Data-driven agent-based modeling of the Hungarian housing market

The Central Bank of Hungary developed a complex, modular, 1:1 scale, agent-based model of the Hungarian residential housing market, where all the 4 million households and their relevant characteristics 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, housing loans, house price dynamics and a procyclical banking sector regulated by a macroprudential authority. After a brief elaboration of the features of this model, the talk will cover four applications:

- Optimal choice of scaling in economic agent-based models: trade-offs between runtime, accuracy, and precision; – Comprehensive evaluation of borrower-based macroprudential policies; – Evaluation of demand- and supply-side policy schemes supporting first-time home buyers; – Interactions between the housing market and the macroeconomic environment using a SVAR extension of the ABM framework.

About the speaker:
András Borsos is an economist at the Applied Research Department of the Central Bank of Hungary and a researcher at Complexity Science Hub Vienna. He holds a BA degree in finance from Corvinus University Budapest, an MA degree in applied economics from Central European University, and a PhD in network and data science from Central European University. András’s research interests include topics related to production networks, economic resilience, financial stability, and economic agent-based modelling.