Urban Planning

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Browsing Urban Planning by Author "Amha Ermias (PhD)"

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    Assessment of Site Suitability for Sustainable Residential Real Estate Investment in Addis Ababa: The Case of Lemi-Kura Sub-City
    (Addis Ababa University, 2025-06-01) Kidist Shuma Ayana; Amha Ermias (PhD)
    This study explores the site suitability for sustainable residential real estate property investment in Addis Ababa, with a specific focus on Lemi-Kura Sub-City. The objective is to identify and map suitable locations for sustainable residential development by integrating Geographic Information System (GIS), the Analytical Hierarchy Process (AHP), and hedonic land value modeling. A geospatial analysis combined with multi-criteria evaluation was undertaken to analyze critical environmental and socio-economic parameters, including land use/land cover, slope, soil, and geology, to establish physical appropriateness. The analysis set the study region into three separate suitability zones for residential development: extremely low, moderate, and very high. Out of the entire area of 78.67 km², about 23.28 km² (30.87%) was found to have extremely low suitability, 8.73 km² (13.64%) was found to be moderately suitable, and 20.27 km² (25.78%) was found to be highly appropriate for building homes. Also, a hedonic pricing model (HPM) was used to measure how property features and location affect the value of residential land. The model construction is based on the Sales Comparison Approach (SCA), employing sample transaction data acquired from various sites in the research area. Based on the model performance, a residential land value map was generated. Finally, the weighted overlay analysis of the physical suitability map and the land value map was done to establish potential zones for residential development. The integrated technique identified that only 8.10% of the land was classified as extremely suitable, 26.51% as moderately suitable, and the rest, 65.39%, as having low suitability for residential use. The integration of GIS, AHP, and HPM generated encouraging results. Future research is encouraged to use sophisticated approaches such as machine learning and deep learning for improved prediction and decision-making. Keywords: Land Value Modelling; Real Estate Development; Residential Site Suitability; Sustainable Housing