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Residential income segregation: A behavioral model of the housing market

Author

Listed:
  • Marco Pangallo

    (INET Oxford - Institute for New Economic Thinking at the Oxford Martin School)

  • Jean-Pierre Nadal

    (CAMS - Centre d'Analyse et de Mathématique sociales - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique, LPS - Laboratoire de Physique Statistique de l'ENS - FRDPENS - Fédération de recherche du Département de physique de l'Ecole Normale Supérieure - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique, Biophysique et Neuroscience Théoriques - LPENS (UMR_8023) - Laboratoire de physique de l'ENS - ENS Paris - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - UPD7 - Université Paris Diderot - Paris 7 - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)

  • Annick Vignes

    (CAMS - Centre d'Analyse et de Mathématique sociales - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique)

Abstract

We represent the functioning of the housing market and study the relation between income segregation, income inequality and house prices by introducing a spatial Agent-Based Model (ABM). Differently from traditional models in urban economics, we explicitly specify the behavior of buyers and sellers and the price formation mechanism. Buyers who differ by income select among heterogeneous neighborhoods using a probabilistic model of residential choice; sellers employ an aspiration level heuristic to set their reservation offer price; prices are determined through a continuous double auction. We first provide an approximate analytical solution of the ABM, shedding light on the structure of the model and on the effect of the parameters. We then simulate the ABM and find that: (i) a more unequal income distribution lowers the prices globally, but implies stronger segregation; (ii) a spike in demand in one part of the city increases the prices all over the city; (iii) subsidies are more efficient than taxes in fostering social mixing.

Suggested Citation

  • Marco Pangallo & Jean-Pierre Nadal & Annick Vignes, 2019. "Residential income segregation: A behavioral model of the housing market," Post-Print halshs-02383410, HAL.
    • Handle: RePEc:hal:journl:halshs-02383410
    DOI: 10.1016/j.jebo.2019.01.010
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02383410
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    2. Adrian Carro & Marc Hinterschweiger & Arzu Uluc & J Doyne Farmer, 2023. "Heterogeneous effects and spillovers of macroprudential policy in an agent-based model of the UK housing market," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 32(2), pages 386-432.
    3. Katarzyna Kopczewska & Mateusz Kopyt & Piotr Ćwiakowski, 2021. "Spatial Interactions in Business and Housing Location Models," Land, MDPI, vol. 10(12), pages 1-25, December.
    4. Benjamin Patrick Evans & Kirill Glavatskiy & Michael S. Harré & Mikhail Prokopenko, 2023. "The impact of social influence in Australian real estate: market forecasting with a spatial agent-based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 5-57, January.
    5. Kirill S. Glavatskiy & Mikhail Prokopenko & Adrian Carro & Paul Ormerod & Michael Harré, 2021. "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model," SN Business & Economics, Springer, vol. 1(6), pages 1-21, June.
    6. Corrado Monti & Marco Pangallo & Gianmarco De Francisci Morales & Francesco Bonchi, 2022. "On learning agent-based models from data," Papers 2205.05052, arXiv.org, revised Nov 2022.
    7. Aneta Chmielewska & Małgorzata Renigier-Biłozor & Artur Janowski, 2022. "Representative Residential Property Model—Soft Computing Solution," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
    8. Bardoscia, Marco & Carro, Adrian & Hinterschweiger, Marc & Napoletano, Mauro & Popoyan, Lilit & Roventini, Andrea & Uluc, Arzu, 2024. "The impact of prudential regulations on the UK housing market and economy: insights from an agent-based model," Bank of England working papers 1066, Bank of England.
    9. Carro, Adrian, 2023. "Taming the housing roller coaster: The impact of macroprudential policy on the house price cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 156(C).
    10. Harting, Philipp & Radi, Davide, 2020. "Residential segregation: The role of inequality and housing subsidies," Journal of Economic Behavior & Organization, Elsevier, vol. 178(C), pages 801-819.
    11. Mérő, Bence & Borsos, András & Hosszú, Zsuzsanna & Oláh, Zsolt & Vágó, Nikolett, 2023. "A high-resolution, data-driven agent-based model of the housing market," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).

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    More about this item

    Keywords

    Agent-based model; Housing market; Spatial segregation; Aspiration level heuristic; Income inequality;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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