[FULL ACCESS] Assessment of House Location for Renovation and Sales

This assignment involves evaluating the suitability of different house locations in South Australia for renovation and sales. The student is provided with a dataset from the South Australian Government Data directory and uses purposive sampling to gather relevant information. They then analyze the data to determine which location (Henley Beach or another unspecified location) would be more beneficial for investment, considering both renovation cost and selling price. The student's analysis includes regression equations, average percentage differences, and comparisons between actual and estimated values.

Statistics

TABLE OF CONTENTS
ASSESSMENT................................................................................................................................3
2...................................................................................................................................................3
a....................................................................................................................................................3
b...................................................................................................................................................3
c....................................................................................................................................................5
d...................................................................................................................................................5
3...................................................................................................................................................6
4.................................................................................................................................................10
Estimations for renovation cost and selling prices on the basis of regression equation............11
5.................................................................................................................................................12
6.................................................................................................................................................13
TASK 2..........................................................................................................................................13
Summary of relevant data set....................................................................................................13
Details of data source.................................................................................................................13
Sampling technique used for obtaining data set........................................................................13
Graphical presentation...............................................................................................................13
Analysis.....................................................................................................................................15
Principles of statistical standards...............................................................................................15
Results........................................................................................................................................15
Recommendations......................................................................................................................15

ASSESSMENT
2.
a.
Computation of correlation
Particulars Figures
Correlation between renovation cost and
selling prices of estimates
.68
Correlation between renovation cost and
selling prices of estimates
.71
b.
Hypothesis:
Null hypothesis (H0): There is no statistical significant correlation between renovation cost and
selling price of houses.
Alternative hypothesis (H1): There is a statistical significant correlation between renovation cost
and selling price of houses.
Regression analysis: Estimates
SUMMARY
OUTPUT
Regression Statistics
Multiple
R 0.680978
R Square 0.463731
Adjusted
R Square 0.441386
Standard
Error 216593.2
Observati
ons 26

ANOVA
df SS MS F
Significa
nce F
Regressio
n 1
9.74E+
11
9.74E+
11
20.753
65 0.000129
Residual 24
1.13E+
12
4.69E+
10
Total 25
2.1E+1
2
Coefficie
nts
Standar
d Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept 311121.5 110798
2.8080
06
0.0097
46 82445.62
539797
.4
82445.
62
539797
.4
Renovatio
n Costs 5.855479
1.28533
1
4.5556
18
0.0001
29 3.202685
8.5082
72
3.2026
85
8.5082
72
P<0.05: Hypothesis accepted
Referring this, it can be presented significant correlation takes place between selling
price and renovation cost of houses.
Regression analysis: Originals
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.70781
4
R Square
0.50100
1
Adjusted R
Square
0.48020
9
Standard
Error
197596.
9
Observations 26
ANOVA
df SS MS F
Significan
ce F
Regression 1
9.40827E+
11
9.41E+
11
24.09
626 5.24E-05

Residual 24
9.37069E+
11
3.9E+1
0
Total 25
1.8779E+1
2
Coeffici
ents
Standard
Error t Stat
P-
value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
278159.
2
101680.10
1
2.735
631
0.011
523 68301.79
488016
.6
68301.7
9 488016.6
Renovation
Costs
5.58299
7
1.1373458
74
4.908
794
5.24E-
05 3.235631
7.9303
64
3.23563
1 7.930364
c.
Particulars Figures
Average figure on the basis of estimation $2070
Average figure on the basis of actuals $2149
Difference 2149 – 2070
= $79
% difference (79 / 2070) * 100
= 3.81%
d.
Assessing regression equation using renovation cost and selling price related to estimates

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
f(x) = 1005.12820512821 x + 66046.1538461538
f(x) = 11504.2735042735 x + 622000
Renovation cost Linear (Renovation cost ) Selling price
Linear (Selling price) Linear (Selling price)
3.
Blakeview quarterly median house prices
Q3 2017 300,000
Q4 2017 326,250
Q1 2018 310,000
Q2 2018 317,500
Q3 2018 315,000
Q4 2018 345,250

Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018
270,000
280,000
290,000
300,000
310,000
320,000
330,000
340,000
350,000
f(x) = 5714.28571428572 x + 299000
Blakeview quarterly median house prices
(Source: South Australian Government Data directory, 2019)
Enfield quarterly median house prices
Q3 2017 432,000
Q4 2017 445,000
Q1 2018 465,000
Q2 2018 425,500
Q3 2018 456,500
Q4 2018 438,600

Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018
400,000
410,000
420,000
430,000
440,000
450,000
460,000
470,000
f(x) = 800 x + 440966.666666667
Enfield quarterly median house prices
(Source: South Australian Government Data directory, 2019)
Gelnunga quarterly median house prices
Q3 2017 880,000
Q4 2017 1,210,000
Q1 2018 1,275,000
Q2 2018 1,115,000
Q3 2018 1,177,500
Q4 2018 1,530,000

Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
f(x) = 85500 x + 898666.666666667
Gelnunga quarterly median house prices
(Source: South Australian Government Data directory, 2019)
Henley Beach quarterly median house prices
Q3 2017 895,000
Q4 2017 828,000
Q1 2018 843,000
Q2 2018 963,000
Q3 2018 880,000
Q4 2018 900,000

Q3 2017 Q4 2017 Q1 2018 Q2 2018 Q3 2018 Q4 2018
750,000
800,000
850,000
900,000
950,000
1,000,000
f(x) = 8600 x + 854733.333333333
Henley Beach quarterly median house prices
(Source: South Australian Government Data directory, 2019)
4.
Revised renovation cost and selling prices on the basis of average percentage difference:
3.81%
Renova
tion
Renovat
ion cost
Revised
renovat
ion cost
Selling
prices
Revis
ed
sellin
g
price
s
1 85,000 88,239 420,000
43600
2
2 60,000 62,286 580,000
60209
8
3 120,000 124,572 740,000
76819
4
4 45,000 46,715 950,000
98619
5
5 40,000 41,524 650,000 67476

5
6 65,000 67,477 680,000
70590
8
7 130,000 134,953
1,250,00
0
12976
25
8 35,000 36,334 860,000
89276
6
9 80,000 83,048 690,000
71628
9
10 75,000 77,858 540,000
56057
4
11 40,000 41,524 350,000
36333
5
12 110,000 114,191 780,000
80971
8
13 90,000 93,429 480,000
49828
8
14 75,000 77,858 920,000
95505
2
15 80,000 83,048 680,000
70590
8
16 55,000 57,096 620,000
64362
2
17 105,000 109,001 920,000
95505
2
18 60,000 62,286 840,000
87200
4
19 50,000 51,905 610,000
63324
1
20 45,000 46,715 700,000
72667
0
21 115,000 119,382
1,300,00
0
13495
30
22 65,000 67,477 630,000
65400
3
23 180,000 186,858
1,680,00
0
17440
08
24 75,000 77,858 540,000
56057
4
25 80,000 83,048 870,000
90314
7
26 110,000 114,191 930,000
96543
3

Estimations for renovation cost and selling prices on the basis of regression equation
Regressio
n equation
for
renovation
cost 1005.1(x)+66046
Regressio
n equation
for selling
prices 11504x+622000
Renova
tion
Revise
d
renovat
ion cost
Revi
sed
sellin
g
price
s
1 67051.1
6335
04
2 68056.2
6450
08
3 69061.3
6565
12
4 70066.4
6680
16
5 71071.5
6795
20
6 72076.6
6910
24
7 73081.7
7025
28
8 74086.8
7140
32
9 75091.9
7255
36
10 76097
7370
40
11 77102.1
7485
44
12 78107.2 7600

48
13 79112.3
7715
52
14 80117.4
7830
56
15 81122.5
7945
60
16 82127.6
8060
64
17 83132.7
8175
68
18 84137.8
8290
72
19 85142.9
8405
76
20 86148
8520
80
21 87153.1
8635
84
22 88158.2
8750
88
23 89163.3
8865
92
24 90168.4
8980
96
25 91173.5
9096
00
26 92178.6
9211
04
5.
Assessment of mean renovation cost and selling prices
Renovation cost Selling price
On the basis of
average percentage
difference
$82649 $806923
As per regression
equation
$79614.9 $777304

6.
Referring the above assessment, it can be presented that for the purpose of renovation and
sales, house located in Henley Beach will prove to be more beneficial. Moreover, assessment
clearly exhibits that average renovation cost and selling price accounts for $79614.9 & $777304
respectively. Hence, it can be presented that by making investment in the houses of Henley
Beach business entity would become able to get desired level of profit margin as well as success.
TASK 2
Summary of relevant data set
On the basis of given case situation, small building company is also involved in building
as well as purchasing new houses. Now, business entity wants to assess location which in turn
proves to be more suitable from the perspective of renovation and purchasing aspect. Thus, such
objective has been fulfilled by making evaluation of previous estimations related to selling price
and renovation cost.
Details of data source
By taking into account following site median (quarterly) data set pertaining to houses
located at different locations have been gathered.
www.data.sa.gov.au
Sampling technique used for obtaining data set
With regards to current analysis, purposive sampling technique has been used for the
collection of data related to housing renovation cost and selling prices.
Graphical presentation
Estimates

0 20 40 60 80 100 120
0
500000
1000000
1500000
2000000
Normal Probability Plot
Sample Percentile
Selling Price
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1,800,000
f(x) = 1005.12820512821 x + 66046.1538461538
f(x) = 11504.2735042735 x + 622000
Renovation cost Linear (Renovation cost ) Selling price
Linear (Selling price) Linear (Selling price)
Actuals

0 20 40 60 80 100 120
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1800000
Normal Probability Plot
Sample Percentile
Selling Price
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
0.00
200,000.00
400,000.00
600,000.00
800,000.00
1,000,000.00
1,200,000.00
1,400,000.00
1,600,000.00
1,800,000.00
f(x) = 1100.51282051282 x + 67796.9230769231
f(x) = 14184.6153846154 x + 548123.076923077
Renovation Costs Linear (Renovation Costs)
Selling Price Linear (Selling Price)