Framework for assessing municipal tax delinquency, a case study area Nashik

Abstract

This thesis develops a framework for assessing municipal tax delinquency in Nashik, a growing metropolitan city in Maharashtra, India. Municipal property tax is the main source of municipal revenue, and timely collection is essential for maintaining public services such as roads, water supply, waste management, and public health. By combining spatial mapping, statistical modelling, and expert feedback, this study creates a process that transforms data sources into a decision-support tool. The framework covers all forty-four wards of Nashik and produces a ward-level risk index highlighting areas with the highest tax delinquency. This index and maps enable municipal officers to focus their enforcement, communication, and service improvement efforts precisely where they are needed most. Nashik’s municipal records show a substantial and persistent backlog of unpaid property taxes. Many property owners either miss payment deadlines or fail to pay, forcing the municipal corporation to operate with less revenue. These shortfalls directly affect the city’s ability to plan and fund essential services, leading to maintenance lapses, service interruptions, and a decline in citizen satisfaction. Without a clear understanding of which areas and which socio-economic groups are driving the backlog, city authorities must rely on broad, untargeted collection drives that consume time and resources. While the corporation holds extensive records on tax demands and receipts, these are maintained in tabular form and lack spatial context. Similarly, socio-economic data from the census and planning agencies remain isolated in separate databases. As a result, city officials lack a systematic way to link unpaid taxes to neighborhood conditions, making it difficult to diagnose underlying causes or priorities follow-up visits, awareness campaigns, or policy reforms. This research addresses this by integrating these datasets into a single framework. By mapping arrears and correlating them with factors such as income, literacy, employment, and valuation. To achieve these goals, the research follows four main objectives. First, it seeks to map and visualise the spatial distribution of property tax arrears across Nashik’s six administrative zones and 44 wards. Creating maps revealing clusters of high delinquency that are unclear in spreadsheet form. Second, it aims to analyse the relationship between arrears and socio-economic factors. Through Pearson’s correlation and regression, the study measures how variables such as median income, poverty rate, unemployment rate, literacy rate, and outdated property valuations influence non-payment. Third, it incorporates the insights of experts: municipal tax officers, urban planners, and revenue staff, through a two-round Delphi survey. This step assigns ranks to each factor. Finally, the research combines the statistical weights in a Multi-Criteria Decision-Making (MCDM) model to produce a Delinquency Risk Index. This index ranks each ward by its overall risk of tax non-payment. The research methods follow a two-stage design. In the first stage, property-level arrears figures and valuation details are obtained from the Nashik Municipal Corporation’s tax department. Shapefiles and ward boundaries come from the city’s planning department. Socio-economic indicators like literacy rate, median household income, poverty rate, and unemployment rate are taken from the 2011 Census and supplemented by state surveys where available. These layers are cleaned, standardised, and overlaid to produce choropleth maps of total arrears, arrears per hectare, and arrears per capita. In the second stage, Pearson’s correlation coefficient measures the bivariate relationships between each factor and tax delinquency. Standardised ordinary least squares (OLS) regression assesses each variable’s independent effect while controlling for the others. The Delphi survey engages twelve experts in two rounds: round one collects initial judgments on the importance of each factor, while round two refines those judgments toward consensus. The resulting weights from both the regression and the correlation are then integrated in an MCDM model. Each ward’s normalised scores on the various indicators are multiplied by the corresponding weights and summed to yield a composite Delinquency Risk Index. In the findings, Spatial maps highlight that the highest concentration of unpaid taxes lies in wards with lower median incomes, higher poverty rates, and higher unemployment. Literacy emerges as a strong negative predictor of delinquency, indicating that efforts to improve tax awareness and customer communication could yield substantial compliance gains. Color-coded maps and ward scores guide the allocation of field inspectors, the design of targeted reminder campaigns. Moreover, the simple structure of the framework allows it to be transferred to other cities facing similar challenges. This thesis offers a practical blueprint for cities to move beyond generic collection drives and toward data-driven, targeted interventions. While Nashik serves as the case study, the approach is designed for replicability: any city with basic tax records, socio-economic data, and GIS capability can apply the same steps to diagnose delinquency patterns, weigh their causes, and priorities actions. Keywords: Municipal Tax Delinquency, Property Tax Collection, GIS Mapping, Spatial Analysis, Correlation, Regression, Delphi Survey, Multi-Criteria Decision Making (MCDM), Delinquency Risk Index, Revenue Recovery, Nashik Case Study