Analysis and Findings

The global financial crisis of 2008 affected almost every economy of the world. This in turn affected foreign business hence a ripple effect on other sectors too. One of the sectors that were not spared was the housing sector. Till today, the housing sector has experienced many challenges fluctuating housing prices being one of the main challenge (Shisong & Hongmei, 2009). Several factors have been put forward as the immediate determinants of housing market prices (Hua, 2008). However some have been discovered not to have a direct effect on the prices. Major urban areas experiencing this problem have hinged the fluctuation on a number of factors that include economy of a given area, industrial development of the particular area, demand for housing in the area, closeness to an urban center and the size of the land (Dongsheng & Zhong, 2010). It is against this background that this research is conducting a research on factors that are influencing the housing market price with the main focus being the city of Sidney in Australia. Preliminary reports have shown that the factors that have influenced housing market prices in Australia are the ages of the houses, proximity to the urban area, demand for the houses, housing price index, area of land where the house is sitting and annual rates of change of the market prices. The study is determined to establish a model that can accurately be used to estimate the market prices in future in the city of Sidney (Nellis, 2011). For this reason, the research identified one dependent variable (housing market price) and four independent variables such as housing price index, annual percentage change, land area and age of the house. The age of the house was considered a major determiner since new houses are demanded more than the old houses. To add on, new houses fetch higher prices than old houses. On the other hand, if the area where the housing is built on is large, the housing market price will automatically be high as opposed to where the land is small. Annual rate of change also affect the market price for houses. If the annual rate is high, it means that the housing units will also be having high market prices every year (Quigley , 2009). However, when the annual rate is low, the housing prices will also tend to be low.

Scatterplots

Market price and house price index scatterplot

 

Figure 1

The figure above shows the relationship between housing market price and house price index. From the trend line, it can be concluded that there is a linear relationship between the two variables. The relationship can also be said to positive since the coefficient of the independent variable is positive. The R² value is 0.65. This means that the independent variable (Sidney price index) is responsible for 65% of variation in the dependent variable (market price).

Scatterplots

 

Figure 2

The figure above shows the relationship between housing market price and annual percentage change. From the trend line, it can be concluded that there is a linear relationship between the two variables. The relationship can also be said to positive since the coefficient of the independent variable is positive. The R² value is 0.16. This means that the independent variable (annual percentage change) is responsible for 16% of variation in the dependent variable (market price).

Market price versus area in meters scatterplot

 

Figure 3

The figure above shows the relationship between housing market price and area of land in square meters. From the trend line, it can be concluded that there is a linear relationship between the two variables. The relationship can also be said to positive since the coefficient of the independent variable is positive. The R² value is 0.1. This means that the independent variable (area of land in square meters) is responsible for 10% of variation in the dependent variable (market price).

 

Figure 4

The figure above shows the relationship between housing market price and age of house in years. From the trend line, it can be concluded that there is a linear relationship between the two variables. The relationship can also be said to positive since the coefficient of the independent variable is positive. The R² value is 0.46. This means that the independent variable (age of house in years) is responsible for 46% of variation in the dependent variable (market price).

 

Table 1

 

From the regression results above, it can be observed that the value of the constant (y – intercept) is 548.98. This value indicates that without the independent variables or when all the independent variables are equal to zero, the minimum house market price will be 548.98 thousand dollars. It can also be observed that a unit change in Sidney price index, land area, annual percentage change and age of the houses in years causes 1.96, 0.52, 5.62 and 2.49 units change in the dependent variable (market price) respectively.

When it comes to significance of the coefficients of the variables, p-value was used to determine significant and non-significant variables. The Sidney price index coefficient was confirmed to be significant at 95% confidence interval. The p-value (0.01) was less than the level of significance (0.05). However, the other coefficients were found not to be significant at 95% level of confidence. Their p-values were found to be greater than 0.05.

Regression Equation Model

The R² value of 0.71 shows that there a strong correlation between the dependent variable and the independent variables. On the other hand, it implies that 71% of the variation that is occurring on the dependent variable is as a result of the independent variables.  

Interpretation of 95% confidence interval for each parameter

Table 2

The limits within the coefficients of the independent variables lie have been computed as observed in table 2 above. The third column gives the lower limit while the fourth column gives the upper limit. The level of precision is 95% confidence level. This means that if the coefficients were to be picked 100 times, 95 of the times, the limits will lie within what has been computed in the table above.

Linear regression model for relationship between the market price and land size in square meters

Table 3

The regression equation for the above is as below;

The R² value of 0.1 shows that there a weak correlation between the dependent variable and the independent variables. On the other hand, it implies that only 10% of the variation that is occurring on the dependent variable (market price) is as a result of the independent variable (area in square meters).  

R² value can be employed to determine the best model to be used.

The two regression models are as shown below;

With R² value of 0.79

The second model is therefore the best since it has a greater R² value. The model explains much of the variation that occurs in the dependent variable as compare to the first model where only 10% of the variation is explained.

Predicting market price of a house given land area is 400 m²

The equation of the relationship is;

Conclusion

The objective of this research report was to establish the factors influencing market price of houses in Brisbane, Sidney and Melbourne in Australia. However more focus was on Sidney. A sample of prices and other variables such as land area, percentage change in price index and annual percentage change and age of the houses were obtained for 15 years starting from the year 2002 to the year 2017.  From the above, the research found that various variables played a role in determining house prices in Sidney. However, the major determinants were the ones that have been mentioned above. Among the four factors, housing price index had a great influence on market price of houses compared to the rest of the variables. The size of the area in meters squared was found to be the least influencer of housing market price. All variables were found to have a positive correlation with housing market price except the age of the house in years. This had a negative correlation with housing market price. The second model was found to be the best since it has a greater R² value. The model explains much of the variation that occurs in the dependent variable as compare to the first model where only 10% of the variation is explained.

References

Dongsheng, C., & Zhong, M. (2010). The bad effects of high housing price on urbanization of China. Yangtze Forum, 3, 3-7.

Hua, Z. (2008). An analysis of supply and demand curve of real estate market and its policy implication.. (Vol. 3). Jianghuai Tribune.

Nellis, J. G. (2011). An empirical analysis of determination of house prices in the United Kingdom. Urban Studies. (1 ed., Vol. 19).

Quigley , M. J. (2009). Real estate prices and economic cycles. International Estate Review, 2, 5-8.

Shisong, H., & Hongmei, C. (2009). The mystery of housing price. Beijing: Social Sciences Academic Press.