International Students' Housing Challenges in Australia

This assignment examines the difficulties international students encounter when seeking accommodation in Australia. It delves into issues such as ethnic discrimination in private rentals, the impact of factors like place (e.g., Sydney) on rental prices, and the challenges faced by students with disabilities in accessing inclusive housing options. The report also highlights the need for government intervention to regulate the rental market and ensure equitable access to affordable housing for international students.

Running head: DATA ANALYSIS OF LODGING IN AUSTRALIA
Data Analysis of Lodging in Australia
Name of the Student:
Name of the Student’s family:
Student ID:
Name of the University:
Author Note:

DATA ANALYSIS OF LODGING IN AUSTRALIA 1
Executive Summary
Students from many countries come to various cities of Australia for mainly study purpose. Australian
students also travel from their locality to other cities for same reason. Therefore, they need to hire lodging
in houses, flats or apartments for dwelling. It is true that students try to optimize their weekly rent and
bond amount and get a luxurious dwelling place. The report concentrates on the dwelling aspects and
approaches of international and Australian students and few factors that are correlated with that.

DATA ANALYSIS OF LODGING IN AUSTRALIA 2
Table of Contents
Introduction:-.................................................................................................................................................3
Section 1: Introduction to the datasets:-.........................................................................................................4
Section 2: Weekly Rent of International Students:-......................................................................................4
Section 3: Dwelling Type Rental Bond Board Data:-...................................................................................9
Section 4: Weekly Rent of Rental Bond Board Property Data:-.................................................................12
Section 5: Bond Amount Analysis:-............................................................................................................14
Section 6: Conclusion:-................................................................................................................................19
Conclusion of the report:-............................................................................................................................20
References:...................................................................................................................................................21

DATA ANALYSIS OF LODGING IN AUSTRALIA 3
Introduction:-
The Australian colleges, universities give chances to Australian and international students to
complete their studies (Behnke, Seo and Miller, 2015). Students come to different cities for studying and
lodge in flat, houses or apartments. Australian education is now days publicly demarcated in student’s
interest (Bryne and Hall, 2013). House-owners find a profitable business in renting their flats and houses
(Tigney and Garwood, 2015). Handicapped students sometimes search the availability of lodging
according to their adversities (Leppo, Cowthon and Bond, 2013). In that case, increment of cost becomes
the major issue of those students.
The report shows the contrast between weekly rent and amount of bond paid by the international
and Australian students. Students keep balance between these two factors keeping the factors like number
of bedrooms, suburb, dwelling type in mind (Martin, 2013).
The report elaborately describes different correlation effect between the factors, distributions of
their standard of living, many calculations and graphs. The two datasets separately indicates the
preferences of dwelling types in different suburb. The report also interprets the comparison between two
datasets.

DATA ANALYSIS OF LODGING IN AUSTRALIA 4
Section 1: Introduction to the datasets:-
Answer (a):
This assignment provides the data of Australian students who boards in the rent for various
purposes. The dataset mainly provides the factors such as amount of bonding, weekly rent of the dwelling
place, dwelling type, number of bedrooms and suburb. We would like to find out and analyze the
different aspects and interrelationship of dwelling type, weekly rent and suburb preference. We also can
draw conclusions from the analyzed data and corresponding graphs, calculations.
This assignment also invites us to collect some data of international students who boards in
Australia. In addition, we have to analyze the data numerically and graphically. The report also elaborates
the findings and comparisons between these two datasets.
Answer (b):
The international students’ data who are boarding in rent in Australia is a secondary data (Irwin,
2013). The data was primarily collected by any organization or data-collector. Then we collected the data
from them via internet and analyzed them. It involves the data of 100 students. So, the data is taken at
random and might not be biased.
The data involves total nine columns. Among them, bond amount, weekly rent, number of
bedrooms, Postcode and Length of Tenancy (in month) is numeric factor. Suburb, bond lodgment date,
bond activation date and dwelling type are attributing in nature. Here, we note that, factors like suburb,
bond amount, weekly rant, number of bedrooms and dwelling type are present in both the data.
Answer (c)
The Australian students’ data has six factors. Bond amount (in dollar), Weekly rent (in dollar),
number of bedrooms and postcode is numerical data whereas dwelling type and suburb is attribute type
data. The dataset has the collection of boarding profiles of 500 students.
The data is also secondary. The data is primarily collected by anyone other than the student and
the student receives the data as an assignment from the professor to analyze. This secondary data is the
backbone of the report structure. First five cases of our dataset is displayed in the following:
BondAmoun
t WeeklyRent
DwellingTyp
e
NumberBedroom
s
Postcod
e Suburb
$1,840 $460 Flat 2 2150 PARRAMATTA
$2,380 $595 Flat 2 2031 RANDWICK
$2,880 $720 House 4 2144 AUBURN
$3,600 $900 Flat 2 2000 SYDNEY
$2,160 $540 House 3 2144 AUBURN
Section 2: Weekly Rent of International Students:-
Answer (a):

DATA ANALYSIS OF LODGING IN AUSTRALIA 5
Figure 1: The box plot shows the scatterings of Suburbs of the students.
> summary (DwellingType3)
FLAT OR UNIT OR APARTMENT OTHERS
41 1
SEPARATE HOUSE TERRACE OR TOWNHOUSE OR SEMI-DETACHED
49 4
UNSPECIFIED
4
The summary shows that dwelling type of only 41 of international students is flat. Most of the
students (49+4=53) students live in the house.
> summary (lm(WeeklyRent3~BondAmount3))
Min 1Q Median 3Q Max
-923.63 -247.67 -41.74 203.65 1349.64
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12207.0037 1974.1351 6.183 1.49e-08 ***
BondAmount3 -1.5537 0.2746 -5.659 1.55e-07 ***
---

DATA ANALYSIS OF LODGING IN AUSTRALIA 6
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 398.2 on 97 degrees of freedom
Multiple R-squared: 0.2482, Adjusted R-squared: 0.2404
F-statistic: 32.02 on 1 and 97 DF, p-value: 1.546e-07
> cor(WeeklyRent3,BondAmount3)
[1] -0.4981778
The value of multiple R2 in linear regression model and correlation coefficient between weekly
rent and Bond Amount shows that in this dataset both of these factors are neither linearly related nor
highly correlated.
5 0 0 1 0 0 0 1 5 0 0 2 0 0 0 2 5 0 0
distribution of weekly rent
amount of weeklyrent
Figure 2: The box plot shows the distribution of amount of weekly rent of the students.
The median of the weekly rent is 1000 and it involved few outliers too.

DATA ANALYSIS OF LODGING IN AUSTRALIA 7
7000 7100 7200 7300 7400
distribution of amount of bond
amount of bond
Figure 3: The box plot shows the distribution of amount of bond of the students.
The median of the bond is 7250 and it involved no outliers.
Answer (b)
> range(WeeklyRent3)
[1] 82.5 650.0
> range(BondAmount3)
[1] 330 2600
The ranges of Weekly rent and Bond Amount show the widely spread of these two factors. From,
the previous graphs, we observed that weekly rent contains outliers whereas amount of bond pay does not
contain outliers.

DATA ANALYSIS OF LODGING IN AUSTRALIA 8
Histogram of WeeklyRent3
WeeklyRent3
F r e q u e n c y
0 100 200 300 400 500 600 700
0 1 0 2 0 3 0 4 0
Figure 4: Histogram of Weekly Rent of the international students.
Histogram of BondAmount3
BondAmount3
F r e q u e n c y
0 500 1000 1500 2000 2500 3000
0 1 0 2 0 3 0 4 0
Figure 5: Histogram of Bond Amount of the international students.

DATA ANALYSIS OF LODGING IN AUSTRALIA 9
Both the histogram plots of Weekly rent pay and bond amount shows that their distributions are
positively skewed. Their distribution does not follow normal distribution perfectly. An interesting point is
that most of the international students pay weekly rent of $200 to $300 and most of the students pays the
bond amount of $500 to $1000.
> summary(bedroom1)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 2.000 2.000 2.404 3.000 5.000
> summary(bedroom2)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 1.000 2.000 1.748 2.000 4.000
The summary measures of number of bedrooms indicate that the international students have
greater number of bedrooms than Australian students have (in comparison with mean). It shows that
international students are more luxurious than Australian students.
Section 3: Dwelling Type Rental Bond Board Data:-
Answer (a)
Numerical Summary of Australian students’ Dwelling Type:
> summary(DwellingType)
Flat House
479 21

DATA ANALYSIS OF LODGING IN AUSTRALIA 10
0
100
200
300
400
500
Flat House
DwellingType
fre q u e n c y
Showing Dwelling Type of the students
Figure 6: A Bar chart to show dwelling types of the students.
Answer (b)
1-sample proportions test without continuity correction
/////output///////
data: rbind(.Table), null probability 0.5
X-squared = 419.53, df = 1, p-value < 2.2e-16
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.9366484 0.9723677
sample estimates:
p
0.958
The information about dwelling types of the students is attribute type data. Definitely, the data
informs that students having dwelling type “House” is 21 in number. Now, we apply test of significance

DATA ANALYSIS OF LODGING IN AUSTRALIA 11
of proportion of house rent and flat rent type by 95%. According to the estimated sample of Dwelling
Type, the p-value interprets that null hypothesis is not rejected.
Therefore, we can conclude that, rent type of house has proportion of less than 10% according to
the values of 95% confidence interval.
Answer(c)
Numerical Summary of Suburb of the students:
> summary(Suburb)
AUBURN PARRAMATTA RANDWICK SYDNEY
57 148 118 177
Flat
House
AUBURN PARRAMATTA RANDWICK SYDNEY
Suburb
D w e llin g T y p e
Relation of Suburb and dwelling type of the students
Figure 7: A comparative scatter plot between Suburb and dwelling type of the students.
The graph shows that the main Suburb is distributed in four parts such as Auburn, Parramatta,
Randwick and Sydney. Similarly, dwelling types is mainly divided in two parts such as House and Flat.
The people whose suburb is Sydney do not live in house. House dwellers are maximum in number in case
of Auburn. The maximum number of student has suburb in Sydney and minimum number of student has

DATA ANALYSIS OF LODGING IN AUSTRALIA 12
suburb in Auburn. The number of house dwelling students is significant in Auburn but insignificant in
Randwick.
Answer (d)
The suggestion that could be advised to the students who prefer to rent house than flat is that the
flat dwelling students are maximum in number in big suburb like Sydney. Therefore, according to the
luxury and facilities, flat is better than house. Students prefer flat than house in general. It can be
deducted that to transform their status more urbanized, student should shift to flat from house.
Section 4: Weekly Rent of Rental Bond Board Property Data:-
Answer (a)
> Suburb1=dataset3 [,6]
> summary(Suburb1)
AUBURN PARRAMATTA RANDWICK SYDNEY
31 111 85 60
The numerical summary of the students who dwell in only 2 bedrooms show that they live in
Parramatta mostly and in Auburn rarely.
Figure 8: The box plot shows the distribution of weekly rent of the students who dwells in 2 bedrooms.
The plot shows that most of the weekly rent of the students who dwells in 2 bedrooms pays $450
to $690. The median of the weekly rent of the students is $550.
In the next part, we are eager to see that how weekly rents of the students vary with their suburb.

DATA ANALYSIS OF LODGING IN AUSTRALIA 13
Figure 9: The box plot shows the distribution of weekly rent of different suburb student who dwells in 2
bedrooms.
The data plot confirms that the students who dwell in only 2 bedroom are more or less located in
all suburbs. Clearly, the students of Sydney pay a large weekly rent than the others suburb dwellers.
Auburn dwellers pay lowest median value of weekly rent. The median weekly rent of Randwick dweller
is greater than Parramatta dwellers.
Australian students should live in Auburn for their cheapest livelihood.
Answer (b)
2-sample proportions test without continuity correction
/////output///////
data: rbind(.Table), null probability 0.5
X-squared = 311.37, df = 3, p-value < 2.2e-16
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.649092 0.672347
sample estimates:
p
0.6531

DATA ANALYSIS OF LODGING IN AUSTRALIA 14
This implies that Weekly rent pay of students who dwells in different suburb has significant
effect at 5% level of significance. Sample estimates shows the proportion of weekly rent in different
suburb has effect according to the null hypothesis. Here, according to the p-value null hypothesis is not
rejected.
Therefore, according to the p-value, we have enough evidence of the high difference in weekly
rent among the different suburbs at 5% level of significance.
Answer (c)
The rent payers who pay their rent in different suburbs should come to Auburn. Here the weekly
paying rent is minimum and cost of living is cheap here.
Section 5: Bond Amount Analysis:-
Answer (a)
1 25 49 73 97 121145169193217241265289313337361385409433457481
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
$3,500
$4,000
$4,500
BondAmount
WeeklyRent
Figure 10: The bar plot shows the amount of bond and amount of weekly rent.
Call:
glm(formula = BondAmount ~ WeeklyRent, family = binomial(logit),
data = Dataset)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.000002409 0.000002409 0.000002409 0.000002409 0.000002409

DATA ANALYSIS OF LODGING IN AUSTRALIA 15
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.657e+01 2.518e+05 0 1
WeeklyRent[T.$1,030] -1.691e-09 4.362e+05 0 1
WeeklyRent[T.$1,050] 4.638e-06 3.251e+05 0 1
WeeklyRent[T.$250] -5.313e+01 3.561e+05 0 1
WeeklyRent[T.$280] -7.835e-22 3.561e+05 0 1
WeeklyRent[T.$290] -1.898e-19 4.362e+05 0 1
WeeklyRent[T.$300] -1.487e-20 4.362e+05 0 1
WeeklyRent[T.$315] 2.880e-21 4.362e+05 0 1
WeeklyRent[T.$324] -3.556e-21 4.362e+05 0 1
WeeklyRent[T.$330] -5.785e-21 4.362e+05 0 1
WeeklyRent[T.$335] 6.997e-21 4.362e+05 0 1
WeeklyRent[T.$345] -4.807e-22 4.362e+05 0 1
WeeklyRent[T.$350] -3.995e-21 2.980e+05 0 1
WeeklyRent[T.$355] 2.587e-21 4.362e+05 0 1
WeeklyRent[T.$356] -5.680e-21 4.362e+05 0 1
WeeklyRent[T.$360] -2.015e-21 3.251e+05 0 1
WeeklyRent[T.$365] 2.436e-21 4.362e+05 0 1
WeeklyRent[T.$370] 2.059e-21 3.561e+05 0 1
WeeklyRent[T.$375] -3.189e-21 3.561e+05 0 1
WeeklyRent[T.$380] -3.620e-21 3.561e+05 0 1
WeeklyRent[T.$385] 3.321e-21 4.362e+05 0 1
WeeklyRent[T.$390] -1.382e-21 2.815e+05 0 1
WeeklyRent[T.$400] 4.441e-22 2.738e+05 0 1
WeeklyRent[T.$410] 1.551e-21 2.908e+05 0 1
WeeklyRent[T.$415] 1.517e-23 3.251e+05 0 1
WeeklyRent[T.$420] -2.716e-22 2.681e+05 0 1
WeeklyRent[T.$425] 1.657e-21 3.084e+05 0 1
WeeklyRent[T.$430] 1.144e-21 2.980e+05 0 1
WeeklyRent[T.$435] 3.761e-22 4.362e+05 0 1
WeeklyRent[T.$440] -2.005e-21 2.908e+05 0 1
WeeklyRent[T.$450] 1.550e-22 2.681e+05 0 1
WeeklyRent[T.$455] -2.001e-21 3.251e+05 0 1
WeeklyRent[T.$460] 4.619e-06 2.815e+05 0 1
WeeklyRent[T.$469] -4.499e-25 4.362e+05 0 1
WeeklyRent[T.$470] -1.226e-24 2.815e+05 0 1
WeeklyRent[T.$475] -1.336e-24 4.362e+05 0 1
WeeklyRent[T.$480] -1.485e-24 2.720e+05 0 1
WeeklyRent[T.$490] -8.173e-25 2.855e+05 0 1
WeeklyRent[T.$495] -1.028e-21 3.251e+05 0 1
WeeklyRent[T.$500] -1.876e-24 2.662e+05 0 1
WeeklyRent[T.$510] -1.720e-24 3.084e+05 0 1
WeeklyRent[T.$520] 6.674e-22 2.681e+05 0 1
WeeklyRent[T.$525] 7.998e-22 3.084e+05 0 1
WeeklyRent[T.$530] -2.287e-23 2.855e+05 0 1
WeeklyRent[T.$540] -5.493e-22 2.738e+05 0 1
WeeklyRent[T.$545] -7.293e-25 3.561e+05 0 1
WeeklyRent[T.$550] -1.587e-22 2.610e+05 0 1
WeeklyRent[T.$560] 3.223e-22 2.759e+05 0 1

DATA ANALYSIS OF LODGING IN AUSTRALIA 16
WeeklyRent[T.$565] 1.269e-24 4.362e+05 0 1
WeeklyRent[T.$570] -5.411e-25 2.815e+05 0 1
WeeklyRent[T.$575] -4.991e-25 2.980e+05 0 1
WeeklyRent[T.$580] -1.996e-24 2.681e+05 0 1
WeeklyRent[T.$585] -8.730e-25 4.362e+05 0 1
WeeklyRent[T.$590] 2.888e-22 2.738e+05 0 1
WeeklyRent[T.$595] 2.598e-13 3.561e+05 0 1
WeeklyRent[T.$600] 3.168e-24 2.596e+05 0 1
WeeklyRent[T.$610] -1.029e-24 2.784e+05 0 1
WeeklyRent[T.$615] 3.125e-25 3.561e+05 0 1
WeeklyRent[T.$620] -5.396e-25 2.759e+05 0 1
WeeklyRent[T.$625] -3.630e-25 3.561e+05 0 1
WeeklyRent[T.$630] 6.985e-22 3.084e+05 0 1
WeeklyRent[T.$635] -6.310e-25 3.251e+05 0 1
WeeklyRent[T.$640] -1.230e-24 4.362e+05 0 1
WeeklyRent[T.$645] -1.380e-21 3.561e+05 0 1
WeeklyRent[T.$650] 2.690e-22 2.647e+05 0 1
WeeklyRent[T.$660] -9.182e-26 2.980e+05 0 1
WeeklyRent[T.$665] -7.084e-25 3.561e+05 0 1
WeeklyRent[T.$670] -4.092e-25 3.251e+05 0 1
WeeklyRent[T.$675] -1.191e-24 3.084e+05 0 1
WeeklyRent[T.$680] -1.313e-24 2.908e+05 0 1
WeeklyRent[T.$690] 1.841e-20 3.561e+05 0 1
WeeklyRent[T.$695] -6.132e-25 3.251e+05 0 1
WeeklyRent[T.$699] -8.238e-25 4.362e+05 0 1
WeeklyRent[T.$700] -2.935e-24 2.784e+05 0 1
WeeklyRent[T.$710] -1.568e-24 4.362e+05 0 1
WeeklyRent[T.$720] 1.048e-15 2.815e+05 0 1
WeeklyRent[T.$725] 6.175e-23 4.362e+05 0 1
WeeklyRent[T.$730] -6.241e-25 3.251e+05 0 1
WeeklyRent[T.$740] -4.754e-25 3.084e+05 0 1
WeeklyRent[T.$750] -1.545e-24 2.720e+05 0 1
WeeklyRent[T.$775] -1.838e-24 3.561e+05 0 1
WeeklyRent[T.$780] -4.673e-25 3.561e+05 0 1
WeeklyRent[T.$785] 2.350e-25 4.362e+05 0 1
WeeklyRent[T.$795] 3.093e-21 3.561e+05 0 1
WeeklyRent[T.$800] -9.691e-23 2.815e+05 0 1
WeeklyRent[T.$810] -7.057e-25 4.362e+05 0 1
WeeklyRent[T.$825] 1.426e-24 4.362e+05 0 1
WeeklyRent[T.$830] -3.611e-21 3.561e+05 0 1
WeeklyRent[T.$850] 2.256e-24 2.855e+05 0 1
WeeklyRent[T.$870] -1.918e-24 3.561e+05 0 1
WeeklyRent[T.$875] -3.554e-25 4.362e+05 0 1
WeeklyRent[T.$880] -1.177e-24 4.362e+05 0 1
WeeklyRent[T.$895] -1.142e-24 4.362e+05 0 1
WeeklyRent[T.$900] -3.735e-09 2.855e+05 0 1
WeeklyRent[T.$920] -3.227e-25 2.980e+05 0 1
WeeklyRent[T.$925] -1.160e-24 4.362e+05 0 1
WeeklyRent[T.$930] -1.770e-25 3.561e+05 0 1
WeeklyRent[T.$950] -1.113e-25 2.855e+05 0 1
WeeklyRent[T.$960] -1.764e-25 3.561e+05 0 1

DATA ANALYSIS OF LODGING IN AUSTRALIA 17
WeeklyRent[T.$975] -1.773e-25 4.362e+05 0 1
WeeklyRent[T.$980] -1.776e-25 3.561e+05 0 1
WeeklyRent[T.$990] -1.766e-25 3.561e+05 0 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 2.6078e+01 on 499 degrees of freedom (d.f.s)
Residual deviance: 2.9008e-09 on 398 degrees of freedom (d.f.s)
AIC value: 204
Number of Fisher Scoring iterations: 25
The result of multiple regression model, indicates that each level of weekly rent has significant
effect on their bond amount. Therefore, for increased weekly rent, the bond amount also is increased.
The binomial logit model gives AIC value 204. The null deviance and residual deviance are
almost very near to each other. Therefore, the multiple regression fitting is good.
Figure 11: The scatter plot shows the linear relationship between amount of bond and amount of weekly
rent.
Answer (b)
The correlation coefficient between amount of bond and amount of paying rent of the students is
(0.988039). Therefore, we can interpret that for the large amount of rent pay there is big amount of
bonding. Simultaneously, for small amount of rent pay, we observe little amount of bonding.
The important suggestion to deliver to the students is that if they borrow small amount of rent
paying flat, then they could pay little amount of bonding.

DATA ANALYSIS OF LODGING IN AUSTRALIA 18
Section 6: Conclusion:-
Answer (a)
We can draw some conclusions from international student’s lodging data and Australian student’s
lodging data that amount of bond and amount of rent pay is positively high correlated in Australian
student’s lodging data but not in case of international student’s lodging data. International students who
are dwelling in Australia prefer houses rather than flat and apartment. However, the scenario is different
in case of Australian local students.
The median of weekly rent currently paid by international students is $1000; which is far more
than Australian students of median pay $550. Clearly, the larger rate of rant paying is for House lodging.
From the analysis, it can be said that cost of lodging in case of international students in much higher
according to the market.
Answer (b)
An important suggestion could be given to the international students that if they change their
lodging from houses to flat, they can save money. However, they could face the problem of high amount
of bond pay rate. After all, the proposal is going to be profitable to the international students.
Conclusion of the report:-
The expenses of marginal rental houses are increasing day by day in Australia (Goodman et al.,
2013). The demand of Australian education is growing to the international students (Byme and Hall,
2013). Sometimes the presence of ethnic discrimination of private rental housing market is also found
(Nelson et al., 2015). Place as Sydney proves the hike in rental prices (Perks and Maruyama, 2015). The
students with disability face discrimination in educational accountability tests and social inclusion
(Cumming and Dickson, 2013).
According to the report, a regular government planning is to be accepted in the renting market of
different suburb of Australia.

DATA ANALYSIS OF LODGING IN AUSTRALIA 19
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