Kingdom of the Netherlands—Netherlands: Selected Issues

This Selected Issues paper for the Netherlands highlights that Dutch cost competitiveness deteriorated significantly in the years prior to 2004. The relatively poor economic performance during the new millennium contributed to these concerns generally, as did restrained export performance more specifically. The deceleration of private consumption growth started earlier than the deceleration of per capita real disposable income growth, and occurred at a time when the unemployment rate has been at a historic low and interest rates had declined.

Abstract

This Selected Issues paper for the Netherlands highlights that Dutch cost competitiveness deteriorated significantly in the years prior to 2004. The relatively poor economic performance during the new millennium contributed to these concerns generally, as did restrained export performance more specifically. The deceleration of private consumption growth started earlier than the deceleration of per capita real disposable income growth, and occurred at a time when the unemployment rate has been at a historic low and interest rates had declined.

III. House Prices in the Netherlands22

A. Introduction

27. Between 1995 and 2001, average Dutch house prices rose by almost 80 percent in real terms, raising concerns about the sustainability of the resulting price levels. Various observers have warned that house prices appear to be out of line with economic fundamentals, and some—notably ING, a large commercial bank and mortgage supplier—have suggested that a substantial correction of prices could be imminent.

28. There is some historical justification for such concerns. In the late 1970s, the Netherlands also experienced an episode of rapidly rising house prices, which was then followed by a painful complete reversal of the price gains in the early 1980s.

29. Some indicators seem to support the claims that house prices have been divorced from market fundamentals. A basic—and rather crude—measure of housing affordability that is used by analysts is the ratio of house prices to the disposable income of households. Since 1995, this ratio has risen by more than 50 percent in the Netherlands, suggesting a sharp decline in the affordability of housing (Figure 1). This development certainly appears reminiscent of the housing boom of the late 1970s. And, at its current level, the ratio lies some 40 percent above its 30-year average.

Figure 1.
Figure 1.

Netherlands: Ratio of House Prices over Disposable Income 1970:Q1-2004:Q2

(1995:Q1=100)

Citation: IMF Staff Country Reports 2005, 225; 10.5089/9781451829518.002.A003

Sources: De Nederlandsche Bank; Kadaster; and IMF staff calculations.

30. Another simple measure of the relative valuation of housing is the ratio of house prices to the going rent. This “price-rent ratio” is broadly comparable to the “price-earnings ratio” that is used to assess the valuation of stocks; it measures the return on investment in housing and says something about the attractiveness of residential property as an asset class. On this measure, concerns about overvaluation appear even more well-founded than those arising from the affordability ratio. The price-rent ratio has risen by more than 75 percent since 1995, and its current level is more than 50 percent above the average of the past 30 years (Figure 2).

Figure 2.
Figure 2.

Netherlands: Price-Rent Ratio, 1970:Q1-2004:Q2

(1995:Q1=100)

Citation: IMF Staff Country Reports 2005, 225; 10.5089/9781451829518.002.A003

Sources: Statistics Netherlands; Kadaster; and IMF staff calculations.

31. Housing supply conditions in the Netherlands may make the market prone to an overshooting of prices. The Netherlands is one of the most densely populated countries in the world (with 478 people per square kilometer), undeveloped land is in short supply, and zoning laws tend to be very strict. Consequently, the supply elasticity of housing is usually regarded as very low (Swank, Kakes, and Tieman, 2002, provide empirical evidence). Studies for regional markets in the United States have found that low supply elasticities are associated with a tendency for prices to overshoot (see below). However, some observers in the Netherlands see the structural supply shortage as a factor that supports and justifies the current high house prices (e.g., Rabobank, 2003).

32. Thus far, in contrast to the experience of the early 1980s, the Dutch housing market seems to be making a soft landing. After a period of exceptionally rapid growth, economic conditions started to deteriorate in 2001 on the heels of the global collapse of equity prices and the subsequent slowdown of the world economy. The Dutch downturn was compounded by a unusually sharp contraction of domestic consumption, and in 2003 average annual growth turned negative for the first time in 20 years. While house price increases have moderated substantially during this period, average prices have so far not shown any decline except at the very high end of the market.

33. Against this background, the question remains of whether the remarkable rise in house prices has been the result of a change in fundamental conditions, the reflection of a catch-up process—that is, an adjustment toward a long-run equilibrium level—or an unsustainable upward deviation from their equilibrium level, as is suggested by the affordability and price-rent ratios. In the latter case, a correction of house prices would be expected at some point.

34. This paper examines the extent to which the Dutch housing boom can be explained by fundamentals. Section B briefly discusses the relevant literature in the field of housing markets. Then, in Section C, a basic error-correction model of house prices is developed. Sections D and E discuss the data series and various econometric issues. Section F presents the empirical results from the analysis, followed by some concluding remarks in Section G.

B. Brief Review of the Literature

35. An extensive empirical literature examines house price movements in terms of their fundamentals. However, there is no clear consensus on the theoretical framework that should underpin such exercises.

36. One strand of the literature views housing primarily as a durable consumption good and examines the relationship of house prices to the real economy and demand factors such as general economic conditions and housing affordability. For instance, Hendry (1984) models the market for “second-hand” housing in the United Kingdom using real disposable income, interest rates, retail prices, and mortgage credit as explanatory variables. Also included is a cubic lagged house price term in the short-run dynamics, to pick up bubble behavior; the resulting model performs well in tracking the volatile British housing market. Similarly, Vladkova Hollar (2003) estimates an equation for house prices, again in the United Kingdom, using an error-correction model with disposable income and the real interest rate as the only other variables. Both variables appear in the long-run relationship as well as in the short-term dynamics of the model (together with the lagged house prices themselves), and provide it with considerable explanatory power.

37. Other research focuses primarily on the function of housing as an asset or investment. These studies typically compare house prices to developments in rents and the rates of return in other classes of assets. An example of such an approach is a study by Krainer and Wei (2004) that analyzes house prices in the United States using concepts from the finance literature. Specifically, they decompose the increase in the price-rent ratio into two parts: one that is related to expected future rent increases, and another that corresponds to expected increases in house prices, concluding that the latter part is the main driver of the U.S. pricerent ratio.

38. From a theoretical perspective, it would appear desirable to include a supply-side in any model for the housing market. As in the above examples, however, supply factors (e.g., building costs) are frequently omitted in the literature. Indeed, there can be a good reason for doing so, in particular for markets where the supply elasticity is deemed to be very low. In such cases, at least in the short term, housing supply is essentially given and equal to the existing housing stock. The United Kingdom, for example, is often considered as a country where supply restrictions are binding, and therefore various studies for the U.K.—including the ones quoted above—do not attempt to model supply factors (see also Muellbauer and Murphy, 1997).23 In contrast, many studies for the United States do include supply factors (e.g., McCarthy and Peach, 2004). One interesting result from this literature, which is also relevant for cases where the supply elasticity is low, is that the relative responsiveness of supply can affect the volatility of house prices. In a panel data survey covering 65 metropolitan areas in the United States, Capozza and others (2002) find that high building costs—related, inter alia, to barriers to new construction—increase the persistence in house prices and reduce the speed of mean reversion, thus creating fertile ground for price overshooting and speculative bubbles.

39. From the perspective of housing as a store of wealth, (expected) inflation is another factor that can affect house prices. In a cross-country study, including multiple variables, Tsatsaronis and Zhu (2004) find that on average across the countries in their sample, inflation explains more than half of the total variation in house prices. This finding could also be related to the common practice of financing houses with debt, which is fixed in nominal terms, and to the attractiveness of such financing in periods of high inflation.

40. Some recent work also includes wealth effects from stock market developments in housing demand equations. One example is Sutton (2002), who relates house prices in a sample of industrial countries to fluctuations in national income, interest rates, and stock prices, and finds a significant contribution from the last variable. Similarly, for the Netherlands, Van den End and Kakes (2002) find a positive long-run correlation between the stock market and house prices. The relationship is found to be complex and running in both directions, but it seems strongest from stock prices to house prices, at a two-three year lag. As the Dutch stock market has lost about half of its value since 2000, this finding suggests that there is substantial scope for downward movement in current house price levels.

41. Few recent studies focus exclusively on the Dutch housing market, such as the one by Van den End and Kakes discussed above. The main other study is a recent analysis by Verbruggen and others (2005), who explain Dutch house price movements between 1980 and 2003 using an error-correction model with several variables, including household wealth. They conclude that current Dutch house prices are “somewhat” overvalued. They also find that adjustment of actual house prices to their equilibrium level takes place faster when the equilibrium price is on the rise than when it is falling (i.e., they find that house prices are sticky downwards).

42. These specific papers aside, the Netherlands has been included in various recent cross-country studies on housing. Most of these find that Dutch house prices are currently overvalued by some margin. Research by PricewaterhouseCoopers (2002) finds that Dutch house prices are influenced by past changes in long-term interest rates, inflation, the amount of new construction, and past house price changes. But their model fails to explain the strong price increases in 1999–2001, which they conclude are likely to represent speculative behavior. Lopes (2004), using a model based on real disposable income growth, mortgage interest rates, and the equity market, finds that Dutch house prices are overvalued by about 10 percent, a result that is broadly confirmed by the IMF (2004), which also includes population and credit growth as explanatory variables in its analysis. Incidentally, consistent with the recent strength of house prices during the economic downturn, Catte and others (2004) find that the correlation of house prices and the business cycle is weak in the Netherlands.

C. The Conceptual Framework

43. Since the supply of new housing is very inelastic in the Netherlands, this paper will focus exclusively on demand factors to explain the behavior of prices. Some empirical backing for this choice can be found in Van Rooij (1999), who fails to find any long-run effects of housing supply on house prices in the Netherlands. Consistent with data properties (see below), we employ an error-correction model, which allows a distinction to be made between short-term dynamics (including possible persistence in house prices) and long-run relationships that might exist between house prices and their main determinants. The broad framework is given by

ΔZt=ΠZt1+Σi=1k1ΓiΔZti+μ+ϵt(1)

where Z is a vector containing the n variables of the system, matrix Π captures information on the long-run relationships among the variables in Z, matrix Γ contains information on the lagged variables, and µ is a vector with constants.

44. For the vector Z, we considered a range of variables including, besides house prices, the disposable income of households, the mortgage interest rate, consumer price inflation, and rents (as measured by the rent component in the Dutch consumer price index). In addition, we examined the variables in both nominal and real terms. Using a “General-to-Specific” modeling approach (see e.g., Enders, 2004, or Charemza and Deadman, 1997, for a general description), the model was subsequently narrowed down. In the final model, three variables were left:

Z=(p,y,r)(2)

where p denotes real house prices, y the total real disposable income of households, and r the real mortgage interest rate.

45. In most studies on house prices, disposable income per household is used rather than the aggregate measure of income that is included in our equation—total disposable income. The latter is used in this study because it captures two effects simultaneously: (1) the increase in average household income; and (2) the rise in the number of households. The latter has been especially important in the Netherlands because an increasing proportion of single-person households has contributed substantially to housing demand (Kakes, 2004). The volume effect is more important than the change in average household income, which—due to composition effects—has risen only very modestly over the past decades. Under these circumstances, using average income per household is clearly insufficient and, moreover, fraught with difficulties: for example, while the decreasing average household size raises housing demand, it also reduces average household income, causing the coefficient for the latter variable, inappropriately, to turn negative. As an empirical matter, we found that there was a highly significant long-run relationship between house prices and total household income, but not between house prices, the number of households, and average income separately.

46. Also, we use real rather than nominal mortgage interest rates. Assuming that the size of the debt-service payment in relation to disposable income matters for the affordability of housing, it could be argued that the nominal interest rate is more relevant. However, after testing both real and nominal interest rates, we found the real rates to be a better estimator of house price developments. This result is supported by that of a similar test for the United Kingdom (Vladkova Hollar, 2003), and our use of real interest rates is consistent with several other studies on—or including—the Netherlands (among others, Verbruggen, 2005, Lopes, 2004, Sutton, 2002, and Van Rooij, 1999).

D. The Data

47. We use quarterly data covering the period 1974:Q1 through 2004:Q2. The house price data originate from the Netherlands’ land registry (Kadaster), while the real disposable income series is obtained from De Nederlandsche Bank. Both series have been transformed into logs. The mortgage interest rate data are from the Dutch home owners association (Vereniging Eigen Huis) and are deflated with the CPI, obtained from Statistics Netherlands.

48. Figure 3 presents the real house price data in levels (top panel) and in year-on-year growth rates (bottom panel). The series clearly show the pronounced boom and bust of house prices in the late 1970s and early 1980s, followed by a relatively long period of more gradual price developments. But in the second half of the 1990s, prices start to accelerate again, culminating in particularly strong increases during 1999–2001. This is best shown in the plot of the year-on-year growth rates. This presentation also reveals, however, that, even while the level of real house prices has now surpassed that of the late 1970s, the growth rates during the latest “boom” have been appreciably more moderate. Since 2001, the growth rate of house prices has slowed quite abruptly.

Figure 3.
Figure 3.

Netherlands: Real House Prices, 1970:Q1-2004:Q2

Citation: IMF Staff Country Reports 2005, 225; 10.5089/9781451829518.002.A003

Sources: Kadaster; and IMF staff calculations.

49. Figure 4 presents the other two data series: disposable income and the mortgage interest rate. The real disposable income measure is shown in the top panel, together with its breakdown into income per household and the number of households. Over the range of the sample, it is clear that important composition effects are at play: the number of households is increasing steadily—and at a much faster pace than would be explained by population growth—while real income per household is almost flat, which seems at odds with the sizable increases in overall prosperity recorded over this 30-year period. Looking at the aggregate series, disposable income appears to have reacted quite strongly to the turning points in the business cycle, which broadly coincided with the turn of each decade. For the bust of the early 1980s, as well as for the recent downturn, this accords well with the developments in the housing market. For the developments in the early 1990s, however, the link between income and house prices is less pronounced.

Figure 4.
Figure 4.

Netherlands: Household Disposable Income and Mortgage Rates, 1970:Q1-2004:Q2

Citation: IMF Staff Country Reports 2005, 225; 10.5089/9781451829518.002.A003

Sources: De Nederlandsche Bank; Statistics Netherlands; and Vereniging Eigen Huis.

50. The bottom panel shows the real mortgage interest rate, together with its breakdown into the nominal rate and inflation. The figure illustrates how real interest rates took off around 1976 on account of rapidly falling inflation and, after peaking in 1987, gradually descended during the 1990s in line with developments in nominal interest rates.

51. All three data series are I(1), i.e., they contain a unit root in levels, but not in their first differences. For each variable, the nonstationarity condition was tested using the augmented Dickey-Fuller (ADF) test. The tests were conducted with a constant in the test specification and both including and excluding a trend. The lag length was determined on the basis of the Schwarz information criterion. The test results are summarized in Table 1.

Table 1.

Unit Root Tests

article image

denotes rejection of the null hypothesis that the series contain a unit root at the 5 and 1 percent significance level, respectively.

52. It must be noted that the evidence with regards to the order of integration of the real disposable income variable is mixed. While the ADF test rejects the null hypothesis of a unit root in the first differences at the 5 percent level when only a constant is included in the test, it fails to reject when a trend is added. In principle, this suggests that the variable might possibly be integrated of a higher order than one. Nonetheless, we accept disposable income to be I(1), since also in the second test specification, the test statistic is very close to the 5 percent critical value, and because on theoretical grounds, there is no reason to suspect integration of a higher order for this variable.

E. Econometric Issues and Hypothesis Tests

53. A vector autoregression system was constructed with the three variables (p, y, r) over the period 1974:Q1–2004:Q2. The appropriate lag length of the system was determined on the basis of a range of widely-used selection criteria. The results of these lag order tests are summarized in Table 2. Unanimously, the criteria select an order of seven lags.

Table 2.

Lag Order Selection Criteria

article image

Indicates lag order selected by the criterion;

LR: sequential modified LR test statistic (each test at 5% level);FPE: Final prediction error;AIC: Akaike information criterion;SC: Schwarz information criterion;HQ: Hannan-Quinn information criterion.

54. Subsequently, we tested for possible cointegration between the variables in the system using the Johansen cointegration test. The test results are shown in Table 3. While the trace statistic is too close to the critical value to be conclusive, the λmax value strongly indicates the existence of one cointegrating equation, including all three variables, at the 10 percent level. The coefficients of the variables in the cointegrating equation are very significant and their signs are consistent with economic theory. On the basis of this result, the error-correction model was estimated. In the initial system, the cointegrating vector was significant only in the house price and the mortgage rate equations. The disposable income variable was found to be weakly exogenous, and the income equation could be dropped without loss of information, leaving a two-equation system.

Table 3.

Johansen Cointegratior Test

article image

Denotes rejection of the null at the 10 percent level

Corrected for small sample bias following Cheung and Lai (1993)

1 Cointegrating relation: Log Likelihood 713.3379

Normalized cointegrating coefficients (std. err. in parentheses)PYR1-1.500278(0.19089) 0.09423(0.01439)Adjustment coefficients (std. err. in parentheses)D (P)-0.050980  (0.01716) D (Y)0.007261  (0.00389) D(R)0.918144  (0.44643) 

55. With two equations, each containing a constant, an error correction term, and six lags for each of the three variables, the resulting system comprised 40 coefficients. For parsimony, and because various coefficients in the system were not significant, the number of variables in the system was then reduced by applying Wald tests to various groupings of variables. While it was not possible to eliminate complete lags from the system without substantially reducing its explanatory power, one variable could be omitted from each equation. Specifically, the Wald tests indicated that the lagged values of the interest rate were not significant in the short-term dynamics of the house price equation. Similarly, the lagged house prices did not appear to be a short-term determinant of the interest rate.

F. Empirical Results

56. After the reductions described in the previous section, the final empirical model for house prices has the following form:

Δp=0.0020.046*(p1.500y+0.094r+3.557)+Σi=16β1iΔpti+Σi=16+β2iΔyti(3)
Δr=0.034+0.087*(r+10.64p15.96y+37.84)+Σi=16β3iΔyti+Σi=16β4iΔrti(4)

57. For presentational reasons, Equations (3) and (4) do not show the coefficients for the lagged variables, but these can be found, together with the t-statistics, in Table 4 which summarizes the estimation output. The error-correction term (ECT—i.e., the term between the parentheses) in Equation (3) indicates that equilibrium house prices rise with disposable income and fall with increases in interest rates, which is consistent with economic theory. The long-run equilibrium is defined by ECT = 0. The coefficient of the ECT can be interpreted as the “speed-of-adjustment” parameter. The low value of this parameter indicates that house prices adjust only slowly (in the course of about 20 quarters) toward their long-run equilibrium. This is consistent with the persistence that we find in the short-run dynamics for house prices: the lagged house prices are significant at various lags.

Table 4.

Estimation Results

article image

58. In order to answer the question of whether house prices are overvalued or not, in Figure 5 we plot the equilibrium house price as derived from the cointegrating equation and compare it with actual prices. The picture that emerges is striking. For the boom-bust episode of the late 1970s/early 1980s, it is still clear that prices were way out of line with fundamentals. In the second half of the 1970s, house prices rose, with a considerable lag, in response to an actual rise in the equilibrium price that was associated with increasing disposable income and declining mortgage rates. However, around 1976, the favorable environment of low real interest rates started to change as inflation fell rapidly in the aftermath of the first oil crisis, while nominal interest rates rose. This change in fundamentals was initially ignored in the market, and house prices continued to rise through 1979. By that time, however, disposable income had also stopped growing, and reality finally set in. House prices fell in order to realign with their fundamental value, a process that was completed around 1982.

Figure 5.
Figure 5.

Netherlands: Actual House Prices Versus Their Long-Run Equilibrium, 1970:Q1-2004:Q2

Citation: IMF Staff Country Reports 2005, 225; 10.5089/9781451829518.002.A003

Sources: Kadaster; and IMF staff calculations.

59. Turning to recent periods, our analysis does not show an upward deviation from fundamentals. In fact, for most of the 1990s, actual house prices were below their long-run equilibrium, although there has been some catching-up in the last few years. Overall, actual house prices appear to have moved largely in line with their fundamental value—that is, in line with developments in total disposable income and interest rates. Apparently, the gradual decline of interest rates during the 1990s has had a large positive impact on the affordability of housing, which—in combination with a steady increase in total disposable income—has justified the rapid increases in house prices.

60. However, by no means does this imply that house prices are secure at their current levels. Indeed, fundamentals may change—as they did in the late 1970s. A key feature of our estimated equilibrium price is that it is quite volatile. Even small changes in income or interest rates have a relatively large impact on the fundamental value of housing. For example, it can be calculated on the basis of our results that a one percentage point increase in the real interest rate reduces the long-run equilibrium house price by about 10 percent. However, the short-term volatility of the fundamental value is normally not reflected in actual house prices because of the slow pace of adjustment.

G. Concluding Remarks

61. As was shown above, we fail to find evidence for a deviation from fundamentals in the current Dutch housing market. There is only limited comfort in this finding, though, because our analysis also shows that the equilibrium price of housing can change quite rapidly with developments in income and interest rates. The current weaknesses in disposable income growth therefore pose significant risks to the housing market, and if interest rates were to rise substantially in the period ahead, house prices may still fall.

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22

Prepared by David Hofman.

23

Of course, there are studies on the United Kingdom that do include supply factors. For example, Ericsson and Hendry (1985) focus specifically on the economics of house building.

Kingdom of the Netherlands—Netherlands Selected Issues
Author: International Monetary Fund
  • View in gallery

    Netherlands: Ratio of House Prices over Disposable Income 1970:Q1-2004:Q2

    (1995:Q1=100)

  • View in gallery

    Netherlands: Price-Rent Ratio, 1970:Q1-2004:Q2

    (1995:Q1=100)

  • View in gallery

    Netherlands: Real House Prices, 1970:Q1-2004:Q2

  • View in gallery

    Netherlands: Household Disposable Income and Mortgage Rates, 1970:Q1-2004:Q2

  • View in gallery

    Netherlands: Actual House Prices Versus Their Long-Run Equilibrium, 1970:Q1-2004:Q2