Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System

Chmielewska Aneta (), Adamiczka Jerzy () and Romanowski Michał ()
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Chmielewska Aneta: Institute of Spatial Economy and Geography, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
Adamiczka Jerzy: Adamiczka Consulting
Romanowski Michał: Independent researcher

Real Estate Management and Valuation, 2020, vol. 28, issue 4, 1-14

Abstract: Every real-estate related investment decision making process calls for the careful analysis of available information even though it is often carried out in conditions of uncertainty. The paper attempts to minimize the impact of the factor on the quality of real estate investment decisions through the proposal of application of tools based on the simulation of the process of natural selection and biological evolution. The aim of the study is to analyze the potential of methodology based on genetic algorithms (GA) to build automated valuation models (AVM) in uncertainty conditions and support investment decisions on the real estate market. The developed model facilitates the selection of properties adequate to the adopted assumptions, i.e. individuals best suited to the environment. The tool can be used by real estate investment advisors and potential investors on the market to predict future processes and the proper confrontation of past events with planned events. Even though genetic algorithms are tools that have already found particular application on real estate market, there are still areas that need further studies in the case of more effective uses. The obtained results allow for the possibilities and barriers of applying GA to real estate market analyses to be defined.

Keywords: genetic algorithm (GA); uncertainty; decision support system; automated valuation model (AVM); real estate market (search for similar items in EconPapers)
JEL-codes: C00 C10 D81 R30 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:28:y:2020:i:4:p:1-14:n:1

DOI: 10.1515/remav-2020-0027

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