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An Agent-Based Simulation of Rental Housing Markets

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  • John Mc Breen

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique, IXXI - Institut Rhône-Alpin des systèmes complexes - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UJF - Université Joseph Fourier - Grenoble 1 - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique, Phys-ENS - Laboratoire de Physique de l'ENS Lyon - ENS de Lyon - École normale supérieure de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - CNRS - Centre National de la Recherche Scientifique)

  • Florence Goffette-Nagot

    (GATE - Groupe d'analyse et de théorie économique - UL2 - Université Lumière - Lyon 2 - ENS LSH - Ecole Normale Supérieure Lettres et Sciences Humaines - CNRS - Centre National de la Recherche Scientifique)

  • Pablo Jensen

    (LET - Laboratoire d'économie des transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique, IXXI - Institut Rhône-Alpin des systèmes complexes - ENS de Lyon - École normale supérieure de Lyon - UL2 - Université Lumière - Lyon 2 - UJF - Université Joseph Fourier - Grenoble 1 - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique, Phys-ENS - Laboratoire de Physique de l'ENS Lyon - ENS de Lyon - École normale supérieure de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - CNRS - Centre National de la Recherche Scientifique)

Abstract

We simulate a closed rental housing market with search and matching frictions, in which both landlord and tenant agents are imperfectly informed. Homogeneous landlords set rents to maximise revenue, using information on the market to estimate the relationship between posted rent and time-on-the-market (TOM). Tenants, heterogeneous in income, engage in undirected search accepting residences based on their idiosyncratic tastes for housing and a disagreement point derived from information on the distribution of offers. The steady state to which the simulation evolves shows price dispersion, nonzero search times and vacancies.The main results concern the effects of increasing information on either side of the market. When tenants see a greater percentage of the distribution of offers, tenants learn to refuse high rents and so the population rises and tenants' utilities rise as does overall welfare. Conversely, when landlords have less information, their utility can rise as over estimations in best posting rent move the market to higher rents.

Suggested Citation

  • John Mc Breen & Florence Goffette-Nagot & Pablo Jensen, 2009. "An Agent-Based Simulation of Rental Housing Markets," Post-Print halshs-00374157, HAL.
    • Handle: RePEc:hal:journl:halshs-00374157
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00374157v2
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    References listed on IDEAS

    as
    1. Grenadier, Steven R, 1995. "The Persistence of Real Estate Cycles," The Journal of Real Estate Finance and Economics, Springer, vol. 10(2), pages 95-119, March.
    2. Marcus Allen & Ronald Rutherford & Thomas Thomson, 2009. "Residential Asking Rents and Time on the Market," The Journal of Real Estate Finance and Economics, Springer, vol. 38(4), pages 351-365, May.
    3. Diamond, Peter A., 1971. "A model of price adjustment," Journal of Economic Theory, Elsevier, vol. 3(2), pages 156-168, June.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Demetris Demetriou, 2017. "A spatially based artificial neural network mass valuation model for land consolidation," Environment and Planning B, , vol. 44(5), pages 864-883, September.
    2. John Mc Breen & Florence Goffette-Nagot & Pablo Jensen, 2011. "Information and Search on the Housing Market: An Agent-based Model," ERSA conference papers ersa11p1395, European Regional Science Association.
    3. Horn, Keren & Merante, Mark, 2017. "Is home sharing driving up rents? Evidence from Airbnb in Boston," Journal of Housing Economics, Elsevier, vol. 38(C), pages 14-24.
    4. Mark Merante & Keren Mertens Horn, 2016. "Is Home Sharing Driving up Rents? Evidence from Airbnb in Boston," Working Papers 2016_03, University of Massachusetts Boston, Economics Department.

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    More about this item

    Keywords

    Real estate; Rental markets; Search; Information; Simulation; Multi-agent systems;
    All these keywords.

    JEL classification:

    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs

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