[go: up one dir, main page]

IDEAS home Printed from https://ideas.repec.org/a/taf/specan/v4y2009i1p53-72.html
   My bibliography  Save this article

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Author

Listed:
  • Philip Kostov
Abstract
Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un modèle hédonique à régression quantile spatiale des prix des terrains agricoles Résumé Les études sur le prix des terrains font généralement usage d'une analyse hédonique pour identifier l'impact des caractéristiques des terrains sur le prix. Toutefois, du fait de la fixité spatiale des terrains, la question d'une éventuelle dépendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une dépendance spatiale dans le prix des terrains agricoles peut avoir des conséquences importantes sur l'analyse du modèle hédonique. En ignorant cette corrélation sérielle, on s'expose au risque d'évaluations biaisées des modèles hédoniques du prix des terrains. Nous proposons l'emploi d'une estimation à base de régression flexible du modèle hédonique à décalage spatial, tenant compte de différents effets des caractéristiques, et surtout de différents degrés de corrélations sérielles spatiales. En appliquant ce principe à un échantillon de ventes de terrains agricoles en Irlande du Nord, nous découvrons que le marché se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au modèle hédonique traditionnel, sans dépendance du décalage spatial, tandis que le deuxième segment du marché, plus petit et beaucoup plus étroit, présente une dépendance considérable du décalage spatial. Un modelo hedónico de regresión cuantil espacial de los precios del terreno agrícola Resumen Típicamente, los estudios del precio de la tierra emplean un análisis hedónico para identificar el impacto de las características de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuestión de una posible dependencia espacial en los precios del terreno agrícola. La presencia de dependencia espacial en los precios del terreno agrícola puede tener consecuencias graves para el modelo de análisis hedónico. Ignorar la autocorrelación espacial puede conducir a estimados parciales en los modelos hedónicos del precio de la tierra. Proponemos el uso de una valoración basada en una regresión cuantil flexible del modelo hedónico del lapso espacial que tenga en cuenta los diversos efectos de las características y, particularmente, los diversos grados de autocorrelación espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agrícola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El más grande de estos dos segmentos se ajusta al modelo hedónico convencional sin dependencia del lapso espacial, mientras que el segmento más pequeño, y mucho más fino, muestra una dependencia considerable del lapso espacial.

Suggested Citation

  • 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.
  • Handle: RePEc:taf:specan:v:4:y:2009:i:1:p:53-72
    DOI: 10.1080/17421770802625957
    as

    Download full text from publisher

    File URL: http://www.taylorandfrancisonline.com/doi/abs/10.1080/17421770802625957
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/17421770802625957?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. Lars Nesheim, 2002. "Equilibrium sorting of heterogeneous consumers across locations: theory and empirical implications," CeMMAP working papers CWP08/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    2. Lee, Sokbae, 2007. "Endogeneity in quantile regression models: A control function approach," Journal of Econometrics, Elsevier, vol. 141(2), pages 1131-1158, December.
    3. Liangjun Su & Zhenlin Yang, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Development Economics Working Papers 22476, East Asian Bureau of Economic Research.
    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. Philip Kostov & Julie Le Gallo, 2015. "Convergence: A Story of Quantiles and Spillovers," Kyklos, Wiley Blackwell, vol. 68(4), pages 552-576, November.
    2. Philip Kostov, 2013. "Empirical likelihood estimation of the spatial quantile regression," Journal of Geographical Systems, Springer, vol. 15(1), pages 51-69, January.
    3. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    4. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    5. James J. Heckman, 2019. "The Race Between Demand and Supply: Tinbergen’s Pioneering Studies of Earnings Inequality," De Economist, Springer, vol. 167(3), pages 243-258, September.
    6. Nicholas Apergis & Alexandros Gabrielsen & Lee Smales, 2016. "(Unusual) weather and stock returns—I am not in the mood for mood: further evidence from international markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 30(1), pages 63-94, February.
    7. repec:hal:wpspec:info:hdl:2441/3vl5fe4i569nbr005tctlc8ll5 is not listed on IDEAS
    8. Xin Liu, 2024. "Averaging Estimation for Instrumental Variables Quantile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1290-1312, October.
    9. Jing You & Katsushi S. Imai & Raghav Gaiha, 2014. "Decoding the Growth-Nutrition Nexus in China: Inequality, Uncertainty and Food Insecurity," Discussion Paper Series DP2014-28, Research Institute for Economics & Business Administration, Kobe University, revised Dec 2014.
    10. Jia-Young Michael Fu & Joel L. Horowitz & Matthias Parey, 2015. "Testing exogeneity in nonparametric instrumental variables identified by conditional quantile restrictions," CeMMAP working papers 68/15, Institute for Fiscal Studies.
    11. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    12. Ingo E. Isphording, 2013. "Returns to Foreign Language Skills of Immigrants in Spain," LABOUR, CEIS, vol. 27(4), pages 443-461, December.
    13. repec:clu:wpaper:0607-14 is not listed on IDEAS
    14. Silvia Mendolia & Alfredo R Paloyo & Ian Walker, 2018. "Heterogeneous effects of high school peers on educational outcomes," Oxford Economic Papers, Oxford University Press, vol. 70(3), pages 613-634.
    15. Philip Kostov, 2010. "Do Buyers’ Characteristics and Personal Relationships Affect Agricultural Land Prices?," Land Economics, University of Wisconsin Press, vol. 86(1), pages 48-65.
    16. Christina Christou & Ruthira Naraidoo & Rangan Gupta & Won Joong Kim, 2018. "Monetary Policy Reaction Functions of the TICKs: A Quantile Regression Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(15), pages 3552-3565, December.
    17. Blume,L.E. & Durlauf,S.N., 2005. "Identifying social interactions : a review," Working papers 12, Wisconsin Madison - Social Systems.
    18. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    19. Jane Cooley Fruehwirth & Sriya Iyer & Anwen Zhang, 2019. "Religion and Depression in Adolescence," Journal of Political Economy, University of Chicago Press, vol. 127(3), pages 1178-1209.
    20. Hiroaki Kaido & Kaspar Wüthrich, 2021. "Decentralization estimators for instrumental variable quantile regression models," Quantitative Economics, Econometric Society, vol. 12(2), pages 443-475, May.
    21. Serneels, Pieter & Beegle, Kathleen & Dillon, Andrew, 2017. "Do returns to education depend on how and whom you ask?," Economics of Education Review, Elsevier, vol. 60(C), pages 5-19.
    22. Tae-Hwan Kim & Christophe Muller, 2012. "A test for endogeneity in conditional quantile models," Working papers 2012rwp-49, Yonsei University, Yonsei Economics Research Institute.

    More about this item

    Keywords

    Spatial lag; quantile regression; hedonic model; C13; C14; C21; Q24;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land

    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:taf:specan:v:4:y:2009:i:1:p:53-72. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RSEA20 .

    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.