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Econometric guidance for developing UrbanSim models. First lessons from the SustainCity project

  • Nathalie Picard


  • Constantinos Antoniou


In the context of the SustainCity project (, three European cities (Brussels, Paris and Zurich) will be modelled using the land use microsimulation platform UrbanSim. This platform relies on various models interacting with each other, to predict long-term urban development. The aim of this paper is to provide some econometric insight into this process. A common set of notation and assumptions are first defined, and the more common model structures (linear regression, multinomial logit, nested logit, mixed MNL and latent variable models) are described in a consistent way. Special treatments and approaches that are required due to the specific nature of the data in this type of applications (i.e. involving very large number of alternatives, and often exhibiting endogeneity, correlation, and (pseudo-)panel data properties) will also be discussed. For example, importance sampling, spatial econometrics, Geographically Weighted Regression (GWR) and endogeneity issues will be covered. Applications and specific options of the following models: (i) household location choice model, (ii) jobs location/firmography, (iii) real estate price model, and (iv) land development model, will be demonstrated using examples from the on-going case studies in Brussels, Paris and Zurich. Finally, lessons learnt in relation to the econometric models from these on-going case studies will be summarized.

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Paper provided by European Regional Science Association in its series ERSA conference papers with number ersa11p1494.

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Date of creation: Sep 2011
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Handle: RePEc:wiw:wiwrsa:ersa11p1494
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  1. J. A. Hausman, 1976. "Specification Tests in Econometrics," Working papers 185, Massachusetts Institute of Technology (MIT), Department of Economics.
  2. Louviere,Jordan J. & Hensher,David A. & Swait,Joffre D., 2000. "Stated Choice Methods," Cambridge Books, Cambridge University Press, number 9780521788304.
  3. Florence Goffette-Nagot & Isabelle Reginster & Isabelle Thomas, 2009. "A spatial analysis of residential land prices in Belgium: accessibility, linguistic border and environmental amenities," Working Papers 0929, Groupe d'Analyse et de Théorie Economique (GATE), Centre national de la recherche scientifique (CNRS), Université Lyon 2, Ecole Normale Supérieure.
  4. André de Palma & Kiarash Motamedi & Nathalie Picard & Paul Waddell, 2007. "Accessibility and Environmental Quality: Inequality in the Paris Housing Market," THEMA Working Papers 2007-16, THEMA (THéorie Economique, Modélisation et Applications), Université de Cergy-Pontoise.
  5. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September.
  6. 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.
  7. J. Barkley Rosser, 2009. "Introduction," Chapters, in: Handbook of Research on Complexity, chapter 1 Edward Elgar.
  8. de Palma, Andre & Motamedi, Kiarash & Picard, Nathalie & Waddell, Paul, 2005. "A model of residential location choice with endogenous housing prices and traffic for the Paris region," European Transport \ Trasporti Europei, ISTIEE, Institute for the Study of Transport within the European Economic Integration, issue 31, pages 67-82.
  9. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
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  11. David Brasington & D. Hite, . "Demand for Environmental Quality: A Spatial Hedonic Analysis," Departmental Working Papers 2003-02, Department of Economics, Louisiana State University.
  12. Mohamed Abdel-Aty & M. Abdalla, 2004. "Modeling drivers' diversion from normal routes under ATIS using generalized estimating equations and binomial probit link function," Transportation, Springer, vol. 31(3), pages 327-348, August.
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