A Novel Machine Learning Model for Estimation of Sale Prices of Real Estate Units
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 2
Abstract
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.
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Published In
Journal of Construction Engineering and Management
Volume 142 • Issue 2 • February 2016
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Nov 24, 2014
Accepted: Jun 18, 2015
Published online: Aug 10, 2015
Discussion open until: Jan 10, 2016
Published in print: Feb 1, 2016
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