INF80028 Business Process Management Modelling Assignment
Delivery in day(s): 3
This is a solution of descriptive analysis visualization assignment help in which we discuss objective of carrying out exploratory, descriptive and regression analysis for gaining comprehensive understanding of house price in city
This report has been undertaken with the main objective of carrying out exploratory, descriptive and regression analysis for gaining comprehensive understanding of house price in the Shiraz region. It is also going to lay down understanding of most important factors that has been laying down impact over the housing prices in the area of Shiraz. The different analysis that has been carried out in this regard are as follows descriptive statistics, factors influencing house prices, development of multiple regression model and time series analysis.
Table 1: Descriptive statistics
Price($'000)




Mean 
886.575 
Standard Error 
29.66343575 
Median 
852 
Mode 
811 
Standard Deviation 
324.9466579 
Sample Variance 
105590.3305 
Kurtosis 
0.14778497 
Skewness 
0.426005063 
Range 
1569 
Minimum 
192 
Maximum 
1761 
Sum 
106389 
Count 
120 
From the above scatter diagram it could be interpreted that there is positive correlation between house price and area of the house in square meters as there is rightward movement and a straight line if drawn is going to originate out to high x and yvalues.
From the above scatter diagram it could be interpreted that there is positive correlation between house price and Street appeal as evaluated by the real estate agency as there is rightward movement and a straight line if drawn is going to originate out to high x and yvalues.
The above figure indicates that there is perfect positive linear relationship between the variables that are between house price and number of storey’s or levels in the house. It is because both the variables are moving towards the rightward direction and are seen to be away from each other.
Table 2: Correlation ship

Price($'000) 
Price($'000) 
1 
Rooms 
0.505469281 
Street 
0.722570048 
Storey’s 
0.565098455 
Weekly Rent $ 
0.665590917 
Bedrooms 
0.539744531 
Bathrooms 
0.331222685 
This model represents the independent variable that has been identified in order to present the dependent variable.
Y=α + βX1+BX2+BX3+BX4+BX5+ BX6…………..………………………. (Model 1)
Y= 386.984+ 0.174X1+0.212X2+89.86X3+188.42X4+106.80X5+13.37X6
Dependent variable= Selling price of house in $'000
Independent variable= Rooms, Weekly rent, Street, Storey’s, Bedrooms and Bathrooms
Table 3: Multiple regression model 1
Regression Statistics



Multiple R 
0.913271 


R Square 
0.834063 

Adjusted R Square 
0.825253 

Standard Error 
135.8368 

Observations 
120 



ANOVA 


df 
SS 
MS 
F 
Significance F 

Regression 
6 
10480214 
1746702 
94.66378 
9.95E42 

Residual 
113 
2085036 
18451.64 



Total 
119 
12565249 







Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Intercept 
386.984 
102.3464 
3.78111 
0.000251 
589.75 
184.217 
Rooms 
0.17444 
25.05619 
0.006962 
0.994457 
49.4664 
49.81528 
Weekly Rent $ 
0.212072 
0.07814 
2.714014 
0.007689 
0.057263 
0.36688 
Street 
89.8622 
7.429578 
12.0952 
4.13E22 
75.14287 
104.5815 
Storey’s 
188.4243 
26.93576 
6.995319 
1.98E10 
135.0597 
241.7889 
Bedrooms 
106.8086 
31.44359 
3.396831 
0.000942 
44.51315 
169.104 
Bathrooms 
13.3787 
40.95154 
0.3267 
0.744502 
94.5111 
67.75365 
RSquare
Rsquare value for the following model is 83.4%, indicates that 83.4% total variance in selling price of house in $'000 can be explained by independent variable Rooms, Weekly rent, Street, Storey’s, Bedrooms and Bathrooms
FValue
The calculated Fvalue is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.
PValue
Rooms, Street, Storey’s and Bathrooms have no influence on selling price of house in $'000 as it is not statistically significant because Rooms, Street, Storey’s and Bathrooms Pvalue is greater than 0.01% at 1% level of significance. On the other hand, weekly rent and bedrooms have influence on selling price of house in $'000 as it is statistically significant because weekly rent and bedrooms Pvalue is less than 0.01% at 1% level of significance.
Coefficients
Coefficient value indicates the rooms (0.17), weekly rent (0.21), street (89.8), storey’s (188.42) and bedrooms (106.8) have got dependability on selling price of house in $'000. In a case if Rooms, Weekly rent, Street, Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 17%, 21%, 8900%, 18800% and 10600%. On the other hand, Bathrooms $ 13.37 have got no dependability on selling price of house in $'000. In a case if Bathrooms changes by one unit then selling price of house in $'000 will decrease by 1300%.
Linear regression model
This model represents the independent variable that has been identified in order to present the dependent variable.
Y=α + βX1+BX2+BX3…………..………………………. (Model 2)
Y= 409.72+ 97.7X1+200.19X2+127.61X3
Dependent variable= Selling price of house in $'000
Independent variable= Street, Storey’s and Bedrooms
Table 4: Multiple regression model 2
Regression Statistics



Multiple R 
0.906903 


R Square 
0.822474 

Adjusted R Square 
0.817882 

Standard Error 
138.6718 

Observations 
120 



ANOVA 


df 
SS 
MS 
F 
Significance F 

Regression 
3 
10334586 
3444862 
179.1413 
2.25E43 

Residual 
116 
2230663 
19229.86 



Total 
119 
12565249 







Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Intercept 
409.729 
58.34096 
7.023 
1.58E10 
525.28 
294.177 
Street 
97.711 
7.009674 
13.93945 
1.52E26 
83.82745 
111.5945 
Storey’s 
200.197 
27.0691 
7.395776 
2.39E11 
146.5833 
253.8108 
Bedrooms 
127.6109 
11.84395 
10.77435 
3.52E19 
104.1525 
151.0693 
Rsquare value for the following model is 82.24%, indicates that 82.24% total variance in selling price of house in $'000 can be explained by independent variable Street, Storey’s, Bedrooms
FValue
The calculated Fvalue is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.
PValue
Street, Storey’s and Bedrooms have no influence on selling price of house in $'000 as it is not statistically significant because Street, Storey’s and Bedrooms Pvalue is greater than 0.01% at 1% level of significance.
Coefficients
Coefficient value indicates the street (97.71), storey’s (200.19) and bedrooms (127.61) have got dependability on selling price of house in $'000. In a case if Street, Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 9700%, 20000%, and 12700%.
Linear regression model
This model represents the independent variable that has been identified in order to present the dependent variable.
Y=α + βX1+BX2+BX3…………..………………………. (Model 3)
Y= 60.14+ 97.7X1+317.87X2+137.10X3
Dependent variable= Selling price of house in $'000
Independent variable= Storey’s and Bedrooms
Table 5: Multiple regression model 3
Regression Statistics



Multiple R 
0.724641 


R Square 
0.525104 

Adjusted R Square 
0.516986 

Standard Error 
225.8353 

Observations 
120 



ANOVA 


df 
SS 
MS 
F 
Significance F 

Regression 
2 
6598066 
3299033 
64.68494 
1.21E19 

Residual 
117 
5967183 
51001.56 



Total 
119 
12565249 







Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 
Intercept 
60.1449 
85.78547 
0.70111 
0.484627 
230.039 
109.7487 
Storeys 
317.8746 
41.88497 
7.589228 
8.52E12 
234.9236 
400.8255 
Bedrooms 
137.1081 
19.25664 
7.120045 
9.4E11 
98.97137 
175.2449 
Rsquare value for the following model is 52.5%, indicates that 52.5% total variance in selling price of house in $'000 can be explained by independent variable Storey’s and Bedrooms.
FValue
The calculated Fvalue is greater than critical value of F thus it can be said that model is accepted. Even it can be said that value ration of explained to unexplained variance is seen to be very high. Hence, it can be said that regression variables are significant for explaining the dependent variable.
PValue
Storey’s and Bedrooms have no influence on selling price of house in $'000 as it is not statistically significant because Storey’s and Bedrooms Pvalue is greater than 0.01% at 1% level of significance.
Coefficients
Coefficient value indicates the Storey’s (317.87) and bedrooms (137.10) have got dependability on selling price of house in $'000. In a case if Storey’s and Bedrooms changes by one unit then selling price of house in $'000 will increase by 31700% and 13700%.
Table 6: Mean absolute percentage error calculation
Time Period 
Quarter 
Median House Price ($'000) (A) 
Forecasted (F) 
Deviation(AF) 
Absolute deviation(/AF/) 
Absolute percentage of error=100(/AF/)/ A 
1 
2012Q1 
554 
950 
396 
396 
71 
2 
2012Q2 
589 
1320 
731 
731 
124 
3 
2012Q3 
661 
1500 
839 
839 
127 
4 
2012Q4 
522 
1090 
568 
568 
109 
5 
2013Q1 
610 
950 
340 
340 
56 
6 
2013Q2 
700 
1320 
620 
620 
89 
7 
2013Q3 
850 
1500 
650 
650 
76 
8 
2013Q4 
592 
1090 
498 
498 
84 
9 
2014Q1 
770 
950 
180 
180 
23 
10 
2014Q2 
880 
1320 
440 
440 
50 
11 
2014Q3 
1090 
1500 
410 
410 
38 
12 
2014Q4 
725 
1090 
365 
365 
50 
13 
2015Q1 
932 
950 
18 
18 
2 
14 
2015Q2 
1150 
1320 
170 
170 
15 
15 
2015Q3 
1330 
1500 
170 
170 
13 
16 
2015Q4 
940 
1090 
150 
150 
16 
Total 





943 
A= actual valueWhere,
F= Forecasted value
n= Time period
From the above calculation of Mean absolute percentage error it could be interpreted that the measure of prediction accuracy of a forecasting value for Quarterly median house prices was seen to be around 58.9%. This shows price of the house vale at Shiraz to be accurate by 58.9%.
The present research proposal have been taken on the basis of appropriate variable for measuring the suitability of selling price of house in $'000 on the basis of various factors selected for carrying out the study. Further, if this analysis need to be repeated in the future then the various other factors that can be used for studying the suitability of Selling price of house in $'000 to be charged could be through inflation, personal disposable income, connectivity of city and location of Shiraz city at highway.
From the study it has been found that majority of the variables that has been laying down influence over the house price are Street, Storey’s and Bedrooms. This shows that Shiraz local government area must these independent variables to be important while carrying out their development process.