Kingdom of the Netherlands—Netherlands: Selected Issues

International Monetary Fund. European Dept.

doi:10.5089/9781484357866.002.A002

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Kingdom of the Netherlands—Netherlands: Selected Issues

Author: International Monetary Fund. European Dept.

Publication Date:: 28 May 2018

eISBN:: 9781484357866

Language:: English

Keywords:: ISCR; CR; cost; pay; Netherlands; rent control; valuation gap; rental housing; housing investment; spurring housing demand; Mortgages; Income; Personal income

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Selected Issues

Abstract

Selected Issues

Fundamental Drivers of House Prices in the Netherlands? A Cross-Country Analysis¹

The Netherlands had seen a long housing boom since the early 1990s with house prices rising to high levels, including by comparison to other countries. The boom turned into a bust following the 2007–09 global financial crisis, which left the household sector with excessive debt and a significant share of underwater mortgages. Over the past years, house price growth rebounded strongly in most parts of the country, with price level surpassing pre-crisis highs in the main cities. Given the importance of the housing market to both financial and macroeconomic stability, it is essential for policymakers to monitor the extent to which house prices deviate from economic fundamentals. This paper examines various factors driving the uptrend in house prices, with a particular focus on institutional and structural factors. The extent of a possible valuation gap and the role of structural polies in shaping house price development are gauged empirically in the context of a cross-country panel analysis of long-run fundamental determinants of house prices using data from 20 OECD countries, with policy implications drawn at the end of the paper.

A. Introduction

1. The Netherlands had seen a long housing boom since the early 1990s, which turned into a bust following the 2007–09 global financial crisis (GFC). While real house prices have also been up strongly during early 1990s–2007 in the majority of advanced economies, the Netherlands experienced one of the highest increase among OECD countries, driven in part by easy financial conditions and accompanied by debt accumulation. During 1991–2007, real house prices almost tripled, with average annual nominal house price growth of 8.6 percent (7.1 percent during 2000–07). Real house prices subsequently declined by 25 percent before bottoming out around the end of 2013.

2. Nevertheless, house prices growth recovered strongly in most parts of the country over the past years, with price level surpassing pre-crisis highs in the main cities. Following the trough in 2013, house price inflation reaccelerated sharply in recent years, with house price inflation exceeding income growth by a wide margin. House prices nation-wide rose at an average pace of about 7½ percent y/y in the first ten months of 2017―up from 5 percent y/y in 2016, and the growth is particularly high in major cities (e.g., over 10 percent y/y in Amsterdam and Rotterdam in 2017:Q3). House price level are now over 20 percent higher than the post-crisis low in 2013, with price level surpassing pre-crisis highs in main cities. Transaction volumes have also exceeded the pre-crisis highs.

3. The house prices-to-income ratios are also high by international standards. With house prices rising ahead of income over the most part of the past three decades, the average cost of a home relative to the median household income nationwide has more than doubled since the early 1990s, rising much faster than the OECD average. Currently, the house price-to-income (PTI) ratio stands about 15 percent above its 30-year historical average. In absolute terms, the PTI ratio is also relatively high compared to a wide range of countries, hindering affordability especially in the major cities.

4. The recent boom-bust housing cycle left the household sector with excessive debt and a significant share of underwater mortgages. Households have started to deleverage gradually from record debt levels over the past years however, partly owing to the relaxed tax exemption for gifts used for housing down payments or mortgage repayments.² But household debt as a share of disposable income―standing at 270 percent at end-2016―remains the second highest in the OECD, with household asset holdings are mostly illiquid in the form of pension entitlements and housing. Against the backdrop of rapidly rising house prices in recent years, the share of mortgages in negative equity―which is particularly prevalent among young borrowers―has gradually declined to 14 percent as of 2017:Q2. However, new mortgages with loan-to-value (LTV) ratio over 90 percent kept rising along with higher house prices.

5. For policymakers, it is important to monitor the extent to which house prices deviate from economic fundamentals. If house prices significantly exceed fundamentals, this raises the risk of a house price correction. A large correction in house prices―driven by slower real income growth, a reverse in sentiment, or interest rate hikes―could weaken household balance sheets and depress private demand through wealth effects (Mian et al., 2013). Moreover, this impact tends to be more pronounced for households with high LTV mortgages than for those with low LTV mortgages, as the high LTV group tends to have higher levels of consumption in the very similar Danish housing and household wealth environment (Andersen et al., 2014). While arrears and bank losses related to residential mortgages would remain low―notably due to the full recourse on borrowers and the national mortgage guarantee system, both financial and macroeconomic stability could be undermined if lower consumption impairs business activity and corporate earnings, which would negatively impact output and pushes up unemployment and bank losses associated with enterprise loans. While it is difficult to detect housing `bubbles’ in real time, it is helpful to gauge the degree of overvaluation or undervaluation in the housing market by comparing actual price levels to those that would be justified by fundamental demand, supply, institutional and structural factors.

6. The paper is organized as follows. Section B discusses the driving forces behind the uptrend in house prices, including demand, supply, institutional, and structural factors. Section C presents the cross-country analysis of long-run equilibrium house prices using data from 20 OECD countries. Section D concludes with policy implications.

B. Factors Contributing to the Uptrend in House Prices

Demand Factors

7. Household income and financial net wealth played an important role in shaping house price dynamics. Real personal disposable income (RPDI) grew by 2.0 percent per year on average in the 1990s. This, coupled with a rapid decline in unemployment and rise in female labor participation, have supported strong demand for housing. Following the GFC, the sluggish RPDI growth and sharply rising unemployment have contributed to the housing downturn during 2007—13. In more recent years, the favorable economic and labor market trends, combined with a rapid accumulation of financial net wealth of households, exerted renewed upward pressure on housing demand.

8. Demand has also been fueled by declining interest rates. Mortgage rates have gone down substantially since 2000 to historically low levels in recent years. In addition, housing investment returns have become increasingly attractive after the GFC as long-term bond yields declined along with the slide of policy rates, which stimulated purchases for investment purposes by the wealthier—further driving up the prices.

9. Population trends reinforced the high demand for owner-occupied housing, particularly in large cities. Annual population growth averaged about 0.5 percent from 1990–2016—comparable to the average level in advanced economies. Meanwhile, urbanization— with an average annual rate of 1.1 percent over 2010–15―has been exerting additional pressure on demand for housing in the main urban areas. The housing demand pressure is most pronounced in the four major cities, with an average annual population growth rate of 1.2 percent during 2007–16—substantially outstripping the national average due to a steady inflow of foreign immigrants as well as domestic migration. According to Statistics Netherlands’ projections, the number of households will continue to grow over the next few decades, by some 640,000 to 8.4 million (8 percent) by 2030.

Supply Factors

10. Meanwhile, housing supply in large cities has not kept up with housing demand since the GFC. The issuance of new-building permits has stagnated since the crisis and the supply of housing has been lagging behind the expected growth in households in most provinces (Economic Institute for Construction and Housing, 2016). The situation is most acute in the four major cities, where housing completion fell to a record low in 2014 despite the post-GFC surge in population. While residential investment recently rebounded in response to higher prices, housing completion remained well below the estimated household formation, contributing to fast price increases.

Institutional and Structural Factors

11. The slow supply response to rising demand can amplify price increases. According to staff’s updated estimates using 1989–2016 data based on the same methodology used in the OECD study (Caldera Sanchez et. al., 2011), The Netherlands has the second lowest price responsiveness of housing supply among OECD countries, with the long-run price elasticity of new housing supply estimated at about 0.2 compared to the OECD average of 0.6. The sluggish supply of housing may reflect both natural (i.e. topographical) and man-made constraints (e.g. stringent local regulations on land use and cumbersome building permitting process, including restrictive zoning codes and building aesthetics criteria). In addition, the capacity constraint of construction sector following the onset of the GFC is an important cause of the slow recovery of construction output in response to fast rising house prices. Subject to a given increase in long-run demand, markets with inelastic long-run supply curve cannot build as much new dwellings as can markets with elastic supply, resulting in greater increase in prices (Anundsen et al., 2016).

12. Generous tax incentives for mortgage financing and home ownership have substantially reduced the user cost of housing, contributing to high and rising house prices. Like in many other advanced economies, housing investment receives favorable tax treatment relative to other investment in the Netherlands. Interest on mortgages is fully tax deductible,³ which effectively reduces the debt service costs, thereby incentivizing households to borrow more and purchase more expensive houses. The authorities have started gradually reducing the maximum tax rate that mortgage interest can be deducted against by 0.5 percentage points annually from 52 percent in 2013, to 38 percent in 2041 (50 percent in 2017).⁴ However, the tax relief for housing financing in the Netherlands remains one of the most generous in the OECD, and leads to higher house and land prices.⁵ In addition, the capital gains tax is one of the lightest in the European Union (Hilbers et. al., 2008; ESRB, 2015) and the recurrent tax revenue from immovable properties is low compared with the OECD average.⁶ The favorable tax treatment on housing investment may crowd out capital from more productive uses than housing, resulting in efficiency losses and housing demand distortions by reducing the user cost of owner-occupied housing and encouraging excessive leverage (OECD, 2009; Geng et al., 2016). Moreover, it tends to favor higher-income earners, (e.g., tax savings from mortgage interest deductibility tends to be larger when income are higher). Other things equal, housing demand in markets with more favorable tax treatment on housing would be higher for a given level of income, pushing up house prices relative to markets with lower tax preferences.

13. Structural weaknesses and strict regulation in the rental market intensifies supply-demand imbalances, putting further pressure on the owner-occupied housing market. The size of the Dutch rental market is at about the OECD average, accounting for about 40 percent of the total dwelling stock. However, both private and social rental housing are subject to strict rent regulation—the third most stringent in the OECD—and social rental housing receives large direct/indirect public subsidies. Social rental housing dominates the rental market and is one of the largest in Europe, accounting for 30 percent of the total dwelling stock, compared to 19 percent in France, 15 percent in the UK, and only 5 percent in Germany (BPD, 2016; Whitehead et al., 2016). But there are allocation issues, with some being occupied by households earning too much relative to their rent—estimated at 18 percent in 2015 (Ministry of Economic Affairs, 2016). In addition, while strict rent control allows low-income earners to rent in the regulated market with rent below the market-clearing level, it also creates “locked-in” effects and hinders efficient use of existing housing stock, resulting in long waiting lists. Meanwhile, rent regulation for private rental, combined with the large subsidies for both homeownership and renting in the regulated market have crowded out public and private investment in unregulated rental dwellings.⁷ As a result, the private rental sector has contracted substantially since the 1970s to less than 10 percent of all housing stock despite the slight recovery in recent years. The supply shortage of the unregulated rental housing, especially in large cities, limits the functioning of the housing market, hindering mobility to areas with greatest job availability. This adversely affects the part of the population that is not willing or able to enter the owner-occupied market and that has no access to the social housing market (e.g. young people, singles, and couples without children). This leaves many households with no option but to purchase housing, creating excess demand for owner-occupied housing and debt at high debt levels and possibly amplifying price increases for owner-occupied houses.

C. A Cross-Country Housing Valuation Model

14. To gauge the extent of a possible housing valuation gap, the long-run relationship between real house prices and their potential determinants discussed above is estimated in a cross-country panel model. Following the literature on modelling the housing market,⁸ housing demand (D) can be expressed as a function of the real price level of housing (P) and other factors shifting demand (summarized in X). In the long run, the equilibrium price of housing (P^*) is that at which the demand for housing matches the stock of housing (S):

\begin{matrix} D (X, P^{*}) = S & (1) \end{matrix}

In practice, the actual price will not always be at the long-run equilibrium, such that for each country i, and time t, there is an error term $ϵ_{i t}^{p}$ between the observed price p_it and the long-run equilibrium real house prices $p_{i t}^{*}$ . Assuming that (1) is log-linear, $p_{i}^{*}$ , can be written in the form of an inverted demand function of the housing stock and the long-run demand shifters (discussed below), giving the following formula for p_it:

\begin{matrix} p_{i t} = p_{i t}^{*} + ϵ_{i t}^{p} = f (y_{i t}, m o r r_{i t}^{p o s t - t a x}, w_{i t}, s_{i t}) + e_{i t}^{p} & (2) \end{matrix}

With households maximizing an inter-temporal utility function with non-separability between housing and non-housing consumption (Skaarup and Bodker (2010), the long-run housing price can be derived as a reduced form of its fundamental determinants, which include real per capita household disposable income y_it, the real after-tax interest rate for mortgage borrowing $m o r r_{i t}^{p o s t - t a x}$ , real per capita household net financial wealth w_it, and the housing stock per capita s_it (column (1). A square term of real mortgage rate is also added to capture any non-linear relationship between house price and interest rate following the present value theory (column (2–5)).

In practice, for most countries it is difficult to calculate the effective after-tax interest rate, so we use the updated version of tax relief index from the OECD—which also reflects recent reforms that took place after the original index was created in 2009—to proxy for the generosity of tax incentives for home ownership and mortgage financing. ⁹ Tax relief such as MID is usually capped at a nominal amount¹⁰ (ESRB, 2015) and hence the tax savings tend to be larger when income is higher, which is captured by including an interaction term between tax relief and income in the augmented model presented in column (3). In addition, an interaction term of s_it with the OECD rent control index (also updated to incorporate recent reforms, and rescaled to 0–1) is added to test if rent control hinders the efficient use of existing housing stock (column (4)). Last, to test the differential impact of demand shifters on long-run equilibrium prices resulting from variations in long-run elasticity of housing supply across countries (i.e., the slope of the long-run supply curve), the estimated coefficients of demand factors are allowed to differ across countries through additional interaction terms of the demeaned long-run supply elasticities sr_i with demand variables in the full augmented model presented in column (5). In summary, the long-run relationship between real house prices and their potential determinants discussed above is estimated in a cross-country panel model as follows:

\begin{matrix} p_{i t} = (β_{1} + β_{2} {s r}_{i}) * y_{i t} + (β_{3} + β_{4} {s r}_{i}) * {m o r r}_{i t} + (β_{5} + β_{6} {s r}_{i}) * w_{i t} + (β_{7} + β_{8} {r c}_{i t}) * s_{i t} + β_{9} y_{i t} * {t a x r e l i e f}_{i t} + β_{10} {m o r r}_{i t}^{2} + α_{i} + ϵ_{i t}^{p} & (3) \end{matrix}

All variables are in log terms except for mortgage rates, the housing stock to population ratio, the tax relief and rent control indices, and long-run supply elasticities. Country fixed effects are used in the panel estimation to control for other factors resulting in permanent differences in the level of housing prices across countries, which may include time-invariant unobserved housing market characteristics such as cultural attitudes toward housing, etc. In addition, robust standard errors are clustered at the country level. The estimation sample covers 20 advanced countries in the OECD over the period of 1991:Q3–2016:Q4.¹¹

15. Estimation results confirm that these factors play important roles in shaping long-run house price developments (Table 1). The explanatory variables all have the expected sign and most are statistically significant. On the demand side, a one percent increase in per capita disposable income raises the long-run equilibrium house prices by a cross-country average of 1.5–1.7 percent, confirming that housing is a luxury good. Tax relief on housing also contributes to spurring housing demand and driving up house prices, with a positive income shock translating into a greater price impact in countries having more generous tax relief. Take the Netherlands for example; tax relief results in about 0.5 percent higher house price from a one percent increase in real per capita disposable income (or 2.0–2.2 percent rise rather than 1.5–1.7 percent). Meanwhile, a one percentage point increase in the real mortgage rate reduces real house prices by a cross-country average of about 1.8–2.8 percent. In addition, household net financial wealth has a small positive impact on house prices. Depending on the long-run supply elasticities, the same increase in demand results in different impact on house prices across countries, with an amplified impact seen in more inelastic markets (e.g., the Netherlands) and a more mitigated impact seen in more elastic markets. On the supply side, one percent increase in housing stock relative to the population is associated with a reduction in house prices by about 1.3 percent, with this dampening effect of supply increases partially offset in markets with rent control. In the case of the Netherlands, rent control leads to 0.3 percentage points less decrease in real house prices for one percent increase in housing stock per capita (in other words, 1.0 percent fall rather than 1.3 percent).

Table 1.

A Cross-Country Panel Model: Long-Run Determinants of Real House Prices

Note: Dependent is the log of real house prices. Significance at 1, 5, and 10 percent levels indicated by ***, **, and *, respectively. Robust standard errors clustered at the country level.

Table 1.

A Cross-Country Panel Model: Long-Run Determinants of Real House Prices

Variables	(1)	(2)	(3)	(4)	(5)
y , log	1.652 [0.034]***	1.638 [0.034]***	1.538 [0.036]***	1.544 [0.036]***	1.533 [0.037]***
morr , percent	−1.922 [0.214]***	−2.759 [0.431]***	−2.234 [0.431]***	−2.116 [0.432]***	−1.776 [0.426]***
morr^2, percent		0.079 [0.035]**	0.066 [0.034]**	0.051 [0.033]	0.058 [0.032]*
w , log	0.031 [0.008]***	0.033 [0.009]***	0.023 [0.009]**	0.020 [0.009]**	0.056 [0.010]**
s, percent	−1.070 [0.062]***	−1.080 [0.062]***	−0.943 [0.063]***	−1.267 [0.103]***	−1.322 [0.102]***
tr y* (log)			0.362 [0.048]***	0.351 [0.047]***	0.487 [0.046]***
rc s* (percent)				1.156 [0.294]***	0.436 [0.230]*
sr y* (log)					−0.007 [0.141]
sr morr*					1.133 [0.154]***
sr w* (log)					−0.060 [0.033]*
Obervations	2042	2042	2042	2042	2042
Adj. R-squared	0.853	0.853	0.856	0.857	0.867
Number of countries	20	20	20	20	20
Country fixed-effect	Y	Y	Y	Y	Y
Corrected for heteroskedasticity	Y	Y	Y	Y	Y
Panel Cointegration Tests for Model (5) Kao (Engle-Granger based)		t-Statistics	−3.806	Prob.	0.0001
Panel Unit Root Test on Residuals of Model (5) Levin, Lin & chu t		Statistics	−2.705	Prob.	0.003

Note: Dependent is the log of real house prices. Significance at 1, 5, and 10 percent levels indicated by ***, **, and *, respectively. Robust standard errors clustered at the country level.

Table 1.

A Cross-Country Panel Model: Long-Run Determinants of Real House Prices

Variables	(1)	(2)	(3)	(4)	(5)
y , log	1.652 [0.034]***	1.638 [0.034]***	1.538 [0.036]***	1.544 [0.036]***	1.533 [0.037]***
morr , percent	−1.922 [0.214]***	−2.759 [0.431]***	−2.234 [0.431]***	−2.116 [0.432]***	−1.776 [0.426]***
morr^2, percent		0.079 [0.035]**	0.066 [0.034]**	0.051 [0.033]	0.058 [0.032]*
w , log	0.031 [0.008]***	0.033 [0.009]***	0.023 [0.009]**	0.020 [0.009]**	0.056 [0.010]**
s, percent	−1.070 [0.062]***	−1.080 [0.062]***	−0.943 [0.063]***	−1.267 [0.103]***	−1.322 [0.102]***
tr y* (log)			0.362 [0.048]***	0.351 [0.047]***	0.487 [0.046]***
rc s* (percent)				1.156 [0.294]***	0.436 [0.230]*
sr y* (log)					−0.007 [0.141]
sr morr*					1.133 [0.154]***
sr w* (log)					−0.060 [0.033]*
Obervations	2042	2042	2042	2042	2042
Adj. R-squared	0.853	0.853	0.856	0.857	0.867
Number of countries	20	20	20	20	20
Country fixed-effect	Y	Y	Y	Y	Y
Corrected for heteroskedasticity	Y	Y	Y	Y	Y
Panel Cointegration Tests for Model (5) Kao (Engle-Granger based)		t-Statistics	−3.806	Prob.	0.0001
Panel Unit Root Test on Residuals of Model (5) Levin, Lin & chu t		Statistics	−2.705	Prob.	0.003

Note: Dependent is the log of real house prices. Significance at 1, 5, and 10 percent levels indicated by ***, **, and *, respectively. Robust standard errors clustered at the country level.

16. Estimation results suggest that current house prices in the Netherlands are modestly overvalued. The model is used to gauge the extent of a possible housing valuation gap. The advantage of our housing valuation model over standard metrics such as the PTI ratio is that it considers a comprehensive list of determinants of long-run equilibrium prices including institutional and structural factors—instead of only one ‘fundamental’ variable, e.g., income―in assessing the degree of over- or undervaluation. Based on the model estimates in column (5), the degree of price deviation from long-run values implied by fundamentals is measured by:

ϵ_{i t}^{p} = P_{i t} - P_{i t}^{*}

The error term is confirmed to be stationary, i.e., equation (5) is a cointegrating relationship. Based on the metric, the average house prices in the Netherlands in 2016:Q4 are found to be about 5 percent above the estimated equilibrium value as implied by fundamentals—much smaller the estimated deviation during the 2007 peak and below the average of the 20 OECD countries covered in the analysis (also see Annex I). However, real mortgage rates are below their 5 percent average since 1990 by about 2 percent (or below their 3½ percent average since 2000 by about ½ percent) and are likely to unwind (at least partially) over time, and this would lower housing prices by up to about 5 percent (or about 2 percent) in equilibrium, implying that house prices could be up to 10 percent (or up to 7 percent) overvalued.

17. The implied valuations from this exercise should be interpreted with caution. The results can only be indicative of potential valuation gap in the sense that the estimated equilibrium price levels are subject to uncertainties. For example, developments in the housing market are complicated by purchases for investment purposes by high-income households—as housing investment returns exceed long-term bond yields. Also, as mentioned above, while low interest rates have driven up equilibrium house prices which mitigates overvaluation concerns, they do not rule out that demand is excessive, nor that it could fall sharply as interest rates normalize. These could complicate the housing valuation analysis and potentially bias up the estimates of long-run equilibrium prices.

D. Conclusions and Policy Implications

18. In sum, apart from the conventional fundamental demand and supply factors, the interaction of various institutional and structural factors seems to have contributed significantly to high and rapidly rising house prices in the Netherlands. The large direct and indirect subsidies for social housing and the highly regulated rental market is likely skewing housing needs and use in the Netherlands. The coexistence of a well-developed mortgage market and large tax preferences for owner-occupied housing and mortgage debt seems to have further fueled the surge in demand for homeownership and household debt. In addition, the sluggish response of housing supply exacerbated the situation by failing to cushion the impact of demand pressures.

19. Overvalued house prices and elevated household debt are a source of vulnerability in the Netherlands in view of the importance of the housing market to both financial and macroeconomic stability. The recent house-price cycle left the Netherlands with elevated level of household debt and a significant share of underwater mortgages. While households have started to deleverage gradually from the record debt levels over the past years, a large correction of house prices, driven by slower real income growth, a reverse in sentiment, or interest rate hikes could weaken household balance sheets further and depress private demand, and in turn adversely affect corporate and bank earnings.

20. The authorities have been vigilant about the risks and have introduced a series of measures to target the owner-occupied housing sector and strengthen the resilience of banks and households, including additional bank capital buffer requirements in line with Basel III/CRD IV, an introduction of LTV and debt service-to-income (DSTI) caps since 2013, a gradual reduction of LTV limit for mortgages to 100 percent by 2018, a tax exemption for gifts used for housing down payments or mortgage repayments, allowing MID only for new fully amortizing loans, and a gradual reduction of the maximum tax rate allowed for MID from 52 percent in 2013 to 38 percent in 2042 in steps of ½ percent per year.

21. Nevertheless, further and comprehensive reforms are needed to address the risks from the housing market and enhance the macro-financial resilience of the economy. A stable housing market (without pronounced boom-bust cycles) would contribute to smoother economic development. It is critical that policies work together to fundamentally address housing market imbalances that pose risks to stability and growth and hinder labor mobility:

Reducing the generous tax preferences for owner-occupied housing and mortgage debt to help prevent demand distortions and excessive leverage: In particular, as discussed above, the Netherlands has the most generous tax relief on the debt financing cost of owner-occupied housing in the OECD. It is hence important to accelerate the phasing down of MID to ultimately bring it to a neutral level relative to the taxation of other assets. Moreover, given the current low interest rate environment which limits the effective benefit of MID, now seems to be the ideal time to implement the reduction. In this regard, it is welcome that the recently released coalition agreement proposes a much more rapid phase-out in steps of 3 percentage points annually until the basic rate of 37 percent is reached;
Improving housing supply responsiveness in large cities to help dampen housing cycles, by streamlining and relaxing stringent building aesthetics criteria, restrictive zoning plans, and cumbersome building permission processes. Addressing impediments to urban redevelopment and improving public transportation would help relieve demand pressures in major centers;
Phasing out rent control and reforming social housing to enhance flexibility. Rents on regulated rental housing should be gradually raised to be aligned with market rates while vulnerable households could be protected through targeted housing allowances, which would promote efficient use of existing housing stock, a larger and more robust private rental market, and mobility across housing types and locations;
Tightening the macroprudential measures to further contain household financial vulnerabilities: This includes gradually lowering the maximum limit on LTV ratios by at least 1 percentage point per year to no more than 90 percent by 2028 (as recommended by the Financial Stability Committee (FSC)) and to 80 percent thereafter and introducing prudential ceilings on DSTI caps by income category that could not be relaxed during periods of strong growth; and
Considering temporarily allowing for a partial use of pension savings for housing purchases to ease liquidity constraints for first-time home buyers, e.g. by meeting part of the down payment. This would reduce debt burdens while easing total savings needs of home purchasers. In the US, money accumulated in 401K plans can be used for first-time home purchases; Switzerland, Canada, and Singapore have adopted similar measures.

Annex I. Actual and Estimated Long-run Equilibrium House Prices in Selected OECD

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Prepared by Nan Geng (EUR).

A once in a life-time gift tax exemption of up to EUR 100,000 for a house purchase was in effect from October 2013 until the end of 2014 and has been reintroduced and made permanent as of January 1, 2017, for people that are between the age of 18 and 40.

100 percent deduction for all pre-2013 loans and for post-2013 fully amortizing loans (within 30 years). While the Netherlands is one of the few countries that tax imputed rent from home ownership, the tax level is low and much smaller than the mortgage interest deductibility (MID). A fully neutral taxation of owner-occupied housing would require full taxation of imputed rents and capital gains on housing, combined with mortgage interest deductibility.

The recently released coalition agreement proposes a much more rapid phase-out in steps of 3 percentage points annually until the basic rate of 37 percent is reached in 2023, but this is still subject to approval by the parliament.

Capozza et al. (1996) and Harris (2010) showed that tax-favoring of housing tends to encourage excessive leverage and be capitalized into house prices, without necessarily expanding housing opportunities for households.

The recurrent property tax in the Netherlands is levied at the local level and varies by region, ranging from 0.1–0.3 percent of property value.

The generous tax subsidy for owner-occupied housing and the resulting high land prices provides municipalities strong incentives for developing owner-occupied instead of private rental housing.

See Meen (2001), Aoki et al. (2002), Poterba (1984), and etc.

This OECD index takes into account if interest payments on mortgage debt are deductible from taxable income, if there are any limits on the allowed period of deduction of the deductible amount, if tax credits for loans are available, and if imputed rent from home ownership is taxed.

The Netherlands, Sweden, and Norway are the few exceptions for which MID is unbounded.

The 20 countries included in the sample are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Israel, Italy, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom, and United States.

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Kingdom of the Netherlands - Netherlands: Selected Issues

Author: International Monetary Fund. European Dept.

Volume/Issue:: Volume 2018: Issue 131

Publisher:: International Monetary Fund

ISBN:: 9781484357866

ISSN:: 1934-7685

Pages:: 52

DOI:: https://doi.org/10.5089/9781484357866.002

Keywords
Fundamental Drivers of House Prices in the Netherlands? A Cross-Country Analysis1
References

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Real House Price Index

(1990 = 100)

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Nominal House Prices

(index, 2010=100)

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Price-to-income Ratio

(1980Q1=100)

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House Price-to-Income Ratio, 2016Q4

(Ratio)

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Household Debt

(Percent of household net disposable income, in 2016 or latest available)

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Loan-to-Value Ratios of New Mortgages

(Average percentage share over 4 quarters)

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House Prices and Household Personal Disposable Income

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House Prices and Unemployment Rate

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House Prices and Net Financial Wealth of Households

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Interest Rates

(Percent)

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Share of Private Investors in Owner-Occupied Housing Market

(Percent of total number of transactions)

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Population and Population Growth

(Million persons and percent)

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Urbanization

(Percent)

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Housing Supply and Demand Indicators

(Thousands)

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Long-run Supply Responsiveness

(Long-run price elasticity of new housing supply)

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Tax Relief for Housing Finance, 2016

(Index increasing in degree of tax relief)

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Recurrent Taxes on Residential Property

(Percent of GDP; in 2015)

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Rent Control ^1/

(Scale 0–6, increasing in degree of control)

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Housing Market Structure, 2015

(Percent of total stock)

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Netherlands: Housing Market Valuation

(Index, percent)

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Housing Valuation Gap, Augmented Model (5)

(Percent, as of 2016Q4 or latest available)

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Table 1.

A Cross-Country Panel Model: Long-Run Determinants of Real House Prices

Variables	(1)	(2)	(3)	(4)	(5)
y , log	1.652 [0.034]***	1.638 [0.034]***	1.538 [0.036]***	1.544 [0.036]***	1.533 [0.037]***
morr , percent	−1.922 [0.214]***	−2.759 [0.431]***	−2.234 [0.431]***	−2.116 [0.432]***	−1.776 [0.426]***
morr^2, percent		0.079 [0.035]**	0.066 [0.034]**	0.051 [0.033]	0.058 [0.032]*
w , log	0.031 [0.008]***	0.033 [0.009]***	0.023 [0.009]**	0.020 [0.009]**	0.056 [0.010]**
s, percent	−1.070 [0.062]***	−1.080 [0.062]***	−0.943 [0.063]***	−1.267 [0.103]***	−1.322 [0.102]***
tr y* (log)			0.362 [0.048]***	0.351 [0.047]***	0.487 [0.046]***
rc s* (percent)				1.156 [0.294]***	0.436 [0.230]*
sr y* (log)					−0.007 [0.141]
sr morr*					1.133 [0.154]***
sr w* (log)					−0.060 [0.033]*
Obervations	2042	2042	2042	2042	2042
Adj. R-squared	0.853	0.853	0.856	0.857	0.867
Number of countries	20	20	20	20	20
Country fixed-effect	Y	Y	Y	Y	Y
Corrected for heteroskedasticity	Y	Y	Y	Y	Y
Panel Cointegration Tests for Model (5) Kao (Engle-Granger based)		t-Statistics	−3.806	Prob.	0.0001
Panel Unit Root Test on Residuals of Model (5) Levin, Lin & chu t		Statistics	−2.705	Prob.	0.003

Note: Dependent is the log of real house prices. Significance at 1, 5, and 10 percent levels indicated by ***, **, and *, respectively. Robust standard errors clustered at the country level.

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Abstract

Fundamental Drivers of House Prices in the Netherlands? A Cross-Country Analysis1

A. Introduction

B. Factors Contributing to the Uptrend in House Prices

Demand Factors

Supply Factors

Institutional and Structural Factors

C. A Cross-Country Housing Valuation Model

D. Conclusions and Policy Implications

Annex I. Actual and Estimated Long-run Equilibrium House Prices in Selected OECD

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Fundamental Drivers of House Prices in the Netherlands? A Cross-Country Analysis¹