Office Rent Determinants Utilising Factor Analysis—A Case Study for İstanbul

Office Rent Determinants Utilising Factor Analysis—A Case Study for İstanbul In recent studies, a wide range of variables has been suggested for modelling the variation in office rent. However, only a few of them are found to influence the explanatory power of the model significantly. Moreover, the significance of these variables varies from model to model, depending on the characteristics of the region or/and the model. It is well established that the regression model of complex phenomena do not perform well, unless the effects of all major determinants are adequately represented. It is also known that complex phenomena may involve a large number of variables, and linear regression models often becomes cumbersome as the number of variables increases. A practical solution to the problem may be to pre-select the significant variables, and leave the less influential ones out. An even better solution could be to include all or most variables, while incorporating the group effect of some variables into a reasonable number factor variables. This way, both the accuracy and practicality of the model can be sustained. Serving this purpose, ‘Factor Analysis’ has been employed in establishing the office rent model for the metropolitan area of İstanbul. The results of four different versions of the model, using linear and non-linear regressions are discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journal of Real Estate Finance and Economics Springer Journals

Office Rent Determinants Utilising Factor Analysis—A Case Study for İstanbul

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2006 by Springer Science + Business Media, LLC
Subject
Economics; Regional/Spatial Science; Financial Services
ISSN
0895-5638
eISSN
1573-045X
D.O.I.
10.1007/s11146-006-8274-5
Publisher site
See Article on Publisher Site

Abstract

In recent studies, a wide range of variables has been suggested for modelling the variation in office rent. However, only a few of them are found to influence the explanatory power of the model significantly. Moreover, the significance of these variables varies from model to model, depending on the characteristics of the region or/and the model. It is well established that the regression model of complex phenomena do not perform well, unless the effects of all major determinants are adequately represented. It is also known that complex phenomena may involve a large number of variables, and linear regression models often becomes cumbersome as the number of variables increases. A practical solution to the problem may be to pre-select the significant variables, and leave the less influential ones out. An even better solution could be to include all or most variables, while incorporating the group effect of some variables into a reasonable number factor variables. This way, both the accuracy and practicality of the model can be sustained. Serving this purpose, ‘Factor Analysis’ has been employed in establishing the office rent model for the metropolitan area of İstanbul. The results of four different versions of the model, using linear and non-linear regressions are discussed.

Journal

The Journal of Real Estate Finance and EconomicsSpringer Journals

Published: May 22, 2006

References

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