The Possibilities and Limitations of Geostatistical Methods in Real Estate Market Analyses

Cellmer Radosław ()
Additional contact information
Cellmer Radosław: Faculty of Geodesy and Land Management University of Warmia and Mazury in Olsztyn

Real Estate Management and Valuation, 2014, vol. 22, issue 3, 54-62

Abstract: In the traditional approach, geostatistical modeling involves analyses of the spatial structure of regionalized data, as well as estimations and simulations that rely on kriging methods. Geostatistical methods can complement traditional statistical models of property transaction prices, and when combined with those models, they offer a comprehensive tool for spatial analysis that is used in the process of developing land value maps. Transaction prices are characterized by mutual spatial correlations and can be considered as regionalized variables. They can also be regarded as random variables that have a local character and a specific probability distribution. This study explores the possibilities of applying geostatistical methods in spatial modeling of the prices of undeveloped land, as well as the limitations associated with those methods and the imperfect nature of the real estate market. The results are discussed based on examples, and they cover both the modeling process and the generated land value maps.

Keywords: real estate market; geostatistics; semivariogram; kriging (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/remav-2014-0027 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:22:y:2014:i:3:p:9:n:6

DOI: 10.2478/remav-2014-0027

Access Statistics for this article

Real Estate Management and Valuation is currently edited by Sabina Zrobek

More articles in Real Estate Management and Valuation from Sciendo
Bibliographic data for series maintained by Peter Golla ().