Optimization of Location Attractiveness Zones for the Purpose of Property Mass Appraisal

Sebastian Kokot () and Sebastian Gnat ()
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Sebastian Kokot: University of Szczecin
Sebastian Gnat: University of Szczecin

Chapter Chapter 20 in Experimental and Quantitative Methods in Contemporary Economics, 2020, pp 277-289 from Springer

Abstract: Abstract Mass land appraisal is a specific type of appraisal. A suitable algorithm used for mass appraisal constitutes one of the main determinants of the quality of achieved results. Many of the algorithms cluster real estate in accordance with specific criteria, such as a real estate type, its characteristics or location. Assuming a “real estate location” as a clustering basis, we use terms such as a taxation zone (which originates from a basic application of mass appraisal, which involves ad valorem real estate taxation), along with an elementary terrain (which originates from the nomenclature used in spatial planning). The main goal of this chapter is to find the solution to the designation of such zones, or real estate clusters for the purpose of mass land appraisal conducted with the use of Szczecin Algorithm of Real estate Mass Appraisal (SAREMA). Each mass appraisal algorithm has its own specificity, and consequently, it may pose various requirements to designated zones or real estate clusters (here referred to as location attractiveness zones—LAZ’s). The chapter presents the methods of LAZ’s designation formed on the basis of the existing cadastral districts, real estate market analysis, and hierarchical clustering. The chapter is additionally focused on the manner of determining LAZ optimal number and consequently the number of properties that will be included in an automated mass appraisal process in a given zone. The uniformity of isolated LAZ’s was subject to verification with the use of an entropy ratio, especially constructed for that purpose. The study was conducted on the land plots located in the city of Szczecin.

Keywords: Mass appraisal; Real estate appraisal algorithms; Hierarchical clustering (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-30251-1_20

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DOI: 10.1007/978-3-030-30251-1_20

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