IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwrsa/ersa10p424.html
   My bibliography  Save this paper

Neighborhood weight matrix in a spatial-quantile real estate modeling environment: Evidence from Brazil

Author

Listed:
  • Bernardo Furtado

  • Frank van Oort

Abstract

This paper analysis two central influences on real estate urban market: (a) neighborhood as cognitively-perceived identity units, and (b) heterogeneity of preferences of households. In doing so, two methodological changes are applied. Firstly, a neighborhood spatial matrix is proposed and compared to regularly used matrices and, secondly, a spatial-quantile regression is tested. The results highlight the fact that spatial influence brought to regression models through weight matrix should be carefully used, and that the matrix's choice might carry unobserved and unwanted effects into the estimation. Results also demonstrated that using information of neighborhood identity can optimize the understanding of city's complex influence on real estate markets. Finally, the quantile estimation should always be tested against in real estate estimation, as preferences of households seem to differ significantly for different levels of prices.

Suggested Citation

  • Bernardo Furtado & Frank van Oort, 2011. "Neighborhood weight matrix in a spatial-quantile real estate modeling environment: Evidence from Brazil," ERSA conference papers ersa10p424, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa10p424
    as

    Download full text from publisher

    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa10/ERSA2010finalpaper424.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. R. Capello & Peter Nijkamp, 2005. "The Theoretical and Methodological Toolbox of Urban Economics: From and Towards Where?," Contributions to Economic Analysis, in: Urban Dynamics and Growth: Advances in Urban Economics, pages 1-27, Emerald Group Publishing Limited.
    2. Sheppard, Stephen, 1999. "Hedonic analysis of housing markets," Handbook of Regional and Urban Economics, in: P. C. Cheshire & E. S. Mills (ed.), Handbook of Regional and Urban Economics, edition 1, volume 3, chapter 41, pages 1595-1635, Elsevier.
    3. Smirnov, Oleg & Anselin, Luc, 2001. "Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach," Computational Statistics & Data Analysis, Elsevier, vol. 35(3), pages 301-319, January.
    4. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Paelinck, J., 1978. "Spatial econometrics," Economics Letters, Elsevier, vol. 1(1), pages 59-63.
    7. Zhenlin Yang & Liangjun Su, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Working Papers 05-2007, Singapore Management University, School of Economics.
    8. Wheaton, William C., 2004. "Commuting, congestion, and employment dispersal in cities with mixed land use," Journal of Urban Economics, Elsevier, vol. 55(3), pages 417-438, May.
    9. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, 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. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    2. Furtado, BERNARDO, 2007. "Real estate market and the relevance of local features in a hedonic prices quantil-spatial analysis – the case of Belo Horizonte – Brazil," MPRA Paper 7340, University Library of Munich, Germany.
    3. Philip Kostov, 2013. "Empirical likelihood estimation of the spatial quantile regression," Journal of Geographical Systems, Springer, vol. 15(1), pages 51-69, January.
    4. 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.
    5. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    6. Witte, Felix & Sponagel, Christian & Bahrs, Enno, 2023. "Used Tractor on the Rise - An Econometric Analysis of German Auction Results," GEWISOLA 63rd Annual Conference, Goettingen, Germany, September 20-22, 2023 344232, GEWISOLA.
    7. Marcelo Cajias & Philipp Freudenreich & Anna Heller & Wolfgang Schaefers, 2018. "Censored Quantile Regressions and the Determinants of Real Estate Liquidity," ERES eres2018_203, European Real Estate Society (ERES).
    8. Jing Zhang & Colin Brown, 2018. "Spatial Variation and Factors Impacting Grassland Circulation Price in Inner Mongolia, China," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    9. Skouralis, Alexandros & Lux, Nicole & Andrew, Mark, 2024. "Does flood risk affect property prices? Evidence from a property-level flood score," Journal of Housing Economics, Elsevier, vol. 66(C).
    10. Herrera Gómez, Marcos & Cid, Juan Carlos & Paz, Jorge Augusto, 2012. "Introducción a la econometría espacial: Una aplicación al estudio de la fecundidad en la Argentina usando R [Introduction to Spatial Econometrics: An application to the study of fertility in Argent," MPRA Paper 41138, University Library of Munich, Germany.
    11. Nana Cui & Hengyu Gu & Tiyan Shen & Changchun Feng, 2018. "The Impact of Micro-Level Influencing Factors on Home Value: A Housing Price-Rent Comparison," Sustainability, MDPI, vol. 10(12), pages 1-23, November.
    12. Zhenlin Yang & Liangjun Su, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Working Papers 05-2007, Singapore Management University, School of Economics.
    13. James Yoo & Thomas Frederick, 2017. "The varying impact of land subsidence and earth fissures on residential property values in Maricopa County – a quantile regression approach," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(2), pages 204-216, May.
    14. Nicodemo, Catia & Raya, Josep Maria, 2012. "Change in the distribution of house prices across Spanish cities," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 739-748.
    15. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    16. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    17. Brunes, Fredrik & Hermansson, Cecilia & Song, Han-Suck & Wilhelmsson, Mats, 2016. "NIMBYs for the rich and YIMBYs for the poor: Analyzing the property price effects of infill development," Working Paper Series 16/2, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    18. Bernd Fitzenberger & Benjamin Fuchs, 2017. "The Residency Discount for Rents in Germany and the Tenancy Law Reform Act 2001: Evidence from Quantile Regressions," German Economic Review, Verein für Socialpolitik, vol. 18(2), pages 212-236, May.
    19. Uematsu, Hiroki & Mishra, Ashok K., 2012. "The Impact of Natural Amenity on Farmland Values: A Quantile Regression Approach," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 119804, Southern Agricultural Economics Association.
    20. Jesus Mur & Antonio Paez, 2011. "Local weighting or the necessity of flexibility," ERSA conference papers ersa11p942, European Regional Science Association.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:wiw:wiwrsa:ersa10p424. 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: Gunther Maier (email available below). General contact details of provider: http://www.ersa.org .

    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.