasting system can also help increase investment in the real estate sector. The ANN model could appropriately estimate the housing prices currently available on the market, according to the findings of the assessments of the model. Thus, this study provides a suitable decision support or adaptive suggestion approach for estimating the ideal sales prices of residential properties. This solution is urgently required by both investors and the general population as a whole.