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Interpolation of Air Quality Measures in Hedonic House Price Models: Spatial Aspects This paper is part of a joint research effort with James Murdoch (University of Texas, Dallas) and Mark Thayer (San Diego State University). Earlier versions were presented at the 51st North American Meeting of the Regional Science Association International, Seattle, WA, November 2004, the Spatial Econometrics Workshop, Kiel, Germany, April 2005, and at departmental seminars at the University of Illinois, Ohio State University, the University of California, Davis, and the University of Pennsylvania. Comments by participants are greatly appreciated. The usual disclaimer holds

Listed author(s):
  • Luc Anselin
  • Julie Le Gallo

Abstract This paper investigates the sensitivity of hedonic models of house prices to the spatial interpolation of measures of air quality. We consider three aspects of this question: the interpolation technique used, the inclusion of air quality as a continuous vs discrete variable in the model, and the estimation method. Using a sample of 115,732 individual house sales for 1999 in the South Coast Air Quality Management District of Southern California, we compare Thiessen polygons, inverse distance weighting, Kriging and splines to carry out spatial interpolation of point measures of ozone obtained at 27 air quality monitoring stations to the locations of the houses. We take a spatial econometric perspective and employ both maximum-likelihood and general method of moments techniques in the estimation of the hedonic. A high degree of residual spatial autocorrelation warrants the inclusion of a spatially lagged dependent variable in the regression model. We find significant differences across interpolators in the coefficients of ozone, as well as in the estimates of willingness to pay. Overall, the Kriging technique provides the best results in terms of estimates (signs), model fit and interpretation. There is some indication that the use of a categorical measure for ozone is superior to a continuous one. RÉSUMÉ Interpolation des Mesures de la Qualité de l'Air dans les Modèles Hédoniste de l'Estimation Immobilière: Aspects Spatiaux Cet article examine la sensibilité de l’évaluation hédoniste des prix de l'immobilier à l'interpolation spatiale des mesures de la qualité de l'air. Nous avons envisagé la question sous trois aspects: la technique d'interpolation utilisée, l'introduction de la qualité de l'air comme variable continue ou discrète dans le modèle et la méthode d'estimation. Nous avons utilisé un échantillon de 115 732 ventes de maisons individuelles, en 1999, dans le district Côte Sud de la gestion de la Qualité de l'Air en Californie du Sud. Nous avons comparé les polygônes de Thiessen, la pondération inversement proportionnelle à la distance, le krigeage et les courbes splines pour mener l'interpolation des mesures ponctuelles de l'ozone, obtenues dans 27 stations de suivi de la qualité de l'air en fonction des lieux où étaient situées les maisons. Nous avons pris une perspective spatiale économétrique et employé aussi bien la probabilité maximale que la méthode générale des moments techniques dans l’évaluation de l'hédonique. Un degré élevé d'auto corrélation spatiale résiduelle garantie l'inclusion d'une variable dépendante spatialement décalée dans le modèle de régression. Nous avons trouvé des différences importantes parmi les interpolateurs dans les coefficients d'ozone, ainsi que parmi les indicateurs de la volonté de payer. Surtout, la technique de krigeage donne les meilleurs résultats pour les estimations (signes), l'ajustement du modèle et l'interprétation. L'utilisation d'une mesure nominale pour l'ozone est supérieure à une mesure continue, semble-t-il. RESUMEN Interpolación de las medidas de la calidad del aire en los modelos de los precios hedónicos de la vivienda: aspectos espaciales En este ensayo investigamos la sensibilidad de los modelos de lo precios hedónicos de la vivienda para la interpolación espacial de medidas de la calidad del aire. Tenemos en cuenta tres aspectos al respecto: la técnica de interpolación utilizada, la inclusión de la calidad del aire como variable continua, en vez de discreta, en el modelo, y el método de cálculo. Con una muestra de 115.732 ventas de viviendas individuales durante 1999 en el Distrito de Gestión de Calidad del Aire de la Costa Sur en California, comparamos los polígonos de Thiessen, la ponderación de la distancia inversa, métodos geoestadísticos o Kriging y métodos basados en splines para llevar a cabo la interpolación espacial de las mediciones puntuales de ozono obtenidas en 27 estaciones de control de calidad del aire en los lugares donde están situadas las viviendas. Desde la perspectiva econométrica espacial empleamos las técnicas de la probabilidad máxima del método general de momentos en el cálculo de precios hedónicos. Debido a un alto grado de autocorrelación espacial residual debemos incluir una variable dependiente espacialmente rezagada en el modelo de regresión. Se observan diferencias importantes entre los interpoladores en los coeficientes del ozono y en los cálculos de la disposición a pagar. En general, la técnica Kriging da los mejores resultados en cuanto a los cálculos (señales), la idoneidad del modelo y la interpretación. Hay indicios de que es mejor usar una medida categórica para el ozono en vez de una continua.

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Article provided by Taylor & Francis Journals in its journal Spatial Economic Analysis.

Volume (Year): 1 (2006)
Issue (Month): 1 ()
Pages: 31-52

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Handle: RePEc:taf:specan:v:1:y:2006:i:1:p:31-52
DOI: 10.1080/17421770600661337
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  1. Raymond B. Palmquist & Adis Israngkura, 1999. "Valuing Air Quality With Hedonic and Discrete Choice Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(5), pages 1128-1133.
  2. Beron, Kurt & Murdoch, James & Thayer, Mark, 2001. "The Benefits of Visibility Improvement: New Evidence from the Los Angeles Metropolitan Area," The Journal of Real Estate Finance and Economics, Springer, vol. 22(2-3), pages 319-337, March-May.
  3. Jeffrey E. Zabel & Katherine A. Kiel, 2000. "Estimating the Demand for Air Quality in Four U.S. Cities," Land Economics, University of Wisconsin Press, vol. 76(2), pages 174-194.
  4. Brasington, David M. & Hite, Diane, 2005. "Demand for environmental quality: a spatial hedonic analysis," Regional Science and Urban Economics, Elsevier, vol. 35(1), pages 57-82, January.
  5. Luc Anselin, 2001. "Spatial Effects in Econometric Practice in Environmental and Resource Economics," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(3), pages 705-710.
  6. Kerry Smith, V. & Sieg, Holger & Spencer Banzhaf, H. & Walsh, Randall P., 2004. "General equilibrium benefits for environmental improvements: projected ozone reductions under EPA's Prospective Analysis for the Los Angeles air basin," Journal of Environmental Economics and Management, Elsevier, vol. 47(3), pages 559-584, May.
  7. Gillen, Kevin & Thibodeau, Thomas & Wachter, Susan, 2001. "Anisotropic Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 23(1), pages 5-30, July.
  8. Kenneth Y. Chay & Michael Greenstone, 2005. "Does Air Quality Matter? Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 376-424, April.
  9. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
  10. Sankar, . Ulaganathan (ed.), 2001. "Environmental Economics," OUP Catalogue, Oxford University Press, number 9780195659139.
  11. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
  12. 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.
  13. Kurt J. Beron & James C. Murdoch & Mark A. Thayer, 1999. "Hierarchical Linear Models With Application to Air Pollution in the South Coast Air Basin," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(5), pages 1123-1127.
  14. R. Kelley Pace & James P. LeSage, 2004. "Spatial Statistics and Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 147-148, September.
  15. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
  16. Phil Graves & James C. Murdoch & Mark A. Thayer & Don Waldman, 1988. "The Robustness of Hedonic Price Estimation: Urban Air Quality," Land Economics, University of Wisconsin Press, vol. 64(3), pages 220-233.
  17. Raymond J. G. M. Florax & Arno J. Van der Vlist, 2003. "Spatial Econometric Data Analysis: Moving Beyond Traditional Models," International Regional Science Review, , vol. 26(3), pages 223-243, July.
  18. Won Kim, Chong & Phipps, Tim T. & Anselin, Luc, 2003. "Measuring the benefits of air quality improvement: a spatial hedonic approach," Journal of Environmental Economics and Management, Elsevier, vol. 45(1), pages 24-39, January.
  19. 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.
  20. Smith, V Kerry & Huang, Ju-Chin, 1995. "Can Markets Value Air Quality? A Meta-analysis of Hedonic Property Value Models," Journal of Political Economy, University of Chicago Press, vol. 103(1), pages 209-227, February.
  21. Moulton, Brent R, 1990. "An Illustration of a Pitfall in Estimating the Effects of Aggregate Variables on Micro Unit," The Review of Economics and Statistics, MIT Press, vol. 72(2), pages 334-338, May.
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