IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v27y2019i3p124-132n10.html
   My bibliography  Save this article

Dissimilarity as a Component of the Property Price Model

Author

Listed:
  • Zyga Jacek

    (Faculty of Engineering and Architecture, Lublin University of Technology)

Abstract

In the course of discussion on an econometric model of property value and its place in property appraisal, the argument of the main goal of the process (property market value prediction itself) was raised in this article. The need for the consideration of an ontologically perceived, particular element of the real estate market with its distinctive characteristics indicates the specific nature of the interpretation of the data which may be used in the appraisal process.Therefore, a new shape of the property value model, based on LSM, was presented. It takes into account a specific description of the appraised property. Thus, the factor of dissimilarity between sold properties used in creating the value model and the appraised property was used in its coefficient matrix. The new model clearly shows the advantages and disadvantages of the dissimilarities between sold properties used in creating the coefficient matrix of the value model.

Suggested Citation

  • Zyga Jacek, 2019. "Dissimilarity as a Component of the Property Price Model," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 124-132, September.
  • Handle: RePEc:vrs:remava:v:27:y:2019:i:3:p:124-132:n:10
    DOI: 10.2478/remav-2019-0030
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/remav-2019-0030
    Download Restriction: no

    File URL: https://libkey.io/10.2478/remav-2019-0030?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. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    2. David Jansen van Vuuren, 2017. "Modified sales comparison method: valuing under (un)certainty," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 35(1), pages 101-110, February.
    3. Hans R. Isakson, 1986. "The Nearest Neighbors Appraisal Technique: An Alternative to the Adjustment Grid Methods," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 14(2), pages 274-286, June.
    4. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    5. Liv Osland, 2010. "An Application of Spatial Econometrics in Relation to Hedonic House Price Modelling," Journal of Real Estate Research, American Real Estate Society, vol. 32(3), pages 289-320.
    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. Zyga Jacek, 2019. "Data Selection as the Basis for Better Value Modelling," Real Estate Management and Valuation, Sciendo, vol. 27(1), pages 25-34, March.
    2. Damian Przekop, 2022. "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 14(2), pages 199-223, June.
    3. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    4. José-María Montero & Román Mínguez & Gema Fernández-Avilés, 2018. "Housing price prediction: parametric versus semi-parametric spatial hedonic models," Journal of Geographical Systems, Springer, vol. 20(1), pages 27-55, January.
    5. Kobylińska Katarzyna, 2021. "The Application of Spatial Autoregressive Models for Analyzing the Influence of Spatial Factors on Real Estate Prices and Values," Real Estate Management and Valuation, Sciendo, vol. 29(4), pages 23-35, December.
    6. Bełej, Mirosław & Cellmer, Radosław & Foryś, Iwona & Głuszak, Michał, 2023. "Airports in the urban landscape: externalities, stigmatization and housing market," Land Use Policy, Elsevier, vol. 126(C).
    7. Mirosław Bełej & Radosław Cellmer & Michał Głuszak, 2020. "The Impact of Airport Proximity on Single-Family House Prices—Evidence from Poland," Sustainability, MDPI, vol. 12(19), pages 1-26, September.
    8. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    9. Prashant Das & Alan Ziobrowski, 2015. "The Relationship between Indian Realty Stocks and Online Searches," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(1), pages 1-19, April.
    10. Liv Osland & Inge Thorsen, 2013. "Spatial Impacts, Local Labour Market Characteristics and Housing Prices," Urban Studies, Urban Studies Journal Limited, vol. 50(10), pages 2063-2083, August.
    11. Chica-Olmo, Jorge & Cano-Guervos, Rafael, 2020. "Does my house have a premium or discount in relation to my neighbors? A regression-kriging approach," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    12. Jiaxuan Liang & Yi Cheng & Yuqi Su & Shuyue Xiao & Yunquan Song, 2022. "Variable Selection for Spatial Logistic Autoregressive Models," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    13. Shang Xie & Muhammad Ateeq ur Reman & Kang Mao, 2022. "Effects of Education Equalization Measures on Housing Prices: Evidence from a Natural Experiment in Suzhou, China," Review of Economic Assessment, Anser Press, vol. 1(1), pages 22-33, December.
    14. Jacek Batóg & Iwona Foryś & Radosław Gaca & Michał Głuszak & Jan Konowalczuk, 2019. "Investigating the Impact of Airport Noise and Land Use Restrictions on House Prices: Evidence from Selected Regional Airports in Poland," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    15. Theologos Dergiades & Costas Milas & Theodore Panagiotidis, 2015. "Tweets, Google trends, and sovereign spreads in the GIIPS," Oxford Economic Papers, Oxford University Press, vol. 67(2), pages 406-432.
    16. Akshita Singh & Shailendra Kumar & Utkarsh Goel & Amar Johri, 2023. "Behavioural biases in real estate investment: a literature review and future research agenda," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    17. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    18. Marko Kryvobokov, 2011. "Defining apartment neighbourhoods with Thiessen polygons and fuzzy equality clustering," ERES eres2011_142, European Real Estate Society (ERES).
    19. Lianne Foti & Avis Devine, 2019. "High Involvement and Ethical Consumption: A Study of the Environmentally Certified Home Purchase Decision," Sustainability, MDPI, vol. 11(19), pages 1-11, September.
    20. Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.

    More about this item

    Keywords

    Dissimilarity; Property price model; comparables selection; property valuation;
    All these keywords.

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    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:27:y:2019:i:3:p:124-132:n:10. 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.