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Housing Market Agent-Based Simulation with Loan-To-Value and Debt-To-Income

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Abstract

This paper introduces an agent-based model of a housing market with macro-prudential policy experiments. Specifically, the simulation model is used to examine the effects of a policy setting on loan-to-value (LTV) and debt-to-income (DTI), which are policy instruments several governments use to regulate the housing market. The simulation model illustrates the interactions among the households, the house suppliers, and the real estate brokers. We model each household in the population as either seller or buyer, and some of households may behave as speculators in the housing market. To better understand the impact of the policies, we used the real-world observations from the Korean housing market, which include various economic conditions, policy variables, and Korean census data. Our baseline model is quantitatively validated to the price index and the transaction volume of the past Korean housing market. After validation, we show the empirical effectiveness of setting LTV and DTI towards house prices, transaction volumes, and the amount of households' mortgages. Furthermore, we investigate the simulation results for the owner-occupier rate of households. These investigations provide the policy analyses in Korea's housing market, and other governments with LTV and DTI regulations.

Suggested Citation

  • Tae-Sub Yun & Il-Chul Moon, 2020. "Housing Market Agent-Based Simulation with Loan-To-Value and Debt-To-Income," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 23(4), pages 1-5.
    • Handle: RePEc:jas:jasssj:2019-122-3
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    Cited by:

    1. Geovanna Hinojoza-Castro & Montserrat Gómez-Delgado & Wenseslao Plata-Rocha, 2022. "Real Estate Developers as Agents in the Simulation of Urban Sprawl," Sustainability, MDPI, vol. 14(15), pages 1-12, July.
    2. Bernardo Alves Furtado, 2022. "PolicySpace2: Modeling Markets and Endogenous Public Policies," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 25(1), pages 1-8.
    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. Ruben Tarne & Dirk Bezemer, 2023. "ousing affordability in a monetary economy: an agent-based model of the Dutch housing market," IMK Working Paper 222-2023, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    5. Sedar Olmez & Akhil Ahmed & Keith Kam & Zhe Feng & Alan Tua, 2023. "Exploring the Dynamics of the Specialty Insurance Market Using a Novel Discrete Event Simulation Framework: a Lloyd's of London Case Study," Papers 2307.05581, arXiv.org.

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