IDEAS home Printed from https://ideas.repec.org/p/hit/remfce/74.html
   My bibliography  Save this paper

Decompositions of Spatially Varying Quantile Distribution Estimates: The Rise and Fall of Tokyo House Prices

Author

Listed:
  • McMillen, Daniel
  • Shimizu, Chihiro

Abstract

We extend Machado-Mata’s (2005) approach for decomposing the differences in the distribution of a dependent variable across two samples to account for location when the models are estimated using conditional parametric procedures. We find that a substantial portion of the change in the distribution of condominium prices in Tokyo between the rapid rise in prices in 1986 – 1990 and the sharp decline in 1991 – 1995 is due to changes in the values of the explanatory variables. Changes in the locations of sales serve to shift the price distribution to the left because later sales were more likely to be farther from downtown Tokyo, where prices are lower.

Suggested Citation

  • McMillen, Daniel & Shimizu, Chihiro, 2017. "Decompositions of Spatially Varying Quantile Distribution Estimates: The Rise and Fall of Tokyo House Prices," HIT-REFINED Working Paper Series 74, Institute of Economic Research, Hitotsubashi University.
    • Handle: RePEc:hit:remfce:74
    as

    Download full text from publisher

    File URL: https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/28997/wp074.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. McMillen, Daniel, 2015. "Conditionally parametric quantile regression for spatial data: An analysis of land values in early nineteenth century Chicago," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 28-38.
    2. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    3. Oaxaca, Ronald, 1973. "Male-Female Wage Differentials in Urban Labor Markets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 14(3), pages 693-709, October.
    4. Tae-Hwan Kim & Christophe Muller, 2004. "Two-stage quantile regression when the first stage is based on quantile regression," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 218-231, June.
    5. Carrillo, Paul & Yezer, Anthony, 2009. "Alternative measures of homeownership gaps across segregated neighborhoods," Regional Science and Urban Economics, Elsevier, vol. 39(5), pages 542-552, September.
    6. Philip Kostov, 2009. "A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(1), pages 53-72.
    7. Chernozhukov, Victor & Hansen, Christian, 2006. "Instrumental quantile regression inference for structural and treatment effect models," Journal of Econometrics, Elsevier, vol. 132(2), pages 491-525, June.
    8. Deng, Yongheng & McMillen, Daniel P. & Sing, Tien Foo, 2012. "Private residential price indices in Singapore: A matching approach," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 485-494.
    9. Zhang, Lei & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    10. Sofie R. Waltl, 2019. "Variation Across Price Segments and Locations: A Comprehensive Quantile Regression Analysis of the Sydney Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 47(3), pages 723-756, September.
    11. McMillen, Daniel P., 2008. "Changes in the distribution of house prices over time: Structural characteristics, neighborhood, or coefficients?," Journal of Urban Economics, Elsevier, vol. 64(3), pages 573-589, November.
    12. 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.
    13. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    14. repec:grz:wpaper:2015-09 is not listed on IDEAS
    15. Zhang, Lei, 2016. "Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 12-19.
    16. Daniel P. McMillen, 2013. "Quantile Regression for Spatial Data," SpringerBriefs in Regional Science, Springer, edition 127, number 978-3-642-31815-3, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marusca De Castris & Daniele Di Gennaro, 2018. "Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach," Papers 1803.05659, arXiv.org.

    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. Zhang, Lei, 2016. "Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 12-19.
    2. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    3. McMillen, Daniel, 2015. "Conditionally parametric quantile regression for spatial data: An analysis of land values in early nineteenth century Chicago," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 28-38.
    4. Zhang, Lei & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    5. Pei-Ing Wu & Yi Chen & Je-Liang Liou, 2021. "Housing property along riverbanks in Taipei, Taiwan: a spatial quantile modelling of landscape benefits and flooding losses," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 2404-2438, February.
    6. Mats Wilhelmsson, 2019. "Energy Performance Certificates and Its Capitalization in Housing Values in Sweden," Sustainability, MDPI, vol. 11(21), pages 1-16, November.
    7. Cartone, Alfredo & Postiglione, Paolo & Hewings, Geoffrey J.D., 2021. "Does economic convergence hold? A spatial quantile analysis on European regions," Economic Modelling, Elsevier, vol. 95(C), pages 408-417.
    8. Mateusz Tomal & Marco Helbich, 2023. "A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in Amsterdam and Warsaw," Environment and Planning B, , vol. 50(3), pages 579-599, March.
    9. Zhang, Lei & Yi, Yimin, 2018. "What contributes to the rising house prices in Beijing? A decomposition approach," Journal of Housing Economics, Elsevier, vol. 41(C), pages 72-84.
    10. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.
    11. Marusca De Castris & Daniele Di Gennaro, 2018. "Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach," Papers 1803.05659, arXiv.org.
    12. Nishi, Hayato & Asami, Yasushi & Shimizu, Chihiro, 2021. "The illusion of a hedonic price function: Nonparametric interpretable segmentation for hedonic inference," Journal of Housing Economics, Elsevier, vol. 52(C).
    13. Bohman, Helena & Nilsson, Désirée, 2016. "The impact of regional commuter trains on property values: Price segments and income," Journal of Transport Geography, Elsevier, vol. 56(C), pages 102-109.
    14. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    15. 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.
    16. Ismail, Muhammad & Warsame, Abukar & Wilhelmsson, Mats, 2020. "Measuring Gentrification with Getis-Ord Statistics and Its Effect on Housing Prices in Neighboring Areas: The Case of Stockholm," Working Paper Series 20/19, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    17. Jose Torres-Pruñonosa & Pablo García-Estévez & Camilo Prado-Román, 2021. "Artificial Neural Network, Quantile and Semi-Log Regression Modelling of Mass Appraisal in Housing," Mathematics, MDPI, vol. 9(7), pages 1-16, April.
    18. Haiyong Zhang & Xinyu Wang, 2018. "The impact of structural adjustment on housing prices in China," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 32(1), pages 108-119, May.
    19. repec:grz:wpaper:2015-09 is not listed on IDEAS
    20. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    21. Alfredo Cartone & Paolo Postiglione, 2016. "Modelli spaziali di regressione quantilica per l?analisi della convergenza economica regionale," RIVISTA DI ECONOMIA E STATISTICA DEL TERRITORIO, FrancoAngeli Editore, vol. 2016(3), pages 28-48.

    More about this item

    Keywords

    Conditionally parametric; quantile regression; decomposition;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

    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:hit:remfce:74. 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: Digital Resources Section, Hitotsubashi University Library (email available below). General contact details of provider: https://edirc.repec.org/data/iehitjp.html .

    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.