2026-09-19T04:48:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/297772022-01-13T07:54:12Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
William C. Wheaton.
Zhao, Yajie, 1970-
Massachusetts Institute of Technology. Dept. of Architecture.
Massachusetts Institute of Technology. Department of Architecture
2006-03-24T16:24:31Z
2006-03-24T16:24:31Z
2003
2003
http://hdl.handle.net/1721.1/29777
54756126
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Architecture, 2003.
Includes bibliographical references (leaf 52).
This paper uses statistical regression techniques, Ordinary Least Squares (OLS), to develop models to explain the relationship between physical characteristics and performance of industrial properties. The physical characteristics studied in this paper include building size, building age, building type, and tenant type. Rent and occupancy rates are used as a proxy for performance. The results show that building size, building age, building type and tenant type are significant variables in the explanation of industrial property performance and volatility of annual growth of performance, but these variables are not found to exert a significant influence on the long-term trend of performance. The results also indicate that the decrease in size of industrial properties increase their performance ( or the relationship is negative) but decrease their volatility of annual growth of performance ( or the relationship is positive); medium-age industrial properties outperform newer and older ones in both performance and volatility of annual growth of performance; R&D industrial properties have the best performance and also the highest volatility of annual growth of performance among the three building types: manufacturing, warehouse, and R&D; single tenant industrial properties have better performance and higher volatility of annual growth of performance than multitenant ones.
by Yajie Zhao.
S.M.
52 leaves
1618208 bytes
1618015 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.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.
http://dspace.mit.edu/handle/1721.1/7582
Architecture.
Industrial property performance and building functionality
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>This paper uses statistical regression techniques, Ordinary Least Squares (OLS), to develop models to explain the relationship between physical characteristics and performance of industrial properties. The physical characteristics studied in this paper include building size, building age, building type, and tenant type. Rent and occupancy rates are used as a proxy for performance. The results show that building size, building age, building type and tenant type are significant variables in the explanation of industrial property performance and volatility of annual growth of performance, but these variables are not found to exert a significant influence on the long-term trend of performance. The results also indicate that the decrease in size of industrial properties increase their performance ( or the relationship is negative) but decrease their volatility of annual growth of performance ( or the relationship is positive); medium-age industrial properties outperform newer and older ones in both performance and volatility of annual growth of performance; R&D industrial properties have the best performance and also the highest volatility of annual growth of performance among the three building types: manufacturing, warehouse, and R&D; single tenant industrial properties have better performance and higher volatility of annual growth of performance than multitenant ones.</Abstract>
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