Estimates and projections for small areas are used extensively in the public and private sectors, and demand for them has been growing. Because of population size and data availability issues, estimates and projections for small areas face methodological challenges not commonly encountered at larger geographical scales.
Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places.
The Bureau of Economic and Business Research Population Program, under contract with the Florida Legislature, has been making three sets of population projections (low, medium, and high) for Florida and its counties for many years. Many decisions in both the public and private sectors are based on expectations of future population change. Planning for schools, hospitals, shopping centers, housing developments, electric power plants, and many other projects is strongly influenced by expected population growth or decline.
The older population in many countries is large and growing rapidly, raising the number of people with disabilities and driving up the need for accessible housing. In a previous
study, we projected the number of households in the United States with at least one disabled resident and estimated the probability that a newly built single-family detached
unit will house at least one disabled resident during its expected lifetime. In this study, we extend our analysis to the subnational level by constructing similar estimates and