Latent Semantic Analysis and Real Estate Research: Methods and Applications

Nicholas Evangelopoulos, Triss Ashton, Kimberly Winson-Geideman and Stephen Roulac

Journal of Real Estate Literature, 2015, vol. 23, issue 2, 353-380

Abstract: In this paper, we discuss the potential applications and methodological challenges of using latent semantic analysis (LSA) to analyze unstructured data in real estate research. LSA is a statistical tool that allows for the analysis of large bodies of textual data by identifying specific topics or themes within the data. The purpose of this paper is to review the use of LSA as a way to understand real estate issues and to provide insight on the practical utility of the method. A typology for data complexity in the real estate context is presented. An in-depth discussion of the analytic process as a series of specific steps is provided, followed by three applications that illustrate the methodological decisions made by the analyst when LSA is performed.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/10835547.2015.12090411 (text/html)
Access to full text is restricted to subscribers.

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:taf:rjelxx:v:23:y:2015:i:2:p:353-380

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rjel20

DOI: 10.1080/10835547.2015.12090411

Access Statistics for this article

Journal of Real Estate Literature is currently edited by Sophia Dermisi and Kimberly Winson

More articles in Journal of Real Estate Literature from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().