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# Milestone 3 - Model development of property prices Belle also recently heard in the media that the proportion of sales are as follows: · 35% of sales are apartments; · 45% of sales are houses; · 10%...

Milestone 3 - Model development of property prices
Belle also recently heard in the media that the proportion of sales are as follows:
· 35% of sales are apartments;
· 45% of sales are houses;
· 10% of sales are townhouses;
· 5% of sales are villas and;
· 5% of sales are other.
Belle would like to know if the sample data is statistically different to what the media is suggesting. Remember to state your assumptions and limitations of the result.
In addition to this, Belle would like you to develop a propriety model to predict Sydney property prices. To begin, Belle would like you to run a simple linear regression of
· price as the dependent variable; and
· Tincome as the independent variable.
As part of your reporting, you need to interpret the coefficients of the model and discuss whether they are economically and statistically significant. Also report the confidence interval of dependent variable (price) and interpret the results.
Next, Belle wants you to run a multiple linear regression. As part of this exercise you need to:
· Explain why a multiple linear regression is beneficial i.e. justify the need for multiple linear regression and the issues associated with running a simple linear regression. Contextualise it in the context of the cu
ent problems are there confounding factors which motivate you to do this?
· Associated with this think about the what type of model you’d like to use e.g. level-level, log-level, log-log or level-log model for each of the considered variables. You need to justify this.
· Run the model with the following independent variables:
· No of bedrooms;
· Total Income;
· Location;
· Pcondition;
· Shops; and
· Bus.
and price as the dependent variable. You should also construct at least one interaction variables but it must make sense and must be justified. Creativity will be rewarded if you do this and you (i) construct it co
ectly, (ii) justify it and (iii) provide an interpretation of this variable.
As part of the reporting requirements for the multiple linear regression:
· Interpret the coefficients.
· Define and comment whether each of the coefficients are statistically significant. Remember to state your assumptions.
· Define and comment whether each of the coefficients are economically significant. Remember to state your assumptions. You’ll also need to define what is economically significant and use a benchmark to determine this.
· Limitations and issues with your model. If you decide to use technical terms e.g. multicollinearity, homoskedasticity, bias, consistency etc. you need to explain what these terminologies are and place them in the context of your problem and how it will affect your results.
Answered Same Day Jul 24, 2021

## Solution

Komalavalli answered on Jul 29 2021
Propriety model
Simple linear regression model
y = β0+β1x
y – Price
x – Tincome
Regression output
Regression Statistics

Multiple R
0.80459
R Square
0.64736
0.64615
Standard E
o
477705
Observations
293

df
SS
MS
F
Significance F
Regression
1
1.2E+14
1.2E+14
534.214
7.9E-68
Residual
291
6.6E+13
2.3E+11

Total
292
1.9E+14

Coefficients
Standard E
o
t Stat
P-value
Lower 95%
Upper 95%
Intercept
213523.54
46132.16
4.63
0.00
122728.55
304318.53
Tincome
3.99
0.17
23.11
0.00
3.65
4.33

Hypothesis
H0: β0= β1=0, H1: β0≠β1≠0
There are 293 observation used for the model. From above regression output we can say that the constant and Tincome variable are significant at 1 % level of significance. Here we reject null hypothesis of T income has no influence and accept alternate hypothesis of Tincome has influence on housing price. Increase in one unit of Total income on an average increases price by 4 units. In general there is a positive relationship between price and Total income. From the regression result we can say that the model is both economically and statistically significant-squared of this simple linear regression model is 0.65 indicates that 65% of variation in this model is explained by the explanatory variable used in this model.
Multiple regressions Model:
There are few other factors which influence the price of property other than total income, when we use simple linear regression model we failed to address others factors that influence property price. By using multiple regression model we able to address other factors that has an influence on property price rather than total income.
I would like to use log-log multiple regression model for the...
SOLUTION.PDF