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dc.contributor.advisorAndrea M. Chegut.en_US
dc.contributor.authorStroud, Ryan Michael.en_US
dc.contributor.otherMassachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development.en_US
dc.date.accessioned2018-02-08T16:25:15Z
dc.date.available2018-02-08T16:25:15Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/113468en_US
dc.descriptionThesis: S.M. in Real Estate Development, Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate, 2017en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 63-64).en_US
dc.description.abstractBig Data Analytics is a term that represents an entire spectrum of analytical applications utilizing significant quantities of data, ranging from optimization at one end of the spectrum, to gaining new insights at the other end of the spectrum. This thesis focuses on the latter, leveraging private, public, and manually developed databases at the MIT School of Architecture and Planning's Center for Real Estate's Real Estate Innovation Lab (REIL) to observe, dissect, and ultimately improve our collective understanding of the current state of urban technology databases. The thesis seeks to explore how companies are providing data within the realm of the built environment, through a study of the information products that they offer. To preserve the confidentiality of the original commercial databases and limit the scope of the investigation, the dataset for this study contains only the data fields from 31 unique databases provided by 14 commercial real estate data aggregators. In essence, the dataset for this thesis is a database of databases, stripped of their numerical information and focused on a study of the variation in data. For analysis this employs computational, statistical, and graphical methods to interpret the information provided by the commercial real estate data aggregators. With an increasingly digital future ahead, this thesis proposes a general framework for examining numerous databases and their respective approaches to the built environment. This thesis also explores the merits of specific processes and presentation methods that translate an immense and disparate array of information into user-friendly analytical tools.en_US
dc.description.statementofresponsibilityby Ryan Michael Stroud.en_US
dc.format.extent64 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCenter for Real Estate. Program in Real Estate Development.en_US
dc.titleInformatics for real estate : urban technology databasesen_US
dc.typeThesisen_US
dc.description.degreeS.M. in Real Estate Developmenten_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Developmenten_US
dc.contributor.departmentMassachusetts Institute of Technology. Center for Real Estate
dc.identifier.oclc1019901777en_US
dc.description.collectionS.M.inRealEstateDevelopment Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estateen_US
dspace.imported2019-09-16T18:44:15Zen_US


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