Boosting the predictive accuracy of urban hedonic house price models through airborne laser scanning

Keywords: Real estate Airborne laser scanning LiDAR GIS Solar radiation Hedonic regression Generalized additive model Vienna (Austria) a b s t r a c t This paper introduces an integrative approach to hedonic house price modeling which utilizes high density 3D airborne laser scanning (ALS) data. In general, it is shown that extracting exploratory variables using 3D analysis – thus explicitly considering high-rise buildings, shadowing effects, etc. – is crucial in complex urban environments and is limited in well-established raster-based modeling. This is fundamental in large-scale urban analyses where essential determinants influencing real estate prices are constantly missing and are not accessible in official and mass appraiser databases. More specifically, the advantages of this methodology are demonstrated by means of a novel and economically important externality, namely incoming solar radiation, derived separately for each flat. Findings from an empirical case study in Vienna, Austria, applying a non-linear generalized additive hedonic model, suggest that solar radiation is significantly capitalized in flat prices. A model comparison clearly proves that the hedo-nic model accounting for ALS-based solar radiation performs significantly superior. Compared to a model without this externality, it increases the model's explanatory power by approximately 13% and additionally reduces the prediction error by around 15%. The results provide strong evidence that explanatory variables originating from ALS, explicitly regarding the immediate 3D surroundings, enhance traditional hedonic models in urban environments. Real estate markets are constantly in motion, thus leading to an increased risk awareness by investors, mortgage lenders, etc. Accordingly, the predictive accuracy of economic models has gained much attention and has stimulated research (e. is an extensively applied framework for mass appraisal and price index construction. These models can be improved in two ways: (a) Through novel estimation techniques (e.g. Brunauer et al., 2010; Koschinsky, Lozano-Gracia, & Piras, 2011) and (b) by ancillary structural, locational, and neighborhood variables on the basis of Geographic Information System (GIS) algorithms (e.g. Hamilton & Morgan, 2010), which have the potential to mitigate violations of model assumptions and advance model reliability. However, recent studies are limited in that they use the raster and 2D vector data model when computing GIS-based variables (e. Nowadays, ALS – also referred to as airborne LiDAR – data are increasingly available because of steadily declining costs, particularly in urban environments. Since the proliferation and substantial advances in ALS as state-of-the-art technology for 3D topographic data acquisition (Vosselman & Maas, 2010), it appears …