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A high-resolution, data-driven agent-based model of the housing market

Year: 2023
Type: article
Abstract: This paper presents a complex, modular, 1:1 scale model of the Hungarian residential housing market. All of the 4 million households with their relevant characteristics and all of the flats with detailed attributes like size, state and neighbourhood quality are represented, based on empirical micro-level data. The model features transactions in the housing and rental markets, a construction sector, buy-to-let investors, credit markets, house price dynamics, a procyclical banking sector regulated by a macroprudential authority and exogenous macroeconomic environment. Thus, the model is suitable for analysing various types of macroprudential, fiscal and monetary policies. After calibrating the model on Hungarian data, it managed to reproduce the key characteristics of the housing market even at disaggregated levels based on regions and the income deciles of the households. We also present an application of the model on assessing the impact of construction cost shocks on the housing market. We found that this shock has strongly heterogeneous effects across the different parts of the country, which is mainly driven by the higher profit margin of the construction sector in the capital compared to the rest of the country. This means broader adjustment opportunities in the capital as the construction sector can not only increase the prices, but also lower its mark-up. There is also substantial heterogeneity in the results across flat types as the price response of newly built flats is more immediate than in the case of used flats. Furthermore, we also show that the deterioration of the housing stock is substantially higher in the shock scenario due to the lower number of constructions and renovations.
DOI: 10.1016/j.jedc.2023.104738
Language: English
Open Access status: hybrid
Estimated APC paid: $2,780
OpenAlex ID: W4386508484
FWCI: 3.357
Cites: 72
Cited by: 19
Related to: 10