A novel machine learning model for estimation of sale prices of real estate units

Predicting the price of housing is of paramount importance for near-term economic forecasting of any nation. This paper presents a novel and comprehensive model for estimating the price of new housing in any given city at the design phase or beginning of the construction through ingenious integration of a deep belief restricted Boltzmann machine and a unique nonmating genetic algorithm. The model can be used by construction companies to gauge the sale market before they start a new construction and consider to build or not to build. An effective data structure is presented that takes into account a large number of economic variables/indices. The model incorporates time-dependent and seasonal variations of the variables. Clever stratagems have been developed to overcome the dimensionality curse and make the solution of the problem amenable on standard workstations. A case study is presented to demonstrate the effectiveness and accuracy of the model.