BBS300 Empirical Research Methods for Business

RUNNING HEADER: REAL ESTATE PRICING DETERMINANTS 1
Real Estate Pricing Determinants
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Real Estate Pricing Determinants 2
Question 1
a.
To determine how well the weekly rent predicts the house price, a linear regression model will
be adopted. A linear regression model is the most appropriate in predicting the values of a
variable on the basis of another variable (Seber & Lee, 2012). In this case, the house price is the
dependent variable while the weekly rent is the independent variable.
b.
Before carrying out a linear regression model, six assumptions have to be checked in order to
ensure that the data adhere to them. The assumptions include, the two variables measured should
be continuous, there should be a linear relationship between the two variables, there should be no
significant outliers, there is the independence of observations, there is homoscedasticity, and
residual of regression line are approximately normally distributed.
c.
The data can be shown to have met all the six assumptions. For starters, the two variables are
continuous in nature thereby meeting assumption 1. The second assumption can be seen in the
scatterplot below where it is seen that there is a linear relationship between the two variables.

Real Estate Pricing Determinants 3
Figure 1: Price against weekly rent scatterplot
Consequently, from figure 1 above, it can be seen that there are no significant outliers.
A Durbin-Watson test was carried out and the result was 1.541. Since the result is between 1.5
and 2.5, we can conclude that the data is not autocorrelated (Chatterjee & Haji, 2015). Thus,
there is the independence of observations. The assumption of homoscedasticity was also
confirmed since the derived VIF was 1. Since the value does not exceed 10, then it is safe to say
that there is no multicollinearity.
Figure 2: Histogram of residual of the regression
In figure 2 above, it is evident that the sixth assumption has been met since the residual of the
regression line is normally distributed.
d.
Table 1: Model 1 Summary

Real Estate Pricing Determinants 4
From table 1 above, it can be seen that the variable accounts for 43.8% of the variability in the
model. On the other hand, 56.2% of the variability is explained by variables, not in the model.
Table 2: Model 1 ANOVA
The model fits a straight line since it can be observed in table 2 above that the model is
statistically significant.
Table 3: Model 1 Coefficients
From the regression model in table 3 above, it can be seen that holding all factors constant, the
constant house price of a house in the suburb in Australia is 309.34 units. The constant is
statistically significant (p < 0.05). A unit increase in the weekly rent leads to a 0.955 unit