IDEAS home Printed from https://ideas.repec.org/a/eee/regeco/v55y2015icp28-38.html
   My bibliography  Save this article

Conditionally parametric quantile regression for spatial data: An analysis of land values in early nineteenth century Chicago

Author

Listed:
  • McMillen, Daniel

Abstract

This paper demonstrates that a conditionally parametric version of a quantile regression estimator is well suited to analyzing spatial data. The conditionally parametric quantile model accounts for local spatial effects by allowing coefficients to vary smoothly over space. The approach is illustrated using a new data set with land values for over 30,000 blocks in Chicago for 1913. Kernel density functions summarize the effects of discrete changes in the explanatory variables. The CPAR quantile results suggest that the distribution of land values shifts markedly to the right for locations near the CBD, close to Lake Michigan, near elevated train lines, and along major streets. The variance of the land value distribution is higher in locations farther from the CBD and farther from the train lines.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:regeco:v:55:y:2015:i:c:p:28-38
    DOI: 10.1016/j.regsciurbeco.2015.09.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166046215000782
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.regsciurbeco.2015.09.001?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.
    2. 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.
    3. Harry H. Kelejian & Dennis P. Robinson, 1993. "A Suggested Method Of Estimation For Spatial Interdependent Models With Autocorrelated Errors, And An Application To A County Expenditure Model," Papers in Regional Science, Wiley Blackwell, vol. 72(3), pages 297-312, July.
    4. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731, January.
    5. McMillen, Daniel P., 1996. "One Hundred Fifty Years of Land Values in Chicago: A Nonparametric Approach," Journal of Urban Economics, Elsevier, vol. 40(1), pages 100-124, July.
    6. José Mata & José A. F. Machado, 2005. "Counterfactual decomposition of changes in wage distributions using quantile regression," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(4), pages 445-465.
    7. Daniel McMillen & Maria Edisa Soppelsa, 2015. "A Conditionally Parametric Probit Model Of Microdata Land Use In Chicago," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 391-415, June.
    8. Daniel P. McMillen, 2012. "Perspectives On Spatial Econometrics: Linear Smoothing With Structured Models," Journal of Regional Science, Wiley Blackwell, vol. 52(2), pages 192-209, May.
    9. 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.
    10. 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.
    11. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    12. 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.
    13. Ahlfeldt, Gabriel M. & Wendland, Nicolai, 2011. "Fifty years of urban accessibility: The impact of the urban railway network on the land gradient in Berlin 1890-1936," Regional Science and Urban Economics, Elsevier, vol. 41(2), pages 77-88, March.
    14. 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.
    15. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    16. Roger Koenker & Ivan Mizera, 2004. "Penalized triograms: total variation regularization for bivariate smoothing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 145-163, February.
    17. Daniel P. McMillen, 2013. "Quantile Regression for Spatial Data," SpringerBriefs in Regional Science, Springer, edition 127, number 978-3-642-31815-3, March.
    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. S.‐H. Arnaud Kanga & Ouagnina Hili & Sophie Dabo‐Niang & Assi N'Guessan, 2023. "Asymptotic properties of nonparametric quantile estimation with spatial dependency," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 254-283, August.
    2. Fritsch, Markus & Haupt, Harry & Ng, Pin T., 2016. "Urban house price surfaces near a World Heritage Site: Modeling conditional price and spatial heterogeneity," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 260-275.
    3. 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.
    4. 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.
    5. Allison Shertzer & Tate Twinam & Randall P. Walsh, 2016. "Race, Ethnicity, and Discriminatory Zoning," American Economic Journal: Applied Economics, American Economic Association, vol. 8(3), pages 217-246, July.
    6. Daniel P. McMillen & Elizabeth T. Powers, 2017. "The eldercare landscape: Evidence from California," Health Economics, John Wiley & Sons, Ltd., vol. 26(S2), pages 139-157, September.
    7. 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.
    8. Xian F. Bak & Geoffrey J. D. Hewings, 2019. "The heterogeneous spatial impact of foreclosures on nearby property values," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 439-466, June.
    9. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    10. 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).
    11. Yu-Hui Chen & Chun-Lin Lee & Guan-Rui Chen & Chiung-Hsin Wang & Ya-Hui Chen, 2018. "Factors Causing Farmland Price-Value Distortion and Their Implications for Peri-Urban Growth Management," Sustainability, MDPI, vol. 10(8), pages 1-18, August.

    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 & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Maria Marino & Alessio Farcomeni, 2015. "Linear quantile regression models for longitudinal experiments: an overview," METRON, Springer;Sapienza Università di Roma, vol. 73(2), pages 229-247, August.
    7. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.
    8. Philip Kostov, 2013. "Empirical likelihood estimation of the spatial quantile regression," Journal of Geographical Systems, Springer, vol. 15(1), pages 51-69, January.
    9. Marusca De Castris & Daniele Di Gennaro, 2018. "Does agricultural subsidies foster Italian southern farms? A Spatial Quantile Regression Approach," Papers 1803.05659, arXiv.org.
    10. 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.
    11. 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.
    12. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    13. 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.
    14. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    15. 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.
    16. Stromberg, Per M. & Öhrner, Erik & Brockwell, Erik & Liu, Zhaoyang, 2021. "Valuing urban green amenities with an inequality lens," Ecological Economics, Elsevier, vol. 186(C).
    17. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.
    18. Cathrine Ulla Jensen, 2016. "Households’ willingness to pay for access to outdoor recreation: An application of the house price method using spatial quantile regressions," IFRO Working Paper 2016/09, University of Copenhagen, Department of Food and Resource Economics.
    19. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    20. Victor Chernozhukov & Iván Fernández‐Val & Blaise Melly, 2013. "Inference on Counterfactual Distributions," Econometrica, Econometric Society, vol. 81(6), pages 2205-2268, November.

    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:eee:regeco:v:55:y:2015:i:c:p:28-38. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/regec .

    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.