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Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo‐Panel Model with Nested Random Effects

Author

Listed:
  • Badi H. Baltagi
  • Georges Bresson
  • Jean‐Michel Etienne

Abstract

This paper estimates a hedonic housing model based on flats sold in the city of Paris over the period 1990-2003. This is done using maximum likelihood estimation taking into account the nested structure of the data. Paris is historically divided into 20 arrondissements, each divided into four quartiers (quarters), which in turn contain between 15 and 169 blocks (�lot, in French) per quartier. This is an unbalanced pseudo-panel data containing 156,896 transactions. Despite the richness of the data, many neighborhood characteristics are not observed, and we attempt to capture these neighborhood spill-over effects using a spatial lag model. Using Likelihood Ratio tests, we find significant spatial lag effects as well as significant nested random error effects. The empirical results show that the hedonic housing estimates and the corresponding marginal effects are affected by taking into account the nested aspects of the Paris housing data as well as the spatial neighborhood effects.
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Suggested Citation

  • Badi H. Baltagi & Georges Bresson & Jean‐Michel Etienne, 2015. "Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo‐Panel Model with Nested Random Effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 509-528, April.
    • Handle: RePEc:wly:japmet:v:30:y:2015:i:3:p:509-528
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    Cited by:

    1. Sergi Basco & Jair N. Ojeda-Joya, 2024. "House Prices and International Remittances: Evidence from Colombia," Borradores de Economia 1273, Banco de la Republica de Colombia.
    2. Yuheng Ling, 2020. "Time, space and hedonic prediction accuracy: evidence from Corsican apartment markets," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 367-388, April.
    3. Deller, Steven & Whitacre, Brian, 2018. "Broadband’s Relationship to Rural Housing Values," Staff Paper Series 591, University of Wisconsin, Agricultural and Applied Economics.
    4. Tim Bollerslev & Andrew J. Patton & Wenjing Wang, 2016. "Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(6), pages 1005-1025, September.
    5. Duran, Nicolas & Elhorst, J. Paul, 2017. "A Spatio-temporal-similarity and Common Factor Approach of Individual Housing Prices," Research Report 2018007-EEF, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    6. Laszlo Matyas (ed.), 2017. "The Econometrics of Multi-dimensional Panels," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-319-60783-2, Juin.
    7. Rumman Khan, 2021. "Assessing Sampling Error in Pseudo‐Panel Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 742-769, June.
    8. Paula Margaretic & Juan Bautista Sosa, 2025. "How Local is the Crime Effect on House Prices?," The Journal of Real Estate Finance and Economics, Springer, vol. 70(4), pages 754-793, May.
    9. Bing Zhu & Stanimira Milcheva, 2020. "The Pricing of Spatial Linkages in Companies’ Underlying Assets," The Journal of Real Estate Finance and Economics, Springer, vol. 61(3), pages 443-475, October.
    10. Myrna, Olena, 2023. "Competition in online land lease auctions in Ukraine: Reduced-form estimation," Land Use Policy, Elsevier, vol. 125(C).
    11. Liebelt, Veronika & Bartke, Stephan & Schwarz, Nina, 2018. "Revealing Preferences for Urban Green Spaces: A Scale-sensitive Hedonic Pricing Analysis for the City of Leipzig," Ecological Economics, Elsevier, vol. 146(C), pages 536-548.
    12. Kuangyu Wen & Ximing Wu & David J. Leatham, 2021. "Spatially Smoothed Kernel Densities with Application to Crop Yield Distributions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 349-366, September.

    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

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