Real Estate Analysis and Data Visualization

This assignment delves into a real estate dataset, providing an in-depth analysis of rental properties. It examines average weekly rents across various suburbs, revealing insights into price variations and affordability. The analysis also investigates the relationship between bond amounts and weekly rent, employing scatterplots and correlation coefficients to illustrate this association. Additionally, the assignment highlights key statistical measures such as mean, median, and mode, and briefly touches upon hypothesis testing concepts.

SECTION 1
This report looks into rented properties in 4 suburbs of Australia and targets only students. It uses
information on not just weekly rents paid but also on other aspects of the accommodation. These
aspects include- type of dwelling, number of bedrooms in the accommodation , suburb chosen, and
bond amount of the property rented .
DATA 1 IS MISSING
The secondary data is taken from the website of Department of Finance, Services and Innovation as part
of Rental Bond Board Property Data. A sample size of 500 is chosen. The table below is snapshot of this
data:
BondAmount WeeklyRent DwellingType NumberBedrooms Postcode Suburb
$2,900 $725 Flat 3 2031 RANDWICK
$2,480 $620 Flat 1 2031 RANDWICK
$1,960 $490 Flat 2 2150 PARRAMATTA
$2,200 $550 Flat 2 2031 RANDWICK
$2,280 $570 Flat 2 2031 RANDWICK
SECTION 2
Data 1 missing
SECTION 3:
Looking at the secondary data , we focus on the categorical variable - Dwelling Type. It has two options -
flat and house. We provide a pivot tale for a 2*2 classification where the 2 attributes are dwelling type
and suburb. We can observe the following:
 Most students live in flats – 462 /500 or 92.4%.
 Most of them prefer to live in Parammatta, while least number in Auburn, despite lowest rents
here.
 Sydney has no student sin houses.

Row Labels Flat House
AUBURN 38 19
PARRAMATTA 151 12
RANDWICK 117 7
SYDNEY 156
Grand Total 462 38
The above information is visually seen below. The high blue bars for flats show their dominance over
houses.
We ten turn to hypothesis testing to check is the proportion of houses is less than 10%
required sample proportion = p = 38/500 = 0.076
Ho: p= 0.1
H1: p < 0.1
Using the left tail hypothesis test with z distribution we get
Test value = (0.076 – 0.1)/ SE where
SE = (0. 1 *.9 /500)^.5 = 0.0134
The z test value = ( 0.076 0.1)/ 0.0134 = -1.789. The test value is more than critical value for 95%
confidence ( -1.645) in absolute terms. This leads to the conclusion that that at a 5% level of significance

or 95% confidence level, we DO NOT ACCEPT the null hypothesis. There is statistical evidence that
proportion of houses in rented dwellings is less than 10%.
This means that flats are dominant in a systematically important way. It is no chance that this sample
has rejected the null hypothesis. However if we choose a 99% confidence then we will be accepting the
null hypothesis. This is because the critical value will be -2.33. thus, the idea that houses are less than
10% of al rented places for students can be debated depending on the confidence level and the
precision level we choose.
SECTION 4:
We turn to the next parameter which is no of bedrooms – looking at flats and houses with 2 bedrooms
only. The table and chart use the same information on average weekly rents across suburbs. Auburn is
the cheapest suburb among the 4 , with rent of $393.17on weekly basis. Sydney is expectedly the most
expensive with a rent of more than double Auburn rents - $840.74
Row Labels
Average of
WeeklyRent
AUBURN 393.167
PARRAMATTA 474.159
RANDWICK 608.278
SYDNEY 840.738

The difference seen above can be challenged in statistical terms. Using ANOVA for checking the
significance in differences, we conclude that differences in average weekly rents across suburbs are
statistically different. The F test value is 261.9, which has p value of zero. This p value automatically
supports differences in rent argument.
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Column 1 113 53580 474.159292 4371.832
Column 2 79 48054 608.278481 11073.23
Column 3 61 51285 840.737705 14925.7
Column 4 30 11795 393.166667 2290.489
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 6522098.798 3 2174032.93 261.9743
8.15E-
81 2.63696
Within Groups 2315322.976 279 8298.64866
Total 8837421.774 282
This result is helpful to pick and choose a suburb based on how much has been allocated for rent or
what student can pay as rent. These average values area good guide to rents in each suburb, and help to
avoid looking at all suburbs when rent constraint exists.
SECTION 5:
The scatterplot tells us:
 A strong positive association between weekly Rent and Bond Amount exists, as shown by
upward sloping regression line.
 The value of R2 is 0.953- so that 95.3% of variation in weekly rent is explained by variation in
bond amount.

 We can see 2 outliers visually as depicted.
 The coefficient of correlation is .953^.5 = 0.972, which is very high.
association. This proves that bond prices are a good indicator/ proxy for weekly rent Any information on
bond amount can help to guess the rent level quite accurately.
SECTION 6
We need data1 so that it can be compared with data 2.
outliers

References
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