Synthetic populations are representations of actual individuals living in a specific area. They play an increasingly important role in studying and modeling individuals and are often used to build agent-based social simulations. Traditional approaches for synthesizing populations use a detailed sample of the population (which may not be available) or combine data into a single joint distribution, and draw agents or households from these. The lattergroup of existing sample-free methods fail to integrate (1) the best available dataon spatial granular distributions, (2) multi-variable joint distributions, and (3) household level distributions. In this paper, we propose a sample-free approach where synthetic individuals and households directly represent the estimated joint distribution to which attributes are iteratively added, conditioned on previous attributes such that the relative frequencies within each joint group of attributes are maintained and fit granular spatial marginal distributions. In this paper wepresent our method and test it for the Zuid-West district of The Hague, theNetherlands, showing that spatial, multi-variable and household distributions areaccurately reflected in the resulting synthetic population.