This paper presents the results of a study which used the classical regression model and the spatial lag model in estimating city and municipal poverty incidence to provide estimates for cities and municipalities with no direct estimates as well as to present estimates with improved precision for cities and municipalities with unreliable direct estimates. In the classical regression model, the identity link performs better than the logit or probit link in terms of a lower mean absolute percentage error. In the spatial regression model, the 25-km distance threshold provides the optimum model with relatively lower mean absolute percentage error and root mean square error compared to other threshold distance matrices. Lastly, the spatial lag model using distance matrix was found to be superior than classical regression model by virtue of lower mean absolute percentage error, lower root mean square error and estimates with lower standard errors and coefficients of variation.