Thesis: S.M. in Real Estate Development, Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 113-117). / Real estate meets machine learning: real contribution or just hype? Creating and managing the built environment is a complicated task fraught with difficult decisions, challenging relationships, and a multitude of variables. Today's technology experts are building computers and software that can help resolve many of these challenges, some of them using what is broadly called artificial intelligence and machine learning. This thesis will define machine learning and artificial intelligence for the investor and real estate audience, examine the ways in which these new analytic, predictive, and automating technologies are being used in the real estate industry, and postulate potential future applications and associated challenges. Machine learning and artificial intelligence can and will be used to facilitate real estate investment in myriad ways, spanning all aspects of the real estate profession -- from property management, to investment decisions, to development processes -- transforming real estate into a more efficient and data-driven industry. / by Jennifer Conway. / S.M. in Real Estate Development
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/120609 |
| Date | January 2018 |
| Creators | Conway, Jennifer (Jennifer Elizabeth) |
| Contributors | Alex Van De Minne and David Geltner., Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development., Massachusetts Institute of Technology. Center for Real Estate. Program in Real Estate Development. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 117 pages, application/pdf |
| Rights | MIT 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., http://dspace.mit.edu/handle/1721.1/7582 |
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