This dissertation models K-12 enrollment within an urban school district using two grade progression ratio (gpr)-based and two housing choice methods. The housing choice methods provide, for the first time, a new spatio-demographic model for projecting school enrollments by grade for any flexibly defined set of individual catchment areas. All methods use the geocoded pattern of individual, address-matched, enrollments within the study district but are different in the way they model this data to estimate key parameters. The conventional method projects the intra-urban pattern of enrollment by assuming no change in grade progression ratios (gprs), which are themselves functions of enrollment change. The adaptive kernel ratio estimation (KRE) of local gprs successfully predicts local changes in gprs from three preceding two-year periods of gpr change. The two housing choice methods are based on different mixtures of a generalized linear and a periodic model, each of which use housing counts and characteristics. Results are clearly sensitive to these differences. Using the above predictions of gpr change, the adaptive KRE enrollment projections are 4.1% better than those made using the conventional model. The two housing choice models were 2.0% less accurate than the conventional model for the first three years of the projection but were 5.1% more accurate than this model for the fourth and fifth years of the projection. Limitations are discussed. These findings help close a major gap in the literature of small-area enrollment projections, shed new light on spatial dynamics collected at areas below the scale of the school district, and permit new kinds of investigations of urban/suburban school district demography.