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Residential income segregation: A behavioral model of the housing market

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  • Pangallo, Marco
  • Nadal, Jean-Pierre
  • Vignes, Annick

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.

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  • Pangallo, Marco & Nadal, Jean-Pierre & Vignes, Annick, 2019. "Residential income segregation: A behavioral model of the housing market," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 15-35.
    • Handle: RePEc:eee:jeborg:v:159:y:2019:i:c:p:15-35
    DOI: 10.1016/j.jebo.2019.01.010
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    3. 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.
    4. Katarzyna Kopczewska & Mateusz Kopyt & Piotr Ćwiakowski, 2021. "Spatial Interactions in Business and Housing Location Models," Land, MDPI, vol. 10(12), pages 1-25, December.
    5. 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.
    6. 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.
    7. 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.
    8. Aneta Chmielewska & Małgorzata Renigier-Biłozor & Artur Janowski, 2022. "Representative Residential Property Model—Soft Computing Solution," IJERPH, MDPI, vol. 19(22), pages 1-24, November.
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    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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    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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