Real estate is more than just an investment opportunity as it has direct impact on development of cities and places we inhabit. Being able to analyze and understand the market dynamics can help in shaping a sustainable growth and development of our cities. In this paper we present and access machine learning methods that can be used to analyze real estate price dynamics, market changes and relate them to customer preferences. We use open data to evaluate different approaches and machine (ML) and deep (DL) learning algorithms to stress on their advantages and methods to overcome their drawbacks.
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