Forecasting Saudi Real Estate Market Trends: A Time-Series Based Comparative Study of 2023 Saudi Real Estate Transactional Data

Abdullah Aldaeej

doi:10.48161/qaj.v5n4a1900

Authors

Abdullah A. Aldaeej Department of Management Information Systems, Imam Abdulrahman Bin Faisal University, Dammam 34221, Saudi Arabia.

DOI:

https://doi.org/10.48161/qaj.v5n4a1900

Keywords:

predictive modeling, Saudi real estate, market trends, forecasting accuracy, machine learning.

Abstract

This study presents a structured comparative evaluation of Saudi Arabia’s real estate market by applying two core time-series forecasting models: ARIMA and a modified SARIMA model with seasonal adjustment. The dataset, sourced from the Saudi Ministry of Justice, comprises over 10,000 real estate transactions collected weekly across five major cities during 2023, including price, size, transaction volume, and category features. The proposed modified SARIMA model incorporates two key enhancements: (1) dynamic parameter tuning using grid search optimized via AIC and BIC, and (2) an extensible hybrid structure that supports integration with machine learning models for residual learning. Forecasting performance was evaluated using standard metrics. ARIMA achieved an RMSE of 599,867.54 and a MAPE of 35.81%, outperforming modified SARIMA which recorded an RMSE of 609,942.58 and a MAPE of 40.64%. Despite its slightly lower accuracy, the modified SARIMA demonstrated a better statistical fit with AIC and BIC scores of 10.00 and 14.76, respectively. These findings support real-world applications in investment planning, infrastructure allocation by city planners, and regulatory oversight to stabilize property markets.

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