Property taxes (PTs) are the primary own-source revenue for Indian urban local bodies (ULBs), yet actual collections fall short of potential, constraining service delivery. Existing studies have documented this gap but have been limited to state or district-level analysis due to the absence of reliable municipal data. This paper exploits new geospatial datasets-ULB boundary maps, nighttime lights, and building volume to estimate PT demand at the ULB level in Karnataka. Using cross-sectional data for 268 ULBs in 2019-20, we show that the sum of building volume (SoBV) within a ULB boundary alone can explain over 80% of the variation in PT demand across ULBs, with a 1% increase in SoBV predicting a 1.09% rise in demand. Benchmarking exercises reveal stark regional disparities aligned with historical administrative boundaries. These findings demonstrate how non-government spatial data can measure fiscal capacity, verify self-reported statistics, and highlight institutional legacies that shape urban tax performance.

Estimating property tax potential in urban local bodies using satellite imagery, Abhishek Seth, Manish K. Singh, Diya Uday, XKDR Forum Working Paper 47, March 2026.