Rafiei, M H and Adeli, H (2016) A novel machine learning model for estimation of sale prices of real estate units. Journal of Construction Engineering and Management, 142(2): 04015066, ISSN 0733-9364
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | quantitative methods |
| Index terms: | seasonal variation, genetic algorithm, accuracy, housing, estimation, machine learning, case study, data structure, forecasting, construction company, real estate, effectiveness, quantitative method, design phase, estimating, integration |
| Subjects: | organization, data collection methods, data analysis and analytics, algorithms, construction type, data science, professional practice, financial and cost management, artificial intelligence, organizational analysis, real estate economics, performance management, climate science, prediction and forecasting, professional development |
| Topics: | Organizational Design, Sustainability, Information Management, Design Practice, Digital Applications, Research Practice, Business Strategy, Quality Management, Urban Studies, Cost Management, Construction Technology |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here