Nonperforming Morgage Loans In Luxembourg And The Eu After Covid-19 (COVID-19-MORTGAGE)

Luxembourg is one of the countries with the highest household leveraging on private debt and mortgage loans. For ensuring financial stability in Luxembourg after the income disruption due to the COVID-19 lockdowns, it is crucial to examine if the COVID-19 shock caused a critical increase in the number of households that have not been able to service their mortgages. An additional crucial aspect of the problem is to study the extent to which banks in Luxembourg and the EU face a risk of increased nonperforming loans. The ability to detect threats of nonperforming loans to banks in a timely manner allows for developing policies that can tackle the financial instability problem. The project will have two parts. The first part will focus on collecting and analyzing data before and after the COVID-19 crisis. The second part will develop simulated household-finance models that can investigate a number of out-of-sample questions and policy questions. The central evaluation question is to predict how income losses due to the COVID-19 lockdown can influence nonperforming loans and how nonperforming loans can cause pressure on the banking system. Because Luxembourg is a very open economy having its banking system exposed banking-sector risks in Europe, studying this question problem both in Luxembourg and in other EU countries, is necessary and insightful. The policy questions involve studying whether, (i) extraordinary aid to households for meeting their mortgage responsibilities and (ii) aid to banks for tackling their nonperforming-loans problems, can avoid bank/household insolvency and a possible collapse in the housing market. In order to ensure that up-to-date post-COVID-19 data can be collected, a team from the Dept. of Finance, U Luxembourg, will collaborate with researchers from STATEC and the Austrian Institute of Economic Research (Wifo), Vienna. Beyond data collection and analysis, computer simulations of household-finance models will guide predictions and out-of-sample policy analysis.