IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v28y2020i4p1-14n1.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chmielewska Aneta & Adamiczka Jerzy & Romanowski Michał, 2020. "Genetic Algorithm as Automated Valuation Model Component in Real Estate Investment Decisions System," Real Estate Management and Valuation, Sciendo, vol. 28(4), pages 1-14, December.
  • Handle: RePEc:vrs:remava:v:28:y:2020:i:4:p:1-14:n:1
    DOI: 10.1515/remav-2020-0027
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/remav-2020-0027
    Download Restriction: no

    File URL: https://libkey.io/10.1515/remav-2020-0027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Marco Helbich & Wolfgang Brunauer & Eric Vaz & Peter Nijkamp, 2014. "Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria," Urban Studies, Urban Studies Journal Limited, vol. 51(2), pages 390-411, February.
    2. d’Amato, Maurizio & Zrobek, Sabina & Renigier Bilozor, Malgorzata & Walacik, Marek & Mercadante, Giuseppe, 2019. "Valuing the effect of the change of zoning on underdeveloped land using fuzzy real option approach," Land Use Policy, Elsevier, vol. 86(C), pages 365-374.
    3. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 2016. "Understanding Booms and Busts in Housing Markets," Journal of Political Economy, University of Chicago Press, vol. 124(4), pages 1088-1147.
    4. Steven D. Levitt & Chad Syverson, 2008. "Market Distortions When Agents Are Better Informed: The Value of Information in Real Estate Transactions," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 599-611, November.
    5. Robert Pereira, 2000. "Genetic Algorithm Optimisation for Finance and Investment," Working Papers 2000.02, School of Economics, La Trobe University.
    6. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    7. Brzezicka, Justyna & Łaszek, Jacek & Olszewski, Krzysztof & Waszczuk, Joanna, 2019. "Analysis of the filtering process and the ripple effect on the primary and secondary housing market in Warsaw, Poland," Land Use Policy, Elsevier, vol. 88(C).
    8. van Groenendaal, W.J.H., 2003. "Group decision support for public policy planning," Other publications TiSEM eecbe70b-1ce7-4885-903f-6, Tilburg University, School of Economics and Management.
    9. Nancy Stokey, 2016. "Wait-and See: Investment Options under Policy Uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 21, pages 246-265, July.
    10. Vincenzo Del Giudice & Pierfrancesco De Paola & Fabiana Forte & Benedetto Manganelli, 2017. "Real Estate Appraisals with Bayesian Approach and Markov Chain Hybrid Monte Carlo Method: An Application to a Central Urban Area of Naples," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Renigier–Biłozor, Małgorzata & Chmielewska, Aneta & Kamasz, Ewelina, 2024. "The soft computing based model of investors’ condition and cognition on a real estate market," Land Use Policy, Elsevier, vol. 141(C).
    2. Elliot Anenberg & Patrick Bayer, 2020. "Endogenous Sources Of Volatility In Housing Markets: The Joint Buyer–Seller Problem," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(3), pages 1195-1228, August.
    3. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    4. Elliot Anenberg, 2012. "Information frictions and housing market dynamics," Finance and Economics Discussion Series 2012-48, Board of Governors of the Federal Reserve System (U.S.).
    5. Elliot Anenberg & Patrick Bayer, 2013. "Endogenous sources of volatility in housing markets: the joint buyer-seller problem," Finance and Economics Discussion Series 2013-60, Board of Governors of the Federal Reserve System (U.S.).
    6. Renigier-Biłozor, Małgorzata & Źróbek, Sabina & Walacik, Marek & Borst, Richard & Grover, Richard & d’Amato, Maurizio, 2022. "International acceptance of automated modern tools use must-have for sustainable real estate market development," Land Use Policy, Elsevier, vol. 113(C).
    7. Han, Lu & Strange, William C., 2015. "The Microstructure of Housing Markets," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 813-886, Elsevier.
    8. William N Goetzmann & Christophe Spaenjers & Stijn Van Nieuwerburgh, 2021. "Real and Private-Value Assets [Gendered prices]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3497-3526.
    9. Elliot Anenberg, 2016. "Information Frictions And Housing Market Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 57(4), pages 1449-1479, November.
    10. Bello Musa Zango & Sanni Mohammed Lekan & Mohammed Jibrin Katun, 2020. "Conventional Methods in Housing Market Analysis: A Review of Literature," Baltic Journal of Real Estate Economics and Construction Management, Sciendo, vol. 8(1), pages 227-241, January.
    11. Hillebrand, Marten & Kikuchi, Tomoo, 2015. "A mechanism for booms and busts in housing prices," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 204-217.
    12. Kuang, Pei, 2014. "A model of housing and credit cycles with imperfect market knowledge," European Economic Review, Elsevier, vol. 70(C), pages 419-437.
    13. Fábio T. F. Silva & Alexandre Szklo & Amanda Vinhoza & Ana Célia Nogueira & André F. P. Lucena & Antônio Marcos Mendonça & Camilla Marcolino & Felipe Nunes & Francielle M. Carvalho & Isabela Tagomori , 2022. "Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-39, October.
    14. Johannes Stroebel, 2016. "EconomicDynamics Interview: Johannes Stroebel on real estate dynamics," EconomicDynamics Newsletter, Review of Economic Dynamics, vol. 17(2), November.
    15. Asongu, Simplice A. & Odhiambo, Nicholas M., 2021. "Inequality, finance and renewable energy consumption in Sub-Saharan Africa," Renewable Energy, Elsevier, vol. 165(P1), pages 678-688.
    16. Badarinza, Cristian & Ramadorai, Tarun & Shimizu, Chihiro, 2022. "Gravity, counterparties, and foreign investment," Journal of Financial Economics, Elsevier, vol. 145(2), pages 132-152.
    17. Goodness C. Aye & Rangan Gupta, 2019. "Macroeconomic Uncertainty And The Comovement In Buying Versus Renting In The Usa," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(3), pages 93-121, September.
    18. Abiodun Ogunyemi & Kevin Johnston, 2017. "Is Server Virtualization Implementation in Business and Public Organizations a Worthwhile Investment?," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 711-736, May.
    19. Charles Ka Yui Leung & Joe Cho Yiu Ng, 2018. "Macro Aspects of Housing," GRU Working Paper Series GRU_2018_016, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
    20. Wenyi Zeng & Deqing Li & Peizhuang Wang, 2016. "Variable Weight Decision Making and Balance Function Analysis Based on Factor Space," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 999-1014, September.

    More about this item

    Keywords

    genetic algorithm (GA); uncertainty; decision support system; automated valuation model (AVM); real estate market;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:vrs:remava:v:28:y:2020:i:4:p:1-14:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.