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

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  • 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.

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  • 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
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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    11. 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).

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