An Analysis on the Norwegian Housing Market

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An Analysis on the Norwegian Housing Market

How can we explain recent changes in the housing prices in Norway? A regional

approach.

Ben Larsen

Thesis submitted for the degree of Master in Economics

30 credits

Department of Economics Faculty of Social Sciences UNIVERSITY OF OSLO

Spring 2018

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An Analysis on the Norwegian Housing Market

How can we explain recent changes in the housing prices in Norway? A regional

approach.

Ben Larsen

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An Analysis on the Norwegian Housing Market http://www.duo.uio.no/

Printed: X-press printing house

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Summary

Recent increases in housing prices in Norway, especially in the capital, Oslo, has once again raised discussions on how long the prices in the housing market can continue to rise before a major correction might take place. In this thesis, I analyze the underlying fundamental driving forces in the Norwegian housing market in order to identify the main factors attributing to changes in the prices. The aim is to find the factors influencing the housing market, how they contribute to changes in the housing prices and, in the end, help understand what might happen in the future. The strength of the empirical approach, compared to previous studies on the Norwegian housing market, is that it tries to capture the demographic effects by using a difference-in-differences approach.

The selection of the explanatory variables is inspired by the results of the models constructed by Jacobsen & Naug (2004) and Levin et al.

(2009). The analysis relies on their approach in order to find the variables associated with changes in the housing prices in three major cities in Norway. The cities analyzed are Oslo, Kristiansand and Stavanger, and they are chosen based on their characteristics and the availability of data.

The results suggest that the interest rate, unemployment rate and the oil prices are the explanatory variables for either some or all three cities. The interest rate affect the housing prices in all three cities, the unemployment rate affects only Oslo and Kristiansand, while the oil price only affects Stavanger.

Further my results suggest that income, the Consumer Confidence index and demographic changes do not have a significant effect on the housing prices for either city. I also find that the level of debt might have a positive effect on the housing prices in Kristiansand and Sta- vanger, while changes in the housing stock might influence the housing prices in Oslo. However, uncertainties regarding the two last variables makes them difficult to interpret.

Finally, by combining the results from the estimations with data on the expected future development for the explanatory variables, I dis- cuss what might happen with the housing prices in near future.

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Preface

This master thesis is written in the spring of 2018, and is the end of a two-year Master’s Program in Economics at the University of Oslo.

The choice of assignment is justified by a combination of the fash- ioned applicable of the subject, and my fascination for the housing market and how it differs from other markets in the economy. Working with this thesis has been very educational and at times demanding. Trying models of my own choice when it comes to finding effects on the housing prices was particularly interesting, and has though me a lot when it comes to assessment of different models and the preparation required in order to get correct estimates.

I would like to use the occasion to thank my supervisor Jørgen Heibø Modalsli, for invaluable support and guidance throughout the whole process. Jørgen has thought me a lot, and always guided me in the right direction when I hit some crossroads. I would also like to thank Erling Røed Larsen from Eiendomsverdi AS and Eirik Åsland from NAV for supplementing me with additional data used in the thesis. I would also like to show my appreciation to the Ministry of Local Government and Modernisation for granting me a scholarship for writing the thesis and Statistics Norway for access to data and a nice place to work.

STATA do files, and other files containing all estimates are available upon request.

Oslo, May 2018

III

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List of Figures

1 Housing prices and level of debt . . . 4

2 Differences between Oslo, Kristiansand and Stavanger . . 6

3 Equilibrium Prices . . . 8

4 Collected variables . . . 20

5 Constructed variables . . . 25

6 House price and income variables for DD regression . . . . 36

7 Demographic variables for DD regression . . . 38

8 Predictions for future population growth and interest rate 43 9 Net vs. disposable income . . . 49

10 Share of population with middle education . . . 60

List of Tables

1 Tools used to evaluate the Norwegian housing market . . . 10

2 Regression estimates for CCI, 2003-2015 . . . 23

3 Regression estimates for housing prices . . . 29

4 Regression estimates for housing prices, all variables . . . 33

5 Regression estimates for demographic changes . . . 40

6 DW and LM serial correlation test results, model 1 . . . 50

7 DW and LM serial correlation test results, model 2 . . . 51

8 ADF unit root test, model 1 . . . 52

9 ADF unit root test, model 2 . . . 54

10 Regression estimates for housing prices, without (INC-HS) 56 11 Regression estimates for housing prices, without CCI . . . 58

VII

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1 Introduction

The development in the housing market in Norway has been a topic for discussion the last decade. Since 1992 the price for housing has on average increased almost every year, making most house owners in Norway richer in terms of more wealth. While most economies in the western hemisphere went into a recession after the housing bubble burst in the U.S. in 2008, the Norwegian economy, including the housing market, managed relatively well.

This thesis will first and foremost try to find the explanatory factors underlying the housing prices in recent years. What differentiate this thesis from the other papers analyzing the housing market, is that it will do the analysis at a regional level, instead of a national level. The thesis will be based on models constructed for analysis on a national level with modifications made to ensure correct measurement and consistency on a regional basis. The regions selected to be analyzed are the three cities Oslo, Kristiansand and Stavanger.

A second deviation is that I will analyze how demographic changes can be related to changes in the housing prices. By using a difference- in-differences approach, I try to check for any effect changes in age and level of education might have had on the housing market in recent years. This is particularly interesting, since such analyzes demands a longer time-line than the traditional analysis on the housing market, meaning the availability of data are often inadequate. Furthermore, I try to use the results in order to have a discussion about the future development in the housing price, using future predictions for the explanatory variables found in the regressions.

The thesis starts by presenting some more on the motivation and background behind why it can be important to analyze the housing market, and it will mention some other work that has been done, both for the Norwegian housing market and for other nations, in chapter 2 and 3. Chapter 4 will go deeper in the theory behind the specific models used, before chapter 5 presents the data used in the two models. The regressions and the results will be presented in chapter 6, before I have a brief discussion on what might happen in the near future in chapter 7. The thesis ends with a short summary and conclusion in chapter 8.

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2 Background

It is important to understand the housing market and how it works, in order to predict its influence on the rest of the economy. Today, there are discussions in the media on whether or not we have seen most of the downside of the housing prices in Norway (Walsgard & Sleire 2018).

The economic growth in Norway has picked up again after a period with weak development after the decrease in oil prices. A combination of expansive fiscal policy and low interest rates have contributed to the upturn in the economy. The need for workers has began to increase, and the unemployment rate has decreased. The housing prices has began to reflect these changes in the economy, as the prices, according to Eiendom Norge (2018), has started to stabilize in the Norwegian housing market.

One way to say something about the future of the housing prices is to analyze how prices has reacted to changes in explanatory factors in the past. Furthermore, it could also be interesting to look at regional data, in order to say something about regional differences within Norway, and their housing prices.

2.1 The Norwegian housing market

More than 75 % of Norwegian households and 83 % of all people in Norway live in a self-owned accommodation. In addition, around 95 % of loans from banks to the private market is with mortgage in housing.¹ Compared to other countries, these numbers are quite high. The percentage of people renting properties in Germany, Austria and Den- mark are respectively 47.4 %, 42.8 % and 37.0 % (Eurostat: Statistics Explained 2015).²

The Norwegian housing prices are exposed to the fluctuation in the rest of the economy, and especially the credit market and the interest rate (Jacobsen & Naug 2004), which was one of the explanatory factors when the housing prices dropped during the so-called Banking crisis in the late 80s and early 90s.

1Numbers from Statistics Norway on households and from public financial report- ing from banks and other finance companies (ORBOF)

2These differences could be explained by different definitions on cooperative residence. In Norway this is widely considered as owning a property, but there are no comments on this in the sources used for Germany, Austria and Denmark.

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2.1.1 Historical housing price development (1945-2016)

After World War II the housing market in Norway got heavily regulated.

Building materials were rationed and there were strict regulations on housing, both with regards to quantity and prices (Anundsen & Jansen 2013). These regulations lasted until July 1982, and in 1984 and 1985 there was a deregulation of the financial markets as well. According to Torsvik (1999), the lending from the banks increased from NOK 157 billion in 1983 to NOK 317 billion in 1987, which is an increase of about 50 percent in four years.³ This led a huge increase in private consumption and investment, which increased the housing prices. With a fall in the price of oil, pressure on the inflation and increases in the interest rate, the boom in the early 1980s ended with a recession in the years between 1987 and 1992, as the housing prices shown in Figure 1a represent. After the recession in 1992 the prices on Norwegian housing have increased with around 585 percent. As Figure 1b show, the prices have increased throughout the whole period, except for years like 2007 and 2013. However, these are small corrections compared to the increases in the other years.

So what are the main explanatory factor for the housing prices? Ja- cobsen & Naug (2004) show, using a time series model on the housing market in Norway, that between 1990 and 2004, interest rates, new construction, unemployment and the income of the households are the explanatory variables when it comes to changes in housing prices.

Their results show that the prices reacts fast and strong to changes in the interest rate, and they claim that a decrease in the interest rates in 2003 explains a significant part of the increase in the housing prices since May 2003. However, they do not find evidence of the prices being overvalued when looking at fundamental values described by underlying explanatory variables.

One of the tools the government in Norway used to cope with the boom in the 1980s was to increase the interest rates (Torsvik 1999).

The interest rate reached its peak in 1988 with an average rate of 16.7

%. As a result of these high interest rates the total level of debt among the Norwegian people did not grow very much in the 10 year period between 1985 and 1995. However, when interest rates began to decrease again the total level of debt in Norway increased rapidly, and between 2006 and 2016 it increased by 108 percent. Figure 1c show how both

3Adjusted for inflation.

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Figure 1: Housing prices and level of debt (a)Housing prices in Norway in the pe-

riod of 1945-2000

0200040006000Housing Price Index

1951 1961 1971 1981 19871991 2001

Year Source: Norges Bank, Eitrheim & Erlandsen (2004)

(b) Housing prices in Norway in the period of 1961-2016

05000100001500020000Housing Price Index

1961 1971 1981 19871991 2001 2011

Year Source: Norges Bank, Eitrheim & Erlandsen (2004)

(c) Level of debt and interest rates 1966-2016

5.0010.0015.0020.00 Percentage

0200000040000006000000NOK million

1966 1976 1986 1996 2006 2016

Year Total debt

Average interest rates on loans Source: Eitrheim, Gerdrup and Klovland (2004). Eitrheim, Grytten and Klovland (2007)

the total debt in Norway and the interest rates has changed between 1966 and 2016.

This increase in debt, combined with a huge increase in housing prices have spurred a discussion on whether the housing prices in Nor- way are overvalued, or if these increases can be explained by other changes suggesting higher housing prices. On the one hand, according to the estimates of International Monetary Fund (2016), the housing prices in Norway are overvalued by 40 percent. Also Elis & Rodrigues- Vales (2017) are skeptical to the stability of the housing market as they find that Norwegian housing prices are most overvalued among developed nations. On the other hand, there are other players in the market who do not believe the prices are as overvalued, and say the prices can be explained by other variables. There will only be a moderate correction in the housing prices in Norway, according to SEB-Group (2017).

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2.1.2 Oslo, Kristiansand and Stavanger

It may be as interesting to look at regional differences as national, when it comes to housing prices in Norway. There are multiple local differences that might influence the housing prices, which is evident if we look at the differences in the level of housing prices between the cities.

This thesis will look at the cities Oslo, Kristiansand and Stavanger. The tables in Figure 2 show how they differ in some of the variables that might influence the housing prices. Figure 2a show the level of mean price per square meter in each city. The price level is highest in Oslo, followed by Stavanger and at last Kristiansand. This is not very surpris- ing as Oslo is the capital of Norway and the biggest city, and Stavanger being both bigger than Kristiansand and they are the oil-capital in Nor- way, making it a popular city for people in the oil industry. Figure 2d, 2e and 2f show the development in number of people living in the cities, and by comparing them one can read the differences in total population.

As for most of the aspects of the Norwegian society, how people move seems to be influenced by the price of oil. Approximately 13 percent of all employed people in Norway work in the petroleum industry, whereas almost half live in either Oslo, Agder or Rogaland⁴ (Blomgren et al. 2015). A graph showing the connection between net moving (the difference between people moving into the city and out of the city) and the price of oil is represented in Figure 2b, for the main cities of the earlier mentioned counties. We can see a clear connection of the net moving declining when the price of oil goes down in all three cities.

This seems to be especially evident when the price of oil dropped in 2014, as we can see that the net moving for all three cities also went down.

The unemployment rate is one of the variables Jacobsen & Naug (2004) found to be explanatory on the housing prices in Norway. The table in Figure 2c show that the trend in the unemployment rate are equal in all three cities, with some small exceptions, until 2015, when there is a big correction in Stavanger. This correction is most likely due to the large drop in the oil prices the year before. The level of unemployment rate is, however, somewhat different. Stavanger has the lowest rate until 2015, when it rises and goes past both cities between 2015 and 2016.

4Oslo: 9.4 %, Agder 6.2 % and Stavanger 30 %.

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Figure 2: Differences between Oslo, Kristiansand and Stavanger (a)Mean average price per square feet

100002000030000400005000060000NOK

2006q1 2009q1 2012q1 2015q1 2018q1

Year

Oslo Kristiansand

Stavanger Source: Statistics Norway, table 05963

(b) Net moving and oil prices

20.0040.0060.0080.00100.00120.00 US Dollar

0500010000Net moving

2002 2004 2006 2008 2010 2012 2014 2016

Year

Oslo Agder

Rogaland Oil price Source: Statistics Norway, table 05539

(c)Unemployment rate

12345Percentage

2006q1 2009q1 2012q1 2015q1 2018q1

Year

Oslo Kristiansand

Stavanger Source: NAV, mail correspondence

(d)Number of poeple, Oslo

550000600000650000700000Number of people

2006q1 2009q1 2012q1 2015q1 2018q1

Year Source: Statistics Norway, table 01222

(e)Number of people Kristiansand

7500080000850009000095000Number of people

2006q1 2009q1 2012q1 2015q1 2018q1

(f) Number of people Stavanger

115000120000125000130000135000Number of people

2006q1 2009q1 2012q1 2015q1 2018q1

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3 Literature on Housing Markets

Trying to understand the mechanisms in the housing market has always been important for economists around the world. The housing market differs from many other markets by offering the biggest investment household make during their lifetime, making the market very important to both the household and the national economy as a whole.

3.1 Supply and demand for housing

The housing market framework applies to the case that economists call a competitive market where there are i) many buyers and sellers, all of whom are small relative to the market, and ii) the goods that sellers produce are perfect substitutes (Cooper & John 2012). In such competitive markets, buyers and sellers take the prices as given, implying that their actions will not have any effect on the price in the market.

The supply for housing, measured by the housing stock, is fairly stable in the short term, as it takes time to build new housing and construction of new housing is low compared to the total stock of housing (Jacobsen & Naug 2004). This indicates that the housing prices will mainly fluctuate with the demand for housing. The stock of housing will, however, adapt to the demand over time.

The supply and demand of housing for the long run is illustrated in Figure 3, together with the price and quantity. For the short run we would have a vertical supply line on the x-axis, as the supply for housing only changes in the long run. We see that as prices goes down in the long run, households will demand more housing, while the supply will go down. Factors affecting the demand for housing are changes in the income of households, concerns about the future health of the economy, a change in the interest rates for mortgages, etc. (Cooper &

John 2012). The shift in the demand curve in Figure 3 shows what hap- pens if, for instance, an increase in interest rates occurs. With higher interest rates, people with excess funds to invest will get a higher rate of return by putting their money in a bank deposit account rather than investing in property. This reduction in demand for housing will lower the quantity of housing fromQ toQ^∗, and decrease the price fromP to P^∗.

Shifts in the supply curve can be caused by changes in production

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Figure 3: Equilibrium Prices Demand

Demand Supply

Quantity Price

Q P

Q^∗ P^∗

costs and changes in regulations in the construction sectors (Cooper

& John 2012). The equilibrium price and quantity are reached at the point where the supply and demand curves intersect. These levels of prices and quantity will be reached as long as the market is perfectly competitive.

The household’s willingness to pay will depend, first and foremost, on their income and wealth. One will find that households with high income and more wealth have higher willingness to pay, compared to households with low income and little wealth.

One can, according to Nordvik & Medby (2007) divide the housing market into different segments in multiple sub-markets based on different characteristics, like type of housing, size, condition and location.

The demand for housing will therefore vary within these sub-markets, where different characteristics will effect the demand. This makes a separate analysis based on regional levels preferable to a national analysis. Furthermore, the demand for housing consist of two components (Jacobsen & Naug 2004): household demand for housing for residential purposes and demand for housing as pure investment objects. The first component is, in almost all cases, larger than the other. I will, in this paper only look at demand for housing for residential purposes including apartments in cooperatives. I then have to assume that this demand is proportional with the demand for housing.

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3.2 The role of the housing market

The influence the housing market has on the households and the economy as a whole became evident during the housing crisis in the US in 2008, when the housing market collapsed after a period of booming mortgage markets and high economic activity. This resulted in household debt overhang and led several over-leveraged financial institutions into distress, which in turn resulted in tighter lending standards in the next period. At least until the crisis, there was a widespread consen- sus in favor of policies in support of housing finance markets. Many considered access to housing finance as essential to promoting home ownership, which in turn was seen as beneficial to social stability and, ultimately, economic growth (Cerutti et al. 2017). This, however, led to a tug of war between increasing access to housing finance and containing the dangers associated with faster growing household indebt- edness.

The financial crisis in the US in 2008 influenced the world economy, and led to a recession in almost every financial market in the world, showing that the housing market interact with other markets in fash- ions not before understood nor predicated (Larsen 2018). Gustafsson et al. (2016) find that a drop in housing prices in Sweden would reduce household consumption growth and increase unemployment. In their analysis they included both domestic and foreign variables, which means their findings also can be used when studying other similar small open economies, such as Norway. This result is due to, as the production of housing goes down other major sectors will take losses when the demand for their services is reduced. This can be the production of new furniture, plumbing, electricians, and other sectors involved in the production of housing. For a small open economy, a reduction in all these sectors will have an influence on the economy overall.

However, André et al. (2006) argue that unique and dramatic house price increases are not necessarily evidence of overvaluation. They explain how one can use evidence from econometric models, affordability indicators - such as Price-to-income ratio - and asset-pricing approaches as tools to examine whether or not the housing market is overvalued. I refer to Table 1 to show the results of some different papers using these tools in order to evaluate the Norwegian housing market. Demographic development is another tool they mention in their paper. They say that, in particular, high rates of net migration, decline in the average size of

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Table 1: Tools used to evaluate the Norwegian housing market

Tools Paper Estimated

overvaluation Econometric models Jacobsen & Naug

(2004)

No overvaluation in recent years.

Price-to-income ratio Norges Bank (2005) Failing household interest burden in recent years Asset-pricing

approach

Norges Bank (2005) Actual price-to-rent ratios remain

noticeably above their "fundamental"

levels, suggesting overvaluation (2004) households and increases in population shares of cohorts of individuals in their thirties will boost housing demand by increasing the share of the population of household formation age. This is also confirmed by the report of Mankiw & Weil (1989), where they find:

. . . that an individual generates little housing demand until age 20 - that is, children do not substantially increase a fam- ily’s quantity of housing. Housing demand rises sharply between ages 20 and 30, and remains approximately flat after age 30 (1989, p. 236).

Another study looking at the effect of change in age structure is a paper by Levin et al. (2009), where they find that differences in demography between Scotland and England/Wales are consistently related to differences in housing prices. They use a difference-in-differences approach in order to find their results. I will also, in one of my models, use the same methodology later in this thesis.

There are a number of features that have impacted the economic role of housing in recent years. Supply of cheap and accessible credit for housing purchases, a low interest rate in most of the western hemisphere, making housing purchase more affordable, and the increase in the world population, and in particular the number of households are

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some of these features. So what has been the role of the housing market in recent years? A report from Regeneris Consulting and Oxford Economics (2010) find that the housing market in the UK impact the macro-economy in two ways - by the impact of activity in the housing sector and via the role of housing wealth in affecting consumption be- havior.

3.3 Housing market bubbles

One can not avoid the discussion about the housing bubble when speak- ing of the housing market. The press often include the expression in their headings in order to capture the reader’s attention, as it is a widely used expression. The term "bubble" is rarely clearly defined.

Case & Shiller (2003) say that the term refers to a situation in which excessive public expectations of future price increases cause prices to be temporarily elevated. Furthermore, the expectation of large price increases may have a strong impact on demand if people think that home prices are unlikely to fall, so that there is little perceived risk associated with an investment in a home.

So is there a housing bubble in Norway? The Norwegian professor at the Norwegian School of Economics (NHH) Ola Grytten already said in 2003 that he believes there might be a housing bubble in Norway (Torvund 2013). Grytten mention seven reasons for why the housing prices has increased to a record high level; low interest rates, high labor migration - especially in the big cities, high increase in income, low housing construction, low unemployment rate, good business conditions and new construction requirement which has given increases in construction costs. He continues by saying that there is not a bubble if these factor stays constant, however, there will be a bubble if they change. Others, as I have mentioned before, do believe that the recent increases can be explained by changes in fundamental factors, meaning there should not be any bubble in the housing market.

The discussion on whether or not we are in a housing bubble will always continue until the bubble bursts, as that is the only clear way of knowing that there was a bubble to begin with. The one thing one can analyze is whether or not the prices deviates from their fundamental factors, which might indicate the presence of a bubble. One would therefore first have to check for what factors influence the prices, and in what way.

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4 Empirical Framework

I will use two different empirical models in this thesis in order to find the explanatory factors underlying the housing prices in Norway. The first model is inspired by the model presented in Jacobsen & Naug (2004). I will here preform a regression with the time line of second quarter in 2003 until fourth quarter in 2014. The goal for the regression is to find which variables have influenced the housing price in recent years, for the cities Oslo, Stavanger and Kristiansand. The second model is inspired by Levin et al. (2009), where the goal is to find the effect demographic changes have on the housing prices. By demographic changes I mean changes in size in different population groups, sorted by age, and level of education. The time line for this model is, due to data concerns I will come back to later, set to 1970 to 2015.

4.1 Model 1: OLS

Model 1 will look at the demand for housing and will be based on an aggregated demand function developed by Jacobsen & Naug (2004), which they used in order to determine the explanatory variables for the housing prices in Norway in the period 1990-2004. With some minor adjustments, this function will also apply on a regional level.

H^D =f µV

P, V H L,Y,X

¶

, f₁<0, f₂<0, f₃>0, (1) where

H^D = demand for housing

V = total housing cost for a typical owner

P = index on prices for other goods and services than housing H L = total housing cost for a typical tenant

Y = disposable income of the household

X = a vector of other fundamental factors that influences the housing prices

f_i = the derivative of f(·)with respect to argumenti

Equation 1 says that the demand for housing increases if income increases, and decreases if the cost of housing increases relative to renting prices, or the prices on other goods and services. The vectorX con-

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tain observable variables capturing effects of demographics, the lending policy of the banks and the household expectation on future income and housing costs. These expectations are important as (i) housing is a lasting consumer good, (ii) the purchase of housing is one of the biggest investments for most households, and (iii) most households use lending as a major part of the total financing for their homes.

The costs of housing measures the value of the goods which the home owner waives by owning and using a home in a period. The real housing costs for a home owner can be defined as:

V P ≡P H

P B K =P H P

h

i(1−τ)−Eπ^{P H}i

, (2)

where

B K = housing cost for each real NOK invested in housing P H = price on average price on housing (measured in NOK) i = nominal interest rate

τ = marginal tax rate on capital income and expenses Eπ = expected inflation

Eπ^{P H} = expected growth inPH (measured as rate)

Equations 1 and 2 describe the demand for housing for living purposes.

It is reasonable to assume that the variables in these equations also will influence the demand for housing as an investment object. Arrondel et al. (2007) find that an increase in household income will increase their investment demand for housing relative to consumption.

The supply of housing will be stable in the short run. The housing price PH is the price that makes sure that the demand for housing is equal to the supply. By inserting Equation (2) into Equation (1), I can solve with respect to PH. Furthermore, I use a semi-logarithmic functional form, in order to better interpret the result:

lnP H = β1lnP+(1−β1) lnH L+β2lnY+

β3B K+β4lnH+β5g(X), (3) whereH is the total housing stock.

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Further more, I define disposable income as:

Y = Y N

P^α¹ H L^α² P H^α³, α1+α2+α3=1, α1<β1,α2<β2 (4) where YN is the nominal disposable income. Equation 4 takes into account that increased housing price will reduce the purchasing power in the housing market for households overall (Jacobsen & Naug 2004).

By rearranging 3 and 4 with respect toPH I get:

lnP H_t = ϕ1lnP₁+ϕ2lnH L_t+ϕ3lnY N_t+ϕ4B K_t+

ϕ5lnH_t+ϕ6g(X_T)+²t, (5) where

ϕ1 =(β1−β2α1)/γ ϕ2 =(1−β1−β2α2)/γ ϕ3 =β2/γ

ϕ4 =β3/γ ϕ5 =β4/γ ϕ6 =β5/γ γ =(1−β2α3)

The subscriptt indicates the period of time and²t is a stochastic residual which captures effects of excluded, non-fundamental conditions.

The variableB Kt in Equation (5) contain the expected real price increase from period t to period t+1. This is an unobservable variable, which I assume depend on the observable (fundamental) right-side variables in Equation (5), the real price increase in period t-1 and a residual vt. This residual captures the effects of psychology and other non- fundamental relationships that influences expectations when it comes to prices. I can now establish the following equation for housing prices:

ln (P H)_t = h(fundamental factors)_t+θ(real housing price- increase)_t₋₁+v_t+²t

= h(fundamental factors)_t+(deviation from fundamental value)_t

(6)

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The housing prices might deviate from its fundamental value ifθ6=0, or if the residualsvt and ²t deviate from zero. A housing bubble occurs in the market if those deviations are positive and significant. A positive change in v_t can occur due to a positive change in the expectation for future housing prices. The increase in prices will give expectations for further increases in housing price if θ > 0. This will make it relatively more lucrative to own property, which increases both the demand and prices for housing. This will, again, increase expectation for future housing prices, which in turn increases the housing prices. This process might push the housing prices way above their fundamental values, ifθ is sufficiently large. It is, however, reasonable to assume that θ<1, so the process cancels (Jacobsen & Naug 2004).

4.2 Model 2: Difference-in-Differences

Jacobsen & Naug (2004) also included variables for development in demographics in their model, but did not find any significant impact regarding these factors. I have used a different method in order to identify the impact of population changes and changes in the level of education on the housing prices. Like Levin et al. (2009), I implemented a difference-in-differences estimation (DD). This method makes it pos- sible to exclude the factors that are constant over time and between regions, such as interest rate, when finding effects on the housing prices.

This will assist in the identification of the factors that are not constant over time and across regions, such as demographic changes. Levin et al. (2009) also gave a simple explanation for the DD model:

. . . let P^Scot_t be average real house prices in Scotland in year t; and P_t^{Eng l} be average real house price in England/Wales in year t. Taking natural logarithms and first differencing, give approximations of the growth rates in Scotland and Eng- land/Wales: ∆P_t^Scot =lnP_t^Scot−lnP_t^Scot₋₁ and ∆P_t^{Eng l} =lnP_t^{Eng l}− lnP_t−1^{Eng l}. That is, the first step of the method is to create sep- arately the ’difference’ in house prices growth over time in the two regions. Subtracting the values for Scotland from the value for England/Wales gives the ’difference’ in growth rates in real house prices over time between the two: ∆P(∆Pt)=

∆¡

∆P_t^{Eng l}−∆P_t^Scot¢

. That is, the second step of the method is

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to create the ’difference’ in the house price growth between the two regions.

The major benefit of using the DD approach is that the model will net out all the other factors common to, in my case, Oslo and Kristiansand that moved the housing prices.⁵ The DD works as a regression, and it can be simplified to the following regression form:

∆(∆Yt) = α+β∆(∆Xt)+µ, (7)

where αand βare parameters to be estimated andµis the error term.

The interpretation of the parameters is quite simple. Ifβ>0, the model suggests that there is a positive relationship between the independent and dependent variable, and that the differences between the growth rate for variable∆X_t can explain differences in the growth rate for ∆Y_t. The DD estimation is not a perfect way of measuring a causal effect. A study by Bertrand et al. (2004) conclude that, because of serial correlation, conventional DD standard errors may underestimate the standard deviation, leading to over-estimation of the t-statistics and the significance levels. As a result, many papers using DD find significant results, when there in fact is no significant effect.

The impact of population size on housing prices can be captured by simply including changes in age structure, by using the share of individuals in particular age groups. The same can be done to test for the impact of changes in the level of education. Therefore, changes in the demand for housing caused by changes in demographic structure are:

D^h_t = f(N_1t,N_2t,N_3t,N_4t) (8a)

D^h_t = f(N_{LE t},N_{M E t},N_{H E t}) (8b)

where

N1t = share of individuals aged 20-29 N_2t = share of individuals aged 30-44 N_3t = share of individuals aged 45-64

5I can not include Stavanger as I was not able to find an index for the housing prices in Stavanger dating back to 1970

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N_4t = share of individuals aged 65+

NLE t = share of individuals with lower education

N_{M E t}= share of individuals with middle education

N_{H E t}= share of individuals with higher education

One can expand these demand functions in order to capture even more effects when using a DD approach. The only criteria for the variables being used is that they must differ from the locations being analyzed, as the model explains connections between differences in the locations. I will get back to this later in the thesis.

In addition, the common trend assumption, saying that Oslo and Kristiansand should face the same shocks over the relevant time period, must hold in order to use the DD approach (Levin et al. 2009). It is reasonable assume this is the case, as the cities are within the same country, meaning they are exposed to very similar shocks, both when it comes to political shocks and economic shocks.

5 Data

I will in this section explain how I collected the data for the variables used in my models, what those data represent and how they are collected in the first place.

5.1 Collected data

The following variables have been collected from public and private institutions, or through mail correspondence with employees of the institutions, making the data sets containing variables applicable for my thesis. I only use data from well known public and private agencies from Norway. More details on how the agencies collect the data can be found in the footnotes below and in their original data sets and tables.

Some of the city-level variables are constructed from individual-level micro data, as discussed below.⁶

Not all data collected are quarterly data, so I have converted some of the variables with the following approach:

6The data were accessed through my participation as a student on the research project "People and their incomes in Norway" at Statistics Norway.

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Y_2003q2 = Y_2003q1+Y_2004q1−Y_2003q1

4 (9a)

Y_2003q3 = Y_2003q2+Y_2004q1−Y_2003q1

4 (9b)

Y_2003q4 = Y_2003q3+Y2004q1−Y2003q1

4 (9c)

where

Y_2003q1= the value given for year 2003 Y_2004q1= the value given for year 2004

5.1.1 The housing price index

The index for housing prices is one of the variables where I have had to use two different sources when collecting the data. For the first model, I use data sent to me by Eiendomsverdi.⁷ These data are produced by Eiendom Norge, in cooperation with Eiendomsverdi and Finn.no. I was given monthly data dating back to January 2003, so I have adjusted them to quarterly data by finding the average values for each quarter.

Figure 4a show how the housing price indices have developed since 2003. We can see that Stavanger has had the highest increase in housing prices, followed by Kristiansand and Oslo. We can see that all three cities have a little ’dent’ in their index in 2008 and 2014.

For the second model I use a data set available from Norges Bank which is made by Eitrheim & Erlandsen (2004).⁸ This data set show the housing prices dating back to 1819. I have rebased the index, so my data use 1970 = 100, instead of 1920 = 100, which it does in the original data. The data from 1970 to 1986 is made by Eitrheim & Erlandsen (2004), while the data from 1986-2013 is collected from Norges Eien- domsmeglerforbund, and the data after 2014 is collected from Eiendom Norge.

7Through mail correspondence with Erling Røed Larsen at Eiendomsverdi AS.

8https://www.norges-bank.no/en/Statistics/Historical-monetary-statistics/House- price-indices/

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5.1.2 Interest rate

The variable for interest rate is the average interest rate on loans from the bank to the households. I only use this variable in Model 1, as I assume the lending rate does not vary between the cities. The data are collected from Statistics Norway⁹ and they are represented as the quarterly average lending rate. I use the nominal interest rate as it is more common to use, compared to real interest rate, when using an empirical model (Jacobsen & Naug 2004). To take tax into account, I calculate the average lending rate after tax by multiplying it with one minus the tax rate for each year. The tax rates were 28 percent from 2003-2013 and 27 percent in 2014.

Figure 4b show how the average lending rate has changed from 2003. The interest rate decreased between 2003 and 2006, before it increased until 2008, after which it decreased again. The interest rate have remained low since 2009/2010.

5.1.3 Unemployment rate

I have collected the data for the unemployment rate from NAV, which is the Norwegian Labor and Welfare Administration. I only use data for unemployment rate in the first model, and the original data set show the monthly unemployment rate.¹⁰ I have, as I did with the data on the housing price index, calculated the average unemployment rate for each quarter to convert the monthly data into quarterly. These are data which specifies the unemployment rate for each city, and are the percentage of unemployed people to the working force.

Also the unemployment rate follow the trend of going down between 2003-2007, before increasing after 2008. By looking at Figure 4c I also read that the unemployment rate in Stavanger is always at a lower level, compared to Oslo and Kristiansand. I suspect that this has something to do with the high level of oil activity in Stavanger, leading to more employment in other sectors in the area.

9SSB.no, table 07200.

10The data was sent to me through a mail correspondence with Eirik Åsland at NAV.

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Figure 4: Collected variables (a)Housing prices, 2003 = 100

100150200250300Index, 2003q1=100

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

Oslo Kristiansand

Stavanger

(b) Interest rate

4.005.006.007.008.00Interest rate

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

(c)Unemployment rate

12345Percentage

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

Oslo Kristiansand

Stavanger

(d)Oil price, US Dollar

050100150US Dollar

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

5.1.4 The oil prices

The prices for oil are collected from the U.S. Energy Information Admin- istration¹¹ and are quarterly average of the Europe Brent Spot Price.

Figure 4d shows how the price on oil fluctuate during the period, and how there were some big corrections in 2008 and 2014.

The oil prices are influenced by global factors, meaning that the oil prices are most likely not influenced by changes in the Norwegian housing markets, as Norway is a small open economy. The oil prices, might however influence the housing prices in Norway, as the Norwe- gian economy is very dependent on the oil sector.

11https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RBRTE&f=M

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5.2 Constructed data

Not all variables I want to include in my model are available or constructed in a way fitting my model. I have therefore made some variables on my own, by doing some calculations on data available from the different agencies.

5.2.1 Housing stock

I have made my own calculations when it comes to the housing stock variable for the first model, as there is no available data for the housing stock for each city in the period of 2003-2015. The values are calculated as follows:

Housing stock_i=Average square meter per residence_i ×

Average square meter price for residences_i × Total number of residences_i,

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where i is either Oslo, Stavanger or Kristiansand. The data for total housing value, number of houses and average square meter price are collected from Statistics Norway.¹² These data are only available in yearly intervals, so I have converted them into quarterly data, as shown in Equation (9). Due to all the calculations, caution must be made when interpreting the results shown for the change in housing stock in the different cities.

In Jacobsen & Naug (2004) the authors argue that the variable for income and housing stock are strongly correlated when adjusted for variations in the seasons. To take this into account they assume that the housing stock and income has the same effect on the long term, with opposite sign.

From Figure 5a I see that the housing stock I have constructed have increased since 2003, for all three cities. My housing stock component will be influenced by both the increase in housing prices and increase in the stock of total housing.

12SSB.no, table 09181, table 06265 and table 06035.

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5.2.2 The Consumer Confidence-index

The Consumer Confidence-index is an index made and published by TNS Gallup in Norway. This index measures the expectations of the households when it comes to their and the country’s future economy by doing different surveys. The data I use are seasonally adjusted and available at the home pages of TNS-Gallup.¹³

Jacobsen & Naug (2004) constructed this variable in order to capture the effect of confidence in the future by households on the housing prices, by adjusting the CCI for the effect of unemployment (ur) and interest rate (IR), as those variables are already included in the regression. So the variable for the Consumer Confidence-index used in the model is given by:

∆E_t = C−β1∆(I R(1−τ))_t−β2∆ur_t−β3E_t₋₁−β4I R(1−τ)_t₋₁−β5ur_t₋₁ + β6∆E_t−1−β7∆E_t−2+β8∆E_t−3+β9S1+β10S2+β11S3,

(11) where ∆Et is the part of the trend indicator that can be explained by interest rate- and unemployment effects. The construction of this variable here differs from the one in Jacobsen & Naug (2004) as I have estimated for different periods, and I have included three lags of the dependent variable in order to correct for serial correlation, see Table 2.

The new variable constructed (CCI) will be given by:

CC I = (E−F)+100·(E−F)³, (12) where E is the variable from TNS Gallup on consumer confidence on the future measured as rate over two quarters, and F is the share ofE that is explained by interest rate and unemployment, expressed by∆Et. The CCI variable is illustrated in Figure 5b.

The regression done when constructing the variable CCI includes the same variables for interest rate and unemployment rate as I use in the regression for Model 1. This means that there could be an issue of endogeneity when including the CCI in a regression with interest rate and unemployment rate. I will come back to how this interfere with the results later in the thesis.

13http://www.tns-gallup.no/globalassets/fra-webnodes/ekspertiseomrader/politikk- og-samfunn/forventningsbarometeret/forventningsbarometeret-q4-2016-tidsserie- uten-link.pdf

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Table 2: Regression estimates for CCI, 2003-2015

∆E_t (1) (2) (3) (4)

∆(IR·(1−τ))t 601.4** 166.1 265.4 -33.15

(2.03) (0.65) (1.26) (-0.16)

∆ur_t -0.149 -0.193** -0.081 -0.175**

(-1.35) (-2.35) (-1.11) (-2.52) E_t₋₁ -0.363*** -0.327*** -0.189*** -0.203***

(-5.60) (-6.68) (-3.96) (-4.73) (IR·(1−τ))t−1 -183.9* -74.91 -67.14 -156.2**

(-1.94) (-0.88) (-0.91) (-2.09)

ur_t−1 0.071** 0.040 0.048* -0.004

(2.30) (1.34) (1.98) (-0.17)

S1 0.063** 0.064*** 0.034* 0.054***

(2.05) (2.91) (1.70) (2.92)

S2 0.002 0.006 0.000 0.007

(0.16) (0.58) (0.10) (1.01)

S3 0.030 0.037** 0.015 0.035**

(1.23) (2.08) (0.93) (2.25)

∆E_t₋₁ 0.512*** 0.828*** 1.099***

(5.26) (8.20) (8.01)

∆E_t₋₂ -0.483*** -0.805***

(-4.81) (-5.15)

∆E_t₋₃ 0.357***

(2.95)

Constant 0.371*** 0.210** 0.224*** 0.053

(3.73) (2.20) (2.95) (0.64)

R² 0.630 0.815 0.889 0.908

LM 0.000*** 0.000*** 0.029** 0.695

N 47 46 45 44

t statistics in parentheses

LM is the Breusch-Godfrey p-value for serial correlation.

* p<0.1, ** p<0.05, *** p<0.01

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5.2.3 Income

The time series variable for income is constructed using individual-level micro data. I use aggregated net labor income for both models. In Ja- cobsen & Naug (2004) they use disposable income, however, there are no data available for either disposable income after 1997, or disposable income for the specific cities, so I am forced to use total income instead.

However, by comparing the variable total net income and disposable income, I see they are highly correlated, meaning the results should not be very effected by me using income and not disposable income.¹⁴ I assume that the same goes for each city.

Figure 5c show that the total income in all three cities have had a steady increase since 2006. It is interesting to see that the income do not vary like the unemployment rate. This is most likely due to the strong labor conditions we have in Norway, meaning the income of the people will not go down even though the economy is in a recession, like in 2008.

5.2.4 Debt

For the debt variable I use an aggregated variable on total household debt for each city, which I have constructed by using individual-level micro data. I have already shown, in Figure 1c, how the level of debt has increased since 1966. My data on the total debt, illustrated in Fig- ure 5d, also show how there have been a massive increase in the debt level since 2003. The increase has been steady and significant for all three cities. There have been implemented some restrictions in Norway when it comes to collection of household debt, especially when it comes to debt with mortgages in housing, in order to cope with the increasing level of debt.

5.2.5 Age groups

In Model 2 I include variables showing the changes in population dis- tribution when it comes to age. The data are collected from individual- level micro data and they represent the share of the total population within the cities of certain age groups. These data are the foundation for the Figures represented in Figure 7 on page 38. The goal here is

14See Figure 9 in the Appendix

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Figure 5: Constructed variables (a)Housing stock, 2003 = 100

100200300400Index, 2003=100

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

Oslo Kristiansand

Stavanger

(b)Consumer Confidence Index

-.010.01.02.03Index, 2003q1=100

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

(c)Total income, 2003 = 100

100120140160180200Index, 2003q1=100

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

Oslo Kristiansand

Stavanger

(d) Total debt, 2003 = 100

100150200250300Index, 2003q1=100

2003q1 2006q1 2009q1 2012q1 2015q1

Quarter

Oslo Kristiansand

Stavanger

to find whether or not differences in the age groups can explain differences in the housing prices.

5.2.6 Education

I also include three variables to check for how changes in different levels of education might effect the housing prices. I have calculated the share of total population in Oslo and Kristiansand with lower education, meaning the share of the population who have only finished mandatory primary schooling, middle education, meaning the share who have finished secondary schooling, and higher education, meaning the share with post-secondary schooling. These data are also constructed from individual-level micro data. The aim in using these variables is to see if differences in the level of education between Oslo and Kristiansand can explain differences in the housing prices.

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6 The Regressions and Results

This section will more thoroughly describe the regressions I have done in order to find the explanatory variables on the housing prices. I also present the results from the regressions before I make some comments on the serial correlation within the data, and how I have solved this issue. In the end I will present some results from a unit root test pre- formed in order to make sure the variables in the regressions have the correct form.

6.1 Model 1: OLS

As I have said before, the aim for this model is to find the explanatory variables underlying the housing prices for each city, in order to explain recent fluctuations in the prices. The regression will try to capture all effects that are not demographic, and the results will tell me if there are any significant effects on the housing prices, and how much each variable can explain changes in the prices.

6.1.1 Regression

The regressions I use are based on the theory presented in the section on the empirical framework. I model a price index for housing for the cities Oslo, Kristiansand and Stavanger. The list of explanatory factors is inspired by the paper of Jacobsen & Naug (2004), and the variables are explained in the previous section. I also include current and recur- ring values of the variables in order to take into account that some of the adjustments might take some time.

When testing for serial correlation in the data set, Jacobsen & Naug (2004) use the Durbin-Watson (DW)d statistic. In their regression they report a d statistics of 2.57. This DW-test statistic is within a region where we can not say anything about whether or not we have serial correlation in the data, meaning we optimally need to use some other test. Also, a Master thesis made by Fredriksen (2007) show, by using a different test, that there are signs of serial correlation in the same data set used by Jacobsen & Naug (2004). My DW results also got within the interval where one can not say anything about the serial correlation, so I therefore use a different test, the Breusch-Godfrey test for my data. I found that the regression for Oslo and Kristiansand have

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no serial correlation while the regression for Stavanger do, see Table 6 in Appendix B. I therefor add a lagged variable for the dependent variable to the regressions in order to be able to compare the results:

y_t = α0+α1y_t₋₁+²t. (13)

A model with a lagged variable of the dependent variable is often called an AR(1) model (Enders 2008).

I have not included any demographic variables in this regression as one will need to analyze a model with a longer estimation period, as demographic conditions change slowly. Jacobsen & Naug (2004) had a similar conclusion. This will be covered in Model 2.

The estimated regressions for Model 1 are:

∆H I_t = α+∆I NC_t−∆(IR·(1−τ))_t−∆(IR·(1−τ))_t−1+CCI_t (14a)

− H I_t₋₁−(IR·(1−τ))_t₋₁−U R_t−(I NC−H S)_t₋₁+∆D_t+∆O_t + ∆H I_t₋₁+S₁+S₂+S₃

∆H It = α+∆I NCt−∆(IR·(1−τ))t−∆(IR·(1−τ))t−1+CCIt (14b)

− H I_t₋₁−(IR·(1−τ))_t₋₁−U R_t+∆D_t+∆O_t+∆H I_t₋₁+S₁+S₂+S₃

∆H I_t = α+∆I NC_t−∆(IR·(1−τ))_t−∆(IR·(1−τ))_t−1+CCI_t (14c)

− H I_t₋₁−(IR·(1−τ))_t₋₁−U R_t+∆O_t+∆H I_t₋₁+S₁+S₂+S₃ where

H I = Housing price index IR = Interest rate

τ = Tax rate, 28% from 1992-2013 and 27% in 2014 CCI = The constructed Consumer Confidence index U R = Unemployment rate

H S = Housing stock D = Debt

O = Price in oil

S_i = Variable equal 1 in quarteri, zero otherwise

∆is a difference operator: ∆X_t=(X_t−X_t₋₁)and cursive letters represent variables measured on a logarithmic scale. These regressions are done for all three cities and the results will be analyzed in the next section.

An Analysis on the Norwegian Housing Market

An Analysis on the Norwegian Housing Market

How can we explain recent changes in the housing prices in Norway? A regional

approach.

Ben Larsen

Thesis submitted for the degree of Master in Economics

30 credits

Department of Economics Faculty of Social Sciences UNIVERSITY OF OSLO

An Analysis on the Norwegian Housing Market

How can we explain recent changes in the housing prices in Norway? A regional

approach.

Ben Larsen

Summary

Preface

Contents

List of Figures

List of Tables

1 Introduction

2 Background

2.1 The Norwegian housing market

3 Literature on Housing Markets

3.1 Supply and demand for housing

3.2 The role of the housing market

3.3 Housing market bubbles

4 Empirical Framework

4.1 Model 1: OLS

4.2 Model 2: Difference-in-Differences

5 Data

5.1 Collected data

5.2 Constructed data

6 The Regressions and Results

6.1 Model 1: OLS