Selected Fundamental Macroeconomic Factors

Based on the analysis above, the analysis of historical development of house prices and an assessment of articles related to the housing market in Oslo, we have chosen the factors we believe is of great importance when it comes to drivers of the housing prices in Oslo. We have decided to divide the factors into two main categories;

demand and supply, whereas demand is divided into directly measurable data affecting the demand and

“supporting” data affecting demand. Regarding directly measurable data we have chosen GDP including oil, disposable income, unemployment rate, key and interest rate development and population growth. The supporting data consist of the credit market in Norway, bank’s lending policies and housing taxation. Within the category of the supply of housing, we have included housing stock, the costs of housing construction and turnover-time. The mentioned factors will be examined in the fundamental factor analysis in chapter 8.

development (cf. Figure 6.4). Therefore, we have developed an extension of the P/R-ratio model, an additional X-factor, enabling us to include more fundamental factors, which can better present the characteristics of the Oslo market and the development in house prices. Thus, in order to test whether including more fundamental factors will have an impact on the fundamental P/R-value, we will apply this model on historical numbers in Oslo.

The choice of fundamental factors to include is derived from a thorough assessment of other house price models and an analysis of the impact factors have on the house prices, in our case Oslo. By including more underlying fundamental factors, we believe the new fundamental P/R-values will reflect the condition in the housing market better, enhancing the possibility to evaluate whether housing is fairly priced. The X-factor will be a part of the denominator and can increase (decrease) the denominator, causing the fundamental value to increase (decrease).

The new formula will be the following:

(7.2) ^{𝑃}_{𝑅}= (_{𝑖} ^{1}

𝑎+ 𝜏+𝑓− 𝜋−𝑋) , 𝑤ℎ𝑒𝑟𝑒 𝑋 = 𝑡ℎ𝑒 𝑓𝑢𝑛𝑑𝑎𝑚𝑒𝑛𝑡𝑎𝑙 𝑓𝑎𝑐𝑡𝑜𝑟 𝑎𝑑𝑑𝑖𝑡𝑖𝑜𝑛

The original fundamental P/R-ratio’s denominator is the user cost of owning housing. The additional factor we present is not directly an additional element of user cost, but a part we believe should be considered in addition to the other factors included. The new denominator in the fundamental P/R-ratio formula can thus not directly be applied as the user cost of owning housing.

Theoretical Framework

We wanted to develop a universal framework to be able to utilize across borders, and not needing to create a new model for each city or country to be evaluated. Therefore, the additional factor includes local, national and international factors, which can be adapted to the city or country being analyzed. An assessment of the most influential factors within each category has to be done in order to see which factors are affecting the housing prices the most. We will test the new X-factor in the formula on the housing market in Oslo and thus evaluate whether the effects from the additional fundamental factors will make the fundamental P/R-ratio better. We will evaluate historical data, with a focus on the last decade, in order to examine if our new model would indicate more accurate fundamental house prices. The fundamental factors we apply will be the ones we find to be most influential in chapter 8.

The theoretical framework of the model is based on an integrated risk-analysis by Dahl, Hansen, Hoff and Kinserdal (2010), used in a different context. House prices are often determined based on both measurable

extent than other models to capture the characteristics of the analyzed housing market. After being divided into local, national and international factors, they will be classified based on the size of the change in the factor from year to year, and then weighted based on the degree of importance to the housing market. These steps will be averaged and result in a corresponding X- factor, which will be applied in the new fundamental P/R-ratio. In the following, a presentation of the theoretical framework of this model and how it will be applied will be presented.

In order to apply this model universally, we will look at the percentage change in the chosen fundamental factors from year to year. As for example population and GDP can vary substantially from country to country, it will be beneficial to use a percentage instead of actual changes. The classification is based on a rating scale from -5 to 5, where -5 gives a very negative impact on housing prices (reduction), whereas 5 gives a highly positive impact on housing prices (increase). Further, the classifications made in this model are based on the results retrieved from the data material on the analyzed market. The changes in the fundamental factor are divided into bins based on intervals and presented in a histogram to see the number of values falling into each interval. The classification intervals are made based on this distribution. All factors are weighted on a scale between 1 and 3, based on the importance related to the housing prices. An example of how the classification is done is shown in the table below:

The weighted fundamental factor classification is: 13/5.5 = 2.36 This number will then give the fundamental factor addition:

**Table 7.1 Classification and Weight of Fundamental Factors **

The additional factor will be a percentage, which, as mentioned, will affect the size of the denominator and thus change the fundamental value in the extended model. A positive additional factor will give a higher fundamental P/R-ratio, while a negative additional factor will give a lower fundamental P/R-ratio.

As our model is an extension of the fundamental P/R-ratio, which consists of the nominal interest rate (after tax), we have applied all data in nominal terms. Although real terms often show a more accurate picture of a situation, there has to be consistency between the use of real and nominal terms. Limitations of this approach will be discussed later.