The religious endowments are one of the important sources, which acquire historical, cultural, and economic importance in all countries of the world. In particular, a religious endowment in Iraq includes several distributed real estates and lands that usually require efficient management systems. One of the most important factors affecting the management of real estates that belong to religious endowments is the rental amount of each real estate. In general, the estimation of the rental of real estates can support the future development of religious endowments. Governmental agencies are faced with some challenges in the management of religious endowments in terms of rental pricing due to numerous economic and geographic factors. The rapid development of artificial intelligence systems and Geomatic techniques can present a framework for rental amount estimation based on spatial and non-spatial factors. In this study, a machine learning algorithm (Support Vector Regression) will be combined with Geographic Information System (GIS) to predict and evaluate the rental amount of real estates that belong to a religious institution in Iraq (Shiite endowment in Hillah city). The final results indicated that the proposed method achieved an overall accuracy of 71%, a root mean square error of 0.2257 million Iraq, Dinar (IQD), and a correlation coefficient of 0.9272. This study can be used as an effective tool for the decision-makers to plan and manage the religious endowments in developing countries.
- The estimation of the rental of real estates based upon the numerous economic and geographic factors with artificial intelligence system and Geomatic techniques.
- Support Vector Regression is combining the Geographic Information System (GIS) with non-spatial data to predict and evaluate the rental amount of real estates.
- Euclidean Distance technique gives us the most effect result in estimation of rental of real estates.
- Accuracy Assessment is Root Mean Square Deviation, RMSD which is used to measure of the difference between values predicted by a model and the values of observed data from the environment that is being modeled.