Economic variables affecting the housing market

Get Complete Project Material File(s) Now! »


This chapter presents the method used to create an understanding for the cause behind the price increase seen in Oslo’s housing market. The purpose of the method is to create a path that in the end leads to the conclusion of whether the price increase on Oslo’s housing market is believed justified or a price bubble is to be suspected. The method will begin with a look at the purpose of the research. Then the research philosophy and approach is discussed. This will be followed by a debate about the quantitative and qualitative data collection, which is continued with a description of the method used to solve each research question. The chapter ends with looking at how validity and reliability of this study is secured.
It is the purpose of this research that decides whether explorative, descriptive or explanatory research is to be used (Yin, 1994). This study initially wants to describe the price increase seen in the market. However the main purpose of this study is to explain the relationship between the price increase of housing with some observed changes in the market, which represents an explanatory research (Robson, 2002). Thus, this research will be mainly explanatory.

Research philosophy and approach

With three research questions guiding the study it was difficult to choose one appropriate research philosophy. The positivist ontology has its roots in natural science where an objective study based strictly on facts should be made, without including personal impressions (Saunders, Lewis & Thornhill, 2007). It was difficult, mainly because of the third research question that includes impressions and different interpretations of the housing buyers’ thoughts and behavior, to only touch upon facts in this research. The different areas of the research questions imply that neither positivist nor pure interpretivist ontology is the appropriate research philosophy for this study. In cases like that the applicable research philosophy to choose, which uses both ontologies is the pragmatist view (Saunders et al., 2007).
The study of Oslo’s housing market will take on mostly a deductive approach, as a main characteristic of the deductive approach is to explain relationships between different variables. However, the deductive approach calls for a structured research (Saunders et al., 2007), and the study’s outcome should either confirm the theory or create a modification to the theory (Robson, 2002). This is not in line with this study’s aims. Yet the alternative; the inductive approach is a study that wants to develop means that can help create a theory (Saunders et al., 2007), which is even further outside the frame of this research.

Quantitative and qualitative methods

A main question in the method choice was to determine whether a quantitative or a qualitative method should be used. Quantitative research method, which is influenced by the researcher being in control and beforehand determining possible results, is highly associated with the usage of statistical data (Holme & Solvang, 1997). Therefore the quantitative research approach fits the study that the author wants to perform, at least partly as market numbers are a big part of the study. However, the purpose of this thesis also concerns a high degree of understanding, which symbolizes a qualitative research approach. Nonetheless, qualitative methods can be made systematically with quantitative influences. A qualitative approach aim is to create an understanding while a quantitative approach wants to explain (Holme & Solvang, 1997). This study tries to do both, which have led to a mixture of both qualitative and quantitative methods being used as an overall method approach. In the end qualitative data creates an overall picture of the research area and an increased understanding (Holme & Solvang, 1997), in line with the aim of this research. Thus, this study strives towards a qualitative result that is gained through the usage of both using qualitative and quantitative methods.
The numbers and graphs used, represent the quantitative approach, even if no statistic calculation has been performed. The statistical data is based on premade numbers and statistical data gathered mainly from Statistisk sentralbyrå (Statistics Norway, SSB). A graph representing the price increase of housing since 2002 initiates the empirical study to visualize what the following study wants to explain.
The qualitative information in this thesis is almost exclusively obtained through interviews with real estate agents.
Since the solution to the problem of this thesis relies on the three research questions, which require adapted research, a method for each question will be given respectively. Each method has been used as a tool for gathering the desired information in order to solve the problem and contribute to some new knowledge, in accordance with the function of a chosen method (Holme & Solvang, 1997).

Question 1 – Market variables

In order to judge whether the price increase in housing in the last decade can be attributed to changes in the market variables, different variables are presented in graphs, representing their annual changes between the years of 2002 and 2012. Unfortunately some variables’ data for 2012 were not compiled yet and consequently the final data is from 2011 instead. Data representing the following variables have been collected:
• Changes in construction costs
• Income development
• Interest rate levels on property loans
• Unemployment and employment numbers
• Population growth, and
• Housing supply development.
The interest rate, income and construction cost increase have not been deflated as the price increase of housing was given in nominal terms and because the inflation has been rather low in Norway. The average inflation rate in Norway, between 2000 and 2011 was around two percent (SSB, 2013b). Interest rate represents part of the owner cost of housing, which also includes tax as stated in the theory. However, in Oslo there is no property tax to take into consideration (SSB, 2013c), interest rate will therefore in this study represent the full cost of housing. Employment statistics was only available from 2006 and the unemployment figure will be complemented with a few other countries’ unemployment rate for reference and to enhance the understanding of Oslo’s level of unemployment.
Housing supply is given in number of completed dwellings and number of started dwellings, both in relation to population, as the amount of increasing supply otherwise would be less interpretable. Interpretation of the variables will be performed continuously as the graphs are presented but an overall interpretation of the aggregated effect of the changes in the market with respect to the given theory comes after the presentation of the variables.

Question 2 – Indicators

The indicators’ that were tested on Oslo’s housing market are mostly presented over time, for comparison reasons. The results are partly presented in graphs and partly in tables with exception of the indicators regarding the credit market that are briefly discussed, while that information is not just given in just simple figures. The indicators listed below are the ones that have been tested on Oslo’s housing market:
• Price vs. income and rent
• Price-to-income ratio
• Price-to-rent ratio
• Number of homeowners vs. tenants
• Age of entering home ownership
• Interest payments in relation to income
• The credit market, loan-to-value ratio
Data is gathered from SSB, but supplemented with information about the banks’ lending policies from Finastilsynet’s webpage. Since housing markets are very heterogeneous it will not give much result to compare the indicators to standardized indicators from other housing markets. To gain result the indicator have in the same way as the economic variables been compared over time to see if the market has made any remarkable changes towards an unstable housing market that would indicate a bubble. The timeframe is however, more flexible due to limitation in the statistical source’s data, regarding some of the chosen indicators. The indicators will similarly to the variables be interpreted systematically as the indicators are given.
Some of the indicators mentioned in the frame of reference not listed above, involving excitement, word-of-mouth, buyer believes and motivations, which are hard to gain information about in statistical numbers, will be researched together with the rest of the study of the buyers’ behavior in Oslo’s housing market.

Question 3 – The housing buyers’ behavior

The third question wants to gain access to the market expectation of Oslo’s housing buyers, since irrational market expectations are said to be the cause of bubbles. “Survey evidence on people’s expectations about future house price appreciation can therefore be a useful tool for diagnosing a bubble.” (Norges bank, 2013, p. 4). It is the belief of this research that information about the buyers’ market expectations can provide valuable information in itself, as well as to support and reflect on the information gained from the two previous research questions. With limited ability to gain access to first-hand information about the buyers’ expectations, real estate agents were targeted as the source of information about the buyers’ behavior on Oslo’s housing market. Thus, information about the housing buyers’ possible motivation, beliefs and expectations has been collected through telephone interviews, made with a number of real estate agents in Oslo.

The survey

The used format in the telephone interviews was a standardized questionnaire, without any given answers to choose from. The first thought was to let the agents think of trends and behavior among the buyers without being directed into already given answers by using completely open-end questions. This kind of qualitative interviews are very flexible but the flexibility can also be a problem (Holme & Solvang, 1997). It was concluded that open questions would lead to too much varying answers with the consequence of little usable or interpretable information obtained. Therefore more standardized questions were needed to gain wanted information but it was still important that the real estate agents had the possibility to express their individual view and make comments. The interpretation of the answers is based on the theory of behavioral finance and the part of the bubble indication theory that involves irrational behavior among house buyers, which have also helped form the questions. The survey in itself was influenced by the survey Case and Shiller (2003), preformed in their study of the US house market.

READ  Given idea of modular sensor

Survey questions

The questionnaire, which can be found in appendix 1, consists of twelve questions. Possible attendant questions, marked with a hyphen were asked if the answer given was not sufficient or if the agent misinterpreted the main question.
In order to separate the agent’s own beliefs about Oslo’s housing market, from what the agent has interpreted as the buyer’s belief, questions about the agent’s opinions initiated the interviews to make the agent aware of her or his own views first (see question 2-4). Question 4, regarding the agent’s opinion of when housing prices would fall, was asked to also to see if the housing market prices, according to the agents, are perceived as predictable. Question 5, simply enquires the buyer’s beliefs and expectations regarding price and future price, while question 6 and 7 concerns demand changes. Especially question 7 that asks whether it is more common today with housing being sold above asking price, wants to tests whether the demand can be symbolized as irrational. Question 8 represents the buyers’ view of housing as an investment as well as question 9 that asks whether the average owning time of housing has decreased since housing sold rather quickly after the purchase would indicate speculation among buyers. Question 10 concerns the buyers’ appreciated risk associated with housing purchase and question 11 requests whether there are buyers who feel a sense of urgency to enter the market because of price expectations or any other expectations. Question 12, the final question wants to measure the general excitement that influences the housing market of Oslo. It brings up the word of mouth phenomena and inquires if there are any topic regarding the housing market that are the more frequently discussed.

The Selection process

The negative aspect of interviewing estate agents instead of homebuyers and recent homeowners directly is that the information about buyers’ beliefs becomes secondary information. Hence, the view and interpretation of the agents will be added to the information. The positive aspect however, is that real estate agents are able to report new trends in buying behavior that they acknowledge over time, which the buyers themselves might not be aware of. They are able to see market differences in relation to former states of the market. Further, interviewing one real estate agent covers a high number of buyers and potential buyers. Even if an estate agent does not remember all people it is more efficient than interviewing or sending out paper forms for homebuyers to fill out. Getting in touch with these new homeowners is also much more difficult than it was to find contact information to real estate agents.
The selections of real estate agents were random with the exception of only targeting agents that have been working five years or more in the housing market to secure their trend analyzing ability. Unfortunately the response among women was lower for unknown reasons. A small amount of nine agents have been interviewed which symbolize a less distanced and more selective method than what is characterized in a strictly quantitative survey (Holme & Solvang, 1997), hence the information obtained in the interviews are foremost qualitative.

The result

In quantitative research, the information gathered is often translated into measurable numbers (Holme & Solvang, 1997), which have not been done with the information gathered in the interviews. Survey questions that showed a lot of similar response is seen as more representative of the market. The aim of the interviews was to be able to present a view of the buyers expectations in the housing market and for that reason the answers have been interpreted by the author in the light if of given theory, which is in line with a qualitative method approach (Holme & Solvang, 1997). The interpretation of the agents’ answers is done successively as the interview answers are presented but a recap of the answers will be in the end of the result.
In a concluding analysis the interpretations and results gained in the three separated parts of the empirical study is analyzed. The three parts; the market variables, the indicators and the observed buyer expectations are analyzed with respect to one another and with respect to theory in order to reach a collective conclusion that fulfills the purpose.

Validity and reliability

Reliability of a study means that a comparable result would be gained if another researcher performed a similar research. This is difficult to sustain in a qualitative research like this, as you not only need to perform the study in the same environment but also during the same time (Bryman & Bell, 2003). As mentioned in the theory there is always a Zeitgeist, a spirit of a certain time era that is influencing the people. That spirit is part of what this study wants to capture, which will be hard to recapture at a later occasion.
The danger of using interviews as a source of material for the study is the great risk of the author, performing the interviews, being biased and influencing the interview participants.
This is a huge threat to a study’s credibility and reliability (Saunder et al., 2007). In this study however, the interviews are just one out of three views presented about Oslo’s housing market. Complementing with information taken from other sources increases the research’s validity (Yin, 1994). Further, the market numbers that are used represent a sort of information that is more difficult to bias than the information gained in interviews.
Validity represents how accurately the research has succeeded to measure and describe the researched area (Bryman & Bell, 2003). In the study of different market powers’ effect on housing price it has been hard to tell the exact effect they have had on price, unfortunately. In order to increase reliability and validity, real estate agents that had been working in the housing market at least five years were targeted. It ensured greater validity of the agents’ answers. Further reliability was increased as the estate agent own beliefs were distanced from the buyers’ beliefs in the interview questions.

Empirical findings and interpretation

The empirical chapter consists of three parts. The three parts are divided after the three research questions, but all the same contain information about Oslo’s housing market. It is also a natural lead in the task of trying to understand Oslo’s housing market, to first look at changes in market variables. The second part presents the result from the housing bubble indicators and the third part will present the result and interpretation gained in the interview process concerning the housing buyers’ beliefs, expectations and behavior.
Before entering the main findings of this research, a look at the price increase of housing in Oslo between 2002 and 2012 is given in graph 4.1, which is the time frame this study focuses on.

Part I. Economic variables

The first part of the findings presents the changes in market variables observed in Oslo’s housing market, which is given in a number of graphs. Further the economic variables are also interpreted in order to gain practical information.

Construction cost, income and interest rate

The three graphs below illustrates Oslo’s construction cost development, income pattern and Norway’s change in interest rate level during the last decade. These are given in the same order as just stated, in graph; 1.1, 1.2 and 1.3 respectively.
Starting with graph 1.1 representing construction cost development for the last decade it can be seen that construction cost has been increasing rapidly and fairly steady during this time. Compared with the price increase in housing in graph 1 above, construction cost development seem to correlate fairly well with housing prices except for the time housing prices were staggering between 2007 and 2008. Construction cost is not mentioned as one of the main factors affecting price increase but inevitably the price of housing has to at least cover the increased cost of construction. As seen in the graph the cost of construction have almost increased 50 percent during the studied time which certainly must have affected price to some extent.
The increase in construction cost has been very similar over the years but between 2006 and 2007 the slope in the graph can be seen as slightly steeper. By observing the income development during the same time in graph 1.2 below, it can be seen that the increase in income between 2006 and 2007 was very high as well. Since labor cost is the biggest part of construction cost the increase in income can probably explain the slightly steeper increase at that time. Except for the fall in income in 2006 not resembled in the construction cost graph the income and the construction cost development can assume visual correlation. Some of the increasing construction costs can be explained with a sudden boom in demand for housing. Because if the construction sector were working at its full capacity any time before an increase in demand, an increase in workload would then increase the average cost of construction. According to the theory this should only have short-term effect on price as the supply is supposed to adapt to increases in demand. Instead it is argued that that an increase in productivity that is indicted by the rise in income seen in graph 1.2 should be able to keep construction cost down, which graph 1.1 show no evidence of. The theory also brought up how housing constructing industry were influenced highly by laws and regulations. It is possible that more laws and tighter regulations have contributed somewhat to the increase in construction cost. Decisively the increased construction cost seems, from the correlation with price, to have had bigger effect on price than what is implied in the theory.
Graph 1.2 that represents average annual income in Oslo fluctuates more than both construction cost and the price of housing. Yet, the overall change shows a decent increase in the average income, which is supposed to be the main factor, affecting price and therefore clearly can be seen as a contributing factor to the price increase on Oslo’s housing market. However, the theory about income’s effect on price mentions that the elasticity between income and price normally is around one, or more commonly above, which means that an increase in income is very likely to cause a price increase of at least one percent as well. Calculating the percentage increase in income using the numbers from the graph, gives an increase of around 30 percent between 2002 and 2011. The percentage price increase of housing during this time is over 90 percent, also calculated from the numbers in the graph. Hence, income might have contributed to an increase in Oslo’s housing prices but it is unlikely to have caused a price increase of three times as much as any increase in income. Consequently there must be other explanations for the price increase seen in Oslo than just the income growth.
The income variable is said to have the highest effect on the price of housing and taking that into account the price of housing does not correlate as strictly as expected with the income graph since income decreased in 2006 while the price kept increasing steadily. This could further indicate that there must be other explanations to the high price increase in housing than just the income increase. It could be explained by the fact that income increase affect demand, hence price but price do not inversely affect the demand at the same level, which is a bit confusing. Consequently an increase in income can cause the price to increase and if the price keeps increasing without an increase in income the demand does not necessarily fall, keeping the price up even at times when income is falling.
Graph 1.3 reveals the nominal interest rate level on loans. In the beginning of the decade the interest was lowered from 8.6 percent, by almost four percentage points to 4.7 percent and have been on a low level ever since. It was lowered also in connection to the financial crisis, after 2008 and according to Boverket (2011), that was a contributing factor to why housing prices were increasing so soon again after the fall in prices connected to the financial crisis. Low interest rates can be said to keep up the demand for housing and Norway has kept a low interest rate despite its booming economy because of recession in the global economy (Finanstilsynet, 2012). This certainly makes housing more affordable for the buyers but it is hard to specify how much it has affected price on housing, as it is not evident between the graphs if price react directly to lowered interest rates. After the decrease in the interest rate level between 2002 and 2003 the price of housing highly increased and surely the interest was a contributing factor at that time. After that decrease in interest rate level, it is however less apparent if there is any evident correlation between changes in interest rate and the price of housing in Oslo.

READ  Brand Image and Consumer’s Self-image

Oslo’s population and employment levels

In the graphs presented below are the population growth, graph 1.4, the net migration, graph 1.5, number of unemployed, graph 1.6 and employment rate located in graph 1.7.
The steady increase in population seen in graph 1.4 symbolizes a growing city and a growing demand, at least in theory. Previous studies of population growth’s effect on housing have shown little influence on the price of housing unless the population density is high and space is limited. In a capital city containing just above 0.6 million inhabitants, in a country with around five million inhabitants in total (SSB, 2013d), where the land area is above 0.3 million square kilometers (Central Intelligence Agency, 2013), space is hardly limited. Yet, the population growth correlates well with price and inevitably the growth in population has increased the competition on the housing market. It is still unclear to what extent it has affected price but like construction cost it looks like the population growth might have affected price more than what the theory suggests. However, the theory also reveals that in cities were space is getting limited this is usually solved by building vertically and increasing the accessibility between the main city and suburban areas. Maybe Oslo has failed to do so, which have caused the population growth to have bigger impact on price than it should have had if suburban areas for example were more available, hence more attractive to the buyers.
The net migration, seen in graph 1.5 shows more fluctuations. Immigration is part of the population growth but it can also be seen as an indication of a market’s attractiveness. The immigration was growing for many years until 2008, which indicates that Oslo’s attractiveness was increasing during that time. Foreigners can possibly have been attracted to Oslo for its prospering economy. Whether this has changed as immigration has decreased in the last years is hard to tell but price rises in housing are said to affect the attractiveness of a market negatively hence, possibly less people chose to not enter Oslo because of the high housing prices.
The number of unemployed in graph 1.6 shows a fairly steady number but past levels of unemployment used to be lower and the relationship between numbers of unemployed and the ongoing price increase is not evident. The employment rate in relation to population in graph 1.7 mirrors the unemployment graph very well as the employment use to be higher than today. Relatively speaking Oslo still has a very remarkable employment rate. Norway’s total unemployment rate, representing unemployed persons as a percentage of the total labor force, was in 2012, 3.2 percent. The same year Sweden had an unemployment rate of 8.0 percent, Germany 5.5 percent, US 8.1 percent and Japan 4.3 percent to mention a few (Eurostat 2013). Oslo’s estimated unemployment rate was 3.1 percent in 2012 (EURES, n.d). Hence, Oslo has a very high rate of employment that makes it possible for a bigger share of the population to afford housing. This keeps up the demand for housing but in Oslo’s case the employment level have affected price less than what theory predicted. Perhaps this is because employment rate has not changed enough to have any affect on price.

Table of Contents
1 Introduction
1.1 Background
1.3 Problem discussion
1.4 Delimitations
1.5 Method approach
1.6 Disposition
2 Frame of references
2.1 Economic variables affecting the housing market
2.2 Bubble Indicators
2.3 Behavioral finance – Human behavior’s effect on housing markets
3 Method
3.1 Research philosophy and approach
3.2 Quantitative and qualitative methods
3.3 Question 1 – Market variables
3.4 Question 2 – Indicators
3.5 Question 3 – The housing buyers’ behavior
3.6 Validity and reliability
4 Empirical findings and interpretation
4.1 Part I. Economic variables
4.2 Part II. Bubble indicators
4.3 Part III. The housing buyers’ behavior
5 Conclusion
5.1 Answering research question one
5.2 Answering research question two
5.3 Answering research question three
5.4 Final conclusion
5.5 Reflections
5.6 Suggested further research
List of references

Related Posts