How valuable is living nearby nature? Does nature have a positive effect on nearby residential property prices? How much are we willing to pay for nature in our living environment, and does this amount decay with distance to nature? Increasing urbanization and stress on natural landscapes makes such questions more and more important in spatial planning. However, quantifying the value of public green space is challenging, especially for large study areas, because of the required high computing power. In a recent conference paper by Daams et al. (2014), over 200.000 (!) individual properties across the Netherlands were analyzed to give insight into the Dutch people’s willingness to pay for living near highly attractive public nature. Unlike existing studies of such kind, not only the relation between property prices and the most nearby nature, e.g. within 1 or 2 kilometer, was analyzed, as effects from the quantity of attractive nature up to 10 kilometers away were evaluated in the initial research process. That analysis required comprehensive and highly detailed spatial data, as the areas of the all natural land use polygons, with many vertices per feature, needed to be summed for each of the 200,000 properties separately. The required resources to do so far exceeded those that a single computer, even with heavy specifications, could provide. In this paper we discuss our solution to this problem that Daams et al. (in prep.) encountered: parallel computing with Python and FOSS4G libraries. More specific, we describe how we supported this project by specifying and applying several python scripts and libraries, and running these on our high performance cluster.