The modeling and control of complex systems, such as transportation, communication, power grids or real estate, require vast amounts of data to be analyzed. The number of variables in the models of such systems is large, typically a few hundred or even thousands. Computing the relationships between these variables, extracting the dominant variables and predicting the temporal and spatial dynamics of the variables are the general focuses of data analytics research. Statistical modeling and artificial intelligence have emerged as crucial solution enablers to these problems. The problem of real estate investment involves social, governmental, environmental and financial factors. Existing work on real estate investment focuses predominantly on the trend predictions of house pricing exclusively from financial factors. In practice, real estate investment is influenced by multiple factors (stated above), and computing an optimal choice is a multivariate optimization problem and lends itself naturally to machine learning-based solutions. In this work, we focus on setting up a machine learning framework to identify an optimal location for investment, given a preference set of an investor. We consider, in this paper, the problem to only direct real estate factors (bedroom type, garage spaces, etc.), other indirect factors like social, governmental, etc., will be incorporated into future work, in the same framework. Two solution approaches are presented here: first, decision trees and principal component analysis (PCA) with K-means clustering to compute optimal locations. In the second, PCA is replaced by artificial neural networks, and both methods are contrasted. To the best of our knowledge, this is the first work where the machine learning framework is introduced to incorporate all realistic parameters influencing the real estate investment decision. The algorithms are verified on the real estate data available in the TerraFly platform.