Predicting Spatial Patterns of House Prices Using LPR and Bayesian Smoothing
AbstractThis article is motivated by the limited ability of standard hedonic price equations to deal with spatial variation in house prices. Spatial patterns of house prices can be viewed as the sum of many causal factors: Access to the central business district is associated with a house price gradient; access to decentralized employment subcenters causes more localized changes in house prices; in addition, neighborhood amenities (and disamenities) can cause house prices to change rapidly over relatively short distances. Spatial prediction (e.g., for an automated valuation system) requires models that can deal with all of these sources of spatial variation. We propose to accommodate these factors using a standard hedonic framework but incoporating a semiparametric model with structure in the residuals modeled with a partially Bayesian approach. The Bayesian framework enables us to provide complete inference in the form of a posterior distribution for each model parameter. Our model allows prediction at sampled or unsampled locations as well as prediction interval estimates. The nonparametric part of our model allows sufficient flexibility to find substantial spatial variation in house values. The parameters of the kriging model provide further insights into spatial patterns. Out-of-sample mean squared error and related statistics validate the proposed methods and justify their use for spatial prediction of house values. Copyright 2002 American Real Estate and Urban Economics Association.
Download InfoIf 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by American Real Estate and Urban Economics Association in its journal Real Estate Economics.
Volume (Year): 30 (2002)
Issue (Month): 4 ()
Contact details of provider:
Postal: Indiana University, Kelley School of Business, 1309 East Tenth Street, Suite 738, Bloomington, Indiana 47405
Phone: (812) 855-7794
Fax: (812) 855-8679
Web page: http://www.blackwellpublishing.com/journal.asp?ref=1080-8620
More information through EDIRC
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Bin, Okmyung, 2004. "A prediction comparison of housing sales prices by parametric versus semi-parametric regressions," Journal of Housing Economics, Elsevier, vol. 13(1), pages 68-84, March.
- Alicia Rambaldi & Ryan McAllister & Kerry Collins & Cameron Fletcher, 2011. "An Unobserved Components Approach to Separating Land from Structure in Property Prices: A Case Study for the City of Brisbane," Discussion Papers Series 428, School of Economics, University of Queensland, Australia.
- Yong Tu & Hua Sun & Shi-Ming Yu, 2007. "Spatial Autocorrelations and Urban Housing Market Segmentation," The Journal of Real Estate Finance and Economics, Springer, vol. 34(3), pages 385-406, April.
- Jose Montero & Beatriz Larraz, 2010. "Estimating Housing Prices: A Proposal with Spatially Correlated Data," International Advances in Economic Research, Springer, vol. 16(1), pages 39-51, February.
- Nancy Lozano-Gracia, 2008. "Semi-Parametric Hedonic Models, and Empirical Comparison," GeoDa Center Working Papers 1006, GeoDa Center for Geospatial Analysis and Computation.
- José-María Montero-Lorenzo & Beatriz Larraz-Iribas & Antonio Páez, 2009. "Estimating commercial property prices: an application of cokriging with housing prices as ancillary information," Journal of Geographical Systems, Springer, vol. 11(4), pages 407-425, December.
- Jorge Chica-Olmo, 2007. "Prediction of Housing Location Price by a Multivariate Spatial Method: Cokriging," Journal of Real Estate Research, American Real Estate Society, vol. 29(1), pages 95-114.
- Wolfgang Brunauer & Stefan Lang & Peter Wechselberger & Sven Bienert, 2008. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," Working Papers 2008-17, Faculty of Economics and Statistics, University of Innsbruck.
- W. Brunauer & S. Lang & P. Wechselberger & S. Bienert, 2010. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," The Journal of Real Estate Finance and Economics, Springer, vol. 41(4), pages 390-411, November.
- Mynbaev, Kairat & Ibrayeva, Saniya, 2011. "Housing market of Almaty," MPRA Paper 36683, University Library of Munich, Germany.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) 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.