Abstract
AbstractEvery 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.
Highlights
Processes which are shaping the real estate market include, among others: instability of property attributes, lack of information coherence, heterogeneity in information access (Levitt & Syverson, 2008), deficiencies in cognitive abilities of real estate market entities (Burnside, et al, 2016) uncertainty of system structures (Su, et al, 2016), and the emotional approach of entities to transactions
In the case of random events describing the image of the real estate market, tools and methods that draw inspiration from processes occurring in nature, especially those based on the concept of natural selection, find application
In order to determine the significance of individual attributes of real estate, the authors used a modified version of Hellwig's method based on Spearman's correlation
Summary
Processes which are shaping the real estate market include, among others: instability of property attributes, lack of information coherence, heterogeneity in information access (Levitt & Syverson, 2008), deficiencies in cognitive abilities of real estate market entities (Burnside, et al, 2016) uncertainty of system structures (Su, et al, 2016), and the emotional approach of entities to transactions. By identifying the degree of uncertainty, we can develop methods that limit, control and account for uncertainty in the decisionmaking process (RAO 1994) For this reason, market analyses require solutions and methods that reflect reality, account for and, most importantly, minimize the degree of uncertainty (Ziółkowski & Niedostatkiewicz, 2019; Janowski, et al, 2018; Brzezicka, et al, 2019). The availability of information, market specificity and unpredictable and sudden changes cause all real estate investments to be subject to considerable risk and uncertainty. In the case of random events describing the image of the real estate market, tools and methods that draw inspiration from processes occurring in nature, especially those based on the concept of natural selection, find application. Genetic algorithms, which are procedures based on the basic mechanisms of biological evolution (Ahn, et al, 2012; Zavadskas, et al, 2017; Wu, et al, 2018; Kontrimas & Verikas, 2011; Chodak & Kwaśnicki, 2002) are an example of such
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