Denmark: Selected Issues
Author: International Monetary Fund. European Dept.
Publication Date:
27 Jun 2016
eISBN:
9781498319805
Language:
English
Keywords:
ISCR; CR; price; housing market; firm; price growth; targeted housing stock; price response; house price fluctuation; supply-demand imbalance; Supply elasticity; Disposable income; Global
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This paper examines the selected issues related to the economy of Denmark: divergence in house prices, house prices in Denmark's cities, macroprudential policies, and product market reform and firm productivity. Recent house price developments in Denmark have been characterized by a growing divergence between different parts of the country, with big cities experiencing much more rapid price increases than other parts. House price booms and busts in Denmark, like in many other countries, are a big-city phenomenon. Macroprudential policies can help contain risks for households, the financial system, and the broader economy, but they should be carefully calibrated to avoid an undue drag on growth.

Abstract

This paper examines the selected issues related to the economy of Denmark: divergence in house prices, house prices in Denmark's cities, macroprudential policies, and product market reform and firm productivity. Recent house price developments in Denmark have been characterized by a growing divergence between different parts of the country, with big cities experiencing much more rapid price increases than other parts. House price booms and busts in Denmark, like in many other countries, are a big-city phenomenon. Macroprudential policies can help contain risks for households, the financial system, and the broader economy, but they should be carefully calibrated to avoid an undue drag on growth.

House Prices in Denmark’s Cities: the Role of Supply1

This paper highlights the role of housing supply constraints in driving prices in Denmark’s owner-occupied housing markets, especially in pressured areas such as Copenhagen. It first briefly discusses the main factors constraining housing supply in Denmark’s largest cities, including a rigid planning regime and rental regulation. The paper then uses municipal-level data to estimate the elasticity of housing supply for each municipality, and show that supply constraints can significantly amplify the price response to a demand shock. Last, a policy experiment is conducted to illustrate the differentiated impact that an interest rate shock or an income shock may have on house prices in areas with different supply conditions.

A. Introduction

1. House price booms and busts in Denmark, like in many other countries, are a big-city phenomenon. There is large divergence between house prices in major urban areas such as Copenhagen and Frederiksberg and other parts of the country, suggesting that housing markets are highly localized. This is not only a feature of the current juncture but also of the earlier house price cycle. For example, between 1996 and 2006, prices increased by a cumulative 85 percent in real terms in the median city, compared to 256 percent at the 90th percentile and over 320 percent in Copenhagen city. The post-2007 price corrections were also larger in cities with previously major price run-ups.

A02ufig1

Real House Prices in Danish Cities

(index, 1996 = 100)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Sources: Statistics Denmark and Fund staff calculations.Note: Prices refer to the price (per square feet) of single family houses, deflated by CPI.

2. Differences in housing supply conditions across cities can contribute to the price divergence. While the literature has traditionally focused on shocks to housing demand (e.g., income shocks and population growth) in explaining house price developments, the role of housing supply constraints—how fast the stock of housing adjusts to accommodate a demand shock—has just started to gain attention. Subject to a given increase in demand, markets with inelastic supply cannot generate new construction quickly to meet the targeted housing stock, resulting in a larger price increase relative to markets with more elastic supply (Figure 1). Thus, in theory, housing supply constraints can have an effect on short-run price changes through its interaction with demand shocks. Supply conditions are determined by both natural (i.e., topographical) and man-made constraints (e.g., local land use regulations, including zoning codes and building permits).

Figure 1.
Figure 1.

Denmark: Price Response to a Demand Shock in Markets with Different Supply Conditions

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

3. Supply conditions vary widely across Denmark’s cities, with Copenhagen and the surrounding cities exhibiting most severe constraints. While Denmark as a whole experienced a construction boom during 2006–07—resulting in an oversupply of housing in some areas—rapid population growth as a result of rural-urban migration and immigration continues to fuel housing demand in large cities, notably Copenhagen. On the supply side, stricter zoning codes, land use regulations, and rental controls in Copenhagen relative to other parts of the country have restricted the supply of both owner-occupied and rental housing. As a result, housing demand has constantly outpaced supply in Copenhagen, causing local land prices to skyrocket (Danmarks Nationalbank’s Monetary Review, 2014Q3). Another manifestation is the wide range of vacancy rates across Denmark’s cities; while some municipalities in Denmark are currently experiencing vacancy rates as high as 30 percent, the vacancy rate remains near zero in Copenhagen area.

A02ufig2

Copenhagen: Population and Housing Stock

(index, 1992 = 100)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Sources: Statistics Denmark and Fund staff calculations.
A02ufig3

Vacancy Rate in Denmark’s Cities, 2015

(share of unoccupied dwellings in total stock)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Sources: Statistics Denmark and Fund staff calculations.

4. The paper is organized as follows: Section B discusses the factors constraining housing supply in Denmark’s largest cities. Section C presents the estimation of local housing supply elasticity. Section D estimates a model of municipal house price growth—with focus on the role of supply, and discusses the policy experiment. Section E concludes.

B. Factors Contributing to Supply Constraints in Denmark’s Large Cities

5. Spatial planning in Denmark is characterized by a top down structure and a strictly enforced zoning system. The zoning law stipulates that urban growth can only take place in designated urban zones. In the Greater Copenhagen Area, which encompasses the municipalities of Copenhagen and Frederiksberg, the Finger Plan (‘Fingerplanen’) introduced in 1947 sets forth that urban development is restricted to each of five ‘fingers’ served by collective rail transport (‘S-tog’) by preserving green areas between the fingers. Even though municipalities were given increased planning powers and responsibilities in the local government reform in 2007, this region is overseen by the Ministry of the Environment, which has the power to veto a municipal plan proposal if the proposal contradicts national interests. As a result, the municipalities in the Greater Copenhagen Area are experiencing greater state regulation of their planning decision making than elsewhere in Denmark (Monk and others, 2013).

6. The approach to land supply in Denmark is relatively passive. For example, Copenhagen does not own a lot of land and is very much dependent on private investors and landowners. There appear to be no specific mechanisms for bringing forward land for housing development. Land value taxation, which taxes the ‘unearned increment’ in land values and arguably provides an incentive to develop, has all but disappeared, and only a remnant remains in the form of a municipal real estate tax (Monk and others, 2013).

7. Strict regulation in the private rental market creates supply-demand imbalances, putting further pressure on the owner occupied housing market. A well-developed and efficient rental market providing a viable alternative to ownership could play a balancing role by alleviating house price pressures and smoothing housing market dynamics. Rent controls, however, impede the functioning of the private rental market by reducing the incentives to invest in rental properties and creating excess demand. While Denmark used to have very strict rental regulation through the early 1980s, subsequent deregulation has introduced a more relaxed regime (e.g., on initial rent setting). The new regime, however, only applies to dwellings built after 1991 and rent controls continue to be important with about 90 percent of private rented dwellings subject to them. Specifically, there are two main sets of rules for rent-setting; one applies a formula that relates rents to costs, and the other allows for administratively determined rents that are based on the rents of comparable dwellings. Both sets of rules produce rents that are well below market levels. Security of tenure for tenants is strong. There are significant differences in how the system is implemented between areas, with Copenhagen having some of the strongest regulation (Cambridge Centre for Housing and Planning Research, 2012).

C. Measuring Local Housing Supply Constraints

8. Measuring local supply constraints is challenging due to lack of data. For the purpose of empirical analysis, one would like to ideally have an indicator of each city’s available and developable land supply, as well as a measure of its regulatory restrictions related to local land use. These indicators have been developed for US metropolitan areas (e.g., Saiz, 2010 and Gyrouko and others, 2008) but are not available for Danish cities. Our empirical strategy gets around this issue in two ways. First, we directly estimate the price elasticity of housing supply using data on house prices and housing starts for Danish municipalities. Second, we use the local population density as a proxy for housing supply conditions, assuming that a more densely populated area reflects a larger degree of supply shortage. These measures are imperfect in that they cannot distinguish the effects of natural land constraints versus regulations; however, the two factors are often highly correlated (e.g., see Saiz, 2010).2

9. We employ data on housing starts and house prices for 92 Danish municipalities to estimate the elasticity of housing supply.3 Starts refer to the number of newly-constructed houses, and prices refer to the realized transaction prices, deflated by the national consumer price index. The sample contains data for the period 1992–2014. Panel cointegration tests indicate that the two series in log levels are cointegrated in the majority of markets (with one cointegrating relationship). Thus, following Wheaton and others (2014), we use the Vector Error Correction Model (VECM) to deal with both stationarity and endogeneity issues. The model is represented by the system of equations:

ΔSt=α0[St−1−(β1+β2Pt−1)]+Σk=0nγkΔPt−k+Σk=1nαkΔSt−kΔPt=α0′[St−1−(β1+β2Pt−1)]+Σk=0nαk′ΔSt−k+Σk=1nγk′ΔPt−k

Here St refers to the logarithm of housing starts, Pt refers to the logarithm of prices, and Δ denotes the difference operator. The parameter of primary interest—our measure of the long-run elasticity of supply—is β2 in the cointegrating equation, which governs the long-run relationship between housing starts and prices. The set of adjustment coefficients (α, γ and α′, γ′) control how quickly starts and prices adjust back to the long-run relationship given a temporary deviation from the equilibrium. We use one lag for each equation, which the lag length selection procedure indicates as sufficient for most markets. The system is estimated repeatedly for each municipality using the single-step Johansen Maximum Likelihood Estimator, which makes the assumption that the errors are normally distributed.

10. The estimated elasticities show substantial regional differentiation and are negatively correlated with population density. Out of 92 municipalities used in the estimation, 10 estimated elasticities are negative—all statistically insignificant (>10%), and another 12 elasticities are positive but insignificant. Focusing on the positive and statistically significant estimates, we have elasticity measures for 70 municipalities ranging from 0.74 to 4.9, with an average of 2.1. As expected, the estimated elasticities are negatively correlated with population density, which is our alternative measure of housing supply conditions. Copenhagen—the most densely populated urban area—also has one of the lowest housing supply elasticities in the country (1.17).

A02ufig4

Distribution of Supply Elasticity

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Sources: Statistics Denmark and Fund staff calculations.
A02ufig5

Estimated Supply Elasticity and Population Density

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

D. Impact of Housing Supply on Prices

11. Our measures of housing supply constraints are correlated with the degree of local house price fluctuations. In virtually all of the municipalities, the period from the early 1990s through 2006–07 exhibits an unprecedented rise in real house prices, followed by a decline of almost similar magnitude over the 2007–13 downswing. These trends resulted from shocks to the housing markets at the national (and even the regional and global) level, which propagate to all local markets to varying extents. We would expect that the degree of price responses naturally depends on how flexible supply is in each location. Indeed, cities with high population density or low estimated elasticity of housing supply recorded relatively larger price run-ups during the boom, but also more severe price declines during the bust (Figure 2). While the relationship is not perfect due to conditions that are unique to each location, it still suggests that inflexibility in the supply of housing contributes to price volatility.

Figure 2.
Figure 2.

Denmark: Relationship Between House Prices and Supply Constraints

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Sources: Statistics Denmark and Fund staff calculations.

12. A dynamic panel regression approach is employed to formally estimate the impact of supply constraints on local house price growth. We apply the methodology in Glaeser and others (2008) and Saiz (2010) while extending to a panel setting. The empirical model relates annual change in local house prices in municipality i (ΔPi,t) to its lag, a vector of national or local demand shocks (ΔXi,t), a measure of the time-invariant local supply constraint (Zi), and an interaction term between the demand shocks and the supply constraint indicator. We’re most interested in the latter variable, which captures the extent to which local supply conditions magnify or mitigate the effect of a demand shock.

ΔPi,t=α+βΔPi,t−1+γΔXi,t+δZi+θΔXi,tZi+μt+ωi+ɛi,t

As discussed, our two alternative measures of Zi are population density and the elasticity of housing supply estimated in the previous section. The demand factors included in the model are changes in the national real mortgage rate4 (which determines the user cost of housing), local real disposable income growth, and growth of local population in the 25–49 age group (the age cohort most likely to demand owner-occupied housing). In addition, several variables on the supply side are included, such as the national real construction cost, and the local vacancy rate to reflect the existing supply of housing. Finally, a full set of year and municipality fixed effects are included, the former to capture time-invariant municipal characteristics and the latter to absorb any common shocks (e.g., a crisis at the national/global level). The dynamic model is estimated using annual data for 94 Danish municipalities over 2001–12 (Table 1 provides summary statistics). As is well known, the correlation between the municipality fixed effects and the lagged dependent variable could give rise to dynamic bias especially in “small T, large N” type of panels; thus, system Generalized Method of Moments (GMM) is used to mitigate this bias (e.g., Arellano and Bond, 1991; Arellano and Bover, 1995).

Table 1.

Denmark: Summary Statistics

article image
Sources: Statistics Denmark and Fund staff calculations.

13. Estimation results indicate a significant role for supply conditions in shaping house price developments (Table 2). Results from four models are reported: a baseline model estimated using Ordinary Least Squares (OLS, column 1) or GMM (column 2), an augmented model including supply constraint variables (either population density—column 3; or supply elasticity—column 4). In models 3 and 4, the measure of local supply constraint is interacted with two demand ‘shocks’, i.e., changes in the mortgage rate or real disposable income, to test whether house prices in more constrained locations respond more to changing demand. Results indicate that, subjected to a reduction in mortgage rates or an increase in disposable income, housing prices increase more strongly in cities with higher population density or less elastic supply. In other words, inelastic supply of housing contributes to magnify the impact of shocks to housing demand. Other variables also mostly behave as expected. On the demand side, stronger house prices are most robustly associated with higher disposable income, whereas the effects of population growth and mortgage rates have the correct signs but are not always statistically significant. On the supply side, higher costs of construction nation-wide feed into local increases in house prices, as does a drop in the local vacancy rate.

Table 2.

Denmark: Determinants of Local House Price Growth

article image
Sources: Fund staff estimates.Notes: Dependent variable is annual growth rate of real house prices. The estimation sample consists of 94 Danish municipalities over the period 2001-2012. A full set of municipality and year fixed effects are included. Estimation method (specifications 2 to 4) is system GMM. Lag of the dependent variables and income growth are treated as endogenous, and instrumented using lags 2 to 4. Robust standard errors in brackets. Statistical significance at *** 1%, ** 5%, and * 10%.

14. An example can further illustrate the economic impact of supply constraints. In a simulation, we apply the estimated coefficients to demand shocks of economically meaningful size, which allows us to gauge the potential impact of supply constraints on prices. We experiment with two types of one-off shocks: (i) a 2 percentage point reduction in the real mortgage rate; and (ii) a 10 percent increase in real household disposable income. The applied interest rate reduction is approximately equivalent to the observed change in the real mortgage rate between 2004 and 2008 in Denmark, and the income shock is roughly comparable to the increase in disposable income in Copenhagen city between 2003 and 2006. We then compare the estimated response of house prices to these shocks in markets with different supply conditions, measured by either population density (model 3) or our estimated supply elasticity (model 4). Subjected to the interest rate shock, house prices would increase by 5 percentage points more in Copenhagen compared to a city with average population density, and 17 percentage points more if subjected to the income shock. If supply constraint is measured by the supply elasticity, the differential price responses between Copenhagen and the average city are somewhat smaller, 1.2 and 3.4 percentage points respectively. The stylized example highlights the importance of being able to adjust the housing stock to different demand conditions, if large run-ups or reversals in prices are to be avoided.

A02ufig6

Impact of Population Density on Real House Prices Growth

(Percentage points)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

Source: Fund staff estimates.
A02ufig7

Impact of Supply Elasticity on Real House Prices Growth

(Percentage points)

Citation: IMF Staff Country Reports 2016, 185; 10.5089/9781498319805.002.A002

E. Conclusion

15. Supply conditions matter for house price developments. The analysis above highlights the often-underestimated role of housing supply constraints in shaping local house prices in Danish cities. Cities such as Copenhagen where the stock of housing is relatively inflexible and responds slowly to changes in housing demand could see higher price growth, ceteris paribus. We demonstrated in a simulation that the potential price impact of supply constraint can be economically significant.

16. Addressing the risks from elevated house prices in certain Denmark’s cities requires a multi-pronged approach, including relaxing supply constraints in pressured areas. While natural land constraints are difficult to overcome, distortions in the housing markets could be reduced to alleviate the supply shortage in high-stress urban areas such as Copenhagen. For example, there is scope for relaxing zoning regulations in certain areas. In addition, reducing rental controls to allow freely determined rents to apply to a larger fraction of the housing stock, and creating the incentives for municipalities and/or private developers to put land to good use would also be helpful. This is particularly relevant in the current juncture, given the low interest rate environment as well as the recent influx of asylum seekers putting additional pressure on the demand for housing.

References

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  • Arellano, M. and O. Bover, 1995, “Another Look at the Instrumental Variable Estimation of Error-Components Models,” Journal of Econometrics, Vol. 68, pp. 29–51.

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  • Cambridge Center for Housing Planning and Research, 2012, “The Private Rented Sector in the New Century: A Comparative Approach.”

  • Danmarks Nationalbank, 2014, “A Multispeed Housing Market,” Monetary Review, Third Quarter.

  • Glaeser, E., J. Gyourko, and A. Saiz, 2008, “Housing Supply and Housing Bubbles,” NBER Working Paper 14193, (Cambridge, Massachusetts: National Bureau of Economic Research).

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  • Gyrouko, J., A. Saiz, and A. Summers, 2008, “A New Measure of the Local Regulatory Environment for Housing Markets: The Wharton Residential Land Use Index,” Urban Studies, Vol. 45, pp. 693–729.

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  • Monk, S., C. Whitehead, G. Burgess, and C. Tang, 2013, “International Review of Land Supply and Planning Systems,” Joseph Rowntree Foundation Report.

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  • Saiz A., 2010, “The Geographic Determinants of Housing Supply,” Quarterly Journal of Economics, Vol. 100, No. 3, pp. 500–30.

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1

Prepared by Giang Ho.

2

Saiz (2010), using data for U.S. metropolitan areas, found that geographic constraints were strongly associated with regulatory constraints. Theoretically, regulation may be endogenous as voters may explicitly restrict the supply of land to keep its value high, but only have an incentive to do so in areas where land was initially scarce.

3

The administrative structure of Denmark’s municipalities underwent a reform in 2007. We use the correspondence between the old structure (over 270 municipalities) and new structure (close to 100 municipalities) to splice together the pre-2007 and post-2007 series.

4

We use the interest rates that mortgage institutes charge households on housing loans (effective rates including fees) from Statistics Denmark.

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Cover IMF Staff Country Reports
Denmark: Selected Issues
Author: International Monetary Fund. European Dept.
Volume/Issue:
Volume 2016: Issue 185
Publisher:
International Monetary Fund
ISBN:
9781498319805
ISSN:
1934-7685
Pages:
54
DOI:
https://doi.org/10.5089/9781498319805.002
  • View in gallery

    Real House Prices in Danish Cities

    (index, 1996 = 100)

  • View in gallery

    Denmark: Price Response to a Demand Shock in Markets with Different Supply Conditions

  • View in gallery

    Copenhagen: Population and Housing Stock

    (index, 1992 = 100)

  • View in gallery

    Vacancy Rate in Denmark’s Cities, 2015

    (share of unoccupied dwellings in total stock)

  • View in gallery

    Distribution of Supply Elasticity

  • View in gallery

    Estimated Supply Elasticity and Population Density

  • View in gallery

    Denmark: Relationship Between House Prices and Supply Constraints

  • View in gallery

    Impact of Population Density on Real House Prices Growth

    (Percentage points)

  • View in gallery

    Impact of Supply Elasticity on Real House Prices Growth

    (Percentage points)

Real House Prices in Danish Cities

(index, 1996 = 100)

Denmark: Price Response to a Demand Shock in Markets with Different Supply Conditions

Copenhagen: Population and Housing Stock

(index, 1992 = 100)

Vacancy Rate in Denmark’s Cities, 2015

(share of unoccupied dwellings in total stock)

Distribution of Supply Elasticity

Estimated Supply Elasticity and Population Density

Denmark: Relationship Between House Prices and Supply Constraints

Impact of Population Density on Real House Prices Growth

(Percentage points)

Impact of Supply Elasticity on Real House Prices Growth

(Percentage points)