Median Housing Price Prediction Model for D. M. Pan National Real Estate Company 1
Report: Housing Price Prediction Model forD. M. Pan National Real Estate Company
Destiny Boniface
Southern New Hampshire University
Median Housing Price Model for D. M. Pan National Real Estate Company 2
Introduction
Hi my name is Destiny Boniface, and I have been hired as an Junior analyst for D.M. Pan
National Real Estate Company. The purpose of my report is to collect data from a region, its
prices of their homes, and their square feet.
Using linear regression is most appropriate when I want to understand and model the relationship
between a dependent variable, and one or more independent variables.
Using linear regression, I expect the scatterplot of the independent variables versus the
dependent variable to show like a roughly straight line pattern.
(y) represents listing price, and (x) represents the square footage of the properties. Yes,
there is an association between x and y. The association is based on the average square footage
properties within the 2800 sq. ft. mostly paying 305,000 for their properties.
Data Collection
Pacifi
c
a
k
anch
orag
e
45
0,0
00
$2
59
1,7
39
Pacifi
c
a
k
fairb
anks
nort
h
star
33
0,6
00
$2
31
1,4
30
Pacifi
c
a
k
mat
anus
43
1,5
$2 1,5
Median Housing Price Model for D. M. Pan National Real Estate Company 3
ka-
susit
na 00 73 83
Pacifi
c
c
a
alam
eda
47
2,1
00
$2
29
2,0
65
Pacifi
c
c
a
butt
e
40
6,2
00
$2
17
1,8
75
Pacifi
c
c
a
cont
ra
cost
a
36
6,5
00
$2
25
1,6
26
Pacifi
c
c
a
el
dora
do
38
8,4
00
$2
11
1,8
39
Pacifi
c
c
a
fres
no
38
9,5
00
$2
79
1,3
95
Pacifi
c
c
a
hum
bold
t
34
9,5
00
$2
34
1,4
94
Pacifi
c
c
a
imp
erial
34
3,9
00
$2
33
1,4
78
Pacifi
c
c
a kern
36
4,8
00
$2
36
1,5
46
Pacifi
c
c
a
king
s
40
8,1
00
$2
46
1,6
56
Pacifi
c
c
a
lake 33
5,7
00
$2
35
1,4
30
Median Housing Price Model for D. M. Pan National Real Estate Company 4
Pacifi
c
c
a
los
ange
les
39
3,1
00
$2
19
1,7
93
Pacifi
c
c
a
mad
era
43
9,8
00
$2
75
1,5
97
Pacifi
c
c
a
mari
n
42
0,8
00
$2
27
1,8
56
Pacifi
c
c
a
men
doci
no
47
8,2
00
$2
37
2,0
20
Pacifi
c
c
a
mer
ced
33
9,6
00
$1
97
1,7
26
Pacifi
c
c
a
mon
tere
y
41
7,2
00
$3
44
1,2
13
Pacifi
c
c
a napa
40
4,1
00
$2
28
1,7
69
Pacifi
c
c
a
neva
da
37
7,4
00
$2
34
1,6
14
Pacifi
c
c
a
oran
ge
35
4,8
00
$3
22
1,1
01
Pacifi
c
c
a
plac
er
31
4,6
00
$2
66
1,1
82
Pacifi
c
c
a
river
side
32
9,5
00
$2
59
1,2
74
Median Housing Price Model for D. M. Pan National Real Estate Company 5
Pacifi
c
c
a
sacr
ame
nto
29
1,5
00
$1
76
1,6
55
Pacifi
c
c
a
san
bern
ardi
no
46
5,5
00
$2
49
1,8
73
Pacifi
c
c
a
san
dieg
o
42
1,4
00
$2
02
2,0
81
Pacifi
c
c
a
san
fran
cisco
41
3,1
00
$2
59
1,5
95
Pacifi
c
c
a
san
joaq
uin
46
8,2
00
$2
25
2,0
83
Pacifi
c
c
a
san
luis
obis
po
37
0,0
00
$2
97
1,2
46
Pacifi
c
c
a
san
mat
eo
31
4,3
00
$2
10
1,5
00
Pacifi
c
c
a
sant
a
barb
ara
35
5,6
00
$2
69
1,3
23
Pacifi
c
c
a
sant
a
clara
32
9,0
00
$2
61
1,2
62
Pacifi
c
c
a
sant
a
cruz
40
5,1
00
$2
07
1,9
55
Pacifi
c
c
a
shas
ta
34
4,9
$2
98
1,1
57
Median Housing Price Model for D. M. Pan National Real Estate Company 6
00
Pacifi
c
c
a
sola
no
34
3,7
00
$2
77
1,2
40
Pacifi
c
c
a
sono
ma
38
1,4
00
$2
29
1,6
63
Pacifi
c
c
a
stani
slaus
38
9,3
00
$2
72
1,4
31
Pacifi
c
c
a
sutt
er
35
4,6
00
$2
55
1,3
90
Pacifi
c
c
a
teha
ma
37
1,2
00
$2
32
1,5
97
Pacifi
c
c
a
tular
e
40
3,9
00
$2
58
1,5
66
Pacifi
c
c
a
tuol
umn
e
36
7,7
00
$2
27
1,6
19
Pacifi
c
c
a
vent
ura
37
3,3
00
$2
44
1,5
32
Pacifi
c
c
a yolo
51
6,3
00
$2
70
1,9
14
Pacifi
c
c
a yuba
42
4,0
00
$2
16
1,9
59
Pacifi
c
h
i
haw
aii
43
7,0
$2
22
1,9
70
Median Housing Price Model for D. M. Pan National Real Estate Company 7
00
Pacifi
c
h
i
hon
olul
u
27
3,3
00
$2
24
1,2
20
Pacifi
c
h
i
kaua
i
51
2,3
00
$2
52
2,0
30
Pacifi
c
h
i
mau
i
34
2,0
00
$2
98
1,1
49
Pacifi
c
o
r
bent
on
35
1,0
00
$2
94
1,1
92
Predictor Variable: the square footage of a home
Response Variable: the cost of the home depending on its square footage
Data Analysis
[Histogram: Create and insert a histogram for the first variable. Be sure to include
appropriate labels.]
[Histogram: Create and insert a histogram for the second variable. Be sure to include
appropriate labels.]
[Summary statistics: Create and insert a table to show the summary statistics (mean,
median, standard deviation) for both variables.]
[Interpret the graphs and statistics: Interpret the center, spread, shape, and any unusual
characteristic (outliers, gaps, etc.) for house sales and square footage.]
[Interpret the graphs and statistics: Compare and contrast center, spread, shape, and
any unusual characteristic for your sample of house sales with the national population. Also,
Median Housing Price Model for D. M. Pan National Real Estate Company 8
determine whether your sample is representative of the national housing market sales. Note: In
the learning management system, under Supporting Materials, see National Summary Statistics
and Graphs Real Estate Data PDF.]
Develop Regression Model
[Scatterplot: Create and insert the scatterplot of the variables with a line of best fit and
the regression equation. [Based on your scatterplot, explain whether a regression model is
appropriate.]
[Discuss associations: Discuss the associations in the scatterplot, including the direction,
strength, and form, in the context of your model.]
[Discuss associations: Identify any possible outliers or influential points and discuss
their effect on correlation.]
[Discuss associations: Discuss keeping or removing outlier data points and what impact
your decision would have on your model.]
[Calculate r: Calculate the correlation coefficient and explain how the calculated r value
supports what was noticed in your scatterplot.]
Determine the Line of Best Fit
[Regression equation: Write the regression equation (i.e., line of best fit) and clearly
define your variables.]
[Interpret regression equation: Interpret the slope and intercept in context. For
example, answer the questions: What does the slope represent in this situation? What does the
intercept represent? Revisit the Scenario section in the learning management system.]
[Strength of the equation: Provide and interpret R-squared. Determine the strength of
the linear regression equation you developed.]
Median Housing Price Model for D. M. Pan National Real Estate Company 9
[Use regression equation to make predictions: Use the regression equation to predict
how much you should list your home for based on the assumed square footage of your home at
1500 square feet.]
Conclusions
[Summarize findings: Summarize your findings in clear and concise plain language for
the CEO to understand.]
[Summarize findings: Did you see the results you expected, or was anything different
from your expectations or experiences?]
[Summarize findings: What changes could support different results, or help to solve a
different problem?]
[Summarize findings: Provide at least one question that would be interesting for follow-
up research.]
