Conventional application of deep neural networks (DNNs) to multi-building and multi-floor indoor localization is based on pure regression of three-dimensional location coordinates (e.g., longitude, latitude and altitude ( i.e. , floor height)), classification of location labels (e.g., building, floor and room information), or hybrid classification/regression of labels and coordinates (e.g., building and floor information and two-dimensional location coordinates), which, however, does not take into account an innate hierarchical auxiliary information (e.g., building-¿floor-¿location) of indoor localization data. Such conventional application of DNNs faces scalability issues in case of large-scale indoor localization where the numbers of buildings and floors are large. Inserting classification tasks as auxiliary networks into a regression neural network , we propose a new framework called a hierarchical auxiliary deep neural network (HADNN), which not only address the scalability issues with an increasing number of classes but also could further reduce the hierarchical information error. In HADNN, hierarchical auxiliary information of given data are provided and used during the training phase. As there are two possible hierarchical information cases in indoor localization data: (1) given only floors and (2) given both buildings and floors, we propose two architectures: one utilizing only floor information and the other taking both building and floor information. At test phase, HADNN predicts building, floor and location coordinate at the same time. Experimental results show that the architecture of HADNN achieves better performance of a coordinate regression task and require a smaller number of parameters than the pure two-dimensional location coordinates regression model. In addition, HADNN does not require the training data and coarse classes (e.g., building and floor information) at test phase while previous methods still require the training data to obtain location coordinates.