Dept of Real Estate and Construction Management
Div of Building and Real Estate Economics
Master of Science Thesis no. 473
The effect of rent regulation on rents, yields and prices
-A comparison between cities and locations within a city
Author:
Daniel Holmkvist
Supervisor:
Hans Lind
Stockholm 2009
Master of Science thesis
Title:
The effect of rent regulation on rents, yields
and prices. - A comparison between a city and
locations within a city.
Daniel Holmkvist
Department of Real Estate and Construction
Management
Division of Building and Real Estate
Economics
473
Hans Lind
Rent, Rent Regulation, Rent Control, Rent
Levels, Yield, Rent comparison, Market rent
Authors
Department
Master Thesis number
Supervisor
Keywords
Abstract
Rent regulation models are used in many countries. The main reason for controlling rents is to
make it possible for everyone in the society to get a decent apartment in the inner city as well as
in the suburbs. There are arguments whether this is the right way to deal with the problem or not.
In Sweden the rent regulation is of the second generation and of a strong type.
The main idea in this thesis is to compare different municipalities in Sweden to see if there are
any signs of the effects of the rent control system in Sweden on the yield levels. The regions
compared are the 25 with highest population according to the Statistics Sweden Bureau in 2008.
The thesis is describes how rent regulation works and theories in the subject. The main focus is
the Swedish rent model. After the explanation of rent controlled system there is a comparison
between the different municipalities in Sweden. This section is divided in to two different
periods. First, a time period between 2003-2005 and second a period from 2006 to 2008. The
comparison over the different periods of years gives a picture of the difference. The factors being
compared are; rent levels, yield levels and transaction prices.
The main hypothesis says that the more population in a municipality the more the rent regulation
affects the rent levels. This is mostly seemed in the inner parts of a region or a city.
From the analysis there are different trends in the rental and transaction market in Sweden. For
example the regions with higher population tend to have higher rent- yield- and transaction
levels. There are municipalities that do not follow this trend, for example Södertälje and Täby,
regions with small population but the fact that they are closely located to Stockholm. This seems
to raise the rents and prices for apartments. Another important factor is the year of construction.
This is mostly seen in the new areas in the inner part of Malmö municipality.
The conclusion is that the steepness of the curves in the diagrams for the different areas of the
municipalities is different. Inner parts of the regions, Area A have much steeper curves than the
outer parts of the regions. The same patterns being seen in all compared regions. This leads to a
buy option in the central parts. Interesting is that there is about the same yield levels in the
regions with high population as the ones with small in the outer parts of the municipalities.
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Acknowledgement
This Master Thesis has been conducted at the Department of Real Estate and Construction
Management. In the Divison of Building and Real Estate Economics.
First of all I want to thank my supervisor, Hans Lind. With the continuous short meetings every
second week the thesis slowly but consistently been taken form. This has been a good way and
interesting way working with the thesis.
Further, I would like to thank my girlfriend and friends for being there with their support during
the period of the thesis writing.
Stockholm, 2009/05/07
Daniel Holmkvist
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Table of Contents 1 INTRODUCTION 2 1.1 1.2 1.3 1.4 1.5 1.6 2 3 5 5 5 6 BACKGROUND HYPOTHESES RESEARCH QUESTIONS AND AIM LIMITATIONS METHODOLOGY DISPOSITION 2 RENT REGULATION 7 2.1 MODELS AND STUDIES 2.2 FIRST AND SECOND GENERATION 2.3 SWEDEN 2.3.1 HISTORY OF SWEDISH RENT POLICY 2.3.2 SWEDISH RENTAL SECTOR IN GENERAL 2.3.3 SWEDISH RENT LEVELS 7 7 8 8 9 10 3 THE MUNICIPAL COMPARISON 11 3.1 THE ANALYZE 3.2 TRANSACTION DATA CONFIGURATION 3.3 EXPLANATION OF FIGURES 3.3.1 RENT LEVELS 3.3.2 TRANSACTION PRICE 3.3.3 YIELD 3.3.4 POPULATION 3.4 MUNICIPALITIES 11 11 12 12 12 12 13 14 4 COMPARISON 2001­2005 15 4.1 RENT LEVELS 4.1.1 HIGH RENT LEVELS 4.1.2 MEDIUM RENT LEVELS 4.1.3 LOW RENT LEVELS 4.1.4 THE AVERAGE RENT LEVELS 4.2 YIELD LEVELS 4.2.1 HIGH YIELD LEVELS 4.2.2 MEDIUM YIELD LEVELS 4.2.3 LOW YIELD LEVELS 4.2.4 THE AVERAGE YIELD LEVELS 4.3 TRANSACTION LEVELS 15 15 18 20 22 23 23 24 26 28 29 5 COMPARISON 2006­2008 31 5.1 RENT LEVELS 5.1.1 HIGH RENT LEVELS 5.1.2 MEDIUM RENT LEVELS 5.1.3 LOW RENT LEVELS 5.1.4 THE AVERAGE RENT LEVELS 5.2 YIELD LEVELS 5.2.1 HIGH YIELD LEVELS 5.2.2 MEDIUM YIELD LEVELS 5.2.3 LOW YIELD LEVELS 31 31 32 34 35 37 37 38 40 5.2.4 THE AVERAGE YIELD LEVELS 5.2.5 LEVEL OF TRANSACTION 2006‐2008 41 43 6 TRANSACTIONS 44 6.1 TRANSACTIONS 2003­2005 6.2 TRANSACTIONS 2006­2008 6.2.1 SUMMARY TRANSACTIONS 44 46 47 7 ANALYSIS 48 7.1 7.2 7.3 7.4 7.5 48 49 49 50 50 RENT LEVELS YIELD LEVELS TRANSACTION PRICE KEY RESEARCH QUESTIONS SUMMARY 8 DISCUSSION 51 Appendix 1 Different areas in the municipalities
Appendix 2 Coefficients for trend lines in figures
References
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1
Introduction 1.1 Background The rent-controlled market in Sweden has been discussed for a long time. Today the market is
controlled on old rental apartments. Buildings that are new or of a high grade refurbished are
allowed to have more like a market rent setting system. This leads to a problem with low rents
in the inner city where the buildings are old. In the suburbs where the buildings are newly
constructed the rents are higher. This creates a market were there is difficult to get a rental
contract in the inner cities. The low rent makes people not giving up their apartments with low
rents in the inner city. This creates segregation and a black market for rental contracts.
The discussion about if this is a problem and how it is going to be solved has been going on
for a while. For example, in different journals and other forums in Sweden (Ekonomisk debatt
etc.). There are persons arguing from different perspective and gives different opinions about
which is the right way and which is the wrong way. In 2004 there was a proposition (SOU
2004:91) from the government handed in to the parliament about a reformed rent setting
system (reformerad hyressättning) in Sweden. The proposition included suggestions about law
changes and suggestions about how to set the rent on new constructed rental apartments etc.
In 2006 these suggested changes got legal force. Today the system does not act as a market
rent system. It is still very far from a free market where, supply and demand setting the price
levels. It is very interesting that different countries have so different view and legal system
controlling the rental markets.
Finland, for example, changed from a controlled market to a market rent system about ten
years ago. Maybe this could be the case in Sweden in the future as well. The difference
between countries and their legal system preventing unfairness in the society is interesting. By
having a look at the housing policies and comparing countries could lead to a better
understanding and a result how it would affects the Swedish market with a free rent system.
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1.2 Hypotheses The thesis is going to compare regional data in a variety of municipalities to see how the
different variables are affected by regulated rents. The idea is to compare the 25 municipal
regions with highest population in Sweden. The comparison is based on the hypotheses
diagram showed later in this chapter. The data being compared are rent levels for each of the
municipalities. Another figure that is being compared is the yield levels for residential
buildings. The last comparable figure that is going to be used is transaction prices. Data that
will be required in all cases are; yield, rent levels and transaction price and the size of region
for the different municipalities. Three basic hypotheses will be used to compare data in
different municipalities in a good way.
Hypothesis number one; the greater the Region is, the lower yield, this compared to regions
with lower population levels, this is the main assumption. Another assumption being made is
that the rent regulation to a higher degree affects the inner parts of cities and municipalities.
This is symbolized in the figure below where the red line shows the outer parts of
municipalities and the blue line is the inner parts of the municipalities. The population is on
the X-axis and is starting from zero to the left. The Y-axis shows the yield levels in
percentage.
Hypothesis number two; Rents are growing with the city's size. The comparison with
municipalities with a high population and the one’s with lower population shows according to
the hypothesis. That the higher population, the higher rents per square metre and year. This is
referred to as the rent level in the report. This is symbolized in the figure showing the rent
level hypothesis on the next page, where the blue line is the inner parts of the municipalities
and the red line the outer parts of the municipalities.
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Hypothesis number three; The property value is greater in the inner parts of the
municipalities. This relationship is growing, the larger the region the higher market value.
This is symbolized in the figure below where the blue line is the inner parts of the
municipalities and the red line is the outer parts of the municipalities. The correlation is
growing, the higher population the higher price per square metre.
To get a connection between these three graphs the gap between property value and the rent
level figure and the gap between the figure showing the transaction price hypothesis are going
to be compared. One issue is that the gap between the value will be higher than the gap
between the difference of rent in different municipalities / regions. This can be seen as a buy
option. With a high rent level and a low transaction price the region are very interesting in an
investor’s point of view.
Hypotheses number four; The difference between areas in the city is higher within the more
central parts than within the parts out from the centre. This leads to a buy option in the inner
parts of a city but not in the outer parts were the yield levels are higher.
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In summary; the greater metropolitan region, the more affected by the rent regulation will the
variables be. To compare the data in a proper way, some assumptions will be made. One of
these is that value year will not be taken into account; the assumption is that urban regions
have about the same range of age in the real estate portfolios.
1.3 Research Questions and aim There are mainly two questions this report is supposed to answer. These questions are the
foundation where the information searching is based upon.
Is there a difference in the municipalities in the markets due to rent regulation?
Answering these two questions with the collected data from different sources for example,
Statistics Sweden. Also by the empirical analysis of literature, collected and written about the
systems in the different countries. These questions will be the main theme through the report.
The primary goal with the thesis is to answer the key research questions in a proper way.
Further, by looking at the different levels of rent, yield and transaction price to see differences
and get a good knowledge about what reasons are the basic to this difference and come up
with explanations to the differences.
1.4 Limitations This report will try to answer the research questions with the information collected from
literature and information collected from companies and statistics collected from databases.
The thesis will have focus on the Swedish residential market.
There could be problem in collecting and comparing data. Mainly, problems comparing
figures and different properties in the data. This because there are no value year included and
transactions being made are just compared to each other without further considerations.
1.5 Methodology The thesis will mainly consist of a comparative cross analysis from 25 different municipalities
of Sweden. Comparing different cities to see how the rent regulation system is affecting the
price levels etc. The main focus is to put the rental systems in a Swedish perspective but to do
this it is important to compare with countries that have a free market to see the differences.
Finland can be a quite good comparison because they had a rent regulated system just as
Sweden to 1995. By, comparing how Finland changed their system and which effects it had
on the Real Estate companies is a very interesting point, but unfortunately the data needed
was hard to get and compare to the data collected for the Swedish market. Hans Lind and
Stellan Lundström have made an interview with different finish companies that were really
interesting (Lind & Lundström, 2008).
Analysis of the different data will have both one empirical and one theoretical part. The
empirical part’s focus will be on comparing the different municipalities and according to the
facts and figures found base result on this. The theoretical part will handle theoretical
frameworks and models and try to apply them on the differences rent to see if there are any
differences in the key research questions.
The 25 municipalities with highest population in Sweden are being compared. The regions are
different and the economic growth could be different. The same thing with the employment
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level in the labour market. To get a good view and proper and comparable values there are
control variables used in the report. One of these variables is population. This is to compare
the municipalities and see how and if the figures can be explained by these variables.
The data mainly used is from Datscha, a company that has a service for Swedish Real Estate
firms. Data used in Datscha’s database is collected from Newsec, DTZ and Forum
Fastighetsekonomi. Data included in the data service are; Rent- and yield levels for different
municipalities with different data for different parts of the municipalities. Real Estate
transactions in the different municipalities. The database also includes a lot of information as
average income per municipality, age structure and so on. For demographic differences
statistical databases as the SCB (Central Bureau of Statistics) will be used which also is
included in the datscha service.
1.6 Disposition The thesis consists of 8 different parts. The number in the parenthesis after the text is showing
the number of the chapter. The report starts with presenting a background, key research
questions, aim, limitations and methodology (1). After this a brief introduction about rental
housing markets, explaining differences in a market rent policy and a controlled market
system. This makes it easier to follow the discussion later in the report (2). After the
introduction the report start focusing on the municipal comparison. Factors and figures being
used are introduced (3). Next part of the thesis is where the figures are being compared. The,
municipal comparison, in this part the 25 different regions are compared according to their,
rent- yield and transaction levels in the time period 2003-2005 (4). Next part is comparing the
next time period, 2006-2008 (5). Transaction levels are presented in the next part. In this
chapter the transaction data are presented year-by-year (6). Next, is the conclusion, it is here
the most important findings and a conclusion of the report is being presented (7). The last part
is the discussion, here are thoughts and further discussions taken up (8).
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Rent regulation The property prices and regulations are not just limited by rent control. Planning processes,
zoning regulations, conversion restrictions are also a part of it (Malpezzi & Tuner, 2003:15).
Rent control nowadays often combines different mechanisms. The rent control has two main
types; the first one is control of fair rent levels. The second is protection against future rent
increases. The normal way rent regulations are supervised is through a governmental
authority. Some rent policies accept rent increased if there has been cost for refurbishment etc
for the landlord. The main thought of rent control is to have a stable level in the private
market but often the public housing is involved as well (Malpezzi & Tuner, 2003:17). For
example private market in the United States is 98 percent and in Italy the government relies
mostly on private investors and landlords. The turnover rate of an apartment is much higher in
a free market. In New York city the rate is four times higher to change apartment than if the
market would be regulated (Malpezzi & Tuner, 2003:40). It is the same in Denmark and here
is the duration six times longer for regulated markets. In New York people in rent controlled
apartments pay less and are poorer than others. Rent regulation is a bad tool for fighting
segregation. Not even second generation of rent regulation does not improve better quality in
apartments. There are still not enough incentives for landlords (Glaeser, 2003:188). A study
by Moon and Stotsky shows that there really is better quality in apartments in a market rent
situation (Glaeser, 2003:189). An interesting point is that in New York the apartments with
free rents are not just much more expensive, they are smaller as well (Glaeser, 2003:193).
2.1 Models and studies Most of the studies in recent years using old models that are very simple and shows the
surplus of the consumer in different matters. Other points that the models show are effects of
subsidies and tax regulation. There are two different types of study approaches in the area;
one is an empirical study that focus on a single market area. A study like this shows costs and
benefits to individuals consumers and producers. The good thing about a study like this is that
it highlights just one single market and everything there. The disadvantage is that it is hard to
know if it is generalizable (Malpezzi & Tuner, 2003:21). The second approach is to examine
different markets, a cross-market comparison. This kind of study focuses mostly on averages
and medians in the output data. The disadvantage here is that there is no deepness in a single
market but it is easy to generalize and use as a comparison (Malpezzi & Tuner, 2003:28).
Rent control leads to a decrease in production of housing of private landlords and increasing
transaction costs is the prediction in many models (Malpezzi & Tuner, 2003:30-31). Many
studies on benefits and disadvantages with rent control focuses on the marshallian1 or the
money-income-constant surplus (Malpezzi & Tuner, 2003:30). A study by Olsen in 1972
shows that housing in rent control parts of New York consume 4,5 percent less housing that
persons living in a free market (Malpezzi & Tuner, 2003:33).
2.2 First and second generation There are two different forms of rent control; first generation which is the freeze of nominal
rents. The second generation of rent control is when rent can be raised with inflation. The
second generation has some advantages compared to first generation for example; no falling
property values reduced maintenance and reduced mobility. Further on, Lind divide secondgeneration rent control in to five different groups (Elsinga et al, 2008:219-220).
A) Weak rent regulation, protects against high transaction costs
1
Economic welfare divided into consumer and producer surplus
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B) Strong, limited regulation for sitting tenant. Specific return to landlord
C) Regulation in all types of contracts
D) Monopoly related regulation, no rent higher than market rent
E) Limiting short-term price effect.
2.3 Sweden In this chapter the Swedish model is being discussed. The history of the Swedish system and
why it is as it is today. Further, the Swedish rental sector is being discussed.
2.3.1 History of Swedish rent policy Rent regulations started during the second world war to avoid unfair prices when the
government started military production instead of housing (Lind, 2005:2; Hellström & Lind,
2006:167). The first intention was that the law was temporary. The main point of the law was
that the only way rent could adjust upwards was if some main refurbishment that had
increased the cost for the landlord a lot. After the war there was a boom in the economy and
the demand for housing increased in the big cities. The problem now was if the regulation
were taken away it would have the effect that the prices for housing in the metropolitan areas
would increase a lot. To solve this issue the municipalities started housing companies and
produced low cost housing to solve the increased prices. The competition was thought to hold
down the prices from the private housing companies. At the end of the 1950s and the
beginning of 1960 a committee was created to solve the issue with the rent regulation that did
not work properly. The problem at this time was that newly produced apartments in the
suburbs had higher prices than the same sized apartment in the city centre (Lind, 2005:3). The
apartments in the city centre often had lower quality, but still their location was in the city
centre.
In 1961 a new proposal was created and the main point in this was that when an apartment
were vacant the landlord could set the price he wanted. But he could not increase the rent for a
sitting tenant more than to the market rent level. The million programme started in the early
1960s. This construction era created homes for millions of people. The architecture was not
good and the location where mainly in suburbs (Donner, 2000:summary). In 1967 a new
proposition was presented and tried to deal with some problems in the last one. The new point
here was that the reference of the market rent was newly produced apartments from the
municipal companies. In the proposition of 1961 it was the rent level on recent contracts with
private landlords. However this proposal were pulled back after a short time because low
income households in the city centre would suffer a lot from this new proposal; they had low
rent apartments with low quality and the proposal did not take this in mind. Now the demand
for apartments with low rents in the city centre started to increase rapidly.
The next step to the rent regulation we have today were in the early 1970s, the labour market
were the model for creating the bargaining model (Lind, 2005:4). The point of the new
legislation was: bargaining between landlords and tenants should set the rent level. This lead
to that the government no longer was responsible for the rent setting. Sweden changed the
rent regulation settings from cost-based to use value system (Elsinga & Haffner & Hoekstra,
2008:224). After the second world war the demand for housing increased but the government
did not see any causes taking away the rent control. They thought that a deregulation would
increase the rent in the expanding areas and cities. Under this period housing mainly was
produced by municipal housing companies with non profit intention.
Another big change in the Swedish rental sector is that transactions of rental apartments to
condominiums have increased rapidly since the late 1990s. The reason is that there is a profit
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both for the tenants and for the landlords. If the majority of tenants in the property want to
change to condominium then they could if the landlord wants to sell the property. There is no
rent regulation on private owned apartments, the rent level and the market rent difference
makes a profit for the buyer of the dwelling (Lind, 2005:6).
2.3.2 Swedish rental sector in general The aim of rent control is having a balance and mix between rich and poor in a city or a part
of a city. Another is that the rent could not be increased for a sitting tenant more than to a
limit extend. Rent control in best case creates opportunities for poor people living in rich
neighbourhoods or expensive cities (Glaeser, 2003:188).
The background to the controlled rent in Sweden is to provide everyone with a modern
comfortable apartment. The politician had this in mind when started the million apartments
program (Donner, 2000:summary). Rent regulation in Sweden covers all rental apartments,
old such as new constructions. The share of rental apartments has decreased from 52 percent
in the early 1970s to 17 percent in the early 2000 (Elsinga et al, 2008:223; Lind, 2003:144).
Rents in the private sector could not be set more than 5 percent over rents in municipality
companies that are non-profit organisations (Elsinga et al, 2008:224). This could be problem
with a monopoly situation where municipal companies can set strategically rents to make it
hard for private landlords to compete in the market due to non-profit there. It has been
complained to the European union about the Swedish legislation of rents and there is a
committee working on revising the rules so it will be in line with the EU-legislation.
The rental sector has been declining in Europe as well, and reason for this is the rent
regulation models used in most of the countries. Elsinga makes an assumption that the stricter
rent control the more decrease in the rental sector (Elsinga et al, 2008:217). Rent regulation
creates a non-efficient market and ineffective use of the housing stock is one of the most
traditional economics points about controlled rent systems. Here the black market has a
“positive” effect; it creates a more effective market if the illegal points are taken out of the
aspect. People willing to pay more normally get the apartment.
Segregation is another main point of the rent regulation system in Sweden. It gives a chance
for poor people to live in rich and attractive areas and neighbourhoods. The two biggest
problems by rent regulation in Sweden are; the black market and that it is hard for new “poor”
households to get an apartment (Lind, 2005:8). The rent levels are negotiated with
cooperation for tenants. And this level is the one set for private landlords. More and more
apartments are converted to condominiums. Migration from smaller cities has decreased the
demand there and some municipalities had to demolish housing because of the low demand
(Donner, 2000:summary).
The labour market is the model for the bargaining rent regulation system in Sweden. The
Sweden rent control system can be seen as a collective bargaining system (Lind, 2005:2-4).
As shown before there has been a big change in Sweden the last decade. The Swedish rental
sector sometimes called social housing and that is not right; the intention is to offer housing
for everyone even the low socioeconomic groups. The rents in Sweden are based on utility
value, which mainly refers to the standard of the apartment as base for the renting cost.
Compare this to market rent where location could have a higher influence of the rent level
(Hägred & Martinsson, 2006:4). The Swedish regulation policy can be summarized in a
decent living for all to a reasonable cost with a minimum standard fulfilled.
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2.3.3 Swedish rent levels There is no law in Sweden that decides the rent level. The rent levels are set by negotiations
between Hyresgästföreningen (Tenant association organisation) and the municipal housing
company. It is important to mention that this occurs in all municipalities, all have one
negotiation. The cost change determines the demand from the municipal companies. This
shows how much the rents in total have to be raised. This cost is divided between the
properties and their apartments. The correlation here is big between age and rent level and not
so high correlation between location and rent level (Lind, 2003:153; Eriksson & Lind,
2005:32). The private landlords can still partly make profit from this rather hard sector
because they can select tenants compared to municipal companies that have to house all kinds
of people (Lind, 2003:153). The regulation leads to corruption, nepotism, black market and
criminality. To protect the unwealthy and the young there is a need of total deregulation or
alternative methods to strengthen their influence on the market (Andersson & Söderberg,
2005:46)
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The Municipal Comparison 3.1 The analyze The thesis first idea was to compare countries in Europe and their rent regulation models. The
data needed for making a research like this were; yield, rent levels, market value and the size
of the region. This seemed to be quite hard to collect the data around Europe. Companies that
were contacted are IPD and DTZ.
From here the point of the thesis changed a bit to a more Swedish perspective. The main
thought is to compare different regions in Sweden and see if there is a difference in yields fore
regions with high or low population. The main comparison is about the gap between rent level
and market value in different parts of cities with different size. According to the hypothesis
the gap is less in the diagram with rent levels than in the diagram with market value.
The rent regulation model in Sweden is hard and the rent increases have to be negotiated with
the tenant association (Hyresgästföreningen) every year. The tenant association in Stockholm
agreed for the first time to increase the rent in a different than normal this time. The
suggestion is that the location of the apartment will have a bigger role for the rent increase.
Normally, the value year of the building and the condition is the most common aspect to set
the rent increase against.
The analyse is going to involve the 25 biggest regions in Sweden. Stockholm is the biggest
one and Borlänge is the smallest one. The information is collected from Statistics Sweden and
is from 2007. As mentioned in the hypothesis there is three different things being compared
and set in to context with the population number and the location within the city. These three
are: rent levels, yield levels and transaction price.
3.2 Transaction Data Configuration All data are put in a datasheet. Transactions with a transaction value lower than 3 000 000
Swedish crowns was excluded. All transactions with a purchase price per square metre with
zero were excluded. All transactions where no residential area were involved were taken
away. Such properties are not mainly residential; some of them are development properties
and can not be counted as a residential transaction.
In general there were some transactions with the same price. This means that the transactions
have more than one property in the acquisition. This makes a calculation difficult in the end.
Because all of them have the same price per square metre. This makes the average tend to
head in that direction of where there are many properties in the transaction. Only one of the
deals could be taken into account when there are multiple properties involved. In this model
the only transaction with the same price were used in the calculations.
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3.3 Explanation of figures This section is explaining which figures are used and why they are used. The four main
figures used in the research are; rent levels, transaction price, yield and size of the region and
location within the city. The figures are collected from Datscha, which is a web-based service
for the Swedish property market.
3.3.1 Rent levels The rents are divided in different periods of years. The first period is from 2001-2005 and the
other is from 2006 and forward. The problem here is that the data from the sales comparison
just are going back just to 2003. The comparison is made with the data for the rent levels and
yield levels with the period 2001-2005 and 2006 and forward. In the transaction data the
average transaction price per square metre are divided into the same groups (2001-2005 and
2006-). The problem is that there are no transaction data for 2001 and 2002. Therefore the
average price for 2003, 2004 and 2005 are assumed to be the average over all the years (20012005). With the second group, 2006 and forward data is not missing and the average over
2006, 2007 and 2008 is used.
The data are reported in different locations in the municipality called A, B and C. This is a
measure of how central the properties are and in which group they are grouped. There are for
example a lot of areas in the different parts. This is to make it easy to see the difference in rent
levels. According to the hypothesis the effect is higher of the rent regulation in the inner parts
of a city. Further, the rent levels are divided into three segments; High, Medium and Low.
This is to see the difference between the highest and lowest rent in the different regions.
3.3.2 Transaction Price The figures of market value are collected from different property transactions with start in
2003. In the most of the 25 regions there were over 200 transactions. In some of them there
are less because there were less transactions in the city in total that specific year. Transaction
used in this comparison is dated between the 1st of January 2003 and the 31st of December
2008. The interval is divided into two different parts; 1st of January 2003 to 31st of December
2005 and 1st of January 2006 to 31st of December in 2008. Transactions that are not a good
example of data are excluded from the comparison, example on this could be transactions
with a very low price, with no or very little residential area, and residential buildings included
in other deals. For example the lowest transaction price included in the data are 3 000 000.
There are different numbers of transactions in all the regions; the lowest number of
transactions had Täby with just one in some of the compared years.
3.3.3 Yield There are different yield measures and the word yield have different meanings in different
sources. The income return, often called current yield is classified as CFt/Vt-1. This
expression is mostly used in stock market terminology. The yield used in this report is the
“total yield” or “yield to maturity”. The definition of the income return and the formula used
is: NOIt/Vt-1 (Geltner & Miller 2007). This is the Net operating (NOI) income divided by the
Property value (V). In Real Estate terms the yield is the profit earned by rental income minus
maintenance etc. In general it is the net rental income per month multiplied with 12 divided
12
by the property cost. Property tax, maintenance and deduction are not normally taken into
account when calculating the yield property investments.
In example:
Property price
Net Rental income/month
2 000 00
1000
In the example above the yield is (1000*12)/200000 = 6 % in yield
The yields from Datscha are grouped in different areas in the each of the regions. The most
central is called area A. Then there is area B and area C as well. These areas have different
yields and rent levels. To make an accurate research, a comparison between area A and
locations of area B and C will be done. Further the yields are listed as low, medium or high.
The hypothesis is that the rent control mostly affects the inner parts of a city with high rents.
Therefore the high rent- and yield levels in area A are the most interesting in this perspective
and it is here the biggest differences between the regions are expected. The same with the
comparison between the areas, the difference between area A and B are expected to be larger
than between B and C. The reason to this is that the rent regulation mainly affects the central
parts on high price levels (See the yield hypothesis diagram in chapter one).
3.3.4 Population In Sweden the population is around 9,2 million. The capital Stockholm stands for around 800
000 (Datscha 2008) of this number. The population of the Stockholm region if Stockholm
includes the outer suburbs in the count is over 1,25 million people (SCB 2008). The smallest
region in this comparison is Borlänge with a population of 47 756 people. There are just three
regions with more than 200 000 inhabitants, Stockholm, Gothenburg and Malmö. Most of the
municipalities compared are in the interval from 100 000 down to 50 000. All the
municipalities and their population are listed in Figure 1.
Figure 1 Population
13
3.4 Municipalities Different regions in Sweden have different levels of rent. Reasons for this could differ. Some
of them are population density, labour market and income level etc. In a model with market
rent, the rents are set by supply and demand formulas. In markets where the rent levels are
controlled the rents have a lower level than the level would have been with the supply and
demand model. In Sweden as mentioned before the rents are set according in an old way
called “bruksvärdesmetoden”. In words this means that the rent of the apartment is set at the
same price as equivalent apartments. Rents are set by the condition of the apartment,
modernity. The cost of maintenance, production or the costs of loans for the landlords are not
taken into account when the rents are set. Another important aspect of valuing properties is
the value year. This is the year when the buildings are produced. This is another aspect that
not is taken into account when the rents are set by this method. Normally, the rents are not
taking location in to account either. Comparable apartments are the ones in the municipal
housing company portfolios. Residential properties owned by private companies are not
classified as comparable apartments.
The comparison between the 25 regions is made with data from Datscha. The data are listed in
three different areas on Regional level (A, B and & C) where A is the inner part of the
municipality. There are also three levels of rent in each of the location groups. These are
listed as High, Medium and Low. Rent in the high group is the highest rent possible in the
specific area. Low is the lowest rent paid in the same area.
Examples of districts in the three municipalities with the highest population are listed in this
section. Stockholm, Gothenburg and Malmö are the three municipalities with highest
population. This is to give a good picture of which areas are in each of the groups. The rest of
the areas in all different municipalities are listed in Appendix 1.
In Stockholm:
• A, Old town, Kungsholmen, Södermalm, Östermalm and Norrmalm
• B, Farsta strand, Western suburbs, Hässelby and Southern suburbs.
• C, Rågsved, Skärholmen, Skarpnäck and Rinkeby-Tensta.
In Gothenburg:
• A, Gårda, Gullbergsvass, Lindholmen, Lilla bommen, Johanneberg and Masthugget.
• B, The central parts of Hisingen, ladugårdsgärdet, Frölunda torg, Guldheden and
Änggården.
• C, Hisingen without the central parts, Northeast parts of Gothenburg, Southwest of
Gothenburg.
In Malmö:
• A, Ribersborg, West parts of the old town, Bo01, Västra hamnen and Davidshall.
• B, The other part of central Malmö
• C, Oxie and Other outer parts including Kirseberg
14
4
Comparison 2001‐2005 The comparison starts with the first interval of data between 2001-2005. The data that being
compared in this chapter are rent- and the yield levels in all municipalities. The data are listed
in high, medium and low levels. Different parts of the municipalities also have different
values. The effects of rent control are highest in the central parts with high rents according to
the hypothesis. The municipalities in the diagram are showed and compared to the population
size. The cities with highest population are to the right on the figures.
4.1 Rent Levels The levels are divided into three different levels. First of all there is a high, medium and low
level. Besides this each region are divided into A, B and C area, this to separate the central
area of a city/municipality from the outer parts. This according to the hypothesis that the rent
regulation has highest influence on the inner parts. The number in parenthesis is price per
square metre per year for renting an apartment in the parts mentioned in the text.
4.1.1 High rent levels The blue line is showing area A which is the central areas in the different municipalities. High
means that this graph shows the figures with the highest rent level in the cities. As can be seen
the population is on the X-axis and the Y-axis shows the rent levels. The highest rent level in
A area as can be seen in Figure 2, has the third biggest city which is Malmö (2073).
Figure 2 High Rent Levels
This does not really going in line with the hypothesis. There are some good explanations why
Malmö have a high level of rent in general, especially in the central area. As can be seen in
Figure 2 and Figure 3 the gap between the different curves is big in Malmö compared to the
other municipalities. The fact that the rent is higher than in Stockholm (2043) could probably
be explained by the agreement that the tenant association and the municipal housing company
agreed upon in the early 1990s. The rents in the inner areas of Malmö were allowed to be
increased more than in the outer parts. The difference between the inner and outer parts is
nowadays about 25% (Hellström & Lind, 2006). Another reason could the new and fancy
area, Västra hamnen in Malmö. This central area with new buildings has high rents compared
to the rest of the city. This area is built in the old shipyard in Malmö and a big exhibition in
2001, Bo01 was situated here. This was the building boom for the area. The famous
15
residential building, Turning Torso is located here. Nowadays 1300 apartments are located at
Västra hamnen
(http://www.malmo.se/vastrahamnen).
Another reason for the general high rent level in Malmö could be the location. The city is
connected to Copenhagen with a bridge and this makes the rent level higher than it would be
without the close connection and location to Copenhagen. The other city with relative high
rent compared to the population is Helsingborg. This can be seen as the high blue dot to the
left in Figure 2. Helsingborg is also located in the south of Sweden and situated very close to
Denmark and to Malmö. Helsingborg is also connected to Denmark via ferry route to
Helsingör. This close location to Denmark, Malmö and Copenhagen makes the rent level
higher. One explanation is that Danish people live in Sweden and work in Denmark because
of the low living costs in Sweden compared to Denmark. This, and the higher salary levels in
Denmark make it profitable. Helsingborg also had a refurbishment of an old shipyard to
apartments. This was in the end of the 1990th and this is another explanation to the high rent
level.
Figure 3 High Rent Levels -Area A
Area A
Figure 3 shows the inner parts of the municipalities, Area A. The trend line shows the average
rent level as a linear line. This is the black linear line in the middle of the figure. As can be
seen most of the cities are in line with the hypothesis. The three dots that are higher than the
general is; Helsingborg (1654), Södertälje (1534) and Täby (1488). The reasoning about
Helsingborg is already done. Södertälje is located close to Stockholm and this could be the
main reason why the rents are high compared to the population. Täby is closely located to
Stockholm and could be seen as a suburb. Another thing about Täby to be mentioned is the
average income. Most people in Täby live in single-family houses. The average income is 385
000 per person (Datsha). This is higher than the average income in the country and could be
another reason for the high rent level.
16
Figure 4 High Rent Levels –Area B
Figure 5 High Rent Levels –Area C
Area B
Figure 4 shows area B, Stockholm (1640) has the highest level followed by Malmö (1555),
Södertälje (1534), Täby (1488), Uppsala (1361) and Helsingborg (1349). These cities are
shown as the six dots with highest rent levels in Figure 4. All these cities have been
mentioned before about the reason to the high rent levels. Umeå is the municipality with the
lowest rent and this with a population of 111 000 people. As can be seen in the end of the
curve the two cities with lowest population have an upward sloping curve.
Area C
The most remarkable thing is that Uppsala (965) has a very low rent compared to the
population. Uppsala is the municipality with the fourth highest population. The rent level is
one of the lowest in this comparison. Stockholm (1514), Malmö (1267), Västerås (1295),
Helsingborg (1349), Södertälje (1534) and Täby (1488) are the six peaks from right to the left.
There are many municipalities with a lower rent level than the trend line in this area. See the
bottom left of Figure 5. Otherwise there are not really any outstanding trends in this figure.
Further, it is of interest that Stockholm does not have the highest rent in this area. Södertälje
have the highest but the interesting thing about this is that Södertälje and Täby have the same
rent levels in all the three areas.
Summary High Rent Level:
The most noteworthy things about the high rent levels are Malmö municipality. The rents here
are highest in Area A and not in Stockholm as expected. Other cities with a rent level higher
than expected are Helsingborg, Västerås, Södertälje and Täby. Umeå have remarkable low
rent in area B and there is no obvious explanation to this. An interesting point with Malmö is
that the rent in area A are very high, in area B it is higher than expected. In area C the rent are
different, with Södertälje as the highest rent followed by Stockholm. As mentioned there is an
interesting point that both Södertälje and Täby have the same rent levels in all areas.
17
4.1.2 Medium rent levels The Medium level of rent shows the segment of rent between the High level and the Low
level. As can be seen in Figure 6 the highest rent level is in Malmö. The second highest is in
Stockholm and the third is Helsingborg. Gothenburg rent level is low compared to the
population of the city. Södertälje has a high rent level compared to the population and to
regions are around the same size.
Figure 6 Medium Rent Levels
The highest rent level in the comparison of the medium rent levels in 2003 has Malmö. This
can be seen in Figure 6 on the blue dot to the upper left. Second is Stockholm, the expected
position on Stockholm was the first place. The explanation and the expected value for Malmö
is that the Medium levels are high in area A as it was in this area in the High level. Many
building in area A is newly built and therefore it is expensive. Turning Torso can be
mentioned, this nowadays famous sky scrape for residential are located in area A. The rent
levels in Turning Torso are high.
Normally this number more reflects the situation in High rent level. The rent in Turning Torso
is probably the most expensive in area A in Malmö. But it should be mentioned that there are
a lot of newly built residential houses in Västra Hamnen that also have high rent levels. As
can be seen on Figure 7 the blue line curve is much steeper than the other colour. This reflects
that the rent levels are more volatile in the central areas (A area presents by the blue line). As
forecasted Stockholm has one of the highest level in all three areas. It is quite remarkable that
Malmö has higher rent levels in area A. The reasons are already mentioned above.
Figure 7 Medium Rent Levels –Area A
Figure 8 Medium Rent Levels –Area B
18
Area A
Figure 7, with the blue line showing rent level medium in area A. The highest level has
Malmö (1975), Stockholm (1945) and Helsingborg (1575). The cities that do no fit the
hypothesis line here are from the right Malmö, Helsingborg, Södertälje (1460) and Täby
(1417). These dots can be seen as the four a bit above the trend line. These are the
municipalities mentioned before. Possible explanations why the regions do not fit the
hypothesis line has also been presented. Apart from these regions the line perform as expected
line in a good way.
Area B
Figure 8, shows area B and here are some other differences than in area A. First, there are two
cities falling below the average trend line. These are Linköping (1050) and Umeå (891),
which are the 5th and 10th biggest cities. These two have low levels compared to the rest of the
group. Other extra ordinary things are that Västerås (1234) is high the 6th ,city in the
population rank. The fourth dot from the left above the average line. After Västerås, the
municipalities mentioned before have levels over the trend line, which is not in line with the
hypothesis. Another thing is the two smallest cities due to population, Trollhättan (1070) and
Borlänge (1101), have higher rents that most of the municipalities with around the same
population.
Figure 9 Medium Rent Levels -Area C
Area C
In area C, see Figure 9, Malmö (1207) is not as high as it is in A and B area. Stockholm
(1441), Västerås (1234), Helsingborg (1283), Södertälje (1460) and Täby (1417) have higher
rent levels than Malmö in this area. One explanation to this can be that in many cities the
areas do not differ much in rent levels. The bigger city the more difference in rent levels in the
different areas. Another thing here is that Uppsala (918) has low rents compared to the other.
Uppsala is a traditional student town and the population consists to great extents of students.
This could be one explanation to why the rents level is low. Inn this case the student
accommodation is excluded from the data because normally these kinds of dwellings have
high rent per square metre. Another rare thing about the highest rent level is that Södertälje
has a higher level than Stockholm at medium level in area C.
Summary Medium Rent Level
This level of rents follow the high in the way that Malmö has the highest rents in Area A. The
same pattern with Helsingborg, Södertälje and Täby as other municipalities with high rent
levels compared to the other regions. In area B Umeå has a remarkable low level of rent, the
same as in area A. The same pattern as in high rent levels is in all the three different areas. It
19
is the same thing in medium level as in high that Södertälje and Täby have the same rent
levels in all the three areas.
4.1.3 Low rent levels The low levels means that the rent level is lowest in the three graded scale, High, Medium and
Low. As before Malmö has a high level in the inner parts of the city. Second are Stockholm
and then Helsingborg on the 3rd place and then Gothenburg. This data and numbers are
explained before. Västra hamnen in Malmö is newly built and therefore the rents are high in
all levels especially in area A. Gothenburg has an area called Eriksberg that also is built in an
old harbour. The difference with Västra hamnen can be explained that the main type of
apartments in Gothenburg newly built are condominiums and not rental apartments. In this
diagram it seems like the low rent areas (A and B) have more municipalities that differ from
the hypothesis line.
Figure 10 Low Rent Levels
Area A
The diagrams below shows area A and B, see Figure 11 and Figure 12. There are some
differences between the graphs. The blue curve, Area A has just as before high rents excluded
the ones mentioned above in (Stockholm, Gothenburg and Malmö); Helsingborg (1480),
Södertälje (1460) and Täby (1417). This is the same municipalities, which had high levels
before in the High and Medium rent levels. There are no municipality with really low rent
levels and as before Malmö (1857) is the most remarkable with its very high rent levels in the
central parts of the city. Reason to this as discussed before are the high share of newly
constructed apartments. Stockholm has as expected high levels and is second in the
comparison with a level on 1828 sek/sqm/year.
Figure 11 Low Rent Levels –Area A
Figure 12 Low Rent Levels -Area B
20
Area B
The red line, which symbolizes area B can be seen in Figure 12. Here is Helsingborg (1207)
lower and Södertälje (1374) and Täby (1333) just as high as on Medium levels of rent.
Exactly the same tendencies with Linköping (987) and Umeå (838) mentioned before with
relatively low rent levels compared to the population. Uppsala is a region that shows
relatively low rents in area B and C but not in area A.
Figure 13 Low Rent Levels –Area C
Area C
As can be seen and mentioned before it is Uppsala (864), that has one of the lowest rent levels
in C area in the comparison (see the fourth dot from the right in Figure 13). This low rent with
the 4th highest population in the comparison makes the low rent level remarkable. Linköping
(902), the 5th region with highest population is also low in the comparison this is seen as the
dot after Uppsala in the diagram. After Linköping the curve raises and this is Västerås (1171).
Örebro (918) comes after with a lower rent level compared to Västerås. After these two comes
Helsingborg (1207) with a high level. The most outstanding rent levels are as before
Södertälje (1374) and Täby (1333), and this is explained by the location close to Stockholm .
Other noteworthy things are in this case Borlänge (1023) that have a much higher rent than
Trollhättan (832) in C areas but not in A and B areas.
Summary Low Rent Levels
The patterns as could be seen in the high and medium rent level are almost the same in this
part of the municipalities. In area A, Malmö is highest as in the other cases. This is logical
that Malmö has the highest in all the three levels in Area A. The municipalities not following
the trend line are as before Helsingborg, Södertälje and Täby. Stockholm has the highest level
in area B and Södertälje in area C
21
4.1.4 The average rent levels All municipalities have different rent levels and there are some patterns across the data but
some rent levels are hard to explain. The average on rent levels High, Medium and Low are
shown in the diagram below. This is to see a pattern in the different areas. The most rare
things about this diagram as mentioned before are Malmö and Helsingborg especially in the A
area. On B and C areas the most remarkable patterns are Södertälje and Täby were the rent
levels are high compared to municipalities with the similar populations as this regions. In
general the curve follows the trend line but there are some exceptions. In municipalities were
the population are very similar the rent levels can vary a lot and this is interesting. In the most
cases there are explanation about why the rent levels are high in this particular region and not
in the one with similar population. The small difference in population makes the diagram not
really looking like the hypothesis line.
Figure 14 Rent Levels in Average 2003-2005
The trendlines follows the average linear curve for the different areas in the municipalities in
the comparison. A thing of interest about the trendlines is the steepness. It is easy to see that
the trend line, which follows the blue line, which shows the central parts of the city, is much
steeper than the other two lines. The difference between Area B and Area C is not as big as
the difference between Area A and Area B. This follows the hypothesis that the rent
regulation has the most effect on the inner part of a city.
22
4.2 Yield levels The yield is the income return for property owners. How the yield is calculated is explained
earlier in chapter 3. Here, yield in different regions are being compared. The hypothesis says
that the higher population the lower yield. This has to do with the supply and demand.
Normally there is harder to get contract for a rental apartment in bigger regions especially in
the inner parts. It is more probable that the apartments are not empty and do not generate any
income. Therefore the risk is lower in cities with higher population. This is what the
hypothesis says. The numbers in the parenthesis shows the actual yield level in percentage.
4.2.1 High Yield Levels The high yield level is the highest yield in different areas in different municipalities. The blue
line represents the central areas of a city, area A. The red line represents the closer suburbs
and parts of the city area B. And area C, the green line represents outer suburbs. The yield in
the inner parts of Stockholm is about 4 %, see Figure 15 and Figure 16. The four cities with
the highest population follow a straight line. This is exactly what the hypothesis says (see the
introduction chapter). Taking the rent levels into account the regions with high rents could
with further reasoning have a low yield. The supply and demand probably has something do
to with the higher rents, which in turn probably would lead to lower yields?
Figure 15 High Yield Levels
Figure 16 High Yield Levels -Area A
Area A
On the curve above with the blue colour, Figure 16, Uppsala is the fourth dot from the right
and this follows the assumptions. The fourth biggest city has the fourth lowest yield level.
Next cities are Linköping, Västerås and Örebro. These three cities have higher yield levels
than expected. Linköping, Norrköping and Helsingborg, have the same yield level 5,75.
Västerås and Örebro have the same yield level, 6 %. Lund has a low yield level, 5 %, this
compared to the population and the other municipalities with the same size. Borås, Sundsvall
are next and both these two regions have a yield level on 6,5 %. Halmstad and Täby are two
regions with low yields compared to the population with a yield level on 5,5. Södertälje
almost is on the same level, 5,75 %. The highest yield in the data has the city with the lowest
population Borlänge. This city is situated in the interior and is small which makes the yield
high.
23
Figure 17 High Yield Levels -Area B
Figure 18 High Yield Levels -Area C
Area B
The medium level follows the same structure as the high level except that there are
municipalities with lower yields than the cities with highest population, see Figure 17. The
municipalities with small population and lower yields than Stockholm, Gothenburg and
Malmö are; Jönköping and Täby (5,5%). Lund has in this area the same as Stockholm with a
yield level on 5,75%. Next is Södertälje with a yield level on 6%. After Södertälje,
Gothenburg, Malmö and Uppsala are at the same level (6,25%). It is obvious in the diagram
that the less population the higher yield, except the exceptions already mentioned. In the C
area (the green curve) Täby has the lowest yield level on 5,5%. And after Täby; Jönköping
(6,0%) followed by Västerås, Örebro, Norrköping with a yield on 6,5 %. Täby has the lowest
level in area C but it has to be mentioned that Täby has the same yield level in all the three
areas. Reasons to this could be that there is not enough data in area B and C. Other way to see
it could be that Täby actually have the same yield level in all areas. The vacancy rate in Täby
is low, 0 % and therefore the yield level should be low.
Summary high yield level
Things of interest about the high yield levels in area A are that the cities with the highest
population follow the hypothesis line well. The cities with lower yields than expected from
the line are; Lund, Södertälje, Halmstad and Täby. The reason why Lund has a lower yield
level could be connected to the fact that Lund is a student city with a long tradition of
academia. This makes the apartments very attractive in the inner city where the university is
located. There are a lot of students who want to have their own apartment and this makes it
easy to find new tenants for the apartments. The cities with lowest population also follow the
expected structure. The city with no explanation in this comparison is Halmstad. This city is a
typical summer city. There are plenty of summer villas in Halmstad owned by people all over
the country. One reason could be that there is not so many apartments for rent here and they
are therefore easy to rent out all the time. Another explanation could be that apartments are
very easy to rent out in summertime and therefore people sticking with their old contracts.
4.2.2 Medium Yield Levels The level where the yield is medium high is supposed to act in the same way as the high level.
As can bee seen on the figure below, Figure 19 the yield is lowest in Stockholm in almost all
of the three different areas in the city.
Area A
In the inner parts of the different cities the blue curve behaves as supposed. Stockholm has the
lowest yield with around 3 %. After that, Gothenburg and Malmö have the same level.
Uppsala is the fourth biggest city to population and the yield level follows the curve to
24
Uppsala then it goes up. Jönköping, the city with the 10th biggest population have a down
going yield levels compared to the regions before. Then Lund comes as the region 12 due to
population. Lund has a low yield level compared to the other regions around the same size.
Next remarkable region is Täby with a low yield level. In the end it is as supposed; the three
cities with smallest population have the highest yield. The first sight on the diagram, it looks
like the yield line follows the hypothesis curve well, better than in area B and C. This could
be explained with that the rent regulation is strongest, in the inner parts of the city. So this
theory is the same as before in the rent levels. The yield levels are affected in the same way as
the rent levels.
Figure 19 Medium Yield Levels
Figure 20 Medium Yield Levels -Area A
Area B
The red line, area B, see Figure 21, has as the blue line the lowest yield furthest to the right
which is Stockholm. The difference is that both Gothenburg and Malmö have higher yields
than four other cities with much lower population. These regions are; Jönköping 5%, Lund
5,25%, Södertälje 5,5 % and Täby 5,25%. The regions are listed in population order with the
highest population first. These dots can be seen on the diagram below. As before the three
cities with lowest population have the highest yield levels. The reasoning around these
regions have been presented previously in the chapter.
Figure 21 Medium Yield Level - Area B
Figure 22 Medium Yield Level - Area C
Area C
Next area is the outer area of the municipality, area C and this is the green line, see Figure 22.
As can be seen in the diagram down to the right the municipalities with the lowest yields are
not the biggest ones. The two regions with lowest yields in these areas are Jönköping and
Täby both with a yield level on 5,25 %. The remarkable thing is that Täby have the same
yield. This could be explained that there do not exists any bad areas like part of the million
25
program2. Another reason could be that Täby municipality do not own any apartments. The
big residential company in the municipality is privately owned
(http://taby.se/templates/TswComplexPage.aspx?id=2995).
Summary medium yield levels
The interesting things about these levels are the difference between the areas. There are for
example big difference between the inner parts and outer parts of the municipality. The yield
levels are of course supposed to be higher in the outer parts. The interesting issue is that the
municipalities that have low yield levels vary. The steepness of the curves is of interest. In
area A the curve are much steeper than in area B and C. And this is in line with the hypothesis
that the rent control affects the inner parts of municipalities most, reducing the yield because
there is an option to change it to condiminiums.
4.2.3 Low Yield Levels The low levels seem at a first sight on the figures look the same as the medium and high level.
The only difference is that the yield levels are higher.
Figure 23 Low Yield Level
Figure 24 Low Yield Level -Area A
Area A
Stockholm and Gothenburg have the lowest yields, see Figure 23. The third biggest
municipality due to population size Malmö, has a yield on 3,75% which is higher than
Stockholm and Gothenburg. The thing in this comparison is that Lund has the same yield and
Lund is the 12th biggest municipality. Other region with low levels of yield is Jönköping,
which, have a yield on 4,25%. These are the two dots above the curve. If these two
municipalities are excluded the curve looks very good according to the hypothesis line.
2
A Residential expanding program that were supposed to create more than one million apartments in the early 1970ths.
26
Figure 25 Low Yield Level -Area B
Figure 26 Low Yield Level -Area C
Area B
The same pattern as before on medium level and area B can be seen on low as well, see
Figure 25. Jönköping and Lund have lower level than the fourth biggest municipality,
Uppsala. Södertälje and Täby have the same yield level as Uppsala. Stockholm, have a lower
yield but Gothenburg and Malmö have higher yields than these regions. The difference from
the inner parts is that Uppsala in parts not in the city centre has a lower yield than Gothenburg
and Malmö, see Figure 25.
Area C
In area C, Södertälje has the lowest yield level on 4,75%, see Figure 26. Täby is on the second
place with 5,0%. After that Stockholm and Uppsala have the same 5,25%. One remarkable
thing here is that Helsingborg has a high yield level with 6,75 % compared to around 5,5 %
for regions with the same size. An explanation to this could be that Helsingborg built many
new apartments in the inner municipality about ten years ago (BO99) and this could lead to
investors invests in the inner parts. This because there are a lot of apartments in the inner
municipality. And also could make the yield higher because it is a higher risk to invest in the
outer parts. Halmstad and Sundsvall are in the same situation, the yield in these municipalities
are higher than with compared regions around the same size. Sundsvall is the dot that have the
same yield level as Borlänge, see Figure 26 (the dot longest to the left). Trollhättan and
Borlänge, the regions with smallest population have the highest yield levels.
Summary low yield level
As mentioned before, it is interesting that the difference is so big between different regions.
Another thing about the yield is that the area C is different from the others. The pattern is
clear that the more central the lower yield in almost all of the municipalities. This goes in line
with both area A and area B. In Area C the difference is that two municipalities have lower
yield levels than for example Stockholm and one have the same level. Södertälje and Täby
that are closely located and goes away from the average before. Besides this there are no
really remarkable things about the yield levels.
27
4.2.4 The average yield levels Figure 27 shows the average of all levels of yields; high, medium and low in the different
areas of the municipalities. The three different diagrams showed before illustrates a more
detailed view of the different parts and the yield level in that specific part. This diagram is to
show the whole picture of the yield levels. In general Stockholm, the municipality with
highest population has the lowest yields. This is true in area A and B but not in area C, the
outer parts of a municipality. This could maybe be explained with for example that there are
no “bad” areas with segregation or apartments below the standard in smaller municipalities
like Täby and Jönköping, which were the regions with lower level than Stockholm and other
regions with high population in the outer parts.
The average yields levels steepness is interesting. In Figure 27, the curves steepness is easy to
see. The curve for inner parts, area A is steeper than for the B and C areas.
Figure 27 Yields in Average 2003-2005
The trendlines in Figure 27 shows the different in the steepness of the three curves. As can be
seen the blue curves trend line are much steeper than the two others. The hypothesis says that
the inner parts are more affected of the rent regulation than the outer parts. The steepness of
the trendlines shows this in Figure 27 above. The trendlines between the blue line and the red
line are more than between the red and the green curve. This is also in line with the
hypothesis.
28
4.3 Transaction levels The level of transactions is the average across different years. The first data that will be
compared is the average. This number is seen as market values for the residential properties in
the different municipalities. There is one factor missing in this data and it is the different areas
that the data on rent levels and yield levels are presented in. The transactions are only shown
in the different regions.
Figure 28, shows the different levels of transaction price in average between 2003-2005. The
regions are listed in order of population starting with Stockholm to the right. If the hypothesis
line is correct all bars should go down on a line from Stockholm to Borlänge. This is not the
case. The most interesting thing about the diagram below is that Uppsala has so high price
levels. Lund is on the third place according to the highest transaction price. Gothenburg, the
second biggest municipality is the fourth and Malmö the third due to population is in the
seventh place. Täby is a municipality with low population but the transaction levels were high
in this comparison. One explanation for Täby’s high level is that there were few transactions
made this year. In general there are many regions with transactions between 60008000/sek/sqm. About one third of the municipalities in the data is in this group. Stockholm
had the highest transaction price 2003 with 15369 and Borlänge the lowest with 3950.
Figure 28 Transaction Levels in 2003-2005
29
Figure 29, shows the different levels as dots in similar diagrams as the rent levels and yield
levels earlier in this chapter. As can be seen the trend line looks similar to the one in the
figures showing the rent levels. The transaction price per square metre is showed on the Yaxis and the population size on the X-axis. The same can be seen as in the bar chart above.
Uppsala (12333) has a high transaction level compared to the population size. The other two
dots that are above the other in the diagram are: Helsingborg (10146) Lund (11480) and Täby
(9057).
Another thing of interest is that the average price in Norrköping was low compared to other
regions around the same size. It was only just over 5000 sek/sqm, which is one of the lowest
in the comparison.
Figure 29 Transaction Average 2003-2005
30
5
Comparison 2006‐2008 First the rent levels are going to be compared in the different regions exactly like the 2003
comparison. Then the rent, the yield are being compared, last the transaction data are
compared. The numbers that are shown in parentheses after municipalities are listed in
Swedish crowns per square metre and year.
5.1 Rent levels The rent levels are data from Datscha and in the interval 1st of January 2006 and forward to
31st December 2008. There are three levels of rents; High, Medium and Low, same as in the
comparison 2003-2005. Four figures are showed in every level. The data is showed in this
way to get a good view of the interval in the different areas in each city. First, all areas are
shown in the same diagram. This diagram shows the main picture of how it looks in this
specific level. The figures coming after are showing a chart with only on area included, one
figure for each of the areas. This is to get a better picture of how it looks. The reason for this
is that it is hard to see regions that do not have high difference in population. The rent levels
are showed in price in Swedish crowns per square metre and year and are showed in the
parenthesis after the municipality name.
5.1.1 High rent levels Area A
The rent is as high as expected in Stockholm (2145) but Malmö is higher. The regions that
have to be noticed in the comparison are Malmö (2178), Helsingborg (1736), Södertälje
(1610) and Täby (1563). This can be seen as the high dots on the diagram with the blue line
below. These municipalities do not follow the expected line. There are explanations to the
high levels in all this regions and they are mentioned in the previous chapter when the rent
level in 2003-2005 was analysed. The curves look similar to the one with the compared data
between 2003-2005.
Figure 30 High Rent Levels
Figure 31 High Rent Levels – Area A
Area B
Figure 32, area B shows the same as for area A with the difference that the rent levels are
higher. The main difference is that Helsingborg (1415) does not have as high level of rent as
in area A. Besides that, Malmö (1634), Södertälje (1610) and Täby (1563) are high compared
to the rest of the regions. Stockholm has a higher level than Malmö in area B (1640 compared
to 1634). Another remarkable thing about the curve is Umeå, which has a rent level on 1006.
This is very low compared to regions around the same size. This can be seen as the lowest dot
31
on the red line, see Figure 32. One explanation to this could be the reason that Umeå are
located up north and most people live in the city centre.
Figure 32 High Rent Levels –Area B
Figure 33 High Rent Levels –Area C
Area C
In area C Stockholm as the city with most population is not in the first position. Both
Södertälje (1610) and Täby (1563) have a higher rent level than Stockholm (1514). Other
regions with high levels is Västerås (1359) and Helsingborg (1415). One notable thing is the
low level in Uppsala municipality (1013). One explanation to this could be that the most
dwellings are located in the centre of the municipalities. The same pattern is for Linköping
(1058) probably with the same explanation. Another one is that these two regions are
municipalities with a high share of students and student accommodations mainly are located
in centre close to the university.
Summary high rent level
In area A, Malmö has the highest level of rent. Explanation to this has been discussed
previously. This pattern with the highest rent in Malmö follows in area B. In area C the levels
in Malmö are as expected. Södertälje and Täby are the two municipalities with the highest
rents in C area besides Stockholm. Another remarkable rent level is Umeå’s low one in area
B. The overall picture is the same as in the previous period, 2003-2005.
5.1.2 Medium rent levels Area A
The same pattern as shown in high rent levels are shown in the medium level rents. Malmö
has the highest rent levels (2075). Stockholm is the second with (2043). Helsingborg (1654),
Södertälje (1534) Gothenburg (1497) and Täby (1488) follows. Linköping (1244) and Örebro
(1173) have low levels compared to municipalities around the same size. This can be seen in
the diagram with the blue line below.
32
Figure 34 Medium Rent Levels
Figure 35 Medium Rent Levels -Area A
Area B
In area B Stockholm (1562) has the highest level followed by Malmö (1555). The same
pattern here as before with Södertälje and Täby on the following places. Helsingborg do not
have so high rent levels in area B as in area A. Umeå has the lowest level in this comparison
with (958) and this is remarkable low compared to municipalities with the same size. But the
difference is not so big compared to the high level for Umeå. This means that rents are around
the same in the whole region.
Figure 36 Medium Rent Levels -Area B
Figure 37 Medium Rent Levels –Area C
Area C
Area C in the medium rent level is quite the same as with high rent levels. Stockholm (1441),
Södertälje, (1534) Täby (1488), Helsingborg (1348) and Västerås (1295) are the five
municipalities with the highest levels. Uppsala (964) and Linköping (1008) have low levels as
in the high level of area C compared to regions in the same size. The region with lowest level
is, Trollhättan, see Figure 37.
Summary medium rent levels
The levels in medium range are showing the same pattern as in High. Malmö is, as before, in
top of the highest rent levels. Helsingborg, Södertälje and Täby are the regions with
uncommonly high rent levels in area A. There are many dots diverge from the trendlines in
area A and area B. In area C the three regions with most population follows the trend line
well. Instead there are municipalities as Södertälje and Täby here, which have much higher
rents than supposed. One interesting point is that the gap between area A (blue line) and area
B (red line) is bigger in the high levels of rent.
33
5.1.3 Low rent levels Rent levels in the low level can be seen in the figures below. First, area A is shown after that
the red and green lines. These different curves show the areas the different municipalities are
compared in. A is in the inner parts of the city. B is suburbs and central parts and area C is
outer suburbs. In the introduction of this chapter there is a description of the different areas in
the three biggest regions this to get a good picture of what the areas are like.
Area A
The blue curve shows the inner parts of the municipalities, area A see Figure 39. As can be
seen Malmö is the city with highest rent level with 1949 crowns per square metre and year.
Stockholm, the biggest city has a rent level on 1919. Next is Helsingborg (1553) and after that
Södertälje (1442) and Täby (1400). Low rent levels in this comparison have Linköping (1169)
and Örebro (1101) and this compared to the regions with around the same size. Other low
rents are found in Umeå (1107), Eskilstuna (1059) and Halmstad (1034). These levels are low
compared to regions with around the same size.
Figure 38 Low Rent Levels
Figure 39 Low Rent Levels -Area A
Area B
Next area is area B, this curve can be seen in Figure 40. Here Stockholm has the highest level
on 1468. Malmö is second with 1462. Södertälje (1442) and Täby (1400) are in this area as all
others over the average compared to regions with around the same size. This can be explained
by the location close to Stockholm and that there are not many apartments in Täby. Umeå has
lowest rent level of all municipalities in this comparison and this is remarkable. An
explanation that is mentioned before could be the location, up north. Another region with low
rent is Linköping (1036), which is lower than many of the regions after in population order.
Area C
The green curve in Figure 41 shows area C and here can be seen that Södertälje (1442) and
Täby (1400) have higher levels than the big regions as Stockholm (1335), Gothenburg (1136)
and Malmö (1191). Uppsala the fourth city due to population has a rent level on just 907, one
of the lowest in the comparison. Umeå, Borås, Eskilstuna, Luleå and Trollhättan have lower
levels. Another remarkable number is Borlänge, which has the level 1074. This is high
compared to the municipalities with around the same size.
34
Figure 40 Low Rent Levels -Area B
Figure 41 Low Rent Levels -Area C
Summary on low rent levels
The pattern mentioned before about which regions that diverging from the trend lines are the
same as in the low level in the comparison between 2003-2005. Malmö has the highest rents
in the inner parts of the municipalities and Södertälje and Täby the highest in the outer parts
of the regions. In this case there is no difference between the two different year periods. The
same regions have the highest rents going away from the average; Malmö, Helsingborg,
Södertälje and Täby. The trend is the same, Malmö has high rent levels in Area A but not as
outstanding levels in Area B and Area C. In Area C, Malmö are far away from the top rent
levels, which are in Södertälje and Täby.
5.1.4 The average rent levels The average level is the average between the three different levels of rent; High, Medium and
Low. As can be seen in, Figure 42 the three different areas have the same colours. The blue
line represents area A, the red area B and the green area C. It is interesting that Malmö (2067)
even in the average comparison have a higher level of rent than the city with most population,
Stockholm (2036). The third top region in the average rent level comparison is Helsingborg
(1648). After Södertälje (1529), Gothenburg (1486) and Täby (1484). The region with the
lowest average level is Östersund (1082) followed by Halmstad (1096). Halmstad does not
have the lowest rent level in any of the areas so there is interesting the region is so low in the
average comparison. The thing about Halmstad is that the rent levels are the same in all three
different parts of the city.
35
Figure 42 Rent Levels In Average 2006-2008
The steepness of the curves is different; compare the trend line following the blue line to the
trend line following the red- and green line. Here it easily could be seen that the rent levels are
higher in the inner parts of the area. But it is also easy to see that the more population a region
has the higher rents in the inner parts compared to the outer parts. As mentioned before the
pattern is not 100 % clear. For example, in the case about Malmö. The gap between the rent
levels can be seen as a buy option in rental apartments in the inner parts of a region.
36
5.2 Yield levels The expected number is that the regions with high population have low yield levels. One
reason to this is that the expected risk is lower in big regions. The rent levels are supposed to
be higher, the transaction prices higher and the risk therefore lower for investing in residential
properties in the municipalities with highest populations. There are three levels of yields,
high, medium and low. This shows the interval of the yield in the different municipalities.
Apart from the three levels, the regions are divided into three different areas; A, B and C and
this represents different parts of the municipalities. This are explained deeper in the
introduction of this chapter. The yield levels are showed in the text as a number in the
parenthesis after the name of municipality, these numbers are in percentage.
5.2.1 High yield levels Area A
The high yield levels in area A is the curve that mostly reflects the hypothesis line and this
because the hypothesis are saying that the effects of the rent regulation are highest in the
central areas where the rents are highest. Stockholm (3,75) has the lowest yield, followed by
Gothenburg (4,5), Malmö (4,5) and Uppsala (5,0). The fifth lowest yield has Lund (5,0) and
sixth Jönköping (5,25). This is interesting, either Lund or Jönköping had especially high rent
levels but the yield is rather low. The three regions with the lowest population have the
highest yield levels and this is exactly according to the hypothesis line. Täby (5,5) has a very
low yield level compared to the comparison and the municipalities with around the same size.
This is the line so far that is most alike the hypothesis line.
Figure 43 High Yield Levels
Figure 44 High Yield Levels -Area A
Area B
In area B the yield levels do not match the hypothesis line as good as in Area A. The
municipalities with lowest yields are; Jönköping (5,5), Täby (5,5) and Lund (5,75). Next is
Stockholm with a level on 6 %. Both Umeå and Södertälje also have a yield level on 6%. One
interesting thing is that there IS a big group of municipalities of around the same size that
have a yield level around 7 %.
37
Figure 45 High Yield Levels -Area B
Figure 46 High Yield Levels -Area C
Area C
The green line, area C as can be seen on the summary above the regions with most population
are not the ones with lowest yields. Täby has the lowest level with a yield on 5,5 % which is
the same as in area A and area B. This makes the figures not so reliable. Another outlook is
that there is a small different in risk between different areas in Täby municipality. Jönköping
has a yield on 6 % and is the municipality with second lowest level. There is no really good
explanation to this low level in Jönköping. The yield levels are different in all the three areas,
which still makes the figures accurate. The regions supposed to be in the top are not there.
The pattern is not clear at all in area C. Stockholm (6,75), Gothenburg (7,25) and Malmö (7,5)
have high levels of yield compared to regions with less population. The highest level is in
Sundsvall (9,0) and this is worthy of notice higher than the rest of the municipalities.
Summary high yield levels
This level of yields is similar to the other year period. Stockholm has the lowest yield level in
the inner parts of the city. In the outer and middle parts other regions have lower yields. In
area B Jönköping and Täby have the lowest yield and in the outer areas Täby alone has the
lowest yield. Jönköping is a municipality where the yields are relatively low compared to
regions around the same population.
5.2.2 Medium yield levels The medium yield levels are seen in the diagrams below.
Area A
The blue line represents the inner parts of the city, area A. This is following the hypothesis
line very good. The two dots, which are lower than the rest of the regions in the medium size,
representing Jönköping and Lund. Stockholm has the lowest yield levels as expected.
Borlänge and Trollhättan have higher level than expected. There is a difference in over 4 % in
yield level between the lowest and highest.
38
Figure 47 Yield Levels Medium
Figure 48 Medium Yield Levels -Area A
Area B
The red line represents the B area in the medium interval. The lowest yield level has
Stockholm (4,75). The city after is not Gothenburg as expected but Jönköping (5,0). The next
on low yield levels is Lund (5,25), Täby (5,25) and Södertälje (5,5). The most remarkable
yield is Täby and this because the level is so low even that the municipality has so low
population level. And this is explained by the close location to Stockholm and the low level of
apartments in Täby municipality.
Figure 49 Medium Yield Levels -Area B
Figure 50 Medium Yield Levels -Area C
Area C
The green line represents the C in the different municipalities. This diagram is different
compared to the hypothesis line. The regions with highest population do not have the lowest
yield levels in this comparison. The municipalities with the lowest yields are Jönköping and
Täby with a yield level on 5,25%. As can be seen in the diagram below to the right. After
these two, Stockholm (5,75) then Uppsala, Västerås and Norrköping, all with a level of 6 %.
Sundsvall one of the municipalities with the highest yields together with Trollhättan on 8 %.
This is notable with in mind that Sundsvall is on the 14th place in population level.
Summary medium yield levels
This level shows the same pattern as before, in the central parts Stockholm has the lowest
yield levels as expected. The same in area B. the difference is as before in Area C, here are
both Jönköping and Täby below the yield level of Stockholm. It is interesting to see that in
almost all regions the level of yield raises but not in Jönköping and Täby. Other trends are
that Malmö has a yield level under the trend line in Area A. It is difference in Area B and C
where Malmö is at the line and over the line. Other things of interest are that the gap between
area A and area B are bigger than the gap between area B and area C.
39
5.2.3 Low yield levels This level of yield is the lowest level in the three grade scale; high, medium and low. The
diagram to the below left shows all the areas in the same diagram. The general view is that the
higher population the lower yield levels. The blue diagram to the left with the blue curve is
showing the inner parts of the regions in the municipalities and the yield level there. The
lowest levels are seen in Stockholm, Malmö and Gothenburg. Lund is next but not following
the expected line. Other low yield levels could be seen in Helsingborg and Jönköping with a
level on 4,25 %. In general this curve looks good compared to the expected line. Next step in
the yield curve are Halmstad and Södertälje and this is the same level as in Västerås on
4,75%.
Figure 51 Yield Levels Low
Figure 52 Low Yield Levels -Area A
Area B
The red line below to the left represents area B in the low level of yield. Stockholm has the
lowest yield level in this area with a yield level of 4%. Besides Stockholm, Jönköping and
Lund have a level of 4,5% and this can be seen in the diagram below. After this, several
municipalities on a level of 5%, these are Uppsala, Södertälje and Täby.
Figure 53 Low Yield Levels-Area B
Figure 54 Low Yield Levels -Area C
Area C
The green line represents area C and Jönköping (4,75) has the lowest yield level. After this,
Lund with a level of 5%. Stockholm (5,25), Gothenburg (6,25), Malmö (6,25) and Uppsala
(5,25) follows. The four regions with highest population have higher levels of yields than
Jönköping and Lund. One remarkable thing is that Helsingborg (6,75) has a high level
compared to the municipalities around the same size. Sundsvall is another municipality with a
higher level than other comparable regions with a level of 6,25 %.
40
Summary low yield level
Stockholm has the lowest yield levels in area A and area B. The pattern is the same as before.
In area B, Lund has the second lowest yield levels together with Jönköping. In area C,
Jönköping has the lowest yield followed by Täby. The curve almost looks like the hypothesis
the difference is some regions having lower yields than expected. The hypothesis fits best to
the blue curve, which is the inner part of a city.
5.2.4 The average yield levels The average yield levels is the average for the three different levels of yield; high, medium
and low. This average is shown in Figure 55. The three different lines represents the different
areas. The blue line represents the inner parts of the region, area A. The red line represents
area B and the green line area C, which is the most outer parts. Stockholm has the lowest
yield level in area A with a level on 2,92%. After comes Gothenburg (4,0) and Malmö on
4,08%. These three are the regions with most population and therefore this is expected
especially in the inner parts. Next is Lund with a yield on 4,42 and fifth comes Uppsala which
is the fourth region with most population. The regions with the lowest yield in area A are the
three regions with lowest population, Borlänge (6,58), Trollhättan (6,50) and Östersund with a
yield on 6,17%. The steepness of the blue curve is very different from the red and green
curve.
The municipality with lowest yield in this area is Stockholm. This with an average yield on
4,92 %. The difference in area B from area A is that the rest of the regions with high
population do not have so low yield in the comparison. After Stockholm, Jönköping (5,0),
Lund (5,17), Täby (5,25) and Södertälje with a yield level of 5,50% follows. None of these
regions have the same yield level in the different segments; high, medium and low but Täby
has the same levels in all three areas. But the difference between the segments is smaller than
in the bigger regions. For example the difference between Stockholm segments (high,
medium and low) is two percent, from 4-6%. In the same segments in Täby the difference is
just 0,5 percent, from 5-5,5%. In area B, the steepness is not the same as in the blue curve.
41
Figure 55 The Average Yield Level 2006-2008
Area C is the one most diverging from the expected. For example, the municipalities with
lowest yields are Täby (5,25) and Jönköping (5,33). After these two comes Stockholm with a
yield on 5,92%. Another thing to mention is that the regions with lowest population are in the
very top of the yields in all regions. Borlänge and Trollhättan have the highest yields in area
A and area B. In area C, Sundsvall has a higher yield level on 8,08% in average.
The steepness of the curves is interesting. Here it can easily be seen that there is a buy option
in the central parts of a city compared to the outer parts. This is symbolized by the gap
between the trend line to the blue line compared to the trend line for the red and green line.
42
5.2.5
Level of transaction 2006‐2008 The average transactions between 2006-2008 are shown in Figure 56. The expectation about
this compared data is that the higher population the higher transaction sek/sqm/year.
Stockholm is the municipality with highest population and as can be seen on the diagram
below the transaction average is highest in Stockholm with a level of 18043. The city coming
second is Lund and this with a transaction price on 13850 sek/sqm/year. Örebro is next with
12186. Then Uppsala with the transaction price 11948 and after that Gothenburg with a
transaction price of 11776. Next municipality is Södertälje with a price per square metre of
11015. The municipality with a remarkable number is Malmö with a transaction level on
8771. This is a very low level compared to the fact that Malmö has the third highest
population in the data and the rent levels here are high in the rent level comparison. Borlänge
is the municipality with lowest population and the transaction price is very low compared to
the other regions. Figure 56, shows the numbers in a staple diagram. This, to easy see the
different levels of transaction price.
Figure 56 Transaction Average 2006-2008
Figure 57, shows the price in the same way as the rent‐ and yield levels. The trend line showing the same tendencies as the rent level. This follows the hypothesis line. The curve is a bit steeper than the rent level curve. This symbolizes that the price difference is higher than the rent level difference between the regions in the comparison. Figure 57 Transaction Average Curve 2006-2008 As can be seen and as mentioned previously there are municipalities diverging from the trend
line. For example, Borlänge that has a very low transaction price compared to the rest of the
regions. Apart from Stockholm, Uppsala, Lund and Örebro have high price level.
43
6
Transactions The data is divided between the same years as the rent levels and yield levels are shown. This
means that the data in the figures are averages of the transaction prices in the interval. At last
diagrams are showing the average of the time period 2003-2008. And this is the data found in
Datscha and therefore there are no data shown before this year.
Transactions that are reasonable and being made between 2006-2008, the issue with the data
here is that the data in the rent and yield analyses are data from 2006 and forward with an
average number. This chapter is a continuing from the last with the average transaction of
2003. The rent levels and the yield levels is a three year annual average. The transaction data
are in different figures and are shown year by year. The average for all years is shown as
comparison to the price levels per year. The problem with the transaction data is that it is not
divided into the same areas as the rent- and yield levels. The data is just listed as in the
different municipalities.
6.1 Transactions 2003‐2005 In this section, the transaction from the first time period in the comparison are shown as the
transaction average year per year. The average figures are the same as in the chapters before.
In this chapter the differences between the years are shown in more detail. Therefore the
average transaction levels are not discussed in detail.
2003
The city with highest transaction levels in 2003 is Stockholm (17338), Figure 59. Uppsala is
the fourth municipality due to population and has in 2003 the second highest transaction price
with 13779. Next is Lund (11480), followed by Gothenburg (11298). There are different
numbers of residential transaction being made in the different municipalities. Eskilstuna had
34 transactions making the average. Täby is the region with fewer transactions with only one
accurate transaction for the comparison. Eskilstuna (5206) is one of the regions with the
lowest transaction price per square metre. Östersund (5040) and Borlänge (5252) are two
other regions with low transaction price. Norrköping (5142) is the region with most
remarkable price. It is the eight region in highest population rank but has a very low
transaction price. There are 21 comparable sales in this time period for Norrköping.
Figure 58 Transaction average 2003-2005
Figure 59 Transaction Price 2003
44
2004
In the data from 2004, there are five regions with high transaction price. The highest one has
Stockholm (13890). In this year Stockholm is under the trend line and this means that the
average transaction price is lower than expected. Next region is Lund (13381), Uppsala
(12724), Gothenburg (12632) and Helsingborg (12560). One interesting thing compared to
area A is that the difference between Stockholm and the rest of the regions is not that big. In
area A it differs almost 4000 and in area B this number is just about 500 sek/sqm. The regions
with lowest price are Borlänge (4620), Trollhättan (5101) and Östersund (5543), the regions
with lowest population. The transaction span is from 50 accurate transactions in Linköping to
just one in Täby.
Figure 60 Transaction Price 2004
Figure 61 Transaction Price 2005
2005
The data from 2005 showed in Figure 61 shows the price in average this year. Stockholm
(14960) is in the top. After Stockholm, Lund (14312) and Täby (13236) have the highest
prices. The average in Borlänge (1977) is very low in this specific year. Besides the highest
and the lowest levels there are no really remarkable notes about the different transactions
prices in this period. There are from 46 accurate in Borås to three in Täby made under this
year.
Summary 2003-2005
Stockholm has as expected the highest transaction price in all of the comparisons. Regions
with high average prices that were not expected are; Uppsala, Lund and Täby. A municipality
that do not have high transaction prices if taking the yield levels in the calculation is
Södertälje. The rent level was high and the yield level low. This could raise the transaction
prices for residential buildings. The regions with lowest population have low prices as
expected.
45
6.2 Transactions 2006‐2008 In this section, the transaction from the second time period in the comparison are shown as the
transaction average year per year.
2006
The data for 2006 shows the same pattern as before but with some differences, see Figure 63.
Stockholm (15326) is not the region with highest transaction price, Lund (16426) has a higher
price in this particular year. After these two regions comes; Linköping (12410), Gothenburg
(10773) and Helsingborg (10693). Borlänge and Trollhättan have the lowest price levels.
Most transactions were made in Karlstad with 59 ones and the lowest in Täby with just 3.
Figure 62 Transaction average 2006-2008
Figure 63 Transaction Price 2006
2007
Stockholm municipality has the highest transaction price in 2007 and this with a level of
21378 sek/sqm. The difference down to the second which is Lund (15777) is big. After Lund,
Södertälje (14674), Örebro (13046) and Uppsala (12374) follows. Gothenburg has low levels
this year compared to the rest of the regions. Borlänge (1555) and Trollhättan (4875) have the
lowest price this year. Halmstad is the regions with most accurate transactions this year with
37 and Täby the one with fewest, just two.
Figure 64 Transaction Price 2007
Figure 65 Transaction Price 2008
46
2008
In this year, Stockholm has the highest level on 17426, see Figure 65. Gothenburg (15567) is
next in this comparison followed by; Uppsala (14996), Örebro (13863), Västerås (11861),
Karlstad (10451) and Linköping (10359). The lowest value are found in Borlänge (1844) and
Täby (3452). It is remarkable that Täby in one year has one of the highest price levels and in
another one of the lowest. The reason to this is the low number of transactions being made in
Täby during the compared years. In the previous year, 2007, Gothenburg had a low level but
in this year a high level. In Stockholm it was the opposite in 2007 the transaction level were
high, far over the trend line, in 2008 the number in Stockholm is under the trend line.
Summary 2006-2008
Stockholm is on top in all of the compared years apart from 2006, where Lund has the highest
level. For the rest of the regions it fluctuates, Lund for example has a high level in 2006 and
2007 but not in 2008. The same tendencies with Uppsala and Örebro. It can be mentioned that
it seem to differ more in the transaction comparison than in the rent- and yield comparison.
One reason to this is of course that the data not is the same, not divided in to categories as
parts of the municipality.
6.2.1 Summary Transactions The average from the different time periods with the three year average is a better comparison
than year by year. The difference from year to year about which regions that having high
transaction prices fluctuates a lot. One example of this is Gothenburg that has a very low
transaction price in 2007 but in 2006 and 2008 the prices are over the trend line and high.
Therefore it is more accurate to use the average periods as a comparison. There is no really
difference from the two periods of year. The same pattern is seen and no conclusion can be
made from the difference in the two periods. Lund is in the top in most of the years, this is
noteworthy. As Stockholm with the highest population, Borlänge with the smallest population
comes in the end of the rank in the transaction level comparison. Borlänge had the lowest
price in almost all of the comparisons from the different years.
The expectations from the rent level comparison would have been that Malmö having higher
transaction prices in the comparison. And this because the rent levels were noteworthy high in
this region and this makes the transaction price higher. Probably the figures would have been
looking different if they were divided into the areas as in rent- and yield comparison.
47
7
Analysis The rent regulation in Sweden is of a strong character, a second-generation model. This
affects the rent levels and transaction prices for properties and apartments.
The comparison was made between the 25 regions in Sweden with highest population. The
span is from Stockholm municipality with a population of 795 000 and Borlänge with a
population of 47 000. The population spread is not equal over the compared regions. There
are three regions with a population over 200 000. 12 of the 25 compared regions are between
the interval 100 000 to 47 000. And the rest are between 100 000 to 180 000. It had been
better for the analysis if the spread had been more equal over the whole interval.
The data used in the comparison is from Datscha. Companies that are providing Datscha with
data are Newsec, Forum Fastighetsekonomi and DTZ. These are three of the leading real
estate consultants firms in Sweden.
7.1 Rent levels Rent levels in the comparison almost follow the expected hypothesis line. There are a couple
of regions not following the expected curve. One of them are Malmö, the region of Malmö
have the highest rent levels in both the year periods in the central parts of the city. In area B
Stockholm has the highest level and in area C Södertälje has the highest rent level. It is
remarkable that Stockholm not has the highest rent levels in the inner parts. The hypothesis
says that the inner parts are the area that is being mostly affected by the rent regulation. This
together with the fact that the more population a region has, the more affect a rent regulated
model have on the rent levels. The levels in Malmö are in the comparison not in line with the
hypothesis. There are a lot of different explanation to that the rents in Malmö are very high in
the inner parts of the city. One is the area called Västra hamnen, this area were in the
exhibition called Bo01 where an area in a city is rebuilt. Therefore, most of the buildings in
this area are newly constructed which in turn leads to higher rents. For example, Turning
Torso the famous sky scrape is in this area. This building has office spaces but rental
apartments as well. The apartments in Turning Torso are exclusive, with a nice view of
Öresund or the centre of Malmö. This leads to high rent levels. The highest rent in the
comparison was Malmö municipality in the inner parts of the city in 2006 with a level of 2178
sek/sqm/year. The lowest value had Trollhättan in the 2003 comparison in area C with a rent
level of 832 sek/sqm/year. The average in these two periods could be interesting to compare.
The average for 2003 and the inner parts of a municipality is 1331. This is the average of all
three levels; high, medium and high. Further, Umeå is another region with remarkable rent
levels. Umeå is the 10th region with most population in the comparison. The municipality has
one of the lowest rent levels of all in area B and C. This is noteworthy and could be explained
by that the city is located up north and the most people living in the inner parts of the region.
Other regions with higher rent levels than average are Helsingborg and Lund. Both these are
located in south of Sweden and closely located to Malmö and Copenhagen. Helsingborg has
as Malmö a new area built in 1999 to the exhibition Bo99. Lund is known as a city with very
strong student traditions. Almost one third of the population are students. This could be one
reason to the high rent level per square metre in the city centre. Student accommodations
often are small which leads to a higher price per square metre and year. Another issue about
student apartments could be that for example in Lund and Uppsala that students not tend to
stay there after graduation. This creates incentives to have higher rents because they just tend
to stay for a couple of years.
48
Reasons for regions that have rent levels higher than expected according to the hypothesis line
are for example:
• expansion of the region
• refurbishment of parts in the area
• location close to other regions with high populations
• cities with strong student traditions
• other reasons that are unknown.
7.2 Yield levels The yield levels hypothesis line as shown in the introduction is leaning in the other way, low
yield in regions with high population and a high value in regions with low population. This
tendency can be seen in the curves presented in the different yield diagrams. It is most
obvious in area A, the blue line in all diagrams. Here, it is clear that Stockholm has the lowest
yield level and Gothenburg, Malmö and Uppsala follows according to the line. This is the
pattern in all of the three different levels of yields. The only difference is that Uppsala in the
low yield level has a higher yield than expected. Here instead Lund is at the fourth place in
low yield levels.
The analysis of the yield levels in area B and area C is interesting. In these parts of the region,
the pattern is not the same as in area A, there is a clear difference between these two
diagrams. This is in line with hypothesis number four that says; the difference in yield levels
is supposed to be higher in the central parts. In the high level of yield Stockholm has the
lowest in area A but not in area B and area C. Jönköping, Täby and Lund are regions with a
remarkable low overall yield levels. The region with the lowest population has the highest
yield level, Borlänge.
In Sweden, a lot of rental apartments have been converted into condominiums. Stockholm
municipality sold a lot of properties to create a condominium living. The first thought was to
sell off houses in the suburbs to spread the home ownership share in these areas. The problem
with the conversion is that the most conversions have been going on in the inner city. There is
an option of buying a condominium in the inner parts. According to the comparison it can bee
seen that it is just in the inner parts of a city that the option is valid. There is a big difference
between the gaps from the trend line in A area compared to B area. The gap between B area
and C area is smaller. The option is valuable and there have been many persons buying their
apartment for a low amount compared to the actual market value for the apartment.
In the rent level the same patterns were seen in all the three different levels; high, medium and
low. It is not the same in the yield levels. There is a difference in most of the levels.
It is interesting that there are no connection between high rent levels and low yield levels. For
example Malmö has had the highest rent level. The level of yield in Malmö is however as
expected.
7.3 Transaction price The price of transaction in the comparison are in overall following the same pattern as the rent
levels and the yield levels. Stockholm municipality has the highest transaction prices in all
years without in 2006. In this year Lund has the highest transaction level. One of the most
noteworthy things about the transaction price is Malmö. The rent levels in this particular
regions are high but the transaction price is below the average. The transaction prices varies
from year to year and there are different regions coming after Stockholm in the top of the
49
prices. Uppsala is one of the regions with in average higher prices than the regions around the
same population. The same tendencies are seen in Lund. Borlänge is the region with lowest
population in the comparison and the transaction prices reflect this. Borlänge has the lowest
prices in all of the compared years except 2003.
Malmö has low transaction prices compared to the rent levels in the rent comparison. If the
area Bo01 were built in 2001 there have not been many transactions in this new area. The
rents, which are higher in newly constructed apartments, are included in the comparison but
the higher transaction price that follows has not been included because there has not been any
transaction in the area. This could be another probable reason for the low transaction price in
Malmö.
7.4 Key Research questions Is there a difference in the municipalities in the markets due to rent regulation?
There is a difference, according to the comparison made in this thesis the regions with high
population tends to have a higher level of rent, yield and transaction price. This is the pattern
and it reflects the hypothesis well. There are regions that differ from this, for example Malmö
that have a higher level of rent than Stockholm in the central parts of the city. One reason for
this is the more market rent oriented view in Malmö that started in the early 1990s. Other
remarkable findings are that location makes a difference. This could be seen in Södertälje and
Täby, which have higher numbers than the average. One factor to this is probably the
location, close to Stockholm.
7.5 Summary In conclusion, the pattern is that the more population there is in a municipality the higher the
rent level is. In the same way the lower the yields are and the transaction levels, price per
square metre are higher in the municipalities with more population. This is as the hypothesis
predicted. The pattern is there but it is not 100 % in any case. As mentioned before there are
different trends in the comparison. One is that municipal with newly built areas in the inner
harbour city have rather high rent levels compared to others. Other patterns are that
municipalities closely located to Stockholm have tendencies to have higher level than
expected.
Investing in Residential property in Stockholm gives a high rent and a low yield but the
transactions price are high. This is as expected, though. Malmö is a good example of a region
that investors probably would take a look at after reading this thesis. In the central parts in
Malmö the rents are high and the yields are low. The difference from Stockholm here is that
the transaction prices are low compared to the rest of the regions with high population.
Södertälje and Täby are two regions that have higher rent levels and lower yield than regions
with around the same population.
Finally, the different curves have different steepness and this explains for example the buy
option on the yield diagrams. It is interesting that the difference between A area and the outer
parts of the regions are big. The comparison also shows that the difference between the outer
areas in the different regions not is big. This further justify the forth hypothesis that the
difference in yield mostly is seen in the central areas of a region.
50
8
Discussion Problems with the comparison
The comparison is made between the 25 municipalities with the highest population in
Sweden. In Sweden there are not many municipalities with high populations. Stockholm,
Gothenburg and Malmö are outstanding. One problem with this is that the difference in
population is small between the fifth and the 25th municipalities. This makes the graphs hard
to look at. It would have been better with a bigger span of population in the comparison.
Data problems
The data used are taken from Datscha, a service provider in the real estate business. The data
used in the comparison are data in Datscha from Newsec property advisers. It would have
been better to have more data on transactions, rent- and yield levels, and make comparison
between levels from different real estate companies. This to see if there is any difference
between the companies. One of the things with the data that makes the conclusion uncertain is
the transaction part. The transactions are just made per municipality and not divided into the
three different areas as the rent- and yield levels. This makes the conclusion about which
municipalities it would be best to invest in hard to predict. It had been better if the
transactions were divided into the same areas as the other compared factors. If this been the
case it could have been a much more close and accurate conclusion made from the data.
Tenant owned apartments
From the 1st of May so called owner occupied apartments are being allowed in Sweden. This
creates a more effective market for rental apartments. As it is today with condominiums, the
owner of the apartment do not have the fully right to do whatever they want. Normally, the
owner has to ask the tenant-owners association for the right for example to rent it out in
second hand. With the new type of ownership the owner has right to let the apartment without
asking anyone about permission. The apartment is being in legal meaning a 3 dimensional
cadastral property with the owner fully controlling the property. This is probably going to lead
to a more effective market in that sense that people can buy apartments as an investment and
let the apartment out in second hand. This creates more opportunities for people that do not
afford buying their own apartments. (http://www.regeringen.se/sb/d/11132#item114972)
Thoughts about rent regulation?
The main thought about rent regulation is to stop segregation and give all people in the
society a chance to a decent living in all parts of a city. This main thought is nice and there are
no problems with this idea. In Sweden there is a black market of rental contracts that have
growing big. This black market leads to expensive living and very hard to get an apartment
for young people or people with a hard economical situation. Further, the segregation in
Stockholm and Sweden can be seen easily: For example in the suburbs, Rinkeby and Tensta.
Comparison in Europe
The analysis changed from being a cross-country analysis to an analysis focusing on the
Swedish residential market. I believe that the main point of this thesis can be used between
different countries with the same type of rent regulation. Another interesting point would be
comparing countries with rent regulation form countries with a market rent model. In this case
it would be easy to see the difference in rent-, yield levels and transaction price.
51
Appendix 1 – Areas in the different municipalities
Municipality
AA Area2
A Area
B Area
C Area
Stockholm
Old town,
Kungsholmen,
Norrmalm,
Östermalm,
Södermalm
Central parts of
Gothenburg
Bromma, Djurgården,
Essingeöarna, Frescati,
South close suburbs, west
Kungsholmen
Farsta strand, Hässelby,
south of Stockholm,
Western Stockholm
Rågsved, Rinkeby, Tensta,
Hjulsta, Skärholmen,
Vårberg, Skarpnäck
Central parts of
Hisingen, Gamlestaden,
Kallebäck, Kortedala,
Kviberg, Torslanda
Angered, Biskopsgården,
Länsmansgården, Rest of
Gothenburg
Rest of the inner city
Oxie, kirseberg included
Uppsala
Änggården, Heden
Guldheden, Krokslätt,
Masthugget, Majorna,
Norra Älvstranden, Örgryte,
Lunden, Stampen,
Bo01, Davidshall, east of
old town, Limhamn
City centre
Sävja, Nåntuna
Linköping
City centre
Västerås
Örebro
City centre
City centre
Old Town, Gottsunda,
Stenhagen, Sunnersta,
Rest of the city
Linghem, Ljungsbro,
sturefors, Vikingstad,
Rest of the city
Öster Mälarstrand
Rest of the urban area
Norrköping
Helsingborg
City centre, Kneippen
Centre, Tågaborg, Laröd
Rest of the urban area
Rest of the city
Jönköping
City Centre
Rest of the inner city
Umeå
City centre
Lund
City centre
Grubbe, Östra Ersboda,
Rest of the city,
Rörbäck
Rest of the centre
Borås
Rest of the city
Hässleholmen, Norrby, Sjöbo
Sundsvall
Bergdalen, Centre,
Norrmalm, Salängen,
Villastaden
City centre
Rest of the city
Eskilstuna
City centre
Eskilstuna city
Bergsåker, Bredsand,
Granloholm, Matfors, Skön
Råbergstorp, Skiftinge
Gävle
Halmstad
The city
Other urban area
Rest of the municipality1
Andersberg, Vallås
Södertälje
Central parts
City centre, Rotorp,
Strandgatan
City centre
Rest of the city
Karlstad
City centre, Tingvalla
The inner city
Hovsjö, Ronna and Norra
Gneta
Skåre, Skattkärr
Växjö
Rest of the city centre
Rest of the city
Luleå
City centre, East, South and
West of the city
The inner city, Östermalm
Björkskatan
Täby
Östersund
Näsby Park, Täby centrum
City centre
Bergnäset, Rest of the
city
Rest of the city
Outer Urban area
Trollhättan
Borlänge
City centre
City centre
Rest of the city
Rest of the city
Rest of the municipality1
Tjärna, Rest of the
Municipality
Gothenburg
Malmö
1
2
West of old town,
Ribersborg
. In this municipalities the column stands for area D (no area C found in the municipality)
. The municipalities with area AA these are used as area A. And the area A are jumped over.
52
Ryd, Skäggetorp
Rest of the city
Brickebacken,
Varberga/Markbacken,Vivalla
Hageby/Navestad
Outer city area northeast
and southeast
Rest of the city and
Huskvarna
Rest of the municipality
Linero, Norra Fäladen
Hägernäs
Torvalla
Appendix 2 –Coefficients trend lines in figures
Figure
Figure 3 High Rent Levels -Area A
Figure 4 High Rent Levels –Area B
Figure 5 High Rent Levels –Area C
Figure 7 Medium Rent Levels –Area A
Figure 8 Medium Rent Levels –Area B
Figure 9 Medium Rent Levels -Area C
Figure 11 Low Rent Levels –Area A
Figure 12 Low Rent Levels -Area B
Figure 13 Low Rent Levels –Area C
Figure 14 Rent Levels in Average (Blue line)
Figure 14 Rent Levels in Average (Red line)
Figure 14 Rent Levels in Average (Green line)
Figure 16 High Yield Levels -Area A
Figure 17 High Yield Levels -Area B
Figure 18 High Yield Levels -Area C
Figure 20 Medium Yield Levels -Area A
Figure 21 Medium Yield Level - Area B
Figure 22 Medium Yield Level - Area C
Figure 24 Low Yield Level -Area A
Figure 25 Low Yield Level -Area B
Figure 26 Low Yield Level -Area C
Figure 27 Yields in Average (Blue line)
Figure 27 Yields in Average (Red line)
Figure 27 Yields in Average (Green line)
Figure 28 Transaction Levels in 2003-2005
Figure 29 Transaction Average 2003-2005
Figure 31 High Rent Levels – Area A
Figure 32 High Rent Levels –Area B
Figure 33 High Rent Levels –Area C
Figure 35 Medium Rent Levels -Area A
Figure 36 Medium Rent Levels -Area B
Figure 37 Medium Rent Levels –Area C
Figure 39 Low Rent Levels -Area A
Figure 40 Low Rent Levels -Area B
Figure 41 Low Rent Levels -Area C
Figure 42 Rent Levels In Average 2006-2008 (Blue line)
Figure 42 Rent Levels In Average 2006-2008 (Red line)
Figure 42 Rent Levels In Average 2006-2008 (Green line)
Figure 44 High Yield Levels -Area A
Figure 45 High Yield Levels -Area B
Figure 46 High Yield Levels -Area C
Figure 48 Medium Yield Levels -Area A
Figure 49 Medium Yield Levels -Area B
Figure 50 Medium Yield Levels -Area C
Figure 52 Low Yield Levels -Area A
Figure 53 Low Yield Levels-Area B
53
R2
0,49989
0,32452
0,17356
0,50007
0,32537
0,17233
0,4904
0,32242
0,16887
0,49728
0,32443
0,17178
0,5498
0,14974
0,02573
0,66442
0,28528
0,4014
0,64873
0,3194
0,07273
0,63997
0,26262
0,04555
0,40861
0,52159
0,50273
0,26669
0,12597
0,50225
0,268
0,12401
0,49295
0,26468
0,12055
0,49983
0,26678
0,12369
0,59244
0,09181
0,02675
0,69734
0,27631
0,06588
0,68177
0,32038
Coefficient
Y=0,0011x+1161
Y=0,0006+1144,6
Y=0,0005x+1066,8
Y=0,0011x+1106,3
Y=0,0006x+1090,9
Y=0,0004x+1017,1
Y=0,0001x+1046,8
Y=0,0005x+1029,8
Y=0,0004x+959,48
Y=0,0011x+1104,7
Y=0,0006+1088,4
Y=0,0004x+1014,5
Y=-4E-06x+6,3407
Y=-1E-06x+6,7267
Y=-8E-07x+7,373
Y=-4E-06x+5,9132
Y=-2E-06X+6,2823
Y=-9E-07x+6,7785
Y=-4E-06x+5,4034
Y=-2E-06x+5,7971
Y=-1E-06x+6,2709
Y=-4E-06x+5,8858
Y=-2E-06x+6,287
Y=-1E-06x+6,8077
Y=-237,54x+11229
Y=0,0122x+6303,1
Y=0,0012x+1221,2
Y=0,0005x+1214,5
Y=0,0004x+1131,4
Y=0,0011x+1163,6
Y=0,0005x+1157,7
Y=0,0004x+1078,9
Y=0,001x+1101,1
Y=0,0005x+1092,8
Y=0,0004x+1017,9
Y=0,0011x+162
Y=0,005x+1155
Y=0,0004x+1076,1
Y=-4E-06x+6,3693
Y=-1E-06x+6,6735
Y=-8-07x+7,3849
Y=-4E-06x+5,9417
Y=-2E-06x+6,2577
Y=-1E-06x+6,8183
Y=-4E-06x+5,432
Y=-2E-06x+5,7724
Figure 54 Low Yield Levels -Area C
Figure 55 The Average Yield Level (Blue line)
Figure 55 The Average Yield Level (Red line)
Figure 55 The Average Yield Level (Green line)
Figure 56 Transaction Average 2006-2008
Figure 57 Transaction Average Curve 2006-2008
Figure 58 Transaction average 2003-2005
Figure 59 Transaction Price 2003
Figure 60 Transaction Price 2004
Figure 61 Transaction Price 2005
Figure 62 Transaction average 2006-2008
Figure 63 Transaction Price 2006
Figure 64 Transaction Price 2007
Figure 65 Transaction Price 2008
54
0,07094
0,67606
0,23552
0,05405
0,52116
0,50247
0,52159
0,61371
0,41259
0,34624
0,52116
0,33608
0,35326
0,48736
Y=-1E-06x+6,2687
Y=-E-06x+5,9143
Y=-2E-06x+6,2345
Y=-1E-06x+6,824
Y=-299,5x+13004
Y=0,0134x+7097,4
Y=0,0122x+6303,1
Y=0,0144x+5598,4
Y=0,0107x+6584,1
Y=0,0115x+6726,7
Y=-299,5x+13004
Y=0,0108x+6869,1
Y=0,014x+7525,6
Y=0,0152x+6897,5
References
Litterature
Andersson Roland, 2001, Hyresregleringen och stadsbyggandet (eng. Rent regulation and the
construction in the city), Ekonomisk debatt 2001 vintage 29 no2 p. 129-138
Andersson Roland & Söderberg Bo, 2002, Välfärdsvinster vid avveckling av
hyresregleringen, (eng. Welfare gains if there will be a termination of the rent regulation
system) Ekonomisk debatt 2002 vintage 30 no7 p. 621-631
Andersson Roland & Söderberg Bo, 2005 Vad tänker Mona Sahlin göra åt den orättvisa
hyresreleringen? (eng. What are Mona Sahlin going to do about the unfair rent regulation
system) Ekonomisk debatt 2005 vintage 33 no2 p. 44-47
Arnott Richard, 2003, Tenancy rent control, Swedish economic policy review 2003 no10
p.89-121
Donner Christian, 2000, Housing polices in the European union, Vienna
Elsinga Marja, Haffner Marietta, Hoekstra Joris, 2008, Rent regulation: the balance between
private landlords and tenants in six countries, European journal of housing policy vol 8 no2
p.217-233
Eriksson Kimmo & Lind Hans, 2005, Vad vet vi om hyresregleringens effekter? (eng. What
do we know about the affects of rent regulation) Ekonomisk debatt 2005 no4 vintage 33
p. 31-44
Glaeser Edward, 2003, Does rent control reduce segregation? Swedish economic policy review
2003 no10 p. 179-202
Hellström Anders & Lind Hans, 2006, Market rents and economic segregation, European
journal of housing policy vol6 no2 p. 167-189
Hägred Ulrika & Martinsson Lina, 2006, A black market in housing -a problem in Swedish
cities?, Urban planning institute of the republica of Slovenia
Lind Hans, 1994, Hur skulle en fri marknad för hyreslägenheter fungera? (eng. How would a
free market for rental apartment work?), Universitetsservice US AB
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Swedish economic policy review no10
Lind Hans, 2005, Rent regulation in Sweden: Do we see the end of a collective bargaining
experiment, Royal institute of technology
Lyytikäinen Teemu 2006, Rent control and tenants welfare: The effects of deregulating rental
markets in Finland, Government institute for economic research Helsinki, Oy Nord Print Ab
Malpezzi Stephan & Tuner Bengt, 2003, A review of empirical evidence on the costs and
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55
SOU 2004:91, Reformerad hyressättning. Betänkande av hyressättningsutredningen,
Stockholm
(eng. Reformed rent setting. Report of rent setting investigation)
Geltner David & Miller Norman et al, 2007, Commercial Real Estate, Analysis and
Investments, Thomson South-Western, p. 178
Internet sites:
Boservice syd AB, collcted 20090410
http://www.boservicesyd.se/Pages/Lagenheter.aspx?Hyresvard=2
Hyregästföreningen, André Johansson, collected 20090415
http://www.hyresgastforeningen.se/eprise/main/hgfdata/2009/02/press/press20090218_13443
6817/press20090218_134436817?orgId=&Page=&ViewMode=0#
Malmö Municipality, collected 20090410
http://www.malmo.se/vastrahamnen
The Swedish government, collected 2009-05-14
http://www.regeringen.se/sb/d/11132#item114972
Täby Municiplity, search for an apartment, collected 20090422
http://taby.se/templates/TswComplexPage.aspx?id=2995
Database
Data for the comparison; rent-, yield levels and transaction price.
Datscha database (datscha.com)
56