Mass appraisal for property tax purposes: approach using global and local spatial regression

Resumo

Property tax is a vital revenue source for Brazilian municipalities, but the current system based on outdated assessment values results in unequal treatment of taxpayers. To promote fairness, regular property appraisals are necessary, especially in the rapidly evolving Brazilian real estate market. A key challenge in this process is developing a model that effectively addresses spatial effects. This study explores the application of global and local spatial regression models to tackle spatial dependence and heterogeneity in mass property appraisals, aiming to ensure fair property taxation. Analyzing urban properties, we evaluate the performance of the conditional autoregressive model (CAR) as a global regression model and geographically weighted regression (GWR) as a local regression model. Predictions from both models are combined with ordinary kriging interpolation to generate a comprehensive Property Value Map (PVM) representing the study area. Our findings show that the GWR modeling surpasses the CAR model in accuracy and its ability to account for spatial dependence and heterogeneity. This highlights GWR as a viable alternative for establishing a fair and equitable basis for calculating real estate taxes. This research significantly contributes to advancing mass appraisal techniques, ensuring property tax collection aligns with the dynamic nature of the Brazilian real estate market. The study's insights provide valuable guidance for policymakers and practitioners in improving property tax systems, ultimately benefiting municipalities and taxpayers alike.