Understanding and modeling perceived properties of sky-dome illumination is an important but challenging problem due to the interplay of several factors such as the materials and geometries of the objects present in the scene being observed. Existing models of sky-dome illumination focus on the physical properties of the sky. However, these parametric models often do not align well with the properties perceived by a human observer. In this work, drawing inspiration from the Hosek-Wilkie sky-dome model, we investigate the perceptual properties of outdoor illumination. For this purpose, we perform a large-scale user study via crowdsourcing to collect a dataset of perceived illumination properties (scattering, glare, and brightness) for different combinations of geometries and materials under a variety of outdoor illuminations, totaling 5,000 distinct images. We perform a thorough statistical analysis of the collected data which reveals several interesting effects. For instance, our analysis shows that when there are objects in the scene made of rough materials, the perceived scattering of the sky increases. Furthermore, we utilize our extensive collection of images and their corresponding perceptual attributes to train a predictor. This predictor, when provided with a single image as input, generates an estimation of perceived illumination properties that align with human perceptual judgments. Accurately estimating perceived illumination properties can greatly enhance the overall quality of integrating virtual objects into real scene photographs. Consequently, we showcase various applications of our predictor. For instance, we demonstrate its utility as a luminance editing tool for showcasing virtual objects in outdoor scenes.