Document Type : Research Paper

Authors

Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

Abstract

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.  

Highlights

  • 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.

Keywords

Main Subjects

[1] Y. Chen, X. Li, S. Wang, and X. Liu, Defining agents' behavior based on urban economic theory to simulate complex urban residential dynamics, International Journal of Geographical Information Science, 26 (2012) 1155–1172.
[2] E. R. N. E. S. T. O. LOPEZ-MORALES, Gentrification by Ground Rent Dispossession: The Shadows Cast by Large-Scale Urban Renewal in Santiago de Chile, International Journal of Urban and Regional Research, (2010).
[3] S. Frappa and J.-S. Mésonnier, The housing price boom of the late 1990s: Did inflation targeting matter? Journal of Financial Stability, 6 (2010) 243–254.
[4] O. Z. Jasim, N. H. Hamed, and M. A. Abid, Urban Air Quality Assessment Using Integrated Artificial Intelligence Algorithms and Geographic Information System Modeling in a Highly Congested Area, Iraq, Journal of Southwest Jiaotong University, 55 (2020).
[5] O. Zakariya Jasim, Using of machines learning in extraction of urban roads from DEM of LIDAR data: Case study at Baghdad expressways, Iraq, Periodicals of Engineering and Natural Sciences (PEN), 7 (2019) 1710.
[6] W.-X. Zhou and D. Sornette, Analysis of the real estate market in Las Vegas: Bubble, seasonal patterns, and prediction of the CSW indices, Physica A: Statistical Mechanics and its Applications, 387 (2008) 243–260.
[7] D.-Y. Li, W. Xu, H. Zhao, and Rong-Qiu Chen, A SVR based forecasting approach for real estate price prediction, (2009) International Conference on Machine Learning and Cybernetics, (2009).
[8] Y. Ma, Z. Zhang, A. Ihler, and B. Pan, Estimating Warehouse Rental Price using Machine Learning Techniques, International Journal of Computers Communications & Control, 13 (2018) 235–250.
[9] S. Rafatirad, a Technical Report on Real-Estate Rent Prediction, George Mason University., Virginia. United State, 1920/11644, (2017).
[10] M. MejbelSalih, O. Zakariya Jasim, K. I. Hassoon, and A. JameelAbdalkadhum, Land Surface Temperature Retrieval from LANDSAT-8  Thermal Infrared Sensor Data and Validation with Infrared Thermometer Camera, International Journal of Engineering & Technology, 7 (2018) 608.
[11] O. Z. Jasim, K. I. Hassoon, and N. E. Sadiqe, Mapping LCLU Using Python Scripting, Engineering and Technology Journal, 37 (2019)140-147.
[12] O. Jasim, N. Hamed, and T. Abdulgabar, Change detection and building spatial geodatabase for Iraqi marshes, MATEC Web of Conferences, 162 (2018) 03021.
[13] Y. Chen, X. Liu, X. Li, Y. Liu, and X. Xu, Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning, Applied Geography, 75 (2016) 200–212.
[14] X. Zhou, W. Tong, and D. Li, Modeling Housing Rent in the Atlanta Metropolitan Area Using Textual Information and Deep Learning, ISPRS International Journal of Geo-Information, 8 (2019) 349.
[15] A. Chabuk, N. Al-Ansari, H. Hussain, S. Knutsson, R. Pusch, and J. Laue, Combining GIS Applications and Method of Multi-Criteria Decision-Making (AHP) for Landfill Siting in Al-HashimiyahQadhaa, Babylon, Iraq, Sustainability, 9 (2017) 1932.
[16] O. Azeez, B. Pradhan, H. Shafri, N. Shukla, C.-W. Lee, and H. Rizeei, Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks, Applied Sciences, 9 (2019) 313.
[17] A. J. Abdalkadhum, M. M. Salih, and O. Z. Jasim, Combination of visible and thermal remotely sensed data for enhancement of Land Cover Classification by using satellite imagery, IOP Conference Series: Materials Science and Engineering, 73 (2020)  012226.
[18] M. R. Yeadon and M. Morlock, The appropriate use of regression equations for the estimation of segmental inertia parameters, Journal of Biomechanics, 22 (1989) 683–689.
[19] O. Azeez, B. Pradhan, and H. Shafri, Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS, Sustainability, 10 (2018) 3434.
[20] W. Wang ad Y. Lu, Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model, IOP Conference Series: Materials Science and Engineering, 324 (2018) 012049.
[21] O. S., Azeez, B.Pradhan, R. Jena, H. S. Jung,and A. A. Ahmed, Traffic emission modelling using LiDAR derived parameters and integrated geospatial model. Korean Journal of Remote Sensing, 35 (2019) 137-149.