Dasymetric areal interpolation is the process by which data are transferred from a spatial unit system for which they are available (source units) to another system for which they are required (target units) with the aid of ancillary information (control units). We propose a spatially disaggregated areal interpolation model for population data using light detection and ranging (LiDAR)-derived building volumes as an ancillary variable. Innovative methods are proposed for model initialization, iterative regression and adjustment, and stopping criteria to deal effectively with control units of unequal size. The model is derived and applied at the control unit level to minimize the modifiable areal unit problem, and an iterative adjustment process is utilized to overcome the spatial heterogeneity problem encountered in earlier approaches. The use of building volume to disaggregate the population into finer scales ensures maximum correspondence with the unit at which the original population data were collected and models not only the horizontal but also the vertical population distribution. A case study for Round Rock, Texas, demonstrates that the proposed spatially disaggregated model using LiDAR-derived building volumes outperforms earlier areal interpolation models using traditional area- and length-based ancillary variables.