Real estate transactions can be seen as a spatial point pattern over space and time. That means, that real estate transactions occur in places where at a certain point of time certain characteristics are given that lead to an investment decision. While the decision-making process by investors is impossible to capture, this paper applies new methods for capturing the conditions under which real estate transactions are made over space and time. In other words, we explain and forecast real estate transactions with machine learning methods including both real estate transactions, geographical information and most importantly microeconomic data.