Home ownership feasibility for middle income earners in Nigeria

¶ … Linear, Logistic or Multiple Correlation/Regression Proposal

Objective of this paper is to use the statistical test to investigate whether the middle income earners in developing countries are capable of becoming home owners. The paper uses Nigeria as a case study. Unlike the developed countries such as the United States and United Kingdom where middle income earners can become house owners, however, the issues are different in developing countries because various factors prevent middle income earners from purchasing housing units compared to middle income earners in developing countries. Typically, a level of income plays a critical role in homeownership. While different governments in developing countries have implemented various policies in assisting people to become homeowners, nevertheless, factors such as corruption, inflation, and poor implementations have made the policy non-realizable. In Sub-Sahara African countries, nearly 62% of people live in slums, and per capital income of most developing countries are less than $300.(Chica, 2008). Moreover, more than 87% of individual households fund are used to fund the housing units since there are little or no support from the governments and mortgage financial institutions. Moreover, economic and social factors also prevent people from becoming home owners in developing countries. For example, majority of households in developing countries have large families, high price of building materials and low propensity to save inhibit home ownership in developing countries.

Research Question

Can middle income earners in Nigeria able to 1 finance homeownership by using 10-year personal savings amount to N2,400,00?

The study multiple linear regression as a major statistical technique to determine whether middle income earners are able to finance homeownership by using o N2,400,000 of their personal savings.

Two types of linear regressions are available they are multiple linear regression, and simple linear regression. A simple linear regression uses the singe independent variable to predict the dependent variables. In other words, simple linear regression assists in predicting the value of a dependent variable.

A multiple linear regression refers to two or more independent variables that are used to predict a dependent variable. The difference between the linear and multiple regressions there is only one dependent variables however, the independent variables are different in both cases.(Trochim, 2006). The proposal selects the multiple linear regression for the analysis because it is able to use multiple independent variables to analyze the dependent variable. (Sukal, 2013).

The statistical notations for this research are as follows:

Ho = null hypothesis.

H1 = alternative hypothesis.

= significance level.

= probability of Type II error.

x =sample mean.

s = standard deviation of a sample.

Research Hypotheses

H1: Middle income in developing countries are likely to purchase homes using their personal savings. The dependent variable is Income, while independent variables are Employment. Status, Age Range, Housing Size, and Rent.

Ho: Middle income in developing countries are not likely to purchase homes using their personal savings. The dependent variable is Income, while independent variables are Employment. Status, Age Range, Housing Size, and Rent.

4. Results

The paper uses the chi-square to determine the results and based on the probability level alpha table, the chi square of 0.05 determines the level of significant. If the p-value is greater than 0.05 (i.e. p > 0.05), the study will accept the null hypothesis, which means the study will "fail to reject the null hypothesis." "

Probability Level Table (alpha)

Df

0.5

0.10

0.05

0.02

0.01

0.001

1

0.455

2.706

3.841

5.412

6.635

10.827

2

1.386

4.605

5.991

7.824

9.210

13.815

3

2.366

6.251

7.815

9.837

11.345

16.268

4

3.357

7.779

9.488

11.668

13.277

18.465

5

4.351

9.236

11.070

13.388

15.086

20.517

However, the study uses the Likelihood Ratio Tests for the results. Since the chi-squares of all the test carried out are greater than all probability level for (alpha = 0.05), the study rejects the null hypothesis (Ho). The output of the regression is presented in the Appendices.

Thus, the study rejects the following the null hypothesis:

Ho: Middle income in developing countries are not likely to purchase homes using their personal savings.

However, the study accepts the following the null hypothesis:

H1: Middle income in developing countries are likely to purchase homes using their personal savings.

The rejection of the null hypothesis makes the paper to conclude that middle income earners are able to finance the homeownership in Nigeria.

Reference

Chica, E.U. (2008). Obstacles to the Individual Home Ownership in Nigeria. International Journal of Housing Markets and Analysis. 1(2):182-194.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (3rd

ed.). Thousand Oaks, CA: Sage.

Sukal, M. (2013). Research methods: Applying statistics in research. San Diego, CA: Bridgepoint Education, Inc.

Trochim, W. M. K. (2006). Research methods knowledge base (2nd ed.). Web Center for Social Research Methods. Retrieved 01 November 2015 from http://www.socialresearchmethods.net/kb/index.php

Appendices

Appendix 1: Social/Economic Variable Affecting Homeownership

Model Fitting Information

Model

Model Fitting Criteria

Likelihood Ratio Tests

-2 Log Likelihood

Chi-Square

df

Sig.

Intercept Only

1206,951

Final

806,743

400,208

76

,000

Pseudo R-Square

Cox and Snell

,600

Nagelkerke

,626

McFadden

,289

Likelihood Ratio Tests

Effect

Model Fitting Criteria

Likelihood Ratio Tests

-2 Log Likelihood of Reduced Model

Chi-Square

df

Sig.

Intercept

806,743a

,000

0.

EmpStatus

845,487

38,744

16

,001

Age

839,613

32,870

12

,001

FamSize

870,486

63,743

16

,000

Rent

944,216

137,473

16

,000

ResStatus

831,063

24,320

12

,018

Gender

811,521

4,779

4

,311

Case Processing Summary

N

Marginal Percentage

Income

< #20,000

94

21,5%

#20,000 - #39,999

86

19,7%

#40,000 - #59,999

25,2%

#60,000 - #79,999

53

12,1%

>= #80,000

94

21,5%

Employ. Status Unemployed

27

6,2%

Self-employed

50,8%

Employed

32,5%

Retired

23

5,3%

Not applicable

23

5,3%

Age Range

21-30years

25,2%

31-40 years

36,6%

41-50 years

23,6%

51-60 years

64

14,6%

Hse. Size

1-2people

53

12,1%

3-4people

39,8%

5-6people

28,1%

7-8people

33

7,6%

> 8 people

54

12,4%

Rent

< #10,000

28,8%

#10,000 - #24,999

32,0%

#25,000 - #39,999

24,5%

#40,000 - #54,999

39

8,9%

>= #55,000

25

5,7%

Res. Status

Owner/Occupier

60

13,7%

Social tenant

12

2,7%

Tenant

61,6%

Living with friends/family

96

22,0%

Gender

Male

54,2%

Female

45,8%

Valid

100,0%

Missing

0

Total

Subpopulation

282a

Appendix 1: INSTITUIONAL VARIABLES AFFECTING HOMEOWNERSHIP

Model Fitting Information

Model

Model Fitting Criteria

Likelihood Ratio Tests

-2 Log Likelihood

Chi-Square

df

Sig.

Intercept Only

1194,553

Final

884,660

309,893

,000

Pseudo R-Square

Cox and Snell

,508

Nagelkerke

,530

McFadden

,224

Likelihood Ratio Tests

Effect

Model Fitting Criteria

Likelihood Ratio Tests

-2 Log Likelihood of Reduced Model

Chi-Square

df

Sig.

Intercept

884,660a

,000

0.

infrast

924,579

39,919

16

,001

landy

903,912

19,252

16

,256

depositi

908,386

23,725

16

,096

mortgag

900,657

15,997

16

,453

Locatn

914,176

29,516

16

,021

H_PRICE

917,724

33,064

16

,007

bldtype

936,518

51,858

16

,000

sutable

900,549

15,889

16

,461

perm_doc

916,536

31,876

16

,010

bldmat

915,532

30,872

16

,014

Labor

902,931

18,271

16

,308

Case Processing Summary

N

Marginal Percentage

Income

< #20,000

94

21,5%

#20,000 - #39,999

86

19,7%

#40,000 - #59,999

25,2%

#60,000 - #79,999

53

12,1%

>= #80,000

94

21,5%

Infrastructure

SD

36

8,2%

D

56,8%

N

90

20,6%

A

54

12,4%

SA

9

2,1%

Land

SD

85

19,5%

D

62,2%

N

32

7,3%

A

37

8,5%

SA

11

2,5%

Deposit

SD

33,9%

D

38,2%

N

99

22,7%

A

6

1,4%

SA

17

3,9%

Mortgage/LOAN

SD

46

10,5%

D

58,8%

N

61

14,0%

A

57

13,0%

SA

16

3,7%

Location

SD

80

18,3%

D

50,6%

N

26,8%

A

12

2,7%

SA

7

1,6%

HOUSE PRICE

SD

95

21,7%

D

47,1%

N

28,4%

A

5

1,1%

SA

7

1,6%

Building Type

SD

24,0%

D

41,0%

N

29,7%

A

14

3,2%

SA

9

2,1%

Suitability

SD

60

13,7%

D

46,0%

N

33,4%

A

14

3,2%

SA

16

3,7%

Permit & Documentation

SD

77

17,6%

D

54,7%

N

76

17,4%

A

39

8,9%

SA

6

1,4%

Building Materials

SD

78

17,8%

D

57,9%

N

68

15,6%

A

33

7,6%

SA

5

1,1%

Labour

SD

40

9,2%

D

45,8%

N

30,2%

A

54

12,4%

SA

11

2,5%

Valid

100,0%

Missing

0

Total

Subpopulation

340a