{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/70166"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/70166","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Real property portfolio management : a decision-support model","abstract":"In the 1980's corporate real estate has assumed a more active role in the strategic planning of American corporations. However, the tools to accurately evaluate the performance of corporate real property portfolios are still at a very rudimentary stage in their development. This thesis concentrates on the space inventory system of a large corporation and presents a model for determining fair comparisons between buildings across the portfolio. A technique is devised for identifying \"outliers\", that is, buildings whose performance is significantly different from other buildings of the same type. This technique shows how to classify buildings into groups, so that building class standards can be determined and trends identified. Artificial Intelligence tools such as decision-support systems can be helpful to encode the expertise for evaluating buildings' performance levels. Through the design of two working demos the thesis illustrates how that is possible, and points towards future alternatives. The author spent an academic semester as a consultant/ intern in the real estate division of a multinational corporation. For anonymity purposes, the corporation is called the Star Corporation. The Star Corp. provided the data used in the research, as well as the supervision and training in their in-house systems operation.","abstract_html":"In the 1980's corporate real estate has assumed a more active role in the strategic planning of American corporations. However, the tools to accurately evaluate the performance of corporate real property portfolios are still at a very rudimentary stage in their development. This thesis concentrates on the space inventory system of a large corporation and presents a model for determining fair comparisons between buildings across the portfolio. A technique is devised for identifying "outliers", that is, buildings whose performance is significantly different from other buildings of the same type. This technique shows how to classify buildings into groups, so that building class standards can be determined and trends identified. Artificial Intelligence tools such as decision-support systems can be helpful to encode the expertise for evaluating buildings' performance levels. Through the design of two working demos the thesis illustrates how that is possible, and points towards future alternatives. The author spent an academic semester as a consultant/ intern in the real estate division of a multinational corporation. For anonymity purposes, the corporation is called the Star Corporation. The Star Corp. provided the data used in the research, as well as the supervision and training in their in-house systems operation.","abstract_has_math":false,"creators":["Schcolnik, Andres E"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Architecture","school":null,"contributors":[],"advisors":["Ranko Bon."],"committee_chairs":[],"committee_members":[],"year":1988,"date_issued":"1988","date_published":"1988","updated_at":"2026-07-22T22:20:44Z","subjects":["Architecture."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. 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