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How structural factors define trends in the Danish residential market


In pursuit of higher returns, residential property investors have expanded their geographic horizons in recent years. Today, an increasing amount of private capital is funnelled into provincial locations in an attempt to capitalise on underlying demographic trends. Taking a long-term investment perspective, this article feels the pulse of the Danish residential market in terms of a geographic “attractiveness analysis”. 

A growing market

Over the last decade, residential properties have become some of the most sought-after products in the Danish investment property market. The segment is quite rightly associated with low risk and stable cash flows. The coronacrisis has only helped to reinforce this perception. An increasing number of institutional investors, domestic and international alike, therefore plan for higher exposure to Danish residential investment property.

Historically, most professional investors have zoomed in on Greater Copenhagen. However, a shortage of attractive residential properties and, by extension, stronger competition among investors have served to drive down general yield levels in Copenhagen. In pursuit of higher returns, investors have therefore started to look further afield in other geographic areas of Denmark. 

In this respect, mainly Denmark’s large university cities have come into play. Supported by a combination of strong population growth (driven by young newcomers) and many attractive urban development projects (driven by a municipal objective of city centre densification), cities like Aarhus, Aalborg and Odense have started to attract increasing private capital inflows.

As it has become more expensive to settle in the large university cities, and with the large generation of young people born in the 2010s settling down to start a family, migration to the suburbs and slightly smaller towns has accelerated. Obviously, investors want to capitalise on this trend, prompting an increase in capital allocations to provincial locations in recent years.

In this context, we experience mounting demand for analyses of the residential market outside Greater Copenhagen. Due to limited and obsolete historical market data outside the large university cities, there is a risk that classic analyses of market statistics are very short-term or downright misleading. In the following, we will therefore, based on a long-term investment perspective (at least five to ten years), review some of the more fundamental and underlying factors determining housing demand in various geographic areas. The analysis should be put into the context of an overall macro perspective as the attractiveness of a specific property obviously depends on the property’s age, state of repair, location, etc.

Structural factors drive long-term developments

Late US politician Daniel Patrick Moynihan has been credited with saying that the way to create a great city is simply to “create a great university and wait 200 years”. Although this aphorism is of course a gross exaggeration, it carries an important lesson: The long-term development of a city is determined largely by a string of underlying, structural factors. Assuming that the trends in a city’s housing market are closely correlated with the city’s general development, every long-term investor must have an interest in becoming familiar with the long-term, structural factors that constitute the framework. For this article, we have therefore picked five factors that we believe to be of greater or smaller structural importance in a given area:

Jobs within commuting distance

The first factor is the number of jobs within commuting distance, in this context defined as the number of jobs within a 30-minute drive at a given average speed (see figure 1). This factor is structural in the sense that it is driven largely by the geographic location of an area and to a lesser extent the number of jobs in the specific area. To exemplify this let us look at the areas surrounding Esbjerg and the city of Aalborg. Although these two areas are some of the areas of Denmark that have the highest concentration of workplaces, they both rank relatively low in terms of jobs within commuting distance. The opposite pattern applies to areas like Middelfart and Horsens.


Higher education enrolments 

The second factor we have reviewed is Danish first-year higher education enrolments in summer 2020, based on detailed enrolment figures for specific postal codes. Although some of the postal codes may be difficult to decipher, the general trend is clear: The large university cities account for by far the highest student uptake. If we examine the figures more carefully, the area between Roskilde via Kongens Lyngby to Copenhagen S accounts for some 40% of total enrolments, ahead of the city of Aarhus (18%), the city of Odense (just shy of 11%) and the city of Aalborg (10%). This means that the large university cities account for approx 80% of total first-year higher education enrolments, a much higher share than their resident share of the Danish population at large. 

Allocation of national and regional government jobs 

The third factor reviewed is the number of national and regional government jobs (see figure 3). These jobs are structural in the sense that their allocation has more in common with the allocation of funds to major infrastructure projects than with creating private workplaces. As national and regional government jobs are virtually non-cyclical, they serve as a stabilising factor in a given area. In an area like Copenhagen, many government departments will attract highly qualified labour, over time trickling down to the private-sector business community. The figure shows that a municipality like Viborg has an altogether disproportionate over-representation of national and regional government jobs, which ties in with the fact that the administrative offices of the Central Denmark Region are located here.  

Public transport

The fourth factor is public transport, more specifically the number of train stations (see figure 4). As shown in the heat map, the concentration of train stations in the Capital Region of Denmark is much more intense than any other area in the country. Mainly Greater Copenhagen appears well-connected, thanks to S-trains and metro. As the map also shows, the Aarhus Light Rail (connecting Odder and Grenaa) has greatly expanded the vertical concentration. Aalborg on the other hand appears relatively isolated on the railroad grid. Broadly speaking, there are many areas on the Jutland peninsula and the island of Funen where travelling by public transport means long bus rides, making it necessary for most families to own one or two cars.

Leisure opportunities

The fifth and final factor we have chosen for this article is the concentration of leisure opportunities such as cafés, restaurants, bars, museums, music venues, cinemas, etc. (see figure 5). As expected, we see a near-perfect correlation between the concentration of leisure opportunities and the size of a town or city. However, it is worth mentioning that the concentration in the area around Copenhagen is exceptionally high over a very extensive area. It therefore follows that the number of “opportunities” within walking or cycling distance is relatively high across many parts of Copenhagen. In other major towns and cities, the concentration is most pronounced in central districts, with the number of opportunities within walking or cycling distance to a much greater extent being determined by the specific location. 

Has causality been turned upside down? 

When you analyse the effect of structural factors – like, say, infrastructure – you will typically encounter a kind of “chicken and egg” dilemma trying to determine what came first: Did the motorway get constructed in an area because it had many inhabitants, or did many inhabitants settle because the motorway was constructed in the first place? Consequently, caution is advised against jumping to causal conclusions. Nevertheless, it appears to be a fact that the areas scoring high on many of the factors listed in this article have done well and are expected to do well in future - this irrespective of the fact that these factors by no means are the only ones shaping the way an area develops.

In terms of the Statistics Denmark population forecast for Danish municipalities until year 2030 (see figure 6), it is interesting to note just how much its trajectory is mirrored in figures 1-5. More than anything, the “jobs within commuting distance” factor (figure 1) appears to have a high degree of correlation. As the data represented in figures 1-5 more or less speak for themselves, we will omit to comment on minor local variations. Instead, let us conclude with a brief and general summary of the most prominent findings:

  • All large university cities, including respective suburbs, rank high in terms of most key factors. Greater Copenhagen stands out from the group with top scores on all parameters. In second place follows Aarhus, scoring high (but not quite as high) on all factors too. Next come Odense and Aalborg, basically in a close race.
  • Although individual towns in and around the Triangle Region receive low scores on factors like national and regional government jobs, education and leisure opportunities, the region as a whole holds an exceptionally strong position. In brief, the region benefits from an excellent location with many attractive towns in close proximity, with many workplaces within commuting distance. If allowing for a commuting time of more than 30 minutes, it is possible to reach Odense or Aarhus from several locations in the region.
  • In glaring contrast to the Triangle Region stands Esbjerg, located in a more or less isolated part of south-western Denmark. Although Esbjerg is one of Denmark’s largest cities, it scores low on virtually all factors like the rest of western and southern Jutland.
  • The municipalities around Aarhus generally score high on factors like job opportunities and infrastructure. In this respect, mainly the municipalities south-west of Aarhus fare well as they are located in the sweet spot between high-growth municipalities like Horsens, Skanderborg, Silkeborg, etc. Similarly, the area around Herning, Viborg and Holstebro receives fair scores on multiple factors, albeit a far cry from the high scores seen in eastern Jutland.
  • The islands of Lolland and Falster as well as the western part of Zealand in many respects resemble the western part of Jutland, but the areas around Slagelse, Næstved and Holbæk in particular receive far higher scores on most parameters.
  • On Funen, top scores go to the areas that have an infrastructure link to Odense. If we consider Funen in isolation, Svendborg is a close runner-up to Odense, but because Middelfart is situated right next to the Triangle Region, it receives relatively high scores on multiple factors.

It is important to emphasise that a comprehensive analysis of individual areas would also involve factors like demographic composition, housing supply, prices, socio-economic variables, etc. In this article, however, we set out to demonstrate that multiple underlying, structural factors seem to be strongly correlated with long-term developments in a geographical area. As far as a long-term investor is concerned, these factors should therefore be valuable input in the decision-making process before investing in residential properties.