Quantitative sociologists and social policymakers are increasingly interested in local context. Some city-specific studies have developed new primary data collection efforts to analyze inequality at the neighborhood level, but methods from spatial microsimulation have yet to be broadly used in sociology to take better advantage of existing public data sets. The American Community Survey (ACS) is the largest household survey in the United States and indispensable for detailed analysis of specific places and populations. The authors propose a technique, tree-based spatial microsimulation, to produce “small-area” (census-tract) estimates of any person- or household-level phenomenon that can be derived from ACS microdata variables. The approach is straightforward and computationally efficient, based only on publicly available data, and it provides more reliable estimates than do prevailing methods of microsimulation. The authors demonstrate the technique’s capabilities by producing tract-level estimates, stratified by race/ethnicity, of (1) the proportion of people in the census-tract population who have children and work in an essential occupation and (2) the proportion of people in the census-tract population living below the federal poverty threshold and in a household that spends greater than 50 percent of monthly income on rent or owner costs. These examples are relevant to understanding the sociospatial inequalities dramatized by the coronavirus disease 2019 pandemic. The authors discuss potential extensions of the technique to derive small-area estimates of variables observed in surveys other than the ACS.