IDEAS home Printed from
MyIDEAS: Login to save this article or follow this journal

Nonparametric Methods with Applications to Hedonic Models

  • Pace, R Kelley

Current real estate statistical valuation involves the estimation of parameters within a posited specification. Such parametric estimation requires judgment concerning model (1) variables; and (2) functional form. In contrast, nonparametric regression estimation requires attention to (1) but permits greatly reduced attention to (2). Parametric estimators functionally model the parameters and variables affecting E(y x) while nonparametric estimators directly model pdf(y,x) and hence E(y x). This article applies the kernel nonparametric regression estimator to two different data sets and specifications. The article shows the nonparametric estimator outperforms the standard parametric estimator (OLS) across variable transformations and across data subsets differing in quality. In addition, the article reviews properties of nonparametric estimators, presents the history of nonparametric estimators in real estate, and discusses a representation of the kernel estimator as a nonparametric grid method. Copyright 1993 by Kluwer Academic Publishers

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.

Article provided by Springer in its journal Journal of Real Estate Finance & Economics.

Volume (Year): 7 (1993)
Issue (Month): 3 (November)
Pages: 185-204

in new window

Handle: RePEc:kap:jrefec:v:7:y:1993:i:3:p:185-204
Contact details of provider: Web page:

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:kap:jrefec:v:7:y:1993:i:3:p:185-204. 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: (Sonal Shukla)

or (Christopher F. Baum)

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.