Abstract
This study examines the application of Geographic Information Systems (GIS) for property tax assessment within the Shillong Municipal Board (SMB), India, highlighting the transformative potential of geospatial technology in improving urban governance for smaller municipalities. A total of 26,611 built-up plots across 27 wards were digitized and categorized into six zones based on economic value. The study employed the Unit Area Value (UAV) method, integrated with GIS, incorporating factors such as construction type, building age, ownership, usage, and location to calculate property taxes. The results revealed notable variations in tax revenue between wards and zones, with central areas generating significantly higher revenue than peripheral regions. The GIS-based system enhanced accuracy in property identification, standardized valuation methods, and improved data management. However, challenges in maintaining up-to-date data and addressing socio-economic disparities in tax burdens were observed. The study proposes improvements, including refining UAV values, adding new assessment factors, and utilizing machine learning for property classification. This research offers a scalable model for small and medium-sized municipalities, providing insights into more efficient, equitable, and transparent property tax systems. The findings have important implications for urban planning, revenue generation, and policy-making, supporting data-driven approaches to sustainable urban development in India and beyond.