A. Microfinancial Risks

11. Under baseline conditions, bank profitability is likely to decline due to persistent low interest rates and weak credit growth. Interest rates on assets and liabilities of Dutch banks are for a significant part fixed, and adapt to lower rates only gradually. In the recent past, net interest income (NII) of Dutch banks was relatively resilient. As banks perform maturity transformation, they have reduced their funding costs in the low interest rates environment, while interest income from borrowers is declining only gradually. Figure 2 shows the evolution of selected deposit and lending rates; the environment with declining interest rates, however, has also affected non-deposit (securities) funding costs. More specifically, although lending rates on new corporate loans, which are mostly floating, declined sharply in tandem with short-term policy and market rates, mortgage rates, which are largely fixed, ¹⁰ started declining with a lag and gradually. If interest rates continue at their current low levels, the full impact on mortgage rates will materialize with time, further compressing net interest margins, with little room left for further funding cost reductions. It must be noted that stronger credit growth could sustain net interest margins in the future, despite low interest rates.

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Netherlands: Interest Rates on Deposits and Loans to Households (New Business), 2003–16

(In percent)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: DNB.

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12. The Dutch financial sector operates in an uncertain global macroeconomic and financial environment and faces several external risks (Appendix II):

• Reversion in international integration and policy coordination in large economies could weigh on global growth and exacerbate financial market volatility. Protracted uncertainty associated with negotiating post-Brexit arrangements could weigh on confidence and investment more than expected—most prominently in the United Kingdom and the rest of Europe with possible knock-on effects elsewhere. Slower than anticipated external demand associated with diminished confidence and investment in Europe would negatively affect net exports, business confidence, and investment in the Netherlands. Lower domestic growth would exacerbate credit risks for the banking sector.

• Fallout from global economic and political fragmentation could imply a sharp rise in risk premia with flight to safety. Investors could withdraw from specific risk asset classes as they reassess underlying economic and financial risks in large economies—or respond to unanticipated U.S. Federal Reserve tightening and increases in U.S. term premia—with poor market liquidity amplifying volatility. A drop in stock and bond prices would affect the solvency of insurers and reduce coverage ratios in the pension sector; it would also reduce banks’ capital ratios and could trigger redemptions in investment funds. Renewed stress in global wholesale funding markets would increase funding costs for Dutch banks, with adverse effects on their profitability and solvency; it could also trigger funding liquidity strains in a worst-case scenario.

• Structurally weak growth in the Euro Area (EA) could affect demand for Dutch exports and its NFC sector, exacerbating credit risks and triggering deflation. In the EA, weak demand, low productivity growth, and persistently low inflation from a failure to fully address crisis legacies and undertake structural reforms could lead to lower medium-term potential growth and exacerbate financial imbalances, especially among European banks (high likelihood). Such external conditions would weaken the demand for Dutch exports, affecting the profitability and solvency of NFCs, thereby increasing strains on banks’ asset quality. They could also lead to broader deterioration in domestic consumer and business confidence, negatively affecting banks through weak credit demand and growth.

• Global risks with potential indirect effects on the Dutch economy also arise from a potentially significant slowdown in China, while tighter financial conditions could undermine growth in emerging markets (EMs) more generally. In China, a loss of investor confidence, disorderly corporate defaults, and/or a sharp fall in asset prices would impact commodity prices and could roil global financial markets, reducing global growth. Significant slowdown in other large EMs could be exacerbated by excessive household and corporate foreign exchange (FX) leverage. A withdrawal of international investors from EM corporate debt could generate disorderly deleveraging, with potential spillbacks to advanced economies. In these cases, financial and nonfinancial institutions in Netherlands could be particularly affected through trade and financial channels given their wide international exposure.

13. In addition to external risks, the Dutch economy faces domestic risks, notably those related to renewed weaknesses in housing markets. The housing market seems to have turned the corner, households have reduced their mortgage indebtedness through voluntary repayments, and low interest rates support their debt servicing capacity. However, a reversal of the recent recovery in house prices could weaken household balance sheets and dampen domestic demand once again. Dutch banks are highly exposed to households and NFCs. A halt or reversal of the ongoing domestic economic recovery would impact the ability of borrowers to service their loans, which could worsen banks’ asset quality. It would also have implications on economic growth through macrofinancial linkages (lower consumption).

14. Some features of the Dutch economy and its financial system increase its vulnerability to adverse external and domestic shocks. Features that increase the probability of realization, as well as the conditional effects of adverse shocks include: (i) low bank profitability due to a continuation/further decline in interest rates and weak credit growth; (ii) the high indebtedness of the household and NFC sectors; and (iii) banks’ reliance on wholesale funding and exposure to cross-border contagion.

B. Macroeconomic Scenarios

15. Given the risks and vulnerabilities described above, the stress tests examined a baseline macroeconomic scenario and an extreme but plausible adverse scenario (Table 2 and Figure 3). The baseline macroeconomic scenario is based on the World Economic Outlook (WEO) projections of IMF staff as of August 2016. It incorporates post-Brexit downward revisions to real GDP growth equivalent to 0.1 and 0.3 percentage points in 2016 and 2017, respectively; the mild slowdown in growth in 2016–17 is driven by weaker investment and external demand, while consumption remains resilient. Note that the IMF WEO baseline envisages slower growth in 2016–18 than the baseline scenario assumed in the EBA Stress Test. These differences reflect different vantage points in time: the EBA scenarios were announced in February 2016 and do not reflect post-Brexit revisions.

Table 2.

Netherlands: Macroeconomic Baseline and Adverse Scenarios for Stress Tests

Sources: IMF staff calculations.

Table 2.

Netherlands: Macroeconomic Baseline and Adverse Scenarios for Stress Tests

				Paths in Stress Period
			2015	2016	2017	2018
Real GDP growth (in percent change)
	Baseline		2.0	1.7	1.6	1.9
		Contribution of common EA layer		−1.1	−4.0	−1.4
		Contribution of country-specific layer		−0.5	−1.3	−0.3
	Adverse IMF		2.0	0.1	−3.7	0.2
Private consumption (in percent change)
	Baseline		1.8	1.9	1.9	1.7
		Contribution of common EA layer		−0.8	−3.5	−1.8
		Contribution of country-specific layer		−1.0	−2.8	−0.9
	Adverse IMF		1.8	0.1	−4.4	−1.0
Private investment (in percent change)
	Baseline		11.6	6.6	4.6	4.0
		Contribution of common EA layer		−1.6	−7.2	−5.6
		Contribution of country-specific layer		−5.5	−14 3	−2.6
	Adverse IMF		11.6	−0.5	−16.9	−4.2
Consumer price inflation rate (in percent)
	Baseline		0.2	0.2	0.9	1.0
		Contribution of common EA layer		−0.2	−1.9	−3.0
		Contribution of country-specific layer		0.0	−0 3	−0.5
	Adverse IMF		0.2	0.0	−1.4	−2.5
Unemployment rate (in percent)
	Baseline		6.9	6.6	6.4	6.1
		Contribution of common EA layer		0.4	1.8	2.0
		Contribution of country-specific layer		0.2	0.5	0.5
	Adverse IMF		6.9	7.2	8.8	8.6
Fiscal balance ratio (in percent)
	Baseline		−1.8	−1.6	−1.0	−0.7
		Contribution of common EA layer		0.1	0.2	−0.1
		Contribution of country-specific layer		−0.1	−0.6	−0.8
	Adverse IMF		−1.8	−1.7	−1.4	−1.5
Short term interest rate (in percent)
	Baseline		0.0	−0.3	−0.4	−0.2
		Contribution of common EA layer		0.1	0.2	0.2
		Contribution of country-specific layer		0.3	0.8	0.7
	Adverse IMF		0.0	0.1	0.7	0.7
Long term interest rate (in percent)
	Baseline		0.8	0.3	0.5	0.6
		Contribution of common EA layer		0.2	0.4	0.4
		Contribution of country-specific layer		0.3	0.8	0.7
	Adverse IMF		0.8	0.8	1.6	1.7
Mortgage interest rate (in percent)
	Baseline		2.9	2.8	2.7	2.5
		Contribution of common EA layer		0.0	0.1	0.1
		Contribution of country-specific layer		0.2	0.4	0.4
	Adverse IMF		2.9	3.0	3.2	3.0
Interest rate on corporate loans (in percent)
	Baseline		1.7	1.7	1.6	1.5
		Contribution of common EA layer		0.0	0.1	0.1
		Contribution of country-specific layer		0.2	0.4	0.4
	Adverse IMF		1.7	1.9	2.2	2.0

Sources: IMF staff calculations.

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Table 2.

Netherlands: Macroeconomic Baseline and Adverse Scenarios for Stress Tests

				Paths in Stress Period
			2015	2016	2017	2018
Real GDP growth (in percent change)
	Baseline		2.0	1.7	1.6	1.9
		Contribution of common EA layer		−1.1	−4.0	−1.4
		Contribution of country-specific layer		−0.5	−1.3	−0.3
	Adverse IMF		2.0	0.1	−3.7	0.2
Private consumption (in percent change)
	Baseline		1.8	1.9	1.9	1.7
		Contribution of common EA layer		−0.8	−3.5	−1.8
		Contribution of country-specific layer		−1.0	−2.8	−0.9
	Adverse IMF		1.8	0.1	−4.4	−1.0
Private investment (in percent change)
	Baseline		11.6	6.6	4.6	4.0
		Contribution of common EA layer		−1.6	−7.2	−5.6
		Contribution of country-specific layer		−5.5	−14 3	−2.6
	Adverse IMF		11.6	−0.5	−16.9	−4.2
Consumer price inflation rate (in percent)
	Baseline		0.2	0.2	0.9	1.0
		Contribution of common EA layer		−0.2	−1.9	−3.0
		Contribution of country-specific layer		0.0	−0 3	−0.5
	Adverse IMF		0.2	0.0	−1.4	−2.5
Unemployment rate (in percent)
	Baseline		6.9	6.6	6.4	6.1
		Contribution of common EA layer		0.4	1.8	2.0
		Contribution of country-specific layer		0.2	0.5	0.5
	Adverse IMF		6.9	7.2	8.8	8.6
Fiscal balance ratio (in percent)
	Baseline		−1.8	−1.6	−1.0	−0.7
		Contribution of common EA layer		0.1	0.2	−0.1
		Contribution of country-specific layer		−0.1	−0.6	−0.8
	Adverse IMF		−1.8	−1.7	−1.4	−1.5
Short term interest rate (in percent)
	Baseline		0.0	−0.3	−0.4	−0.2
		Contribution of common EA layer		0.1	0.2	0.2
		Contribution of country-specific layer		0.3	0.8	0.7
	Adverse IMF		0.0	0.1	0.7	0.7
Long term interest rate (in percent)
	Baseline		0.8	0.3	0.5	0.6
		Contribution of common EA layer		0.2	0.4	0.4
		Contribution of country-specific layer		0.3	0.8	0.7
	Adverse IMF		0.8	0.8	1.6	1.7
Mortgage interest rate (in percent)
	Baseline		2.9	2.8	2.7	2.5
		Contribution of common EA layer		0.0	0.1	0.1
		Contribution of country-specific layer		0.2	0.4	0.4
	Adverse IMF		2.9	3.0	3.2	3.0
Interest rate on corporate loans (in percent)
	Baseline		1.7	1.7	1.6	1.5
		Contribution of common EA layer		0.0	0.1	0.1
		Contribution of country-specific layer		0.2	0.4	0.4
	Adverse IMF		1.7	1.9	2.2	2.0

Sources: IMF staff calculations.

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Netherlands: Macroeconomic Scenarios for Stress Testing: Real GDP

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: EBA; and IMF staff calculations.

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16. The extreme but plausible adverse macroeconomic scenario reflects the abovementioned downside external risks, and a domestic house price shock.

• The IMF adverse macroeconomic scenario has similar intensity (in terms of real GDP deviation from baseline projections) to the one used in the 2016 EBA Stress Test. The IMF adverse scenario was run through an MCM global macroeconomic model¹¹ and results in a cumulative decline of real GDP relative to the IMF WEO baseline equivalent to 2 standard deviations (6.9 percentage points, calculated based on data covering 1980–2015) over two years, and 1.9 standard deviations (or 8.7 percentage points) over three years.¹²

• As illustrated in Figure 3, however, growth rates under the IMF adverse scenario are lower than in the EBA scenario due to post-Brexit downward revisions in baseline projections. Also, the IMF adverse scenario exhibits a V shape—a sizable decline in the year 2017—while the EBA scenario is U-shaped.¹³

17. Table 3 describes the different layers of numerical assumptions that underlie the construction of the adverse IMF scenario, divided into a common EA layer, and a country specific layer:

• Common EA layer. It is driven by a tightening of global financial conditions accompanied by credit-cycle downturns in EM economies. It features an abrupt decompression of risk asset premia, which is amplified by low secondary market liquidity. Within the EA, these tensions affect, with particularly intensity, high spread economies, and are reflected in higher money market interest rates—and banks’ cost of short-term funding—and long-term government bond yields. Also, the realization of financial stability risks implies that monetary normalization in the systemic advanced economies fails to materialize in the stress testing period (2016–18).

• Country-specific layer. Weakness in economic activity (resulting from the combination of shocks envisaged under the common EA layer) has knock on effects on domestic housing markets. Housing prices decline by 15 percent, with adverse impact on domestic consumption and residential investment (see Appendix III for further details on real estate. In addition, heavy reliance on wholesale funding by Dutch banks makes them vulnerable to turmoil in international financial markets. Under the country-specific layer, the cost of short-term funding rises by 75 basis points. Overall, combining the impact of both layers, banks’ cost of funding increases by 100 basis points.

Table 3.

Netherlands: Macroeconomic Scenarios: Shocks in the Common EA and Country-Specific Layers

Sources: Authorities; and IMF staff.

Table 3.

Netherlands: Macroeconomic Scenarios: Shocks in the Common EA and Country-Specific Layers

			Common to the EA	Country-s pecific	Total
Layer 1: Tightening offinancial conditions in systemic economies, 2016Q3 – 2017Q2
	Long term government bond yield; Duration risk premium shocks
		High Spread Euro Area Economies	+200 basis points		+200 basis points
		Low Spread Euro Area Economies (excl. Netherlands)	+50 basis points		+50 basis points
		Japan, United Kingdom, United States	+100 basis points		+100 basis points
		Netherlands	+50 basis points	+75 basis points	+125 basis points
	Real equity price; Equity risk premiumshocks
		China, Euro Area, Japan, United Kingdom, United States	−20 percent		−20 percent
	Money market interest rate spread; Credit risk premiumshocks
		China, High Spread Euro Area Economies	+100 basis points	+50 basis points	+150 basis points
		Low Spread Euro Area Economies (excl. Netherlands), Japan, United Kingdom, United States	+25 basis points	+50 basis points	+75 basis points
		Netherlands	+25 basis points	+75 basis points	+100 basis points
	Real bilateral exchange rate; Currency risk premiumshocks
		Euro Area	+5.0 percent		+5.0 percent
Layer 2: Fiscal consolidation in the Euro Area, 2016Q3 – 2018Q2
	Primary fiscal balance ratio; Fiscal expenditure shocks
		High Spread Euro Area Economies	+2.0 percentage point		+2.0 percentage point
		Low Spread Euro Area Economies (excl. Netherlands)	+1.0 percentage point		+1.0 percentage point
		Netherlands	+1.0 percentage point		+1.0 percentage point
Layer 3: Credit cycle downturns in emerging market economies, 2016Q3 – 2018Q2
	Loan default rate shocks		+0.3 to +4.7 percentage points		+0.3 to +4.7 percentage points
Layer 4: Suppressed economic risk taking worldwide (excl. Germany and Netherlands), 2016Q3 – 2018Q2
	Private investment; Investment demand shocks		−4.0 percent		−4.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent		−1.0 percent
Germany
	Private investment; Investment demand shocks		−4.0 percent	−8.0 percent	−12.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent	−2.0 percent	−3.0 percent
Netherlands
	Private investment; Investment demand shocks		−4.0 percent	−16.0 percent	−20.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent	−2.0 percent	−3.0 percent

Sources: Authorities; and IMF staff.

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Table 3.

Netherlands: Macroeconomic Scenarios: Shocks in the Common EA and Country-Specific Layers

			Common to the EA	Country-s pecific	Total
Layer 1: Tightening offinancial conditions in systemic economies, 2016Q3 – 2017Q2
	Long term government bond yield; Duration risk premium shocks
		High Spread Euro Area Economies	+200 basis points		+200 basis points
		Low Spread Euro Area Economies (excl. Netherlands)	+50 basis points		+50 basis points
		Japan, United Kingdom, United States	+100 basis points		+100 basis points
		Netherlands	+50 basis points	+75 basis points	+125 basis points
	Real equity price; Equity risk premiumshocks
		China, Euro Area, Japan, United Kingdom, United States	−20 percent		−20 percent
	Money market interest rate spread; Credit risk premiumshocks
		China, High Spread Euro Area Economies	+100 basis points	+50 basis points	+150 basis points
		Low Spread Euro Area Economies (excl. Netherlands), Japan, United Kingdom, United States	+25 basis points	+50 basis points	+75 basis points
		Netherlands	+25 basis points	+75 basis points	+100 basis points
	Real bilateral exchange rate; Currency risk premiumshocks
		Euro Area	+5.0 percent		+5.0 percent
Layer 2: Fiscal consolidation in the Euro Area, 2016Q3 – 2018Q2
	Primary fiscal balance ratio; Fiscal expenditure shocks
		High Spread Euro Area Economies	+2.0 percentage point		+2.0 percentage point
		Low Spread Euro Area Economies (excl. Netherlands)	+1.0 percentage point		+1.0 percentage point
		Netherlands	+1.0 percentage point		+1.0 percentage point
Layer 3: Credit cycle downturns in emerging market economies, 2016Q3 – 2018Q2
	Loan default rate shocks		+0.3 to +4.7 percentage points		+0.3 to +4.7 percentage points
Layer 4: Suppressed economic risk taking worldwide (excl. Germany and Netherlands), 2016Q3 – 2018Q2
	Private investment; Investment demand shocks		−4.0 percent		−4.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent		−1.0 percent
Germany
	Private investment; Investment demand shocks		−4.0 percent	−8.0 percent	−12.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent	−2.0 percent	−3.0 percent
Netherlands
	Private investment; Investment demand shocks		−4.0 percent	−16.0 percent	−20.0 percent
	Private consumption; Consumption demand shocks		−1.0 percent	−2.0 percent	−3.0 percent

Sources: Authorities; and IMF staff.

18. The scenario assumes differentiated impact of banks’ cost of funding shocks across funding sources, and a 50 percent pass-through of these shocks to lending rates (Figure 5 and 6). Cost of funding shocks affect bank’s net interest income in two ways. First, these shocks cannot be passed on to borrowers immediately, due to maturity transformation (e.g., loans have longer maturity and time to repricing than deposits). Second, when banks are able to reprice loans, they can only pass to borrowers 50 percent of the funding shocks. Differentiation of funding shocks across sources also implies differentiated impact across banks; more specifically, banks more heavily reliant on wholesale funding are affected by larger shocks, while banks relying on overnight or short-term deposits are affected by smaller shocks. Note that a highly granular/disaggregated approach to project various interest rates is key to realistically assessing the evolution of bank profitability in a low interest rate (baseline) environment, or under a stress scenario. (Sub-section C below describes how interest rate paths, combined with information of time-to-repricing of assets and liabilities, impact bank’s net interest income and profitability.)

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Netherlands: Housing Prices in Adverse IMF Macroeconomic Scenario

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: DNB; and IMF staff calculations.

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Netherlands: Pass-through of Funding Cost Shocks to Lending Rates

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: IMF staff calculations.

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Netherlands: Interest Rate Assumptions Underlying Net Interest Income Projections

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: IMF staff calculations.

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C. Banks’ Balance Sheets, Profits, and Risk Exposures

19. Large banks have different business models, and hence, are exposed to different types of risks. The largest three banks, ING, ABN Amro and Rabobank, have invested about 70-75 percent of their balance sheets in loans, according to 2015 annual reports. The loan book consists mostly of mortgage loans to households (40–50 percent of the loan book) and corporate loans (30–40 percent of the loan book). These banks finance themselves mostly with deposits (50–70 percent of total assets) and securities (20–30 percent of total assets). In contrast, BNG and NWB are public, focus almost entirely on extending loans to the public sector, and fund themselves predominately with securities. BNG provides financing for (semi-) publicly owned organizations, such as public utilities, public housing and healthcare providers. NWB is a specialist financial institution, providing funding for water boards and local governments. Last, SNS has the largest deposit base and focuses almost entirely on providing mortgage loans.

20. The three largest banks have substantial international exposure. According to data of the 2016 EU-wide stress tests:

• ING holds only about 30 percent of its IRB portfolio in the Netherlands, lending 70 percent to clients outside the Netherlands. The five largest foreign counterparty countries are Germany (14.2 percent), Belgium (13 percent), the United States (5.8 percent), Australia (4.6 percent), and the United Kingdom (4.6 percent); 5.4 percent of ING’s IRB portfolio is invested in Spain and Italy.

• Rabobank is holding about 68 percent of its IRB portfolio in the Netherlands. The five largest IRB foreign counterparty countries are the United States (11.6 percent), the United Kingdom (3.4 percent), Australia (2.1 percent), Switzerland (2.3 percent), and New Zealand (1.5 percent). Rabobank has invested 0.9 percent of its IRB portfolio in Brazil.

• ABN Amro is more domestically focused than ING and Rabobank, holding about 76 percent of its IRB portfolio in the Netherlands. The five largest IRB foreign counterparty countries are France (3.4 percent), the United States (3 percent), Germany (2.1 percent), Belgium (1.6 percent), and the United Kingdom (1.6 percent). ABN Amro is less exposed to Spain and Italy and has larger holdings in Singapore (1.4 percent), the Marshall Islands (0.9 percent) and Bermuda (0.7 percent) compared to ING and Rabobank.

21. Given the international nature of these banks, staff applied a cross-country credit risk model to capture differences in exposures.

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Netherlands: Bank’s Assets and Liability Composition

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: 2015 annual reports; and grouping of various accounting items into larger subgroups performed by staff.

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Netherlands: Banks’ Loan Books Composition

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: 2015 year-end data taken from 2016 EU-wide Stress Test results, and annual reports and investor presentations.

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D. Macrofinancial Risk Transmission: Satellite Models and Methodologies

22. Satellite models and specific methodologies were used to assess the transmission of macroeconomic shocks to individual banks’ profitability and capitalization. These models and methodologies were used to assess the transmission of macroeconomic conditions (summarized in sub-sections A and B, including assumptions on interest rates) to bank-specific losses associated with credit, market, and interest rate risks.

Credit losses

23. Given the international diversification of large banks’ exposures, probabilities of default (PDs) under the adverse scenario were estimated for 11 different countries. A dynamic panel data econometric model was estimated to obtain PDs in the Netherlands and 10 other countries in which banks had significant credit exposures (see Appendix IV for further details). The list of countries includes: Australia, Belgium, France, Germany, Italy, the Netherlands, Poland, Spain, Switzerland, the United Kingdom, and the United States. In the model, bank- and country-specific PDs are determined by real GDP growth rates and real interest rates in a non-linear fashion.¹⁴ Non- linear effects embedded in the estimated model (and consistent with observed data) imply the following:

• Larger subsequent increases in PDs for exposures that have higher PDs at the starting point; and

• PDs rise at an increasing rate as macroeconomic conditions deteriorate further and further.

24. Note that variation in macroeconomic conditions across countries—real GDP growth, deflation, and nominal interest rates—combined with differentiated bank exposures to foreign countries affect the rise in overall PDs of banks. For instance, banks exposed to countries subject to more extreme conditions under the adverse scenario (e.g., high-spread EA economies) would be affected more negatively.

25. Estimates from the credit risk models suggest that PDs would rise sharply in the adverse scenario. System-wide point-in-time (PIT) PDs in 2015 were lowest for portfolios of mortgage loans and credit to financial institutions, and higher for exposures to corporate counterparts and consumer credit. In the adverse scenario, overall the PIT PD multiplier is 2.4 times the starting level, but with significant variation across exposure types and countries:

• Corporate and consumer credit exposures exhibit higher initial PDs and large multipliers. Average multipliers (across all banks and countries) for corporate and consumer credit exposures are 2.8 and 2.5, respectively;

• Mortgage loans exhibit lower initial PDs and smaller multipliers. The average multiplier for mortgage loans is 1.7; and

• As noted above, PD sensitivity and hence multipliers differ significantly by country due to variation in starting PDs and the severity of macroeconomic conditions in the adverse scenario. For instance, multiplier values for corporate exposures range from 2.4 to 3 in low spread EA economies (Netherlands, Germany, Belgium) and from 5 to 6 in high spread EA economies.

26. Loss given default (LGDs) were adjusted to reflect past (international) empirical association between recovery and default rates, as well as the effect of housing price shocks. Empirical evidence indicates that when PDs rise from 1 to 2 percent, average recovery rates decline from 50 percent to 40 percent (Figure 9)—LGDs rise from 50 percent to 60 percent. In terms of “multipliers,” a PD multiplier equal to 2 would be associated with an LGD multiplier equivalent to 1.2. To reflect these relations, LGDs were adjusted according to the following formula: ΔLPD = 0.6 × ΔPD.

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Netherlands: Recovery Rate/Default Rate Association

Dollar Weighted Average Recovery Rates to Dollar weighted Average Default Rates (1982-2009)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: “Default Recovery Rates and LGD in Credit Risk Modeling and Practice: An Updated Review of the Literature and Empirical Evidence,” Edward I. Altman.

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Net interest income

27. A detailed and highly granular approach was used to project net interest income under baseline and adverse scenarios. The authorities provided to the FSAP team detailed information on the maturity structure and time to repricing of asset and liability items for individual banks. Based on these data, we calculated interest income and expenses under the different scenarios. Figure 10 shows a numerical example that illustrates the calculation method: “trees” that track the refinancing times and rates of each type of asset and liability item. Note that:

• As the stress tests were performed under the “constant balance sheet assumption,” maturing loans were always renewed at the rates corresponding to “new business” (lending was not interrupted); and

• It was assumed that in the event of default of a given asset exposure, payments would be suspended. Thus, proceeds from interest payments under the “trees” were adjusted by PDs.

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Netherlands: Calculation of Interest Income in Stress Tests (Example)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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E. Hurdle Rates

29. For the assessment of capital adequacy and determination of hurdle rates, the regulatory frameworks applied were Basel III (i.e., fully loaded), and the European Union and national frameworks, as defined by the Fourth Capital Requirements Directive (CRD IV), Regulation on Prudential Requirements, national law and DNB regulations. The hurdle rates for total capital, Tier 1 capital and Common Equity Tier 1 capital were set according to the current minimum requirements (Table 4). For the leverage ratio, there is no legal threshold; instead, the Basel 3 percent leverage ratio is used. The stress tests were based on the minimum capital ratios under Pillar 1 and did not take into account any individual requirements under Pillar 2. Every ratio was based on Basel III fully loaded definitions.

Table 4.

Netherlands: Hurdle Rates for Solvency Stress Tests

(In percent)

Table 4.

Netherlands: Hurdle Rates for Solvency Stress Tests

(In percent)

	Total Capital ratio (total capital to RWAs)	Tier I Capital ratio (tier I capital to RWAs)	Common Equity Tier I Capital ratio (CET1 capital to RWAs)	Leverage ratio (Tier I capital to total assets)
Hurdle rate	10.5	8.5	7	3

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Table 4.

Netherlands: Hurdle Rates for Solvency Stress Tests

(In percent)

	Total Capital ratio (total capital to RWAs)	Tier I Capital ratio (tier I capital to RWAs)	Common Equity Tier I Capital ratio (CET1 capital to RWAs)	Leverage ratio (Tier I capital to total assets)
Hurdle rate	10.5	8.5	7	3

F. Results

30. Results show that low interest rates are expected to weigh down on bank profitability under the baseline scenario (Box 2). In view of the prolonged low interest rate environment, banks are projected to suffer a significant deterioration in net interest income. Overall profitability is projected to decline but remain positive. Banks’ lending rates are set to decline further as fixed income assets mature and are repriced, with negative effects on net interest income. Net interest income for the largest six banks is projected to decline gradually from €29.4 billion (1.3 percent of assets) in 2015 to €25 billion and €24 billion in 2017 and 2018, respectively (about 1.1 percent of assets). Due mainly to the dominant effect of declining net interest income, overall profitability in the banking system would fall from 0.50 percent of assets in 2015 to about 0.15 percent in 2018—despite the mitigating effects of lower credit losses and taxes.

Box 2.

Effects of a Low Interest Rate Environment on Banks’ Net Interest Income

Banks enter into fixed-to-floating swaps at the time of loan origination such that increases in short-term base rates don’t affect net interest income. Hedging reduces banks’ interest rate risk, as liabilities of banks have shorter maturities than loans.

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However, banks might still be exposed to changes in interest rates. When interest rates are low and the yield curve flattens, maturity transformation will be less profitable, as swaps will be repriced at the time of rolling over the loan, and term spreads are lower. The figure below shows a decomposition of lending and borrowing rates into different components. After hedging, lending and borrowing rates move with the floating rate (in this simple example). However, an unexpected flattening of the yield curve will lead to lower lending rates and lower maturity transformation premium, here called “the term spread.”¹ This implies a certain degree of financial market friction, as yield curve theory would imply no arbitrage on intermediating funding. Another reason for lower net interest income, e.g., lower lending-borrowing margins, is that borrowing rates of some liability categories may hit the zero lower bound, and cannot fall below zero.

The impact of flattening yield curves has a lagged effect on net interest income, as the loan portfolio only gradually is rolling over. The longer the maturity of loans, the longer it takes until long-term yield movements are priced-in. A detailed analysis of banks’ interest income and interest expenses shows that new business loans have significantly lower interest rates than outstanding loans, implying that past yield curve shifts are still priced-in in the loan portfolio of banks.

Source: IMF staff. ¹ For a detailed discussion of the impact of changes in the slope of the yield on net interest income see Borio (2015).

31. The Dutch banking system appears resilient in the face of the risks identified above (the triple “threats”). Stress tests of solvency risk suggest that banks are affected significantly by the realization of extreme but plausible adverse macroeconomic conditions, but all banks maintain capital ratios above the regulatory minimums. The overall results of the tests indicate the following (Figure 11 and Figure 12):

• The shocks have a significant negative impact on (risk-weighted) capital ratios, but all banks maintain capital ratios above minimum regulatory requirements. The common equity Tier 1 capital ratio (CET1) for the largest six banks declines from 13.5 percent in 2015 to 10.2 percent in 2018. As the capital ratio of every bank remains above the minimum hurdle rate of CET1 ratio of 7 percent over the period 2016–18, the system does not have any capital shortfall.

• The results in terms of the leverage ratio, however, indicate that a significant bank could fall below the minimum 3 percent hurdle. In particular, in the adverse scenario, the leverage ratio in the system (six largest banks) would decline from 3.8 percent to 3.2 percent, and the ratio for one of the largest banks would fall below the minimum 3 percent hurdle. This outcome reflects the relative low risk-weighted asset (RWA) density (RWAs to total assets) in the Dutch banking system and implies a capital shortage of Tier 1 capital in the adverse scenario (equivalent to €3.8 billion or 0.6 percent of GDP).

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Netherlands: Stress Tests Results: Evolution of Bank Capital and Its Drivers in the IMF Adverse Scenario

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: IMF staff calculations.

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Netherlands: Stress Tests Results: Evolution of Net Interest Income in IMF Adverse Scenario

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: IMF staff calculations. Other assets and liabilties includes derivatives.

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32. Each of the three main “threats” identified above contribute to a significant decline in bank profitability and capitalization under the stress scenario. The combination of extreme shocks triggers significant losses. Cost of funding shocks increase banks’ interest expenses, reducing further net interest income (which as noted above, is set to decline even in the baseline scenario). A sharp slowdown in the domestic and foreign economies and rising real lending rates—driven by a partial pass-through from the cost of funding shocks as well as by deflation—trigger defaults that exacerbate banks’ credit losses. These defaults, in turn, further reduce net interest income by failing to accrue interest. Sharp upward shifts in sovereign yield curves negatively impact the valuation of marked-to-market and FV securities portfolios.

33. More specifically, the stress test results reveal the following:

• • Overall profitability declines from 0.5 percent of assets in 2015 to -0.3 percent in 2017–18, with significant cumulative impact on capitalization levels. Net interest income declines from 1.3 percent of assets in 2015 to 0.8 percent in 2018. Credit loss impairments rise from 0.2 percent of assets in 2015 to 0.5 percent of assets in 2015 (credit losses decline to 0.3 percent in 2018 as the economy starts recovering). The impact on gains or losses related to securities portfolios is also significant: a net gain of 0.2 percent of assets in 2015 turns into a 0.2 percent loss in 2017 due to sharp increase in sovereign bond yields in the adverse scenario (particularly in some foreign economies where high sovereign bond spreads materialize). Banks post losses on a pre-tax basis, and reduced tax payments partially mitigate the overall decline in bottom-line results.

• Credit risk is a significant driver of overall losses. In the adverse scenario, bank provisions rise with higher expected credit losses (driven by changes in PD and LGD rates). From a system-wide perspective, PIT PDs in 2015 were low for portfolios of mortgage loans and credit to financial institutions, and higher for exposures to corporate counterparts and consumer credit. In the adverse scenario, overall PIT PD rates rise sharply, reaching a peak level equivalent to 2.4 times the initial level, but with significant variation across exposure types and countries (as shown above, in Figure 9).¹⁵

• Banks are also exposed to potential losses from market risk on sovereign and non-sovereign securities portfolios. In the adverse scenario, banks are not only unable to repeat gains received in 2015 (equivalent 0.2 percent of assets), but also suffer from declining valuations as yield curves shift upwards. In the adverse macroeconomic scenarios, the duration-adjusted haircuts applied to all the securities in the held for trading, available for sale, and fair valued books cause losses equivalent to 2 percent of the initial valuations. Potential losses are mitigated by the presence of floating rate securities (not stressed), large allocations to low-spread countries relative to high-spread countries, and short/intermediate average duration of portfolios.

A. Remarks on the Relation between Solvency and Liquidity Stress

A. Recent Developments in Household Indebtedness and Vulnerabilities

48. Households are improving their financial position, but are still highly indebted. Unlike the pre-crisis period, the household sector became a net lender in 2009 and has kept this status since then (text figure).²² During 2010–2015, the outstanding balance of household financial assets increased by 23 percent from €1,772 to €2,181 billion, much faster than that of financial liabilities (6.3 percent).²³ Households’ debt-to-net disposable income ratio also decreased from 294 percent in 2010 to 277 percent in 2015, supported by the economic recovery and employment growth. However, as shown in Figure 16, the household debt-to-disposable income ratio is one of the highest in Europe.

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Netherlands: Household Financial Asset and Liability Transactions

(Billions of euros, non-consolidated)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Sources: CBS; and IMF staff calculation.

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Netherlands: Household Debt-to-Income Ratio

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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49. The overall net wealth of households is strong, but their sizable asset holdings are mostly illiquid in the form of pension entitlements and housing assets. Debt-to-Total Assets stood at 25.7 percent at end-2014.²⁴ However, 80 percent of total assets comprises home equities (34 percent), life insurance (5 percent) and pension entitlements (41 percent), which are illiquid. Considering only financial assets, households had about minus €170 billion net worth at end-2014.

50. Mortgage loans have marginally decreased from the peak in 2012 (text figure). After reaching a peak at €652 billion in 2012, the outstanding balance of mortgages decreased to €639 billion by 2015. Its share in percent of GDP declined from 97 percent to 94 percent during 2013–2015. While securitization via Special Purpose Vehicles (SPVs) has declined significantly through the double-dip recession, insurance companies have doubled their market shares from 3.8 percent to 7.7 percent.

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Netherlands: Mortgage Loans

(Billions of euros)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: DNB.

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51. A sizable share of mortgage loans remains in negative equity.²⁵ Dutch housing markets were severely hit by the recent financial crisis. Through the double-dip recession, residential property prices fell by 21 percent. The share of underwater mortgages still accounts for a quarter of the whole mortgages at end-2015, while it has been declining along a slow recovery in housing markets since 2013. Mortgage interest deductibility (MID), along with the absence of stringent LTV limits, has incentivized high LTV mortgages in the previous boom time which ended up as underwater mortgages afterwards.²⁶ Negative equity is a well-documented cause of default and/or depressed consumption (IMF, 2015). As Verbruggen and others (2015) points out, the large number of underwater mortgages is a latent risk for the Dutch economy, as underwater borrowers are less resilient to shocks and underwater mortgages may lead to higher credit losses in case of default. The share of underwater mortgages is particularly high among young borrowers (e.g., about 40 percent among borrowers at age 20–30, 55 percent among those at age 30–40), who bought houses at the end of the previous boom time with high originating LTV ratios.

52. Aggregate mortgage arrears have been low, but mortgages with higher LTV and loan-to-income (LTI) ratios have higher default rates. Despite sizable underwater mortgages, the aggregate share of mortgage arrears was 2.3 percent of total mortgages in 2013Q1 and has been decreasing in recent years, standing at 1.6 percent at end-2015. This phenomenon is driven by several factors, such as a full recourse mechanism, low and fixed lending rates, a strong social safety net, and a temporary tax exemption for intergenerational monetary gifts.²⁷ However, the share of mortgage loans with high LTV and LTI ratios continue to be very high and is a cause of concern in the Netherlands. Mortgages with originating LTV above 100 percent and originating LTI about four times account for 20 percent of total mortgages at end-2015, according to the DNB loan-level dataset. Even with a gradual reduction of official limits on LTV ratios, 37 and 50 percent of new mortgage loans have LTV and LTI ratios above 100 percent and four times at end-2015, respectively.²⁸ As shown in Table 5, mortgages with higher originating LTV and LTI ratios tend to fall more frequently into arrears: while the share of arrears is only 0.4 percent among mortgages with LTV below 60 percent and LTI below two times, it is 4.9 percent among those with LTV above 120 percent and LTI six times at end-2015.

Table 5.

Netherlands: Share of Mortgages in Arrears by Originating LTV and LTI Ratios

(In percent, as of 2015Q4, loan-level data)

Note: If the balance of mortgage loans, whose originating LTV and LTI are less than 60 percent and 0-2, amounts to €10 billions, and €1 billions out of €10 billions is in arrears, then the share of arrears in this group is 10 percent. Source: DNB.

Table 5.

Netherlands: Share of Mortgages in Arrears by Originating LTV and LTI Ratios

(In percent, as of 2015Q4, loan-level data)

Based on the latest loan-level survey		Originating LTV ratios
		< 60	60-70	70-80	80-90	90-100	100-110	110-120	> 120
Originating LTV ratios	0-2	0.4	0.4	0.4	0.5	0.6	0.7	1.1	0.6
	2-3	0.4	0.5	0.6	0.8	0.9	1.2	1.6	1.5
	3-4	0.6	0.7	0.8	1.1	1.1	1.6	2.3	2.7
	4-5	0.8	1.0	1.3	1.5	1.8	2.1	3.1	4.4
	5-6	1.0	1.3	1.6	2.0	2.3	3.0	4.2	5.9
	> 6	1.4	1.4	1.6	2.0	2.4	2.9	3.7	4.9

Note: If the balance of mortgage loans, whose originating LTV and LTI are less than 60 percent and 0-2, amounts to €10 billions, and €1 billions out of €10 billions is in arrears, then the share of arrears in this group is 10 percent. Source: DNB.

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Table 5.

Netherlands: Share of Mortgages in Arrears by Originating LTV and LTI Ratios

(In percent, as of 2015Q4, loan-level data)

Based on the latest loan-level survey		Originating LTV ratios
		< 60	60-70	70-80	80-90	90-100	100-110	110-120	> 120
Originating LTV ratios	0-2	0.4	0.4	0.4	0.5	0.6	0.7	1.1	0.6
	2-3	0.4	0.5	0.6	0.8	0.9	1.2	1.6	1.5
	3-4	0.6	0.7	0.8	1.1	1.1	1.6	2.3	2.7
	4-5	0.8	1.0	1.3	1.5	1.8	2.1	3.1	4.4
	5-6	1.0	1.3	1.6	2.0	2.3	3.0	4.2	5.9
	> 6	1.4	1.4	1.6	2.0	2.4	2.9	3.7	4.9

Note: If the balance of mortgage loans, whose originating LTV and LTI are less than 60 percent and 0-2, amounts to €10 billions, and €1 billions out of €10 billions is in arrears, then the share of arrears in this group is 10 percent. Source: DNB.

53. The stock of interest-only (IO) mortgages is a potential pocket of vulnerability in the Dutch mortgage market. The share of IO mortgages is about 55 percent of total mortgages at end-2015, the bulk of which will start to mature from 2030 onwards (text charts). Mortgage interest deductibility (MID) previously provided a strong incentive to maximize the loan amount and delay repayment of the principal until maturity by taking out IO mortgages. One quarter of mortgage borrowers fully rely on an IO mortgage, while about 60 percent of IO borrowers combine an IO with an amortizing mortgage.

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Composition of Mortgage Loans by Type of Payment

(Percent of total mortgage loans)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: DNB based on loan-level data.

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54. A large share of mortgage borrowers appears not to be accumulating sufficient financial assets to repay their mortgage loans by maturity. The DNB (2016) estimates that 60 percent of outstanding IO mortgages will not be fully covered by contractual payments or pledged accounts at maturity.²⁹ Therefore, such households will need to negotiate with lenders to roll over a part of the mortgage or sell their homes at maturity, which could create a risk of fire sales or an increase in DSTI ratios, depending upon market conditions at the time.

B. Simulation Analysis of Risks of Mortgage Loans

55. The aggregate picture can mask large variations in financial soundness across households. Notwithstanding the resilience of mortgage borrowers during the double-dip recession, the effects of future macrofinancial shocks could transpire differently if unemployment rates were to rise or housing prices to drop again. Also, the impacts faced by individual borrowers varies substantially across different segments of households, according to current financial conditions. Therefore, policymakers need to know which segments of households are vulnerable to shocks and implement measures to enhance the resilience of the system as a whole as well as individual households.

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Maturity Schedule of Non-Amortizing Mortgages

(Billions of euros)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: De Nederlandsche Bank.

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Methodology and data

56. This subsection describes stress test results with micro-level data to assess the impact of the FSAP adverse scenario and identify the heterogeneity across segments of the Dutch households. Due to the confidentiality of the Income Panel Survey (IPO) data, simulations have been conducted by the DNB staff using its internal mortgage credit risk model for top-down stress tests. The stress test scenarios are reported in Table 2. Household characteristics and their balance sheet information in the dataset are explicitly used to measure the impacts of a stress scenario on the share of households with mortgage loans that become “highly risky,” meaning that debt-service-to-income (DSTI) ratio increases to the level above the recommended limit.³⁰ Explicitly, the scenario inputs affect household income, mortgage characteristics, and each household’s DSTI ratio.

57. The IPO dataset includes a representative sample of the Dutch population and builds on the tax returns households are required to submit. The refined sample of the IPO data covers about 4.5 million units after excluding some observations, such as early retirees, students, and those who are not in the labor force. It has rich information on family size, income, interest payments, property value, mortgage loan, savings, debts, etc. In simulations, the 2010 data wave is used as a starting point for the top-down model, and the DNB loan level data is also used as supplementary information.

58. According to the IPO data, 97.5 percent of mortgage loans in the sample are outside Amsterdam. The most common age categories of the oldest in households are between 35–45 and 45–55 years old; about 50 percent of loans have LTV ratio of below 90 percent, with about 40 percent being in negative equity in the sample, which is slightly above the 2015 level; and borrowers with LTI ratio under four times represent about 86 percent of the sample, as opposed to six percent for those with LTI ratio above five times (Table 6).

Table 6.

Netherlands: Composition of Residential Mortgages

Source: DNB.

Table 6.

Netherlands: Composition of Residential Mortgages

Groups		Percent
Location
	Amsterdam	2.5
	Non-Amsterdam	97.5
Age of the oldest in a household
	Age Under 35	13.5
	Age 35-45	29.3
	Age 45-55	30.0
	Age 55-65	15.5
	Age 65-75	8.8
	Age 75 +	3.0
Gross Income (threshold in euros)
	Income Under 20 percentile	20 (49,220)
	Income 20-40 percentile	20 (66,198)
	Income 40-60 percentile	20 (83,560)
	Income 60-80 percentile	20 (109,101)
	Income 80-100 percentile	20 (277,522)
Current LTV Ratio
	LTV Under 60	29.4
	LTV 60-70	6.2
	LTV 70-80	6.5
	LTV 80-90	6.8
	LTV 90-100	7.2
	LTV 100-110	8.4
	LTV 110-120	9.8
	LTV 120+	25.8
Current LTI Ratio
	LTI 0-2 times	36.0
	LTI 2-3 times	29.5
	LTI 3-4 times	20.8
	LTI 4-5 times	7.6
	LTI 5-6 times	2.7
	LTI over 6 times	3.4

Source: DNB.

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Table 6.

Netherlands: Composition of Residential Mortgages

Groups		Percent
Location
	Amsterdam	2.5
	Non-Amsterdam	97.5
Age of the oldest in a household
	Age Under 35	13.5
	Age 35-45	29.3
	Age 45-55	30.0
	Age 55-65	15.5
	Age 65-75	8.8
	Age 75 +	3.0
Gross Income (threshold in euros)
	Income Under 20 percentile	20 (49,220)
	Income 20-40 percentile	20 (66,198)
	Income 40-60 percentile	20 (83,560)
	Income 60-80 percentile	20 (109,101)
	Income 80-100 percentile	20 (277,522)
Current LTV Ratio
	LTV Under 60	29.4
	LTV 60-70	6.2
	LTV 70-80	6.5
	LTV 80-90	6.8
	LTV 90-100	7.2
	LTV 100-110	8.4
	LTV 110-120	9.8
	LTV 120+	25.8
Current LTI Ratio
	LTI 0-2 times	36.0
	LTI 2-3 times	29.5
	LTI 3-4 times	20.8
	LTI 4-5 times	7.6
	LTI 5-6 times	2.7
	LTI over 6 times	3.4

Source: DNB.

59. In addition to the micro-study, staff used a general equilibrium model with a housing sector to simulate how uncertainty about house prices and household incomes would affect macroeconomic conditions for different LTV ratios (Box 3).

Box 3.

Effects of House Price Shocks on Economic Conditions Under Different LTV Ratios

Staff developed a DSGE model with a housing sector to analyze to which extent changes in the housing outlook affect macroeconomic conditions, conditional on three different LTV ratios—80 percent, 90 percent, and 100 percent. Staff calibrated the model to characteristics of the Dutch economy and simulated how an increase in the dispersion of housing values affects:

• (i) Variables governing home purchase decisions including mortgage rates (the spread above the benchmark rate), mortgage lending, and residential investment; as well as

• (ii) Macroeconomic variables that result from general equilibrium dynamics, including output, employment and consumption.

The model differentiates between households that serve their mortgage loan and households that default on their mortgage loan, and simulates changes in the distribution depending on financial and macroeconomic conditions. Of special interest are high-risk households that are on the verge of default. Households finance their housing purchase p_h,t h_t with a mortgage loan l_t, subject to an LTV-constraint:

$l_t\le LT{V}_t{p}_{h,t}{h}_t$

Next period, households receive a return R_h,t+1 on the house, which is equal to the rental rate¹ plus the increase in the value of the house $(R_{h,t+1}\equiv {\upsilon }_{i,t+1}(r_{h,t+1}+p_{h,t+1}(1-\delta ))/ph)$, and pay the gross lending rate on loans equal to R_l,t = 1 + i_l,t. Housing returns vary across households due to idiosyncratic differences in housing values, represented by the shock υ_i,t+1. Households discount future income at the stochastic discount factor M_t+1 and choose housing and mortgage financing according to the following objective function:

$\underset{h_t,l_t}{\text{max}}\{-p_{h,t}{h}_t+l_t+E_t\left(M_{t+1}\underset{R_{h,t+1}^{*}}{\overset{\infty }{\int }}(R_{h,t+1}{p}_{h,t}{h}_t-R_{l,t}{l}_t)dG(R_{h,t+1},{\sigma }_{h,t+1})\right)(1-{\tau }_H)\}$

Households default if the value of their assets falls below the value of its liabilities, or the return on the house falls below a certain default threshold $R_{h,t+1}<R_{h,t+1}^{*}$:

$R_{h,t+1}^{*}{p}_{h,t}{h}_t-R_{l,t}{l}_t=0$

Households borrow from banks that charge a spread on mortgage loans above the deposits rate to account for credit losses on the mortgage loan portfolio. Banks receive interest and principal on loans from households that serve their debt and take the house of defaulting households into possession, making a loss on defaulting loans. Banks set the lending rate to compensate for expected losses, such that the expected return on the mortgage loan portfolio is at least as high as the cost of funding, e.g., the deposit rate R_d,t:

$E_t\left(M_{t+1}\left({\displaystyle \underset{R_{h,t+1}^{*}}{\overset{\infty }{\int }}{R}_{l,t}{l}_t+\underset{0}{\overset{R_{h,t+1}^{*}}{\int }}{R}_{h,t+1}{p}_{h,t}{h}_t\left(1-{\mu }_H\right)}\right)dG\left(R_{h,t+1},{\sigma }_{h,t+1}\right)-R_{l,t}{l}_t\right)\ge 0$

The model demonstrates that higher tax-deductibility encourages households to increase indebtedness, e.g., to seek a higher LTV ratio. According to Dutch tax laws, mortgage payments are tax-deductible (τ_h >0). In addition, banks suffer a loss (μ_h >0) upon default. Consequently, if households could choose their level of indebtedness freely (e.g., the LTV-ratio), they would increase their indebtedness until the expected cost of default offsets the expected value of tax-deductions. The higher the tax rate, the higher households’ indebtedness.

Simulations show that higher risk associated to housing leads to declining output, employment and consumption; however, the decline is small if the LTV ratio is 80 percent. Staff simulated how an increase in the variance of housing returns that may be associated to increasing uncertainty about households’ incomes and/or house prices, affects macroeconomic conditions. The figure below shows the response of output, employment and consumption to a one-standard deviation increase in the variance σ_h,t of housing returns:

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Output (LTV 80%)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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Employment (LTV 80%)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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Consumption (LTV 80%)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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When housing return values are more dispersed, default becomes more likely, and banks’ expected cost of default increases. Consequently, banks charge higher credit spreads, lending rates increase, and borrowers demand less credit. Residential investment, output and employment declines. As households invest less they have more liquid funds such that consumption only declines gradually with output and employment, owing to habits in consumption.

The higher the LTV ratio, the more vulnerable are households to changes in the housing outlook. The higher the LTV ratio, the earlier a household defaults. Consequently, the larger the LTV-ratio, the more households default in the aggregate. Staff repeated simulations with an LTV-ratio of 90 percent and 100 percent. The response of output, employment and consumption are significantly larger in that case.

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Output

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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Employment

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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Consumption

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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¹ Rental income is equal to a share of household income that is pledged for housing. Source: This box describes staff’s simulations as well as the key part of staff’s model pertaining to housing, borrowing households, and mortgage-providing banks, which extends standard New Keynesian models, such as the one presented in Smets and Wouters (2007).

Results

60. The analysis confirms that a young low income household with high LTV and LTI ratios tends to hit by the negative shock severely (Figure 17). Specifically,

• At an LTV ratio of over 120 percent, there is the largest increase in the share of high risk borrowers, even if the absolute level is fairly low at 2.4 percent given the historically low default rate (the upper left chart in Figure 17). At the LTI over six times, the share increases by 60 basis points from 3.7 percent to 4.3 percent (the upper right chart in Figure 17);

• When variation in the age of the head of a household is investigated, forward-looking vulnerabilities appear to decrease with age (the bottom left chart in Figure 17). Those under age 35 and age 35–45 are more likely to face an income shock, with those over 65 years of age being nearly unaffected. It must be noted that the forward-looking vulnerabilities are unconditional average estimates across the age groups. The current LTV distribution, which reflects a combination of many elements (originating LTV and DSTI, the housing cycle since origination, and location), may be a factor behind this pattern. Based on the DNB loan-level data at end-2015, the share of young borrowers (under 30 years old) with originating LTV and LTI ratios above 100 percent and 5 times was 54 percent and 64 percent, respectively, much higher than the analogous figures in the age over 70 group (4 percent and 24 percent). This largely reflects the higher originating LTV ratios that are prevalent among younger borrowers who bought their houses during the pre-crisis boom time;

• As expected, households with low income will suffer more under the adverse scenario than those with high income (the bottom right chart in Figure 17). Those in the other spectrum (under 20 percentile) would suffer the most with the share of high risk households increasing by about 60 basis points, whereas those in the 80–100 percentile income group are nearly unaffected by the shock due to the ample liquidity buffer; and

• Lastly, borrowers with mortgages outside Amsterdam are found to be more vulnerable than those with loans in Amsterdam under the adverse scenario. On average, borrowers in Amsterdam earn higher income, which functions as buffers against shocks.

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Netherlands: Share of High Risk Households against Adverse Shocks

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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61. Simulations based on the general equilibrium model suggest that the higher the LTV ratio, the more vulnerable are households to adverse changes in house prices and incomes. In a nutshell, the simulations showed that the higher the tax rate and mortgage payment deductibility, the higher is household indebtedness.

C. Conclusion and Policy Implications

62. Segments of households that are particularly susceptible to economic shocks have been identified using aggregate and loan-level data. The analysis of household debt dynamics and the comparison of the simulated shares of high risk households across different groups reveals that borrowers with high LTV and LTI ratios are relatively more vulnerable to adverse shocks.

63. This analysis supports the role of limits to LTV and DSTI ratios; thus, the authorities should further tighten measures aimed at building the resilience of borrowers against future macrofinancial shocks. As shown in IMF (2014), the two tools will complement each other in reducing probabilities of defaults for borrowers and loss given defaults for lenders. LTV limits without a complementary role of DSTI limits could leave borrowers’ capacity to service their mortgages vulnerable to income shocks. DSTI caps without LTV measures could leave lenders highly exposed to severe house price shocks. Combining the Dutch experience during the crisis with results in this section supports the view that it is highly important to build the resilience of households and thus enhance the impacts on lenders’ balance sheets.

64. To minimize the risks associated with a large share of IO loans, the industry and the authorities should act now in a concerted way in order to help borrowers with non-amortizing loans prepare for repayment. It can be done by strengthening the incentive to switch their loan types to annuity or linear mortgages, prepay the loans voluntarily, accumulate financial assets, or use equity release actively (e.g., for older borrowers). The authorities should work with the industry to develop a standardized approach to informing IO mortgage holders of their financial status vis-à-vis these loans and advise them on options for early remedial measures. In the United Kingdom, which faced a similar problem with high IO mortgages, insurers have been sending biennial color- coded letters since 2000 under an instruction from the supervisor. Endowment policyholders receive a red “traffic light” letter if the endowment is not expected to repay their mortgage, amber if it might, and green if it is likely, given prescribed annual growth rates. For example, an endowment in the red category would need to return above 8 percent per annum to meet the principal payment. The letters set out various options to policy holders and encourages them to increase savings or switch to a repayment mortgage at an early juncture.

A. Stylized Facts on Overall Balance Sheet Developments

68. Corporate debt has stabilized at elevated levels in the Dutch corporate sector (Figure 18). After steadily decreasing against the backdrop of strong economic growth during the 2000s, overall gross corporate debt spiked from about 112 percent in 2008 to 127 percent of GDP in 2011, about 20 percent above the EU average.³¹ Since then, total debt has stabilized at this level even as the recovery was gaining momentum. After sharply increasing from 26 percent in 2010 to 34.5 percent in 2014, the share of short-term debt has dropped to 30 percent recently.

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Netherlands: Balance Sheet Developments in the NFC

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

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69. However, the equity position of Dutch firms has markedly improved, improving leverage ratios. The share of equity in total firms’ liabilities has been on an upward trend after bottoming up from the trough of 42 percent in 2008 to reach 50 percent in 2015, above pre-crisis levels. As a result, the average debt-to-equity ratio of the corporate sector as a whole has fallen below 100 percent since 2011. Overall, Dutch firms appear to experience moderate leverage ratios compared to EU country peers.

70. The strengthening of corporate balance sheets has been underpinned by sustained profitability. Throughout the crisis years, Dutch firms have been able to maintain gross profit rates of about 40 percent, above the EU average, while also gaining market shares in Europe. Since 2012, the change in net value added has accelerated to reach about 3 percent per year. These developments have helped Dutch firms preserve their debt servicing capacity in line with EU peers. The liquidity position of the overall corporate sector has been maintained, with the share of currency and deposits hovering over 15 percent of total financial assets.

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Gross operating surplus to gross value added

(Percent)

Citation: IMF Staff Country Reports 2017, 095; 10.5089/9781475593990.002.A001

Source: Eurostat.

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B. Insights on Dutch Firms’ Financial Vulnerabilities using Micro-Level Data

Profitability, liquidity and leverage

71. The analysis relies on disaggregated balance sheet information on an average of 16,000 Dutch firms over the period 2008–2015. The ORBIS database, maintained by Bureau Van Dijk, comprises detailed financial information on each firm, also allowing for a breakdown by size, economic sector (using the NACE Rev. 2 decomposition) and shareholding structure (distinguishing between domestic and foreign ultimate owners). For the Netherlands, the sample obtained after retaining enterprises with sufficient records on operating turnover, liabilities and payroll mostly comprises domestic firms, 97 percent of which employ less than 50 employees or generate a turnover below €500,000 per year. In terms of sectoral decomposition, two-thirds of domestic firms provide financial services, about 20 percent other types of services, while 7 percent operate in the manufacturing and trade sectors.

Average Sample Coverage by Firm Size

Note: Small (1–49 employees), medium (50–249 employees), large (>249 employees).

^1/The sum of small, medium, and large firms tend to exceed their reported number due to some firms switching categories over the years.

Average Sample Coverage by Firm Size

Firm Size	Number of observations	Number of firms 1/
Domestic	74,004	16,237
Small	67,789	15,722
Medium	4,226	1,122
Large	1,990	535
Foreign	4,829	978
Small	2,951	801
Medium	957	241
Large	921	231
Total	78,833	17,215

Note: Small (1–49 employees), medium (50–249 employees), large (>249 employees).

^1/The sum of small, medium, and large firms tend to exceed their reported number due to some firms switching categories over the years.

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Average Sample Coverage by Firm Size

Firm Size	Number of observations	Number of firms 1/
Domestic	74,004	16,237
Small	67,789	15,722
Medium	4,226	1,122
Large	1,990	535
Foreign	4,829	978
Small	2,951	801
Medium	957	241
Large	921	231
Total	78,833	17,215

Note: Small (1–49 employees), medium (50–249 employees), large (>249 employees).

^1/The sum of small, medium, and large firms tend to exceed their reported number due to some firms switching categories over the years.

72. While comprehensive, the ORBIS database is not exempt of limitations that should be borne in mind when interpreting results. First, the number and characteristics of firms vary over time. Further to limiting intertemporal analysis, this likely biases results towards overstating both profitability indicators (as the panel only includes surviving firms, whereas bankrupt ones get dropped) and indebtedness ratios (as these may be boosted by the inclusion of newly-created firms that have not broken even yet, especially among SMEs). Second, the number of observations drops markedly for year 2015 due to reporting lags, undermining the accuracy of forward-looking analyses, especially in a fast-changing environment. Finally, data restrictions emerge as the number of observations decreases when indicators are computed by firm categories. These caveats plead for focusing the interpretation of results on variations over time rather than absolute levels.

Sample Coverage by Firm Sectors

(percent)

Source: ORBIS.

Sample Coverage by Firm Sectors

(percent)

	Share of observations			Share of firms
Sector	Domestic	Foreign	Total	Domestic	Foreign	Total
Agriculture	0.8	1.7	0.9	0.6	1.3	0.6
Manufacturing	4.1	16.5	5.0	2.9	11.5	3.4
Trade	7.1	25.8	8.5	4.5	16.8	5.2
Information	2.4	5.4	2.6	1.4	3.5	1.6
Finance	56.6	33.4	55.0	66.5	51.3	65.6
Housing	3.7	1.3	3.5	3.2	1.7	3.1
Services	22.3	14.6	21.7	19.0	13.0	18.6
Other	3.0	1.3	2.9	1.9	0.9	1.9
Total	100	100	100	100	100	100

Source: ORBIS.

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Sample Coverage by Firm Sectors

(percent)

	Share of observations			Share of firms
Sector	Domestic	Foreign	Total	Domestic	Foreign	Total
Agriculture	0.8	1.7	0.9	0.6	1.3	0.6
Manufacturing	4.1	16.5	5.0	2.9	11.5	3.4
Trade	7.1	25.8	8.5	4.5	16.8	5.2
Information	2.4	5.4	2.6	1.4	3.5	1.6
Finance	56.6	33.4	55.0	66.5	51.3	65.6
Housing	3.7	1.3	3.5	3.2	1.7	3.1
Services	22.3	14.6	21.7	19.0	13.0	18.6
Other	3.0	1.3	2.9	1.9	0.9	1.9
Total	100	100	100	100	100	100

Source: ORBIS.

73. Dutch firms appear profitable, liquid and moderately leveraged in most sectors (Table 7). Disaggregated information reveals that return on equity (ROE) ratios have rebounded across all firm size categories after the 2011–2012 slump, with profit especially high for small and medium-sized domestic firms. However, relative profitability has been lagging behind in the housing industry and in services, and undergone a downward trend in the manufacturing and trade sectors, possibly on account of increased competition. Meanwhile, liquidity buffers have been continuously maintained, especially for small firms and in the finance and services sectors. Also consistent with macro-level data, debt-to-equity ratios have remained stable for all firm sizes and most sectors, although recent developments point to increasing leverage in the manufacturing and trade sectors. By contrast, the housing industry has undergone some continuous deleveraging, with debt-to-equity ratios brought down below 1 along the recovery that started in 2013.

Table 7.

Netherlands: NFC Sector Balance Sheet and Profit and Loss Developments, 2009–15

^1/(Current Assets - Stocks) / Current Liabilities

^1/(Current Assets - Stocks) / Current Liabilities

Sources: ORBIS; and IMF staff calculations.

Table 7.

Netherlands: NFC Sector Balance Sheet and Profit and Loss Developments, 2009–15

Return on Equity (ROE) of Non-Financial Corporations by Firm Size
(median, percent)
		2009	2011	2013	2015
Domestic firms		13.4	12.9	11.0	16.4
	Small	11.8	11.7	10.8	16.5
	Medium	14.5	13.6	10.7	17.9
	Large	13.4	13.7	12.2	14.4
Foreign firms		9.2	11.4	9.0	11.7
	Small	5.0	6.9	4.2	7.3
	Medium	12.4	14.7	11.7	14.3
	Large	12.3	13.2	11.4	14.3
Liquidity Ratio of Non-Financial Corporations by Firm Size 1/
(median, times)
		2009	2011	2013	2015
Domestic firms		2.1	2.3	2.4	2.0
	Small	2.5	2.6	2.8	2.6
	Medium	1.0	1.0	1.0	1.1
	Large	1.0	1.0	1.0	1.0
Foreign firms		1.2	1.2	1.2	1.2
	Small	1.2	1.3	1.3	1.4
	Medium	1.2	1.1	1.2	1.2
	Large	1.1	1.1	1.1	1.1
Debt-to-Equity Ratio of Non-Financial Corporations by Firm Size
(median, times)
		2009	2011	2013	2015
Domestic firms		0.6	0.6	0.6	0.5
	Small	0.7	0.8	0.7	0.7
	Medium	0.6	0.6	0.5	0.4
	Large	0.6	0.5	0.5	0.5
Foreign firms		0.4	0.4	0.4	0.5
	Small	0.4	0.4	0.5	0.5
	Medium	0.3	0.4	0.4	0.4
	Large	0.5	0.4	0.5	0.5
Return on Equity (ROE) of Domestic Non-Financial Corporations by Firm Sector
(median, percent)
		2009	2011	2013	2015
Total		12.6	12.6	10.6	15.3
	Agriculture	11.5	13.8	9.7	15.8
	Manufacturing	17.3	17.8	14.4	14.3
	Trade	18.2	19.9	14.9	17.3
	Information	24.7	22.5	15.4	17.3
	Finance	13.1	11.7	10.9	18.5
	Housing	7.0	6.2	4.8	9.0
	Services	16.2	14.4	12.8	18.8
	Other	12.6	12.5	7.6	9.1
Liquidity Ratio of Domestic Non-Financial Corporations by Firm Sector 1/
(median, times)
		2009	2011	2013	2015
Total		2.1	2.2	2.2	1.9
	Agriculture	1.3	1.4	1.2	1.2
	Manufacturing	1.2	1.2	1.2	1.2
	Trade	1.3	1.3	1.3	1.2
	Information	1.8	1.7	1.8	1.4
	Finance	2.4	2.6	2.7	2.6
	Housing	1.5	1.6	1.7	1.5
	Services	2.4	2.5	2.5	2.3
	Other	1.5	1.7	1.8	1.5
Debt-to-Equity Ratio of Domestic Non-Financial Corporations by Firm Sector
(median, times)
		2009	2011	2013	2015
	Total	0.6	0.5	0.5	0.5
	Agriculture	1.1	1.1	1.3	1.2
	Manufacturing	0.5	0.4	0.4	1.2
	Trade	0.3	0.3	0.3	1.2
	Information	0.3	0.3	0.2	0.4
	Finance	0.6	0.6	0.5	0.5
	Housing	1.8	1.9	1.1	0.5
	Services	0.6	0.4	0.6	0.4
	Other	0.6	0.8	0.9	1.1

^1/(Current Assets - Stocks) / Current Liabilities

^1/(Current Assets - Stocks) / Current Liabilities

Sources: ORBIS; and IMF staff calculations.

View Table

Table 7.

Netherlands: NFC Sector Balance Sheet and Profit and Loss Developments, 2009–15

Return on Equity (ROE) of Non-Financial Corporations by Firm Size
(median, percent)
		2009	2011	2013	2015
Domestic firms		13.4	12.9	11.0	16.4
	Small	11.8	11.7	10.8	16.5
	Medium	14.5	13.6	10.7	17.9
	Large	13.4	13.7	12.2	14.4
Foreign firms		9.2	11.4	9.0	11.7
	Small	5.0	6.9	4.2	7.3
	Medium	12.4	14.7	11.7	14.3
	Large	12.3	13.2	11.4	14.3
Liquidity Ratio of Non-Financial Corporations by Firm Size 1/
(median, times)
		2009	2011	2013	2015
Domestic firms		2.1	2.3	2.4	2.0
	Small	2.5	2.6	2.8	2.6
	Medium	1.0	1.0	1.0	1.1
	Large	1.0	1.0	1.0	1.0
Foreign firms		1.2	1.2	1.2	1.2
	Small	1.2	1.3	1.3	1.4
	Medium	1.2	1.1	1.2	1.2
	Large	1.1	1.1	1.1	1.1
Debt-to-Equity Ratio of Non-Financial Corporations by Firm Size
(median, times)
		2009	2011	2013	2015
Domestic firms		0.6	0.6	0.6	0.5
	Small	0.7	0.8	0.7	0.7
	Medium	0.6	0.6	0.5	0.4
	Large	0.6	0.5	0.5	0.5
Foreign firms		0.4	0.4	0.4	0.5
	Small	0.4	0.4	0.5	0.5
	Medium	0.3	0.4	0.4	0.4
	Large	0.5	0.4	0.5	0.5
Return on Equity (ROE) of Domestic Non-Financial Corporations by Firm Sector
(median, percent)
		2009	2011	2013	2015
Total		12.6	12.6	10.6	15.3
	Agriculture	11.5	13.8	9.7	15.8
	Manufacturing	17.3	17.8	14.4	14.3
	Trade	18.2	19.9	14.9	17.3
	Information	24.7	22.5	15.4	17.3
	Finance	13.1	11.7	10.9	18.5
	Housing	7.0	6.2	4.8	9.0
	Services	16.2	14.4	12.8	18.8
	Other	12.6	12.5	7.6	9.1
Liquidity Ratio of Domestic Non-Financial Corporations by Firm Sector 1/
(median, times)
		2009	2011	2013	2015
Total		2.1	2.2	2.2	1.9
	Agriculture	1.3	1.4	1.2	1.2
	Manufacturing	1.2	1.2	1.2	1.2
	Trade	1.3	1.3	1.3	1.2
	Information	1.8	1.7	1.8	1.4
	Finance	2.4	2.6	2.7	2.6
	Housing	1.5	1.6	1.7	1.5
	Services	2.4	2.5	2.5	2.3
	Other	1.5	1.7	1.8	1.5
Debt-to-Equity Ratio of Domestic Non-Financial Corporations by Firm Sector
(median, times)
		2009	2011	2013	2015
	Total	0.6	0.5	0.5	0.5
	Agriculture	1.1	1.1	1.3	1.2
	Manufacturing	0.5	0.4	0.4	1.2
	Trade	0.3	0.3	0.3	1.2
	Information	0.3	0.3	0.2	0.4
	Finance	0.6	0.6	0.5	0.5
	Housing	1.8	1.9	1.1	0.5
	Services	0.6	0.4	0.6	0.4
	Other	0.6	0.8	0.9	1.1

^1/(Current Assets - Stocks) / Current Liabilities

^1/(Current Assets - Stocks) / Current Liabilities

Sources: ORBIS; and IMF staff calculations.

74. Outstanding corporate sector debt is mostly concentrated in large firms and in the manufacturing sector. While 20 percent of total corporate debt was still held by SMEs in the aftermath of the financial crisis, this proportion drastically diminished since then and 92 percent of total debt was concentrated among large firms in 2015. By this time, more than 60 percent of corporate debt had been incurred in the manufacturing sector, followed by 15 percent in the trade sector.

Outstanding Debt of Domestic Non-Financial Corporations by Firm Size

(Percent of total)

Sources: ORBIS and IMF staff calculations.

Outstanding Debt of Domestic Non-Financial Corporations by Firm Size

(Percent of total)

		2009	2011	2013	2015
Total		100.0	100.0	100.0	100.0
	Small	10.3	15.5	6.3	1.4
	Medium	12.2	15.1	15.2	6.4
	Large	77.3	69.5	78.5	92.3

Sources: ORBIS and IMF staff calculations.

View Table

Outstanding Debt of Domestic Non-Financial Corporations by Firm Size

(Percent of total)

		2009	2011	2013	2015
Total		100.0	100.0	100.0	100.0
	Small	10.3	15.5	6.3	1.4
	Medium	12.2	15.1	15.2	6.4
	Large	77.3	69.5	78.5	92.3

Sources: ORBIS and IMF staff calculations.