| Summary: | 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.
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