A mathematical model developed to predict automobile ownership for individual households residing in New York City is presented. This effort is distinguished from previous disaggregate household-level automobile ownership models primarily by the use of ordered probit models rather than the commonly used multinominal logit (MNL) and sequential logit (SL) models. When the dependent variable involves ordinal categorical data (in this case, automobile ownership level—zero automobiles, one automobile, two automobiles, and three or more automobiles), the ordered probit model will discern unequal differences between ordinal categories in the dependent variable, the MNL model will treat categories as independent choice alternatives, and the SL model (a product of binary logits) will assume independence of the error terms across all binary choices. The modeling approach was based on a behavioral analysis that explained the factors influencing household automobile ownership decisions in a highly urbanized environment. In addition to socioeconomic variables, transportation and land use–related measures were developed and used to test the sensitivity of household automobile ownership choice to transit accessibility, traffic congestion, parking cost and availability, and levels of access to opportunity sites through nonmotorized transportation. The estimation results uncover important interactions between socioeconomic- and location-related elements and automobile ownership. Findings provide exploratory methodological and empirical evidence that could lead to an approach to predicting the change in household automobile ownership as a result of changes in future socioeconomic conditions and transportation and land use scenarios.