Jacobsen and Naug

Jacobsen and Naug published the article "What drives house prices?" in 2004, in an effort to test and detect the most important explanatory factors behind the housing prices in Norway. They estimated an

econometric model for the Norwegian housing market based on quarterly data from 1990 to 2004. Their goal was to explain the massive price increase from 1992 to 2004 where prices almost tripled, and to test if the market prices could be explained by the following fundamental factors:

• households' total (nominal) wage income

• indices for house rent paid and total house rent in the consumer price index (CPI)

• other parts of the CPI adjusted for taxes and excluding energy products (CPI-ATE)

• various measures for the real after tax-interest rate

• the housing stock (as measured in the national accounts)

• the unemployment rate (registered unemployment)

• backdated rise in house prices

• household debt

• the total population

• the shares of the population aged 20-24 and 25-39

• various measures of relocation/centralization

• TNS Gallup's indicator for the households' expectations for their own and the country's economy

Creating one single house price equation that incorporated all the explanatory variables, and that gave a meaningful result, was not feasible due to the large number of factors. Instead they estimated several models that incorporated only a subset of all the variables. These models were then simplified by adding restrictions that were not rejected by the data, which made the interpretation of the dynamics easier.

Rent for housing and other consumer prices generally had insignificant effects on housing prices. The insignificant effect of rent for housing is explained by the fact that the rent in housing cooperatives accounted for a large portion of the housing rent indexes in the CPI for most of the estimation period.

Also, rent has been highly regulated during the period.

The effect of the banks’ lending rate was highly significant in all the models, while the market rate was clearly insignificant on housing prices in the models in which the banks' lending rates were also included.

The insignificant effect of market rates might reflect that the key rate was used to stabilize the

short-term development in the Krone exchange rate during most parts of the 1990's. The households might have used the observed rate as a prediction for future rate, more than they do today. The market rates may also, to a certain degree, incorporate a change in the economic outlook. It is therefore reason to believe that the effect of interest rate expectations might be undervalued in the estimated equations.

No significant effects were found on the households’ debt level on housing prices, neither when the debt variable was included in the entire period nor when it was only included for the period during the bank crisis of 1990-1993. In isolation, this implies that credit for household purchases of real estate was not limited by the profitability of banks' during the estimation period. On the other hand, there is reason to believe that other debt that the households have was affected by their profitability in the period 1990-1993.

There was no evidence that relocation or demographic relations have a strong direct effect on real estate prices as a whole. Indirectly, demographic changes affect prices by changing the personal income in the economy. Personal income had a significant effect on real estate prices, and was included in the final model. As demographic changes happen slowly, it can be hard to capture these changes over a relatively short time span.

Households expectations about the future is largely correlated with the interest rate level and the unemployment rate. These expectations might also shift due to changes in Norway's economic outlook, a change in political conditions or negative shocks such as war, terror or a fall in stock markets. To capture the effect of these expectations an indicator for households’ expectations about their own and the country's economy is included. This indicator is a version of the expectations indicator by TNS Gallup, corrected for the effects of the interest rate level and the unemployment rate. Jacobsen and Naug first estimated a model of the consumer confidence indicator with the interest rate and unemployment as explanatory variables. Next, they calculated the difference between the actual and the fitted value of the consumer confidence indicator for each period. The difference measures changes in expectations that is caused by other factors than the interest rate level and the unemployment rate. Exactly how this is done is further explained in section 10.1.

The analysis done by Jacobsen and Naug concludes that the rise in housing prices over the estimation period can be largely attributed to changes in fundamentals. The expectations variable can also be used to capture the effect of non-fundamental factors, but no evidence was found that expectations had contributed to pushing housing prices up during the period. Instead they found that the rise in prices can be attributed to changes in fundamentals such as housing construction, income, interest rates and unemployment rate.

Testing various variables with quarterly data over the period they estimated a model by the method of least squares, using the factors that had the biggest effect on house prices in the estimation period. By using the nominal interest rate the model got a better fit, therefore the model expresses a connection between nominal sizes and other variables. The model Jacobsen and Naug ended with was this:

Equation 6.5

∆ℎ𝑜𝑢𝑠𝑒𝑝𝑟𝑖𝑐𝑒𝑡 = 0.12 ∆𝑖𝑛𝑐𝑜𝑚𝑒𝑡− 3.16 ∆(𝐼𝑁𝑇𝐸𝑅𝐸𝑆𝑇 ∗ (1 − 𝜏))_𝑡

− 1.47 ∆(𝐼𝑁𝑇𝐸𝑅𝐸𝑆𝑇 ∗ (1 − 𝜏))_𝑡−1+ 0.04 𝐸𝑋𝑃𝐸𝐶𝑡

− 0.12 [ℎ𝑜𝑢𝑠𝑒𝑝𝑟𝑖𝑐𝑒_𝑡−1+ 4.47 ∗ (𝐼𝑁𝑇𝐸𝑅𝐸𝑆𝑇 ∗ (1 − 𝜏))

𝑡−1+ 0.45 𝑢𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑚𝑒𝑛𝑡_𝑡

− 1.66 (𝑖𝑛𝑐𝑜𝑚𝑒 − ℎ𝑜𝑢𝑠𝑖𝑛𝑔𝑠𝑡𝑜𝑐𝑘)𝑡−1] + 0.56 + 0.04 𝑆1 + 0.02 𝑆2 + 0.01 𝑆3

Having an explained variation of 0.8773, the model describes 87.73% of the variation in housing prices experienced in the period.

6.2.1. Weaknesses and Discussion

Even though the model proposed by Jacobsen and Naug is considered a good model for analyzing the forces behind the Norwegian housing prices, there is still uncertainty about its ability to predict future housing prices. Using a model that is based on data from 1990 to the first quarter of 2004 to predict future prices could lead to the wrong conclusions, as the economic environment has changed.

The price increase has been most extreme in the big cities. A variable or an indicator for price differences based on urbanization should therefore have been included in the model. It is likely that urbanization has a major effect on housing prices and that it makes for even stronger bubble tendencies in urbanized areas.

For these reasons, the model might not be able to identify bubbles in the housing market.

The economic situation has changed since the model was estimated. Before the Financial Crisis, the economy was more stable and there were no major banking crises’ during the estimation period used by Jacobsen and Naug. A way of including effects due to changes in the economic environment is by adding a variable that contains expectations about the future. By using the indicator estimated by TNS Gallup, Jacobsen and Naug most likely capture the effect from households changed expectations for the economy. Their estimation period also starts at the very end of a bursting housing bubble.

As this model is one of the most cited and well-known house price models for the Norwegian housing market, we will try to re-estimate it and see how well it can describe the development seen since 2004.