AMDA Project Report_Roll 406

8/8/2019 AMDA Project Report_Roll 406

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9/19/2010

2010

Submitted to:

Mrs. Shailaja Rego

Submitted by:

Subhojit Chatterjee

Roll No.: 406

Division E MBA - Core

Advanced Methods of Data

Analysis Project Report:

Study on what factors peoplelook for when they take a home


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Study on what factors people look for when they take a home

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Contents

Introduction 3

Methodology ..3

Cluster Analysis .5

Factor Analysis .11

Conclusion.....12


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Introduction

Why I Took This Topic For Project?

In India buying a home is one of the biggest aspirations of any household irrespective of the

class to which that household belongs. A lot of thought process and research work goes into the

process and research work goes into the process of selecting a perfect house. Also such

considerations are take place while taking a house for lease or rent.

A house is such a basic need that people do not change it that easily even if they are

unsatisfied with it. Also a person, if asked wont reveal that easily that he/she is unsatisfied with

his/her current house. This kind of natural behavior forces the person to give a biased answer

when asked about the overall satisfaction level from his/her house. My research aims at finding

the relation of some of the factors which affect the overall satisfaction level of a home owner.

For convenience I have targeted NMIMS students specially first year to get genuine response. I

also wanted to verify our analysis as we are using most crucial tool SPSS.

Methodology

As a part of Advanced Methods of Data Analysis project, I chose to study Real Estate becauseof the reason that it is one of the hottest sectors these days. With limited amount of resources

available to a country, it is of utmost importance to manage it properly. Its very essential for a

researcher to understand the satisfaction level of customers while having a home so that they

can more focus to cater their preferences. Being in Mumbai, the costliest city in India & among

the top 100 costliest cities all around the world, the study of factors on which a person chooses

his/her household becomes all the more important.

The objective of the study was to study various aspects as to what influences the behavior of an

individual having a house. These include locality, size, price, availability of electricity as well as

water etc. The detailed questionnaire is as follows:

Q1. What is your total household income level

less than 3 laces between 3 to 6 lacs 6 to 9 lacs


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more than 9 lacs

Q2. Are you satisfied with location of your house? With Highly satisfied as 1 & Not satisfied as 5 (scale 1 to 5)

Q3. Are you satisfied with total square feet area of your house? Highly satisfied to not satisfied (scale 1 to 5)

Q4. Family member to bedroom ratio? Less than 1 greater than or equal to1 but less than 2 greater than or equal to 2 but less than 3 greater than or equal to 3

Q5. How old is your house less than or equal to 3

greater than 3 but less than or equal to 6 greater than 6 but less than or equal to 9 greater than 9

Q6. How much maintenance charges you can afford annually for your house? 0 to 3000 2.3000 to 6000 3.6000 to 9000 4. more than 9000

Q7. Are you satisfied with your neighborhood? 1 for highly satisfied to 5 for Not satisfied(scale 1 to 5)

Q8.Are you satisfied with electricity and water supply? 1 for highly satisfied to 5 for Not satisfied(scale 1 to 5)

Q9.What is the overall satisfaction level with your current house? 1 for highly satisfied to 5 for Not satisfied(scale 1 to 5)

I searched for the questions through internet which becomes the secondary data, discussed it ingroup through our brainstorming sessions & then finalized the above questionnaire.Originally we had a set of 16 questions but then we had to make the questionnaire a bit concise

so that it catches the attention of the respondent & he does not fill the questionnaire for the sakeof it. Through discussion I selected only above mentioned 9 questions which I thought was themost important while having a house.After the questionnaire was designed, I gathered the responses of 97 respondents whichconstitute my primary data. The responses were gathered through online as well as personalsurveys. After the responses were gathered, I analyzed the data through SPSS techniqueswhich included Cluster analysis and Factor analysis.


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Sampling Technique

My motto from this project was to analyze our data by using SPSS tool and for the verification ofanalysis done by me after using SPSS, I asked 5 respondents and asked them to comment on

my analysis and got good response which is mentioned in Final analysis part of this project . I

chose Convenience Sampling as sampling Technique because it is easiest and cheapest to

conduct so that I can keep a genuine and more accurate data. My respondents constitute an

informal pool of friends.

Nature of Data

The data collected is through Questionnaire hence it is primary data.

The date file is attached, along with.

Study on whatpeople look for when

Cluster Analysis

Why it is a 3 cluster analysis?

From the above table we see that the coefficient in agglomeration schedule increases by 5.241

in case of 3 cluster and 3.334 in case of 3 clusters.

Agglomeration Schedule

Stage

Cluster Combined

Coefficients

Stage Cluster First Appears

Next StageCluster 1 Cluster 2 Cluster 1 Cluster 2

1 81 97 1.000 0 0 9

2 86 88 1.000 0 0 18

3 57 78 1.000 0 0 16

4 29 64 1.000 0 0 20

5 17 59 1.000 0 0 39


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6 6 56 1.000 0 0 21

7 26 44 1.000 0 0 16

8 30 38 1.000 0 0 10

9 81 90 1.500 1 0 22

10 8 30 1.500 0 8 38

11 33 77 2.000 0 0 37

12 24 76 2.000 0 0 33

13 62 73 2.000 0 0 71

14 16 71 2.000 0 0 25

15 58 63 2.000 0 0 35

16 26 57 2.000 7 3 1917 10 45 2.000 0 0 30

18 86 93 2.500 2 0 30

19 26 65 2.500 16 0 27

20 9 29 2.500 0 4 42

21 5 6 2.500 0 6 37

22 35 81 3.000 0 9 38

23 18 67 3.000 0 0 40

24 22 50 3.000 0 0 60

25 16 46 3.000 14 0 65

26 3 19 3.000 0 0 52

27 26 83 3.600 19 0 54

28 31 94 4.000 0 0 55

29 68 89 4.000 0 0 59

30 10 86 4.000 17 18 41

31 11 85 4.000 0 0 76

32 55 75 4.000 0 0 54

33 24 70 4.000 12 0 43

34 12 69 4.000 0 0 67

35 25 58 4.000 0 15 45

36 4 43 4.000 0 0 44


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37 5 33 4.333 21 11 53

38 8 35 4.417 10 22 56

39 17 61 4.500 5 0 76

40 7 18 4.500 0 23 55

41 10 54 4.600 30 0 45

42 9 84 4.667 20 0 46

43 24 53 5.000 33 0 60

44 4 15 5.000 36 0 62

45 10 25 5.389 41 35 61

46 9 47 5.500 42 0 77

47 34 95 6.000 0 0 6348 13 91 6.000 0 0 68

49 72 79 6.000 0 0 71

50 23 37 6.000 0 0 53

51 1 28 6.000 0 0 64

52 3 80 6.500 26 0 73

53 5 23 6.600 37 50 75

54 26 55 6.667 27 32 61

55 7 31 6.667 40 28 74

56 8 14 6.857 38 0 69

57 48 87 7.000 0 0 81

58 52 74 7.000 0 0 62

59 21 68 7.000 0 29 66

60 22 24 7.000 24 43 75

61 10 26 7.472 45 54 69

62 4 52 7.833 44 58 77

63 34 60 8.000 47 0 70

64 1 36 8.000 51 0 67

65 16 40 8.333 25 0 79

66 21 27 8.333 59 0 73

67 1 12 8.333 64 34 82


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68 2 13 9.000 0 48 78

69 8 10 9.412 56 61 80

70 34 42 9.667 63 0 72

71 62 72 10.000 13 49 78

72 34 66 10.250 70 0 74

73 3 21 10.250 52 66 83

74 7 34 10.560 55 72 86

75 5 22 11.476 53 60 79

76 11 17 11.667 31 39 85

77 4 9 11.960 62 46 80

78 2 62 12.833 68 71 8379 5 16 13.250 75 65 84

80 4 8 13.324 77 69 84

81 41 48 13.500 0 57 88

82 1 32 14.600 67 0 88

83 2 3 15.347 78 73 87

84 4 5 15.679 80 79 86

85 11 96 16.000 76 0 90

86 4 7 17.742 84 74 87

87 2 4 18.661 83 86 89

88 1 41 21.611 82 81 89

89 1 2 24.423 88 87 91

90 11 20 31.667 85 0 92

91 1 39 33.435 89 0 92

92 1 11 45.791 91 90 0

When case cluster 90 and 91 gets combined the % change = (33.435-31.667)/31.667 * 100 =

5.58 %

When case cluster 90 and 91 gets combined the % change = (31.667-24.423)/24.423 * 100 =

29.67 %

Because of the larger change in the agglomeration schedule I have taken 3 cluster solution as

the optimum solution.


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We now run the K means analysis r for 3 cluster analysis and we got 33, 12 and 48 cases in

cluster 1, 2 and 3 respectively

Number of Cases in each

Cluster

Cluster 1 33.000

2 12.000

3 48.000

Valid 93.000

Missing 4.000

From the ANOVA table:

ANOVA

Cluster Error

F Sig.Mean Square df Mean Square df

Are you satisfied with the

location of your house?27.810 2 .585 90 47.549 .000

Are you satisfied with area of

your house? 28.735 2 .514 90 55.942 .000

Are you satisfied with your

neighbourhood?40.303 2 .689 90 58.466 .000

Are you satisfied with electricity

and water supply?10.566 2 .865 90 12.213 .000

Overall satisfaction level with

your current house?26.034 2 .457 90 56.986 .000

V1 16.156 2 .913 90 17.698 .000

V2 1.212 2 .614 90 1.974 .145

V3 .834 2 1.463 90 .570 .567

V4 13.004 2 1.199 90 10.842 .000

The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences

among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted

as tests of the hypothesis that the cluster means are equal.


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From the ANOVA table we find that all the factors are significant except for V3 which is the

dummy variable for How old is your house?. Thus we infer that people do not take into

consideration of the age of the house in Mumbai while making a purchasing decision.

Now we find mean

Process:- Analyze -> Compare Means ->Means

Preference of attributes correspond to their satisfaction in given cluster both MAXIMUM

Preference to satisfaction (Red colored) MINIMUM Preference to satisfaction (Blue colored)

We can see the figures which are red are preferred maximum and those are blue are minimumpreferred to satisfaction in a particular cluster so we find that :-

Cluster 1:- These respondents most prefer their satisfaction as Area of the house and least

prefer to Electricity and water supply

In the same way in Cluster 2:- Maximum Electricity and water supply and Min Neighborhood

Report

Cluster Number of Case

Are yousatisfied with

the location of

your house?


area of your

house?


your

neighborhood?


electricity and

water supply?

Overallsatisfaction

level with your

current house?

1 Mean 4.47 4.55 4.37 4.35 4.51

N 49 49 49 49 49

Std. Deviation .616 .542 .636 .631 .505

2 Mean 3.37 3.51 2.71 3.93 3.51

N 41 41 41 41 41Std. Deviation .799 .779 .981 .932 .675

3 Mean 1.57 1.57 1.71 2.00 1.57

N 7 7 7 7 7

Std. Deviation .787 .787 1.113 1.528 .787

Total Mean 3.79 3.90 3.47 4.00 3.88

N 97 97 97 97 97

Std. Deviation 1.080 1.056 1.251 1.031 1.003


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Cluster 3:- Maximum Electricity and water supply and similar least preference to 2 attribute

Location and area of the house

KMO and Bartlett's Test of Sphericity. The Kaiser-Meyer-Olkin measure of sampling adequacy

tests whether the partial correlations among variables are small. Bartlett's test of sphericity tests

whether the correlation matrix is an identity matrix, which would indicate that the factor model is

inappropriate.

Factor Analysis

To find which are main attributes which lead to the satisfaction level of the customers buying ahouse?

For this we have done the factor analysis.Steps followed are as follows: Analyze -> Data Reduction -> Factor -> go to descriptive andclick coefficient, significance level and KMO and Barletts test and univariate descriptiveGo to extraction and click correlation matrix and choose Eigen value> and maximum iterationfor convergence = 999

Rotation: Varimax and rotated solution

Options -> Exclude case list wise and suppress small coefficient to 0.1 in coefficient displayThen we find all values in correlation matrix are less than 0.05 so all are significant.If KMO is less than 0.5 then the data is inadequate for factor analysis. We got KMO value of0.770 which means that the data is adequate for factor analysis.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .770

Bartlett's Test of Sphericity Approx. Chi-Square 158.260

df 28.000

Sig. .000

Rotated component matrix: There are two main components. In component 1 we had location ofthe house, area of the house, Neighborhood and Water+ Electricity supply, V1 (Householdincome level) and V4 (Maintenance charge affordability).

In component 2, we had V2 (family member to bedroom ratio) and V3 (how old is your house)

Rotated Component Matrixa

Component


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1 2

Are you satisfied with the

location of your house?

.809 -.082

Are you satisfied with area of

your house?.817 -.095

Are you satisfied with your

neighbourhood?.764 .006

Are you satisfied with

electricity and water supply?.646 -.073

V1 .631 .358

V2 -.085 .732

V3 .038 .635

V4 .405 -.323

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

Conclusion

We get only very few information through forming of clusters and factors, only what are the main

factors on which the choice of the house of a person depends on. We should extend our

analysis to discriminant analysis and multidimensional scaling.

We conclude that there are mainly 3 clusters into which the people can be segregated and also

there are two main factors on which the buying behavior depends. They are: 1) Features and

ambience of the house and 2) requirement-characteristics fit of the house.

Documents

AMDA Project Report_Roll 406