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---

### Main findings
- Growing signs of over-valuation in housing markets across Europe, in the order of 15 to 20 percent in most countries.  
- Under the baseline projections for 2023, the share of households who could struggle to afford basic expenses is likely to increase by 10 percentage points, reaching one-third of all households for the average European country.  
- One out of four consumers could pull back on spending amid real income losses in the baseline.  
- Under the worst-case scenario, 45 percent of households could be financially stretched on average, holding over 40 percent of mortgage debt and 45 percent of consumer debt.  
- One third of consumers may be forced to cut back on consumption of non-essential goods to afford basic expenses, accounting for 30 percent of aggregate consumption.  
- The share of overburdened households among the bottom income tercile could reach between 70 and 80 percent, compared to just 7 and 13 percent of the top income tercile.  
- In the absence of a major house price correction and assuming banks have built adequate provisions for existing risks, capital depletion would likely not exceed 100 basis points in most countries.  
- A 20 percent house price correction would push up losses into the 100-300 basis points range, with Southern and Eastern European countries most severely affected.  
- A subsidy that shields the bottom income tercile from the food and energy price surge in 2022-23 can save about 7 percent of households from financial distress at an estimated cost of 0.8 percent of GDP under baseline projections.

### Policy recommendations and assessment
- Policies can ease the impact of higher living costs and rising mortgage payments on households, but targeting greatly enhances their cost effectiveness.  
- From a financial stability perspective (reducing mortgage debt at risk), policies with broader reach could be more cost effective since richer households are more likely to have mortgages; however, such policies weaken monetary policy transmission and create moral hazard and therefore should be well-designed, at least partially targeted, and with clear sunset clauses.  
- Macroprudential authorities should closely monitor developments and adjust policy settings as needed:
  - Under baseline conditions, countries with elevated vulnerabilities should consider tightening sectoral capital buffers to enhance banking system resilience.  
  - Excessive mortgage debt growth combined with house price overvaluation may warrant tightening of borrower-based measures (BBMs) to mitigate systemic risk build-up.  
  - A sharp downturn may require the swift release of capital-based measures to enable banks to absorb losses and support lending activity and a potential BBM easing to reduce the likelihood of adverse macro-financial feedback loops.

### Household vulnerabilities, scenarios, and distributional insights
- Shocks amplifying household balance sheet pressures include higher mortgage rates and lower real incomes due to high energy/food/broader inflation; mortgage rates at least doubled in most European countries in 2022 and tripled in Finland, Slovakia, Switzerland, and the United Kingdom.  
- Many households that stretched budgets to buy property during the COVID-19 pandemic now face refinancing at much higher rates; prospective buyers face high property values, surging mortgage rates, and lower real incomes as barriers to homeownership.  
- Lower-income households are particularly vulnerable because they spend a higher share of their income on essential goods and housing costs; higher mortgage rates disproportionately affect median-income households that hold larger loans relative to their income.  
- Vulnerabilities vary significantly across countries reflecting household leverage, ability to cut non-essential consumption, and prevalence of floating mortgage rates.  
- Scenario definitions and shocks applied:
  - (i) a 200 basis points further increase in the lending rate;  
  - (ii) a 10 percent decline in gross nominal household disposable income over baseline projections;  
  - (iii) a 20 percent increase in food and energy prices;  
  - (iv) a WEO downside scenario (worsening geopolitical developments, higher commodity prices, sharper slowdown in China property sector, lower potential output);  
  - (v) an ‘adverse’ scenario combining shocks (i) and (ii);  
  - (vi) a ‘cost of living’ scenario combining shocks (i) and (iii).  
- Key vulnerability metrics:
  - Standard DSTI measure (DSTI ≥ 40%): average-country share of financially distressed households could double from 2.5 percent to nearly 5 percent under the average adverse scenario.  
  - Cost-of-living-adjusted measure (DESCTI ≥ 70%): under baseline projections about one third of households are vulnerable—up 10 percentage points from 2021; under the ‘cost of living’ shock, 45 percent of households could be financially stretched, holding over 40 percent of mortgage debt and 45 percent of consumer debt.  
  - Income distribution: over half of all low-income households are estimated vulnerable as of 2021; this could increase to 70 percent under baseline and reach 80 percent under adverse conditions; high-income households vulnerable range between 7 and 13 percent depending on scenario severity.  
  - Consumption-at-risk: baseline one out of four consumers may cut spending; cost-of-living scenario one third of consumers could be forced to cut non-essential consumption, accounting for over 30 percent of aggregate consumption.

### Banking sector implications and stress testing
- Size and composition of retail exposure (as of 2022Q3):
  - EU banks reported EUR 4.2 trillion of outstanding mortgages to households, and 2.4 trillion of other household loans.  
  - Retail portfolio represents over half of total loans to the real economy and more than four times aggregate Tier 1 capital of the EU banking system.  
  - Since onset of the pandemic, retail portfolio rose at 9 percent cumulative growth.  
- Provisions and coverage:
  - Provisions for non-defaulted exposures increased from 48 to 55 basis points since start of the pandemic and peaked at end-2020; since then most banks have released provisions.  
- Three-step procedure to quantify capital impact:
  1. Estimate link between being financially overburdened and likelihood of default using EU-SILC micro data.  
  2. Use simulated increase in share of financially overburdened households from HFCS to estimate increase in average probability of default (PD).  
  3. Use EBA Risk Dashboard data (mortgages, other household loans, risk weights, LGD) to project impact on banks’ capital.  
- Econometric and default findings:
  - Country-level logit regressions find probability of being in arrears on mortgage payments increases by over 50 percent when households are overburdened by mortgage debt service.  
  - Average probability of being in arrears jumps from 6 to 10 percent when the household is overburdened.  
  - DSTI threshold of 40 percent shows the strongest link to likelihood of default for most countries; in some countries best threshold reaches 60 percent.  
  - DESCTI threshold of 70 percent is identified as the most significant threshold for mortgage default risk across countries.  
  - Overburdened households show a 4-percentage point increase in probability of default of consumer loans; average PD on consumer loans exceeds that on mortgages (13 versus 9.5 percent on average).  
- Capital impact magnitudes:
  - Without a major house price correction: capital depletion would likely not exceed 100 basis points in most countries (estimates are a lower bound).  
  - With a house price correction: losses could move into the 100-300 basis points range depending on country and scenario, with CESEE banking systems most exposed.  
  - A 20 percent increase in the loss given default (LGD) on mortgage loans could push up bank CET1 capital erosion into the 100-300 basis points range.  
  - Partially targeted fiscal support could save between 50 and 90 basis points of CET1 capital in countries most affected by a house price correction.

### Methodology and data sources
- House price overvaluation estimation:
  - Cointegration/VECM relating observed real housing price (p) to long-run equilibrium p* determined by housing stock (hs), real household disposable income (di), and real mortgage interest rate (R).  
  - Key equations:
    - p_t = α_1 + β_1 hs_t + β_2 di_t + β_3 R_t + ε_1t = p*_t + ε_1t  
    - Δp_t = α_2 + φ_1 (p_{t-1} − p*_{t-1}) + Σ λ_1 Δp_{t−i} + Σ λ_2 Δhs_{t−i} + Σ λ_3 Δdi_{t−i} + Σ λ_4 ΔR_{t−i} + ε_2t  
  - VECM estimates put over-valuation in 2022Q4 at around 15 to 20 percent in most countries; average speed of adjustment about 20 percent of disequilibria corrected over one year.  
- Microdata and aging:
  - Microdata combined from 2020 EU-SILC and 2017 HFCS, with HFCS aged forward to 2021.  
  - HFCS aged using country gross disposable income per capita growth, aggregate gross debt-to-income ratios, lending rate paths, and consumption adjustments tied to real consumption per capita trends; food and energy prices follow IMF WEO global wholesale prices; non-essential goods adjusted by core inflation.  
  - HFCS durable consumption imputation: estimated that spending on durables, renovations, and insurance represents between 8 and 20 percent of expenditure on non-essential items.  
- Vulnerability measures:
  - Housing cost overburden rate: households spending more than 40 percent of disposable income on housing costs.  
  - New cost-of-living-adjusted vulnerability: essential payments = housing costs + basic expenses (food and utilities) + other debt repayments; vulnerable if essential payments consume more than 70 percent of income (rationale: mortgage default threshold ~40 percent + 30 percent for other essentials).  
- Sample sizes and data sources:
  - EU-SILC observations: 597,364.  
  - HFCS households: 84,075.  
  - Additional sources: EBA Risk Dashboard, Eurostat, national central banks, Statista, Hypostat, IMF staff calculations.

### Cost-effectiveness of targeted fiscal support (Section VI highlights)
- Aggregate framing:
  - Policy cost figures quoted as a share of 2-year cumulative GDP over 2022 and 2023.  
  - Average announced fiscal support (Sep 2022–Feb 2023) cost around 2 percent of GDP, of which 1.3 percent targets households; most measures remain in place through end 2023; only 30 percent of household-targeted schemes considered targeted.  
- Headline results:
  - Narrowly targeted policy (bottom tercile compensated): could save about 7 percent of households from financial distress at an estimated average cost of 0.8 percent of GDP under the baseline forecast; under adverse conditions benefit could rise to 10 percent of households while cost would rise by 0.5 percent of GDP.  
  - Cost-effectiveness (households saved per percentage point of GDP spent): 10.4 percent under baseline; 9.0 percent under adverse for narrowly targeted policy.  
  - Broad policy (all households compensated): cost-effectiveness ratios decline to 6.3 and 6.1 under baseline and adverse conditions, respectively.  
  - From a financial-stability perspective, partially targeted support (bottom two thirds compensated) yields higher cost effectiveness for saving mortgage-debt-at-risk: 3.6 under baseline and 3.1 under adverse (mortgage-debt-at-risk saved per percentage point of GDP spent); narrowly targeted: 2.4 under baseline and 1.4 under adverse.  
- Country patterns and examples:
  - Cyprus: subsidy linked to energy consumption disproportionately benefits lower-income households and shows comparatively higher cost effectiveness.  
  - Greece: ceiling on wholesale electricity prices plus refund of up to 60 percent of surcharges for consumers with annual incomes up to EUR 45,000; relatively cost-effective to save mortgage debt at risk.  
  - Croatia: measures limit electricity rise to 9.6 percent, gas to maximum 20 percent, and limit food price increases to 30 percent.  
  - Advanced economies (Italy, France, Germany): steep cost-benefit curves as support broadens; Emerging economies (Croatia, Hungary, Poland): flatter slopes, higher benefit-to-cost when support includes median-income households.  
- Operational guidance:
  - Targeting matters: narrowly or partially targeted schemes are more cost effective than broad untargeted support, particularly in advanced economies.  
  - Linking subsidies to consumption (e.g., energy use) can disproportionately benefit lower-income households and improve cost effectiveness.  
  - Macroprudential calibration and phase-in are crucial; in downturns authorities could relax capital-based measures to enable banks to absorb losses and continue lending.

*International Monetary Fund — wpiea2023076-print-pdf (References & related sections)*

### References .............................................................................................................

### References

### Main findings
- Growing signs of over-valuation in housing markets across Europe, in the order of 15 to 20 percent in most countries.  
- Under the baseline projections for 2023, the share of households who could struggle to afford basic expenses is likely to increase by 10 percentage points, reaching one-third of all households for the average European country.  
- One out of four consumers could pull back on spending amid real income losses in the baseline.  
- Under the worst-case scenario, 45 percent of households could be financially stretched on average, holding over 40 percent of mortgage debt and 45 percent of consumer debt.  
- One third of consumers may be forced to cut back on consumption of non-essential goods to afford basic expenses, accounting for 30 percent of aggregate consumption.  
- The share of overburdened households among the bottom income tercile could reach between 70 and 80 percent, compared to just 7 and 13 percent of the top income tercile.  
- In the absence of a major house price correction and assuming banks have built adequate provisions for existing risks, capital depletion would likely not exceed 100 basis points in most countries.  
- A 20 percent house price correction would push up losses into the 100-300 basis points range, with Southern and Eastern European countries most severely affected.  
- A subsidy that shields the bottom income tercile from the food and energy price surge in 2022-23 can save about 7 percent of households from financial distress at an estimated cost of 0.8 percent of GDP under baseline projections.

### Policy recommendations and assessment
- Policies can ease the impact of higher living costs and rising mortgage payments on households, but targeting greatly enhances their cost effectiveness.  
- From a financial stability perspective (reducing mortgage debt at risk), policies with broader reach could be more cost effective since richer households are more likely to have mortgages; however, such policies weaken monetary policy transmission and create moral hazard and therefore should be well-designed, at least partially targeted, and with clear sunset clauses.  
- Macroprudential authorities should closely monitor developments and adjust policy settings as needed:  
  - Under baseline conditions, countries with elevated vulnerabilities should consider tightening sectoral capital buffers to enhance banking system resilience.  
  - Excessive mortgage debt growth combined with house price overvaluation may warrant tightening of borrower-based measures (BBMs) to mitigate systemic risk build-up.  
  - A sharp downturn may require the swift release of capital-based measures to enable banks to absorb losses and support lending activity and a potential BBM easing to reduce the likelihood of adverse macro-financial feedback loops.

### Household vulnerabilities, scenarios, and distributional insights
- Shocks amplifying household balance sheet pressures include higher mortgage rates and lower real incomes due to high energy/food/broader inflation. Mortgage rates at least doubled in most European countries in 2022 and tripled in Finland, Slovakia, Switzerland, and the United Kingdom.  
- Many households that stretched budgets to buy property during the COVID-19 pandemic now face refinancing at much higher rates; prospective buyers face high property values, surging mortgage rates, and lower real incomes as barriers to homeownership.  
- Lower-income households are particularly vulnerable because they spend a higher share of their income on essential goods and housing costs; higher mortgage rates disproportionately affect median-income households that hold larger loans relative to their income.  
- Vulnerabilities vary significantly across countries reflecting household leverage, ability to cut non-essential consumption, and prevalence of floating mortgage rates.

### Banking sector implications
- With stretched households increasing default risk and reduced consumption, potential spillovers to the banking sector and the real economy may include higher defaults, increased provisioning needs, capital depletion, curtailed credit supply, and adverse macrofinancial feedback loops.  
- Borrowers with negative home equity could be more likely to default in the face of liquidity pressures in some countries.  
- Provided banks have built adequate provisions, capital depletion would likely not exceed 100 basis points in most countries absent a major house price correction; a 20 percent house price correction would lead to losses in the 100-300 basis points range for more exposed regions.

### Methodology and data sources
- Empirical estimation of house price deviations from model-predicted values using country-level data.  
- Microdata combined from the 2020 EU Statistics on Income and Living Conditions (EU-SILC) and the 2017 Household Finance and Consumption Survey (HFCS), with HFCS aged forward to 2021 to incorporate pandemic-era balance sheet evolution.  
- Simulation of household financial distress and consumption adjustments under baseline and adverse scenarios, linking household finances to default and consumption outcomes.  
- Banking sector impact assessment using European Banking Authority (EBA) Risk Dashboard data, Eurostat, and national data sources.  
- Detailed policy announcements used to evaluate selected policy support measures.

*International Monetary Fund — wpiea2023076-print-pdf (References & related sections)*

### Section VI presents a cost-benefit analysis of fiscal support measures deployed by governments during the

### wpiea2023076-print-pdf - Section VI presents a cost-benefit analysis of fiscal support measures deployed by governments during the

### Indicators of Systemic Risk in the Housing Market — Recent Trends
- Since 2015, real house prices grew by 40 percent across Europe, and almost doubled in Hungary, Iceland, and Portugal.
- The difference between the price-to-income ratio and its long-term trend widened significantly since 2019 in most countries, reaching about 25 percent on average in 2022Q3.
- Price-to-rent ratios are roughly 40 percent above their historical averages.
- Price-to-rent and price-to-income indices are presented with long-term average = 100 (index series covering 2007–2022 with country-specific long-term averages starting at least in 2000).
- Interest rates on new mortgages (percent; end of period) are shown for 2019–2022.
- Real house prices: cumulative and year-on-year annualized growth rates are reported; dots show cumulative growth over referenced periods.
- Authorities across Europe have assessed systemic risk as elevated with concerns about possible price reversals (ESRB, 2022b).

### Extent of Overvaluation — Methodology and Findings
- Approach: cointegration/VECM relating observed real housing price (p) to long-run equilibrium p* determined by housing stock (hs), real household disposable income (di), and real mortgage interest rate (R).
- Key equations preserved:
  - p_t = α_1 + β_1 hs_t + β_2 di_t + β_3 R_t + ε_1t = p*_t + ε_1t
  - Δp_t = α_2 + φ_1 (p_{t-1} − p*_{t-1}) + Σ λ_1 Δp_{t−i} + Σ λ_2 Δhs_{t−i} + Σ λ_3 Δdi_{t−i} + Σ λ_4 ΔR_{t−i} + ε_2t
- VECM estimates put over-valuation in 2022Q4 at around 15 to 20 percent in most countries.
- Over-valuation trend timing:
  - Advanced economies: over-valuation began around the start of the pandemic.
  - Emerging economies: over-valuation became more prominent towards the end of 2021.
- Over-valuation seems to be slowing in 2022Q4.
- Average speed of adjustment: about 20 percent of disequilibria corrected over one year.
- Note: Detailed country estimates are not reported in the paper but are available upon request.

### Measuring Household Vulnerability Using Microdata — Definitions and Rationale
- Common proxy: housing cost overburden rate — proportion of households spending more than 40 percent of disposable income on housing costs (mortgage interest, rent, insurance, regular maintenance and repairs, and utilities).
- Limitations of the 40 percent threshold noted: not consistently meaningful across income distribution or countries.
- Alternative indicators: household income, household leverage.
- New measure constructed: cost-of-living-adjusted vulnerability.
  - Essential payments = housing costs (mortgage repayment and/or rent) + basic expenses (food, and utilities) + other debt repayments (consumer loans).
  - A household is considered vulnerable if essential payments consume more than 70 percent of income.
  - Rationale for 70 percent threshold:
    - (i) Level of mortgage costs as a share of income at which households become more likely to default on mortgage loans, estimated to be around 40 percent in most countries (Section V).
    - (ii) Share of income spent on non-housing essential payments, estimated to be 30 percent of income.
  - Bank of England uses a 70 percent cost-of-living adjusted mortgage debt-servicing threshold; ECB uses a cost-adjusted disposable income ratio with a different illiquidity/default assumption.

### Combining Microdata Sources — HFCS and EU-SILC
- HFCS: rich balance sheet, payments, income, consumption data; most recent wave available is 2017; lacks loan default data; used to predict household financial stress and consumption patterns via simulation.
- EU-SILC: annual, timely cross-sectional and longitudinal data on income, housing, labor, well-being; contains information on loan arrears and material deprivation; used to document current housing conditions and estimate delinquency rates conditional on household financial position.
- Method: combine HFCS (to measure share of consumers that could sharply reduce spending) with EU-SILC (to estimate delinquency rates) to estimate increases in default rates from changes in distressed household shares.

### Aging HFCS Data Forward to end-2021 — Procedures
- HFCS 2017 wave aged to end-2021 following approaches similar to Krimmel et al (2013) and Ampudia et al (2016).
- Updates applied:
  - Income: extrapolated using cumulative growth in country gross disposable income per capita from Eurostat.
  - Debt: projected nominal debt by matching aggregate path in gross debt-to-income ratios at the country level, accounting for principal repayment, new borrowing, and possible pre-payment with savings.
  - Interest payments: adjustable-rate loan payments assumed to track lending rates reported by central banks.
  - Consumption: structure of real consumption basket unchanged; total consumption adjusted to match changes in real consumption per capita by country.
  - Prices: changes in price of food and energy follow global wholesale prices from IMF’s WEO; non-essential goods and services adjusted by core inflation.
  - Durable consumption: imputed by mapping HFCS sub-components to HICP composition and scaling non-essential consumption (excluding rents) at household level; estimated that spending on durables, renovations, and insurance represents between 8 and 20 percent of expenditure on non-essential items, with a smaller share in CESEE countries.

### Housing Affordability across Europe — Patterns and Tenure Effects
- Analysis based on 2020 EU-SILC using a wide definition of housing costs (mortgage principal repayment and interest, rent, mandatory services and charges, regular maintenance and repairs, taxes, and utilities).
- Three key patterns:
  - Affordability varies significantly by tenure status (mortgage, rent, own, free accommodations).
  - Burden of housing costs is significantly higher for lower-income households.
  - Wide cross-country differences in the overburden rate even after accounting for tenure and income distribution.
- Tenure patterns:
  - In all countries except Switzerland, homeowners are more common than tenants.
  - Nordic countries and the Netherlands: owners with mortgages most common.
  - CESEE: outright owners dominate due to post-communist purchasing.
- Overburden statistics:
  - Across Europe, 1/4 of tenants face substantial financial burden from housing costs, compared with 1/5 of mortgage owners.
  - In Greece and Serbia, more than half of tenants are overburdened.
  - Latvia and Malta: overburden rate for tenants below 10 percent.
- Composition effect: lower-income households are more likely to be renters; almost half of the poorest households are overburdened irrespective of tenure.
- Among better-off households, those with a mortgage are twice more likely to be overburdened than tenants.

### Share and Drivers of Vulnerable Households
- Vulnerability measure (essential payments > 70 percent of income) results:
  - One out of five households in the EA are vulnerable.
  - Over one third of households in EE are vulnerable.
  - Households in EE are twice as likely to be vulnerable as households in the EA.
  - Lowest vulnerability rates: Austria, Finland, and Ireland.
  - Highest vulnerability rates: Croatia and Lithuania.
  - Low-income families are most vulnerable.
- Income-tercile findings:
  - Share of vulnerable households among bottom tercile: almost half in the EA and three fourths in EE.
  - Within income sub-groups, little difference in incidence of vulnerability across mortgage owners and tenants.
- Drivers vary by income:
  - Low-income households: proportion spent on food and utilities is main driver of overstretch.
  - Higher income households: debt servicing costs (particularly mortgage repayments) represent an increasing share of income.

### Housing Affordability for New Borrowers and Policy-Relevant Indicators
- To keep buying power at 2021 levels, new borrowers entering the market at end 2022 would see mortgage bills increase by one fourth on average across the sample of European economies.
- Affordability indicators defined:
  - Downpayment indicator: Cash / (PTI * LTV / Income) (notation as presented).
  - Monthly installments (DSTI): DSTI_i = (1 / maturity) * PTI * LTV_i * ... (notation as presented).
- Sustained increases in price-to-income (PTI) ratios raise down payments and monthly installments, reducing affordability.
- Tighter macroprudential limits on loan maturity raise monthly expenses.
- Lower loan-to-value (LTV) ratios have effects on down payment and monthly installments (discussion continued in source).

*Source: IMF working paper content provided in the supplied PDF excerpt.*

### Box 1. Higher Mortgage Rates are Hitting Housing Affordability

### Box 1. Higher Mortgage Rates are Hitting Housing Affordability

### Methodology to Measure Affordability Deterioration
- Two metrics calculated:
  - Increase in the DSTI ratio required to keep buying power at end-2021.
  - Size of mortgage that can be afforded to anchor monthly repayments at end-2021 levels.
- Steps:
  - Price of a 100m2 dwelling: average transaction price of new and existing homes in the most important cities in each country based on data from Statista.
  - Typical LTV ratio on residential mortgage loans: data from the European Mortgage Federation (Hypostat 2022) used to back out size of the average loan.
  - Average maturity for a typical mortgage loan as of 2021: country-specific values from Hypostat kept constant.
  - Lending rate on new mortgage loans: sourced from national central banks.
  - Median income of current mortgage owners: from EU-SILC microdata.
- Debt service to income ratio (DSTI) at time t computed as:
  1
  tt
  t
  t
  iL
  maturity
  DSTI
  I
  
  
  
  
  
  (equation (1.1) as shown in source)
- Size of loan that keeps debt repayment ratio at its 2021 level conditional on mortgage rate i computed as:
  2021   2021
  1
  i
  DSTII
  L
  i
  maturity
  
  
  
  
  
  
  (equation (1.2) as shown in source)
- Note: The 2021 country-specific average maturity of mortgage loans is kept constant in the calculation. Extending mortgage loan maturity could improve buying power by lowering monthly repayments. Example given: in Belgium, banks have recently extended mortgage loan maturities, especially for first time buyers.

### Key Findings on Affordability Impact
- Debt service repayments increases:
  - Increased by one third on average.
  - Increased up to 70 percent in the United Kingdom.
- Under a 300 basis points increase in interest rates:
  - Bills would more than double in the United Kingdom and Portugal.
- To keep monthly payments at 2021 levels (reduction in buying power):
  - Buying power would be reduced by 40 percent in the United Kingdom.
  - Buying power would be reduced by over 20 percent in the average country.
- Under a further 200-basis points interest rate increase:
  - In over 40 percent of the countries, housing buying power would be eroded by over 40 percent.

### Figures and Illustrations (as referenced)
- Box 1.1. DSTI to Keep Buying Power in Main Cities at 2021 Levels (Percent)
  - Captioned with sources: Statista, Hypostat, National Central Banks, and IMF staff calculations.
- Box 1.2. Reduction in Buying Power under Different Scenarios (Percent)
  - Captioned with sources: Statista, Hypostat, National Central Banks; and IMF staff calculations.

### Additional Context and Offsetting Effects
- Higher mortgage rates have offsetting effects on affordability:
  - Push up size of down payment required to purchase property, affecting cash-constrained households disproportionately.
  - Reduce mortgage payments (via amortization effect), decreasing borrower vulnerability to future income shocks.

*Sources: Statista; Hypostat; National Central Banks; IMF staff calculations.*

### Section VI relaxes this assumption by incorporating the effect of announced government policies

### wpiea2023076-print-pdf - Section VI relaxes this assumption by incorporating the effect of announced government policies

### Scenarios and stress tests applied to baseline projections
- Additional shocks applied to the baseline scenario:
  - (i) a 200 basis points further increase in the lending rate;
  - (ii) a 10 percent decline in gross nominal household disposable income over baseline projections;
  - (iii) a 20 percent increase in food and energy prices;
  - (iv) a WEO downside scenario considering a worsening of geopolitical developments, accompanied by an increase in commodity prices, a sharper slowdown in the property sector in China, and lower potential output;
  - (v) an ‘adverse’ scenario, combining the interest rate shock in (i) and the household income shock in (ii);
  - (vi) a ‘cost of living’ scenario featuring the materialization of both the interest rate shock in (i) and the food and energy price shock in (iii).
- Illustrative notes:
  - “three fourths of the shocks in (i) have already materialized due to the increase in interest rates relative to the October 2022 WEO assumptions.”
  - “the likelihood of shocks (iii) has declined since October 2022 with the decline in oil prices in recent months.”
- Table 1 (referenced) shows country-level cumulative shocks assumed for core macrofinancial variables aggregated for EA and EE.

### Definitions and measurement of household vulnerability
- Two measures of vulnerability:
  - Standard creditworthiness measure (outstanding borrowers only): household is financially overburdened if debt service to income ratio (DSTI) exceeds 40 percent.
  - Cost-of-living-adjusted measure (outstanding borrowers and households with no debt): household is economically vulnerable if debt service and essential payments (DESCTI) consume more than 70 percent of gross income.
- Essential consumption includes: food at home, half of food consumed in restaurants, utility bills, and rents. Non-essential consumption includes other goods and services and durables.
- Benchmarking: Section V tests the performance of the 70 percent threshold in identifying risky borrowers.
- Alternative definition of economic overburdening: DSTCTI>1 (debt service and total consumption exceed gross income).

### Modeling approach and assumptions
- Household-level model variables:
  - I = gross household income
  - P = principal repayment of outstanding loans
  - O = amount of outstanding debt
  - i = lending rate
  - N = total number of loans
  - M = number of loans with interest rates to be re-set over the next two years
- Repricing and passthrough:
  - “While the speed of adjustment to market rates varies across countries depending on the re-setting practices of retail prices as documented in Arregui et al (2022), after two years the pass-through is generally complete.”
- Consumption behavior assumptions (conservative):
  - Household spending on food and utilities rises according to growth in energy and food components of consumer prices, respectively.
  - Households keep the structure of consumption unchanged; quantities consumed grow at the pace of real private consumption projected in the scenario.
  - Behavioral responses that could dampen impacts (e.g., higher labor supply, increased participation of second earners) are ignored.
- Projections are contingent on scenario j ∈ {1,...7}.

### Key simulation results on household vulnerability
- Standard DSTI measure (DSTI ≥ 40%):
  - For the average country, share of financially distressed households could double from 2.5 percent to nearly 5 percent under the average adverse scenario.
  - Mortgage debt at risk could rise by over 10 percentage points to reach thirty percent of the total mortgage portfolio under adverse conditions.
- Cost-of-living-adjusted measure (DESCTI ≥ 70%):
  - Under baseline projections, about one third of households are vulnerable—up 10 percentage points from 2021.
  - Under the ‘cost of living’ shock, 45 percent of households could be financially stretched, holding over 40 percent of mortgage debt and 45 percent of consumer debt.
- Income distribution impacts:
  - As of 2021, over half of all low-income households are estimated to be vulnerable.
  - This share could increase to 70 percent under baseline projections and reach 80 percent under adverse conditions.
  - Only between 7 and 13 percent of high-income households would be vulnerable depending on scenario severity.
  - Most of the increase in mortgage debt at risk would come from higher-income households because they hold larger loans.
- Consumption-at-risk:
  - One out of four consumers could pull back on spending amid real income losses in the baseline.
  - Under the cost-of-living scenario, this share could rise to one-third of consumers, accounting for over 30 percent of aggregate consumption.
- Heterogeneity across countries:
  - Prevalence of floating-rate mortgages varies widely; variable-rate mortgages most prevalent in the Nordic countries, the Baltics, the Netherlands, and Portugal.
  - Median household leverage highest in the Netherlands at almost three times gross income.
  - Essential consumption share of income highest in Croatia at almost 40 percent.

### Banks’ exposure to household loan stress and methodology to assess impact
- Size of retail portfolio (as of 2022Q3):
  - EU banks reported EUR 4.2 trillion of outstanding mortgages to households, and 2.4 trillion of other household loans.
  - Retail portfolio represents over half of total loans to the real economy and more than four times aggregate Tier 1 capital of the EU banking system.
  - Since onset of the pandemic, retail portfolio rose at 9 percent cumulative growth.
- Provisions and coverage:
  - Provisions for non-defaulted exposures increased from 48 to 55 basis points since start of the pandemic and peaked at end-2020; since then most banks have released provisions.
- Bank vulnerability factors:
  - Banking systems with higher share of mortgages and lower Tier 1 capital are disproportionately exposed to housing market imbalances.
  - Tier 1 capital examples: Irish banks highest at 34 percent of loans; Netherlands and Slovakia Tier 1 capital represents less than 10 percent of total loans.
- Three-step procedure to quantify capital impact:
  1. Estimate link between being financially overburdened and likelihood of default using EU-SILC micro data.
  2. Use simulated increase in share of financially overburdened households from HFCS to estimate increase in average probability of default (PD).
  3. Use EBA Risk Dashboard data (mortgages, other household loans, risk weights, LGD) to project impact on banks’ capital.
- Default measurement:
  - “In the analysis we measure ‘default’ as being in arrears on debt payments in the past 12 months.”
  - Country-level logit regressions find probability of being in arrears on mortgage payments increases by over 50 percent when households are overburdened by mortgage debt service.
  - Average probability of being in arrears jumps from 6 to 10 percent when the household is overburdened.

### Key econometric findings on DSTI and DESCTI thresholds
- DSTI thresholds:
  - For most countries, DSTI threshold of 40 percent relative to household disposable income shows the strongest link to likelihood of default on mortgage debt.
  - In some countries (Croatia, Iceland, Finland, France, Italy, Norway, Portugal, Spain), best performing threshold reaches 60 percent.
- DESCTI threshold:
  - Country-level logit regressions identify the 70 percent limit (basic expenditure relative to gross income) as the most significant threshold for mortgage default risk across countries.
  - Overburdened households show a 4-percentage point increase in the probability of default of consumer loans relative to non-overburdened peers.
  - Average PD on consumer loans exceeds that on mortgages (13 versus 9.5 percent on average).

### Impact on bank capital and role of house price corrections
- Without major house price correction:
  - Impact of household stress on banks is “manageable” — capital depletion would not exceed 100 basis points in most countries and adverse scenarios considered (estimates are a lower bound).
  - Assumptions: mortgage LGD around 40 percent; LGD of uncollateralized loans 100 percent.
- With a house price correction:
  - A house price correction could push losses into the 100-300 basis points range depending on country and scenario, with CESEE banking systems most exposed.
  - Rationale: house price correction reduces value of mortgage collateral, increases LGD, and amplifies banks’ losses.
- Mitigating factors that could reduce realized losses:
  - Borrowers drawing on pandemic-era savings (but overburdened borrowers may lack sufficient liquid buffers).
  - Borrowers adjusting spending (limited scope for low-income borrowers on essentials).
  - Government support and mortgage relief measures (some quantified in Section VI).

### Government policy response and targeting (Section VI)
- Fiscal support (announced Sep 2022–Feb 2023):
  - Average cost around 2 percent of GDP, of which 1.3 percent target households.
  - Most measures remain in place through end 2023.
  - Main schemes: caps on energy prices, VAT cuts on gas and electricity bills.
  - Only 30 percent of household-targeted schemes considered targeted; many schemes untargeted.
- Forbearance and bank-facing measures:
  - Forbearance measures encouraged (extension of maturities, payment holidays, deferrals).
  - Country examples:
    - Hungary: cap on mortgage rates for outstanding loans with variable interest rates of less than 1 year maturity.
    - Portugal: decree allowing renegotiation for borrowers with first residence floating-rate loans if DSTI is 50 percent (or higher) or has increased by more than five percentage points.
    - Spain: “Code of Good Practices” to standardize debt restructuring solutions, mainly by extending maturities.
- Information gap on cost-effectiveness:
  - Cost of energy price caps is uncertain given high uncertainty around future energy prices.
  - Effective capacity of policy measures to protect consumers depends on income share spent on basic consumption, balance sheet capacity, liquidity position, and debt service obligations.

### Caveats and limitations acknowledged
- Use of “aged” HFCS survey to 2021 with imputations based on macrofinancial aggregates could introduce measurement error.
- Simulations assume full passthrough of global commodity prices into domestic retail prices by 2023; Section VI quantifies effect of governments’ measures to shield consumers.
- Analysis abstracts from potentially important behavioral responses of households (but essential consumption items are likely inelastic).
- Estimates of bank capital impact provide a lower bound because:
  - Some households below thresholds may still see credit quality deteriorate.
  - Banks are assumed to have already built adequate provisions under current conditions, which may not hold uniformly.
  - Mortgage portfolios generally present higher recovery rates but longer recovery times, and provisioning differs markedly across countries and segments.

*Source: IMF Working Paper (wps) content provided in the supplied PDF excerpt.*

### 2.7 percent of annual GDP. Our numbers are quoted as a share of 2-year cumulative GDP over 2022 and 2023.

### Targeted Policies Could Shield More Households from Financial Distress At A Lower Cost, particularly in Advanced Europe

### Cost-effectiveness of Targeted Support: headline results
- Narrowly targeted policy (bottom tercile compensated):
  - Could save about 7 percent of households from financial distress at an estimated average cost of 0.8 percent of GDP under the baseline forecast.
  - Under adverse conditions, benefit could rise to 10 percent of households, while the cost of the policy would rise by half a percentage point of GDP.
  - Cost-effectiveness (households saved per percentage point of GDP spent): 10.4 percent under the baseline; 9.0 percent under the adverse scenario.
- Broad policy (all households compensated):
  - Cost-effectiveness ratios decline to 6.3 and 6.1 under baseline and adverse conditions, respectively (households saved per percentage point of GDP spent).
- From a financial-stability perspective:
  - Partially targeted support (bottom two thirds compensated) yields higher cost effectiveness because median-income households are more likely to hold mortgage debt and larger loans.
  - Cost-effectiveness (mortgage-debt-at-risk saved per percentage point of GDP spent):
    - Partially targeted: 3.6 under baseline and 3.1 under adverse.
    - Narrowly targeted: 2.4 under baseline and 1.4 under adverse.
- Illustrative narrow price subsidy example:
  - Shielding the bottom tercile from the surge in energy and food prices in 2022-23 could prevent about 7 percent of households from becoming financially distressed at an estimated cost of 0.8 percent of GDP under baseline projections.

### Country case studies and variability in effectiveness
- Cyprus (CYP):
  - The targeted policy announced links subsidy size to energy consumption and disproportionately benefits lower-income households; results suggest comparatively higher cost effectiveness to shield households at risk.
- Greece (GRC):
  - Policy announced: ceiling on wholesale electricity prices and refund of up to 60 percent of all surcharges for consumers with annual incomes of up to EUR 45,000.
  - Results suggest relatively more cost-effective to save mortgage debt at risk because it also protects lower-median income households who are more likely to carry mortgage payments.
- Croatia (HRV):
  - Policy announced: limit rise in electricity prices to 9.6 percent and gas prices to a maximum of 20 percent; measures to limit current food price increases to 30 percent.
- Cross-country pattern:
  - Advanced economies (Italy, France, Germany) show steep cost-benefit curves: as support broadens, costs rise sharply for limited benefits.
  - Emerging economies (Croatia, Hungary, Poland) show flatter slopes: relatively higher benefit-to-cost when support includes median-income households.

### Household vulnerability, scenarios, and distributional impacts
- Housing market and vulnerability context:
  - House prices in Europe rose faster than fundamentals can account for, pointing to overvaluation of around 20 percent in most markets in 2022.
  - The surge in cost of credit since early 2022 may intensify housing market cooling.
- Vulnerability projections (using a newly constructed indicator incorporating essential expenses, debt, and income):
  - Under baseline conditions, the share of households that could struggle to afford basic expenses could increase by 10 percentage points by end 2023, reaching a third of all households for the average European country.
  - Under the worst-case (intensified cost-of-living) scenario:
    - 45 percent of households could become financially stretched.
    - These households would represent over 40 percent of mortgage debt and 45 percent of consumer debt.
  - Distributional effects:
    - Share of overburdened households in the bottom tercile could reach between 70 and 80 percent.
    - For well-off peers the share is 7 and 13 percent.
- Consumption adjustment:
  - Baseline: one out of four households could pull back on spending amid real income losses.
  - Worst-case: one third of consumers, accounting for 30 percent of aggregate consumption, may be forced to adjust spending.

### Financial stability implications for banks
- Overall assessment:
  - Risks posed by the household sector to financial stability appear to be contained overall, though pockets of vulnerability persist.
  - Absent an abrupt house price correction and assuming banks are adequately provisioned for existing risks, capital depletion would likely not exceed 100 basis points in most countries.
- House-price shock sensitivity:
  - A 20 percent increase in the loss given default (LGD) on mortgage loans could push up bank CET1 capital erosion into the 100-300 basis points range, with Southern and Eastern European countries most affected.
- Policy support effects on bank resilience:
  - Targeted policies could support the banking system by saving regulatory capital.
  - Results suggest that a partially targeted policy could save between 50 and 90 basis points of CET1 capital in the countries most affected by a house price correction.

### Policy recommendations and operational guidance
- Design of fiscal support:
  - Targeting matters: narrowly targeted or partially targeted schemes are more cost effective than broad untargeted support, particularly in advanced economies.
  - Linking subsidies to consumption (e.g., energy use) can disproportionately benefit lower-income households and improve cost effectiveness.
- Macroprudential and housing-market measures:
  - Authorities may need to adjust macroprudential settings in countries where vulnerabilities continue to build, including by introducing a sectoral systemic risk buffer to increase resilience.
  - Consider measures to improve the functioning of the rental market in some countries.
  - Calibration and phase-in of macroprudential measures are crucial to avoid unintended consequences; associated costs and benefits should be carefully evaluated.
  - In the event of a downturn, authorities could swiftly relax capital-based measures to enable banks to absorb credit losses and continue lending to the real economy.

### Data, methodology, and key statistics
- Policy cost figures and framing:
  - Numbers are quoted as a share of 2-year cumulative GDP over 2022 and 2023.
  - Example: UK “Energy Price Guarantee” cost estimates range from GBP 52 billion tp GBP 140 billion over two years (estimates by UK government, Institute for Fiscal Studies and Cornwall consulting firm).
- Data sources used:
  - HFCS microdata; Eurostat; Sgaravatti et al. (Bruegel dataset); IMF staff calculations.
- Projections and timing:
  - Projections by end 2023.
- Sample sizes (Annex I table totals):
  - EU-SILC observations: 597,364.
  - HFCS households: 84,075.

*Source: IMF staff analysis and calculations (excerpts from wpiea2023076-print-pdf).*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023076-print-pdf.pdf_