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

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This paper introduces an agent-based model on a housing market with macro-prudential policy experiments. Specifically, the simulation model is used to examine the effects of a policy setting on loanto-value (LTV) and debt-to-income (DTI), which are policy instruments for several governments to regulate the housing market. The simulation model illustrates the interactions among the households, the house suppliers, and the real estate brokers. We model the household in population to be 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, and the observations include various economic variables, policy variables, and Korean census data. Our baseline experiment is quantitatively validated to the price index and the transaction volume of the past Korean housing market. After the validation, we show the empirical effectiveness of setting LTV and DTI toward house prices, transaction volumes and credit levels of households. Furthermore, we investigate the simulation results on the owner occupier rate of households. These investigations provide the policy analyses in the housing market of Korea, and other governments with LTV and DTI regulations.
International Conference on Principles and Practice of Multi-Agent Systems
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International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2019)

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IE-Conference Papers(학술회의논문)
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