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dc.contributor.advisorJerome J. Connor.en_US
dc.contributor.authorLi, Heng, S.M. Massachusetts Institute of Technologyen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Civil and Environmental Engineering.en_US
dc.date.accessioned2016-08-02T20:07:44Z
dc.date.available2016-08-02T20:07:44Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103843
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 71-75).en_US
dc.description.abstractCurrent housing price prediction usually employs hedonic or repeat-sales models. The objective is to build a statistical model which is more focused on statistic methods. Neither ordinary nor regularized regression model haven been applied to the field of real estate, even though they are rather well-known statistical procedures. This thesis concludes lots of ordinary and regularized regression models. A theoretical review was performed for these models, and Boston Housing data was used to evaluate their performance. The results were found to be reasonable, from a statistical perspective.en_US
dc.description.statementofresponsibilityby Heng Li.en_US
dc.format.extent75 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleA price prediction method In real estate marketen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Civil and Environmental Engineering
dc.identifier.oclc953870846en_US


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