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Simulating housing prices with UrbanSim: predictive capacity and sensitivity analysis

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Abstract

Housing prices in the Lyon Urban area are simulated with the land use framework UrbanSim interacting with the transportation model MOSART. We focus on the Real Estate Price Model of the UrbanSim framework, which proposes the ordinary least square regression. In our simulation, the alternative geographically weighted regression methodology is applied. The model of housing prices is calibrated using a nine-year back-casting period. The calibrated model, applied in simulation, provides price dynamics similar to actual one in the very centre of Lyon. Farther from the city centre, where the available data on actual sales exist, simulated prices tend to be understated. Thus, mainly only the most central locations manifest realistic price dynamics. Sensitivity analysis demonstrates the model’s ability to capture changes in employment accessibility on price dynamics.

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Notes

  1. In the historical arrondissements Lyon 1 and Lyon 4 located between the two rivers, there is no free space for new residential development, and new construction in fact does not take place; therefore a number of residential units is not updated there. Demolition is not included into the current version of the UrbanSim framework.

  2. MOdélisation et Simulation de l’Accessibilité aux Réseaux et aux Territoires (Modelling and Simulation of Accessibility to Networks and Territories).

  3. The inverse distance weighted method is applied with 12 neighbors, power 2 and cell size of 10 m. The method generates results, which are closer to real values, than in the case of Kriging.

  4. This alternative modeling strategy was implemented only to illustrate the influence of residential supply. In the final version of the calibration as well as in the simulation, residential units are constructed yearly.

  5. Adoptive kernel type is not appropriate to our aggregated data, perhaps due to leaps of prices and other characteristics between neighboring municipalities.

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Acknowledgments

The study is a part of the project PLAINSUDD (Innovative Numerical Platforms of Urban Simulation for Sustainable Development) sponsored through French ANR (number ANR-08-VD-00). Generation of synthetic population by Wisinee Wisetjindawat, provision of data on real estate prices by Perval and Pierre-Yves Péguy, and calculation of coordinates of real estate objects by Nicolas Ovtracht is acknowledged. The authors thank Mats Wilhelmsson for his suggestion of a temporal price lag. The paper benefited from the valuable comments of anonymous reviewers.

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Authors and Affiliations

  1. Laboratory of Transport Economics (LET), 14, Avenue Berthelot, 69363 , Lyon Cedex 07, France

    Marko Kryvobokov, Aurélie Mercier, Alain Bonnafous & Dominique Bouf

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  1. Marko Kryvobokov
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  2. Aurélie Mercier
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  3. Alain Bonnafous
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  4. Dominique Bouf
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Correspondence to Marko Kryvobokov.

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Kryvobokov, M., Mercier, A., Bonnafous, A. et al. Simulating housing prices with UrbanSim: predictive capacity and sensitivity analysis. Lett Spat Resour Sci 6, 31–44 (2013). https://doi.org/10.1007/s12076-012-0084-1

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  • DOI: https://doi.org/10.1007/s12076-012-0084-1

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