<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Institute of Spatial Economy and Geography

University of Warmia and Mazury in Olsztyn

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

Every real-estate related investment decision making process calls for the careful analysis of available information even though it is often carried out in...

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

Data publikacji: 12 gru 2020

Zakres stron: 1 - 14

DOI: https://doi.org/10.1515/remav-2020-0027

Słowa kluczowe
genetic algorithm (GA), uncertainty, decision support system, automated valuation model (AVM), real estate market

© 2020 Aneta Chmielewska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 12 gru 2020

Zakres stron: 1 - 14

DOI: https://doi.org/10.1515/remav-2020-0027

Słowa kluczowegenetic algorithm (GA), uncertainty, decision support system, automated valuation model (AVM), real estate market

© 2020 Aneta Chmielewska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
genetic algorithm (GA), uncertainty, decision support system, automated valuation model (AVM), real estate market