Publications

We study heterogeneity in households’ credit across nine European countries (Belgium, Spain, Hungary, Ireland, Italy, Latvia, Lithuania, Portugal, and Slovakia) during 2022-2024 using granular credit register data. We first document substantial between- and within-country variation in mortgage and consumer lending by borrower age, loan maturity, and interest rate fixation. We then quantify the passthrough of the ECB’s recent tightening cycle to household borrowing costs, and assess its heterogeneous impact across households. Pass-through is nearly complete for mortgages (around 0.9) but considerably weaker for consumer credit (around 0.4). While mortgage pass-through is relatively homogeneous across countries, consumer credit shows pronounced cross-country differences that cannot be explained by borrower or loan characteristics. Younger households face stronger mortgage pass-through but weaker consumer credit pass-through relative to older borrowers, and longer maturities are associated with stronger pass-through in both credit markets.

Keywords: monetary policy transmission; household borrowing; credit registers; interest rate pass-through; cross-country heterogeneity

JEL codes: E52, G21, D14

This document offers an ex-ante assessment of the proposal to allow the withdrawal of funds from the second pillar of the Lithuanian pension system. First, we use a quantitative macroeconomic model to quantify, under alternative scenarios, the potential impact that the withdrawal of Pillar II funds might have on the economy in the medium term. Second, we offer a long-term view of the current pension replacement rates and the consequences that the withdrawal of funds might have for those individuals who decide to opt-out of Pillar II.

The recent surge in inflation hit Lithuania with a 20 percent increase in 2022, affecting many households. This paper examines the heterogeneous wealth effects of the recent inflation surge in Lithuania. Specifically, I consider different channels – wealth, income, and consumption – but also the monetary and fiscal policy responses to the inflationary shock. I quantify these channels by using data from the Household Finance and Consumption Survey (HFCS). The results show that the consumption channel affected all households similarly, while the income channel disproportionately affected low-income and elderly households. Moreover, the impact of inflation was closely related to households’ net nominal wealth position. The wealth channel significantly eroded the wealth of older households but had a positive impact on younger households, especially those with mortgages. Fiscal policy adjustments partially mitigated the impact of inflation on the most vulnerable households. Meanwhile, the monetary policy response helped offset losses for households with substantial nominal asset holdings. In addition, all these channels influenced changes in wealth inequality in the country. While aggregate wealth inequality remained broadly unchanged, distributional effects showed a decrease in inequality measures for renters and a slight increase for homeowners and mortgage holders.

JEL codes: D15, E21, E5, G51

This paper explores how fluctuations in the house price-rent spread influence household spending, taking into account credit constraints. We incorporate a housing spread shock, representing changes in the future value of residential property, into a model of household decisionmaking with borrowing frictions. Using halfcentury data from 28 OECD countries, we find that housing spread shocks are more persistent than credit shocks, which induce ‘boom-bust’ dynamics. We also identify asymmetries once the joint effect of shocks to housing spread and borrowing frictions is analyzed, particularly in crisis periods, underscoring the importance of policies addressing credit conditions and household expectations to stabilize the economy when traditional tools are less effective.

Keywords: household expectations; house price-rent spread; credit frictions; interest rates; household consumption

JEL classification: D15, E21, E5, G51
 

This report presents the stylized facts gathered in the fourth wave of the Eurosystem Household Finance and Consumption Survey, which was conducted in Lithuania as the second wave of results. The survey provides household-level data on wealth, finances, consumption, savings, and additional individual characteristics, covering a sample of 1,664 households. Although the reference period for the data varies across countries, for Lithuania, it pertains to 2021. The report compares new results with those from the previous survey conducted in 2017. The results indicate an increase in measures for household wealth and finances that were only minimally affected by the COVID-19 pandemic.

Keywords: Household-level data, assets, liabilities, net wealth, financial pressure, consumption

JEL codes: D12, D14, D31

I use data from the European Central Bank’s Household Finance and Consumption Survey (HFCS) to examine how house price expectations differ across Europe and to identify the main drivers of such expectations. During the period 2010-2017, housing-related assets drove the household balance sheet evolution. Therefore, house price expectations remained highly heterogenous across European countries. The paper found that changes in income and house prices are the key determinants of house price expectations. Homeownership status, income and wealth distributions also explain part of the heterogeneity in household expectations about house prices in Europe. All these effects appear to be stronger for renters and for households from the bottom quintiles in income and wealth distributions.

Keywords: house price expectations; housing; household portfolio.

JEL Classification: D10, D31, D84, G11

Additional housing equity collateral can loosen borrowing constraints and increase spending for households that value their home highly. However, rising home values also raise the cost of living via higher imputed rental costs, offsetting their impact on consumption. Usage of Household Finance and Consumption Survey microdata and third-party evaluation of housing value enable identification of the causal effect of house price changes on consumer spending. This paper is one of the first that explores this relationship European-wide with an application of an instrumental variable technique. The paper identifies heterogeneities among different households based on their housing status. A $1 increase in home values leads to a $0.127 increase in spending for homeowners overall, and $0.185 for homeowners with mortgages specifically. Results reflect large responses among credit-constrained households, suggesting borrowing constraints as one of the key drivers of the MPC out of housing wealth.

Keywords: Housing Wealth, House Prices, Household Consumption.

JEL codes: E21, G51, O18.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.

This paper reports new data on the household balance sheet and the consumption situation in Lithuania. It uses a unique Household Finance and Consumption Survey (HFCS) dataset, which collects detailed information about different asset classes and outlines the composition of the household balance sheet in Lithuania. At 93.2%, the homeownership rate in Lithuania is the highest in Europe. Real assets correspond to the highest share of households’ wealth and generate a median net wealth of 46 000 €. Lithuanian households participate poorly in financial assets, with only deposits and individual insurance/pensions generating more significant aggregate values. Household participation in debt markets is also limited in Lithuania, with only 11.7% of households having some mortgage-based liabilities. Lithuanian households spend a significant share of their income on food and utilities. This share is among the highest in Europe. A large number of Lithuanian households can be characterized as "hand-to-mouth" households, as they own a significant amount of wealth in illiquid real estate and very little wealth in liquid financial assets.

JEL Codes: D1, D3.

The views expressed are those of the author(s) and do not necessarily represent those of the Bank of Lithuania.