Groups
In the previous section we have generally discussed the effects on the predicted house price for all available samples. It would be interesting to specifically investigate the effects in different groups. For examples, there might be housing bubble in some specific cities like Stavanger, where the housing price is highly determined by the oil price. In 2015, we evident the decreasing of house price due to the sharp downturn
of oil price. However, in some other cities, like Trondheim, the house price may be affected by the long-term change of oil price. Because Trondheim is a high-technology, high-education dominated city and therefore people are linked to oil price not as directly as the people living in Stavanger. To investigate the group differences is the task of this section.
Table 4.7 has 4 columns regression.
In the first two columns, the samples are grouped based on the loca- tion of the house, mainly whether the house is located in the urban area or not. In the next two columns, the samples are separated by the number of properties owned by the households. The households having more than one properties are selected in Column 3 and the left are selected in Column 4. Though we see differences of both sig- nificance and estimated value of coefficients in different groups, the results are fairly consistent. The consistency in some sense give us confidence on the model. The largest differences for the urban and non-urban groups on significance are variables renovation and total financial capital. They could be due to the large difference of the num- ber of samples in these two groups. The largest differences for the urban and non-urban groups on estimated values are loan-to-income ratio and house size. The predicted house price is almost tripled when the same percentage of loan-to-income ratio change existed in the non- urban group than in the urban group.
We then divide the samples into 5 age groups based on the average age of the house adults. From Table 4.8 we see that house size has the largest effect to the age group of 50-60 years old. 1% change of house
Table 4.7: The house price model within different groups: Column 1: the house group in the urban area; Column 2: the house group out of the urban area; Column 3: the house group in which households having more than 1 properties; Column 4: the house group in which house- holds having only 1 property. All columns are regressed by controlling other variables (All Others).
(1) (2) (3) (4) log HPPrice log HPPrice log HPPrice log HPPrice urban 0.000 0.000 0.318*** 0.073** (.) (.) (3.07) (2.47) num property 0.019 -0.002 0.015 0.000 (0.90) (-0.04) (0.23) (.) log HSize 0.284*** 0.346*** 0.194** 0.301*** (11.28) (3.74) (2.09) (11.75) log HRenov 0.019*** 0.021 0.044* 0.020*** (3.01) (0.95) (1.68) (3.07) log HBPrice 0.300*** 0.275*** 0.314*** 0.301*** (17.35) (5.29) (5.15) (17.48) own year 0.020*** 0.019*** 0.022*** 0.020*** (13.70) (4.54) (4.26) (14.06) log TFC 0.029*** -0.018 0.011 0.021*** (4.12) (-0.81) (0.40) (3.01) log TTI 0.188*** 0.216** 0.184* 0.196*** (6.44) (2.15) (1.72) (6.58) log LTI 0.037*** 0.101*** 0.046 0.048*** (4.19) (3.38) (1.30) (5.51) All Others Y Y Y Y N 1403 283 198 1488 R2 0.584 0.457 0.606 0.591 adj. R2 0.573 0.378 0.515 0.581 pseudoR2
Marginal effects;t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1 * p<.1, ** p<0.05, *** p<0.01
size induces the predicted house price to be changed by 0.388%. The renovation of the house is affecting the most for the youngest group. However, the effect of renovation is relatively much smaller compared to the house size. The bought price is most related to the predicted house price for the youngest group, which is fairly easy to be under- stood. Because the youngest group own their houses the shortest. The bought price is thus the closest to the predicted house price. The most important, we see that the coefficient of the loan-to-income ratio is the highest for the youngest group, which could explain that the youngest group have the highest irrational momentums in the Norwegian hous- ing market.
It is also interesting to investigate the discrepancy of housing mar- ket in different region of Norway. In the data, samples are selected from 7 different counties. Therefore we naturally separate the sam- ples into 7 groups. Each group contains samples in one county. Table 4.9 lists the first 4 groups and Table 4.10 lists the next 3 groups, being County Akerhus and Oslo, Hedmark and Oppland, Out of the country, Agder and Rogaland, Vestland, Trondelag and Nord-Norge sequentially. We are not so sure about the regression because number of samples in some groups are small (114 for Group 2, 152 for Group 7). One important observation is that the predicted house prices in all these 7 groups depend mainly on several critical variables like the house size, the bought price, the family total income and the loan-to- income ratio. To pay more attention on the irrational indicator, the loan-to-income ratio contributes not too much on the predicted house price. the largest value of the coefficient of loan-to-income ratio is 0.083 for the Nord-Norge group, which could be interpreted as 1% change
Table 4.8: The house price model within different age groups: Column 1: the group in which the average age of house adults is less than 30 years old; Column 2: the group in which the average age of the house adults is between 30 and 40 years old; Column 3: the group in which the average age of the house adults is between 40 and 50; Column 4: the group in which the average age of the house adults is between 50 and 60; Column 5: the group in which the average age of the house adults is above 60 year old. All columns are regressed by controlling other variables (All Others).
(1) (2) (3) (4) (5) log HPPrice log HPPrice log HPPrice log HPPrice log HPPrice log HSize 0.100 0.244*** 0.118 0.388*** 0.286*** (1.55) (5.14) (1.62) (4.90) (6.15) log HRenov 0.049** 0.026** 0.028 0.018 -0.004 (2.52) (2.34) (1.53) (1.01) (-0.30) log HBPrice 0.638*** 0.507*** 0.365*** 0.136*** 0.292*** (11.78) (14.51) (8.05) (2.90) (10.05) own year 0.050*** 0.035*** 0.018*** 0.003 0.020*** (6.79) (10.54) (3.77) (0.84) (8.59) urban 0.001 0.143*** 0.066 0.168** 0.175*** (0.01) (2.83) (0.86) (2.09) (3.00) log TFC 0.027 0.020 0.019 0.037* 0.046*** (1.38) (1.60) (1.01) (1.90) (3.37) log TTI 0.275*** 0.135** 0.377*** 0.197* 0.220*** (2.64) (2.08) (3.92) (1.96) (4.39) log LTI 0.134*** 0.093*** 0.042** 0.064*** 0.052*** (3.03) (4.66) (1.99) (2.81) (3.23) All Others Y Y Y Y Y N 194 434 359 257 442 R2 0.708 0.635 0.412 0.504 0.569 adj.R2 0.652 0.607 0.357 0.435 0.537 pseudoR2
Marginal effects;t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1 * p<.1, ** p<0.05, *** p<0.01
of loan-to-income ratio causes 0.083% change of predicted house price. Table 4.9: The house price model within different county group: Col- umn 1: the group of houses located in Akerhus and Oslo; Column 2: the group of houses located in Hedmark and Oppland; Column 3: the group of houses located in out of the country. Column 4: the group of houses located in Agder and Rogaland. All columns are regressed by controlling other variables (All Others).
(1) (2) (3) (4) log HPPrice log HPPrice log HPPrice log HPPrice log HSize 0.346*** 0.328*** 0.307*** 0.301*** (8.44) (3.14) (5.25) (3.95) log HRenov 0.010 0.044 0.030** 0.024 (0.93) (1.66) (2.19) (1.19) log HBPrice 0.270*** 0.231*** 0.262*** 0.368*** (9.76) (3.75) (6.76) (6.83) own year 0.024*** 0.011** 0.018*** 0.024*** (9.02) (2.42) (6.08) (5.34) urban -0.041 -0.201** 0.126* 0.035 (-0.52) (-2.16) (1.73) (0.43) log TFC 0.045*** 0.054* 0.016 0.014 (3.60) (1.77) (1.12) (0.63) log TTI 0.256*** 0.119 0.237*** 0.128 (5.49) (1.11) (3.43) (1.49) log LTI 0.061*** 0.077** 0.060*** 0.009 (4.09) (2.53) (3.04) (0.32) All Others Y Y Y Y N 436 114 327 220 R2 0.673 0.722 0.484 0.587 adj.R2 0.648 0.617 0.430 0.519 pseudoR2
Marginal effects;t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1 * p<.1, ** p<0.05, *** p<0.01
Table 4.10: The house price model within different county group (con- tinue): Column 1: the group of houses located in Vestland ; Column 2: the group of houses located in Trondelag; Column 3: the group of houses located Nord-Norge. All columns are regressed by controlling other variables (All Others).
(1) (2) (3) log HPPrice log HPPrice log HPPrice log HSize 0.218*** 0.118 0.282*** (2.85) (1.25) (3.19) log HRenov 0.005 0.010 0.024 (0.25) (0.47) (1.04) log HBPrice 0.262*** 0.512*** 0.342*** (5.26) (7.85) (5.81) own year 0.015*** 0.030*** 0.019*** (3.82) (6.11) (4.22) urban 0.155** 0.038 0.184** (2.17) (0.40) (2.12) log TFC -0.038* 0.038* 0.049** (-1.86) (1.71) (2.08) log TTI 0.256*** 0.024 0.121 (2.87) (0.23) (0.97) log LTI -0.000 0.007 0.083** (-0.01) (0.29) (2.29) All Others Y Y Y N 274 163 152 R2 0.380 0.655 0.640 adj.R2 0.300 0.573 0.547 pseudoR2
Marginal effects;t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1 * p<.1, ** p<0.05, *** p<0.01
irrational is of cause unrealistic. In Norway, most families buy houses by borrowing money from banks and paying back the loans through their whole life. Therefore in our model, only the households having
high loan-to-income ratio should be treated as irrational households. Table 4.11 divides samples into 4 different groups based on the value of loan-to-income ratio. From Group 1 to Group 3, we see that the coefficient of loan-to-income ratio increases significantly from 0.043 to 0.417. It tells that the predicted house prices are affected by the loan- to-income much more for the high loan-to-income group than for the low loan-to-income group. However, it is not statistical significance for the group with loan-to-income ratio higher than 3. The reason is ambiguous. In the end, instead of interpreting how much percentage of predicted house price change due to unit percentage change of inde- pendent variables, we try to normalize the change of predicted house price to be unity and to see that such a unit change is contributed by how much percentage of important independent variables. Table 4.12 shows the results. 4.69% contribution of the LTI to the housing price for all samples in Column 4 infers a minor housing bubble in the Norwegian housing market.
Table 4.11: The house price model with in different loan-to-income ratio (LTI) group: Column 1: the house group in which the LTI is between 0 and 1; Column 2: the house group in which the LTI is between 1 and 2; Column 3: the house group in which the LTI is between 2 and 3; Column 4: the house group in which the LTI is larger than 3. All columns are regressed by controlling other variables (All Others).
(1) (2) (3) (4) log HPPrice log HPPrice log HPPrice log HPPrice log HSize 0.342*** 0.368*** 0.218*** 0.006 (7.60) (7.91) (4.80) (0.07) log HRenov 0.002 0.011 0.028*** 0.052** (0.15) (1.14) (2.66) (2.21) log HBPrice 0.268*** 0.224*** 0.279*** 0.490*** (9.24) (8.12) (8.07) (7.60) own year 0.019*** 0.018*** 0.024*** 0.037*** (8.77) (7.74) (7.74) (5.63) urban 0.130** 0.157*** 0.052 -0.109 (2.53) (3.50) (0.97) (-0.96) log TFC 0.025* 0.024** 0.020* 0.022 (1.81) (1.98) (1.95) (1.05) log TTI 0.179*** 0.297*** 0.363*** 0.130* (2.83) (5.74) (5.82) (1.76) log LTI 0.043** 0.253*** 0.417*** -0.042 (2.36) (3.24) (2.80) (-1.53) All Others Y Y Y Y N 539 604 327 216 R2 0.596 0.611 0.719 0.609 adj.R2 0.566 0.585 0.684 0.528 pseudoR2
Marginal effects;t statistics in parentheses
(d) for discrete change of dummy variable from 0 to 1 * p<.1, ** p<0.05, *** p<0.01
Table 4.12: One unit change of house price is contributed by how much percentage changes of different variables in different LTI group: Column 1: the house group in which the LTI is between 0 and 1; Column 2: the house group in which the LTI is between 1 and 2; Column 3: the house group in which the LTI is between 2 and 3; Column 4: all samples.
(1) (2) (3) (4) 100% HPPrice 100% HPPrice 100% HPPrice 100% HPPrice log HSize 33.93% 25.34% 15.56% 29.34% log HRenov 0.20% 0.76% 2.00% 1.90% log HBPrice 26.59% 15.43% 19.91% 29.64% own year 1.88% 8.13% 1.71% 2.00% urban 12.90% 10.81% 3.71% 11.28% log TFC 2.48% 1.65% 1.43% 2.59% log TTI 17.76% 20.45% 25.91% 18.56% log LTI 4.27% 17.42% 29.76% 4.69%
Discussion
We have performed several regressions of the house prices in Norway based on the empirical data gathered in 2012. Those regressions are logically understandable and interpretable for most variables. The ma- jor find from the regression is that there is little housing bubble in the Norwegian housing market.
However, our conclusion could bias from the real house market due to following issues that are not easily captured by our model.
The first one is the use of loan-to-income ratio as the indicator of the irrational part of house prices. It is reasonable to consider that the households with high loan-to-income ratio have higher irrational mo- ment than the households with low loan-to-income ratio. The former accepts to take higher risk. However, there is no hint of determining a proper loan-to-income ratio so that if the loan-to-income ratio is higher than the proper ratio, the household becomes irrational. Therefore, we really have no idea to identify the irrational part of housing price. We can only conclude a non-housing-bubble when all groups of loan-to- income ratios are not affecting the house prices. Actually, this is not the case in our data. We observed high sensitivity of loan-to-income
ratios to the house prices in the group where the loan-to-income ra- tios are located between 2 and 3. The observation from this group could possibly indicate a housing bubble in this group. Then the next question is whether the housing bubble in one specific group means a housing bubble in the whole housing market. I believe it is nature that people have different risk-preference. It should also be acceptable to have a small group of people inflating bubble of their houses, while on the other hand, there are another group of people who could contribute more on the housing market. It is then hard to say whether a bubble created by a group of people can be absorbed by the entire housing market.
In addition, we have stated that the houses used for investing are not included in our model. We could of cause define similar variables, i.e., house fundamentals, household fundamentals and loan-to-income ra- tio. The problem is that the predicted prices from the households of this part of houses do not rely on the fundamentals any more. The main ambition of the household for holding those houses is just waiting for price increasing in the future. In fact, we should expect this part of houses as full bubbles in the housing market. However, we are not able to capture it and to sum the bubbles up to the houses used for living. Some oral interviews done by the author to the Norwegians indicate that there exist a few amounts of houses for investing. A large amount of investing houses could shake our conclusion of non-housing bubble in the Norwegian housing market. Further research and proper model should be done for this.
In the end, we say that households can make free decision of buy- ing or selling houses. This is an important reason of defining family fundamental in our model. However, one should remember that this
is given by the assumption that households have been given jobs by the market. If the macro-economy is becoming worse in Norway and people are becoming unemployed, a stable housing market could be easily destroyed without financial support. Unfortunately, we are ex- periencing this now. The sharp decrease of crude oil price worldwide makes the unemployment rate climbing up in 2015. Prediction shows that the low oil price will keep steady until the end of 2016. This will seriously affect the housing market in Norway. We are evidencing changes in Stavanger now. It would be interesting to investigate on the possibility of housing bubble caused by this and if does, how fast it will spread out of the whole country. Such type of bubble stimulated by the external and global economy is not able to be captured by our model. Further works should be done to handle the above issues.
Conclusion
We build a housing price model for investigating the existing of housing bubble in Norway. The model is based on the definition of a financial bubble. We also define fundamentals in the family level. We find that the analysis becomes much simpler compared to investigating funda- mental in the country level. The housing price is split into four part, the fundamental of the house, the fundamental of the household, the fundamental of the interactions of the house and the household, and the bubble part which is supported by the irrational expectation of housing increase in the future.
The 2012 data survey conducted by SSB is not necessary to be ideal, but good enough for our analysis. We have regressed the housing prices controlled by different variables and into different groups. The depen- dency of housing price change due to changes of different variables varies in different experiments, yet the results are stable and inter- pretable.We use loan-to-income ratio as the indicator of the bubble term, which could be misleading because a normal loan-to-income ra- tio may not inflate any bubble. The limit of the real data makes us no other choice on the bubble reflection.
Based on our analysis, we conclude that there is no housing bubble in the entire Norwegian housing market. This is consistent with the con- clusion from Duus’s thesis (Duus and Hjelmeland, 2013), where she in- vesting the Norwegian housing bubble based on statistical data. How- ever, we also find that there exist some groups (groups with high loan- to-income ratios) where the bubble indicator remarkably affects the housing price, which might indicate bubbles in these specific groups.
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