IDEAS home Printed from
MyIDEAS: Login to save this paper or follow this series

Clustering Methods for Real Estate Portfolios

  • William N. Goetzmann


    (Yale University, School of Management)

  • Susan M. Wachter


    (Real Estate Department)

A clustering algorithm is applied to effective rents for twenty-one U.S. office markets, and to twenty-two metropolitan markets using vacancy data. It provides support for the conjecture that there exists a few major families of cities: including an oil and gas group and an industrial Northeast group. Unlike other clustering studies, we find strong evidence of bicoastal city associations among cities such as Boston and Los Angeles. We present a bootstrapping methodology for investigating the robustness of the clustering algorithm, and develop a means for testing the significance of city associations. While the analysis is limited to aggregate rent and vacancy data, the results provide a guideline for the further application of cluster analysis to other types of real estate and economic information.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL:
Download Restriction: no

Paper provided by Yale School of Management in its series Yale School of Management Working Papers with number ysm59.

in new window

Date of creation: 03 Jul 1998
Date of revision:
Handle: RePEc:ysm:somwrk:ysm59
Contact details of provider: Web page:

More information through EDIRC

No references listed on IDEAS
You can help add them by filling out this form.

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:ysm:somwrk:ysm59. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.