Increasing Transparency on Housing Markets through Machine Learning

This thesis is designed to shed light on novel aspects of the housing market, which is influenced in recent years by different trends. The data quality and quantity increased rapidly through Big Data, which affects fundamental and non-fundamental variables of the housing market. Furthermore, Artificial Intelligence and Machine Learning, which pave the way for more efficient and fine-grained market analysis and property valuations and eXplainable Machine Learning (XAI), which brings explainability to the complex hidden mechanics of ML approaches. This dissertation finds a way to blur the boundaries of well-established and newly designed approaches, which is not only important for housing markets but is also transferable to other fields of research