Figures and data in Modelling households’ financial vulnerability with consumer credit and mortgage renegotiations

Abstract

The strong growth in consumer credit and the wide use of mortgage renegotiations observed since 2015 has affected households’ ability to repay their loans. In this paper, we provide a novel way to account for these trends, extending Michelangeli and Pietrunti (2014) microsimulation model of households’ financial vulnerability. The extension provides a more accurate assessment of the financial stability risks stemming from the household sector. The growth in consumer credit drives an increase in the share of vulnerable households, but with small effects on the overall debt at risk. Mortgage renegotiations contribute to a decrease in households’ vulnerability.

This article contains 16 figures and 9 tables.

Figures

**Figure 1.** Mortgages and consumer credit (percentages). a) Mortgages and consumer credit (per cent of disposable income), b) Annual growth rate of bank loans to consumer household.

**Figure 2.** Mortgage renegotiations and risks (percentages). a) Mortgage renegotiations and interest rates. b) New non-performing loan rate.

**Figure 3.** Mortgage refinancing. a) Share of renegotiations on outstanding debt (percentages), b) Subrogations and replacements (billions of euro).

**Figure 4.** Financial vulnerability by type of debt (averages 2010–16; percentages). a) Distribution of vulnerable households, b) Distribution of debt at risk.

**Figure 5.** Financial vulnerability and arrears of more than 90 days (averages 2010-16). a) Share of HHs in arrears (percentages), b) Average debt of HHs in arrears (thousands of euros).

**Figure 6.** Subjective perception of economic difficulty (1) (percentages; averages 2010–16).

**Figure 7.** Household debt by income groups. a) Consumer credit, b) Mortgages.

**Figure 8.** Distribution of theoretical and observed debt (1).

**Figure 9.** Backtest exercises – Vulnerability (percentages). a) Share of vulnerable households, b) Debt at risk.

**Figure 10.** Vulnerability in the period 2016-2019 (percentages). a) Share of vulnerable households, b) Debt at risk.

**Figure 11.** Average amount of debt among households in arrears (thousands of euros, average over 2010-16).

**Figure 12.** All households with DSR >30 per cent in the period 2016–2019 (percentages). a) Share of HHs in the population, b) Debt at risk.

**Figure A.2.** Confidence intervals: vulnerability in the period 2016-19 (percentages). a) Share of HHs in the population, b) Debt at risk.

**Figure A.3.** Backtest excercises - All households with a DSR>30 per cent (percentages). a) Share of vulnerable households, b) Debt at risk.

**Figure A.4.** Vulnerability in the period 2016-2019 under adverse scenarios: Interest rates stress (+200 bps) and income stress (-4 p.p.) in 2019 (percentages). a) Share of HHs in the population, b) Debt at risk.

Tables

Table 1. Loans to households (millions of euros and per cent)

	Mortgages		Consumer credit		Total loans		Percentage composition
	Total	Non-performing	Total	Non-performing	Total	Non-performing	A/E	B/F	C/E	D/F
	(A)	(B)	(C)	(D)	(E)	(F)	A/E	B/F	C/E	D/F
2010	329,950	13,938	118,779	10,766	543,902	38,765	60.7	36	21.8	27.8
2011	345,406	16,055	118,476	11,792	565,345	46,760	61.1	34.3	21	25.2
2012	345,255	18,845	116,142	12,140	562,102	52,173	61.4	36.1	20.7	23.3
2013	341,952	21,728	111,937	12,088	554,170	57,161	61.7	38	20.2	21.1
2014	341,221	23,660	108,644	10,879	549,522	59,087	62.1	40	19.8	18.4
2015	342,698	25,530	109,993	9,632	551,824	60,922	62.1	41.9	19.9	15.8
2016	348,643	25,812	113,302	7,804	558,341	57,592	62.4	44.8	20.3	13.6
2017	355,906	23,588	121,992	7,035	567,262	49,521	62.7	47.6	21.5	14.2
2018-Q3	356,792	20,295	129,257	6,618	571,742	40,233	62.4	50.4	22.6	16.4

Source = Supervisory reports.

Table 2. Household debt (averages 2010-16; euros and per cent)

	Consumer credit		Mortgage
	Amount	Interest rate*	Amount	Interest rate*
Vulnerable HHs	10,001	5.0	80,734	4.3
Other low-income HHs	5,446	4.4	52,009	4.1

Notes: Our calculation based on SHIW data.

Interest rates are calculated as weighted averages of the amount borrowed. Other low-income households include households with an income below the median of the population and a DSR below 30 per cent.

Table 3. Mortgage renegotiations (percentages)

	SHIW	MODEL*
a) Share of households that renegotiate their mortgage terms among indebted households
2014	5.0	5.4
2016	11.7	12.6
b) Share of debt held by households that renegotiate their mortgage terms
2014	4.0	2.8
2016	14.9	12.0

Averages over two years.

Table 4. Distribution of households by income and DSR (percentages; averages over the period 2010-16)

	Share of households	Share of debt
Vulnerable HHs	2.3	15.8
Other low-income HHs	47.7	13.9
High-income HHs with DSR >30%	1.5	18.3
High-income HHs with DSR ≤30%	48.5	52.0

Notes: Our calculation based on SHIW data. High-income households are those with an income above the median of the population.

Table 5. Indicators of indebted households (percentages; averages over the period 2010-16)

	Vulnerable HHs	Other low income HHs with DSR <30%	High income HHs with DSR >30%	High income HHs with DSR <30%
Total wealth	118416.5	97649.57	343556.8	364953.6
Financial wealth	4628.9	7228.245	26838.81	46315.7
Liquid assets	3468.2	5614.0	13404.5	21399.0
Liquidity index (fa/instalment)	0.795	6.440	0.828	15.117
Debt to income	4.041	0.118	3.166	0.200
Debt to assets	1.689	0.024	1.608	0.011

Notes: Our calculation based on SHIW data. fa indicates liquid financial assets. High-income households are those with an income above the median of the population.

Table A.1. Participation in the consumer credit market: Linear regression model

Participation at time t-1 in consumer credit market	0.328** (0)
Participation at time t-1 in the mortgage market	0.086*** (4.12e-09)
Income quartile 2	0.027*** (0.00319)
Income quartile 3	0.037*** (0.000154)
Income quartile 4	0.041*** (0.000128)
Durable consumption class 2	0.044*** (0.000649)
Durable consumption class 3	0.055*** (2.91e-05)
Durable consumption class 4	0.081*** (1.14e-05)
Durable consumption class 5	0.212*** (0)
Constant	0.023*** (1.34e-05)
Observations	17,368
R-squared	0.182

Notes: Probability weights have been used.

Robust p-values in parentheses. ***P<0.1, **P<0.05, *P<0.01

Table A.2. Change in consumer credit amount: Linear regression model

Growth rate of consumer loans in period T in italy	452.014* (258.903)
Income quartile 1	–56.722 (131.299)
Income quartile 2	–79.546 (134.427)
Income quartile 3	–78.194 (132.996)
Income quartile 4	–110.160 (136.147)
Durable consumption class 1	–56.990 (131.605)
Durable consumption class 2	–52.810 (141.930)
Durable consumption class 3	–84.604 (141.588)
Durable consumption class 5	1,204.870*** (194.763)
Observations	17,368
R-squared	0.021

Notes: Probability weights have been used. Robust standard errors in parentheses.

***P<0.01, **P<0.005, *P<0.1

Table A.3. Spread: Linear regression model

Primary school certificate	2.067** (0.853)
Lower secondary school certificate	1.518* (0.824)
Upper secondary school	1.326 (0.825)
University degree	1.069 (0.827)
Postgraduate qualification	0.903 (0.851)
Centre	0.159 (0.097)
South	0.806*** (0.113)
Self-employed	0.241** (0.113)
Not working	0.269** (0.126)
Constant	–0.738 (0.824)
Observations	2,593
R-squared	0.048

Notes: Robust standard errors in parentheses.

***P<0.1, **P<0.05, *P<0.01

Table B.1. Macroeconomic inputs

	Growth rate of income	Growth rate of mortgages	Growth rate of consumer credit	Annual change in 3-month Euribor	Annual change in 10-year IRS	Growth rate of durable consumption
	(%)	(%)	(%)	(basis points)	(basis points)	(%)
	(1)	(2)	(3)	(4)	(5)	(6)
2013	0.51	−1.22	−1.88	0.08	−0.06	−4.62
2014	0.74	−0.80	−0.85	−0.19	−0.45	6.39
2015	1.49	0.41	4.59	−0.21	−0.57	8.23
2016	1.47	1.79	8.25	−0.19	−0.35	5.01
2017	1.66	2.26	9.06	−0.012	0.30	4.25
2018*	2.77	2.61	9.01	0.025	0.16	0.16
2019*	3.02	1.19	8.07	0.217	0.31	0.31

Notes: Historical data for 2013–17 are based on national accounts (Columns 1 and 6), supervisory reports (Columns 2 and 3) and MIR data (Columns 4 and 5). * indicates the macroeconomic projections for 2018–19 used in the paper, which may differ from realized data and from confidential forecasts based on the macroeconometric model developed at the Bank of Italy.