ALLANA MANAGEMENT JOURNAL OF RESEARCH, PUNE - Volume 8, Issue 1, January 2018- June 2018

Pages: 62-62
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Impact of Customers Demographic Characteristics during buying homes.

Author: Dr. Rashmi Mahajan , Dr. Darshan Mahajan

Category: Marketing Management

Abstract:

Customer decision making is one of the most important areas of customer behaviour. Demography is the study of human population and its distribution such as age, gender, income, education, density, occupation etc. The marketer studies these variables to understand changing needs of consumers. Consumer Behaviour helps us understand the buying tendencies and spending patterns of consumers. There are various factors that play an important role in affecting consumer buying behaviour. Real estate refers to things that are not moveable such as land and improvements permanently attached to the land.The main purpose of this study is to identify what factors have an impact on house purchase decision of customers and examine how these factors influence their decision of buying house in Pune City. The statistical analysis chi-square tests were carried out with all the identified parameters and it was observed that the parameters like Gender, Educational Qualification of the customer has significant relations with house purchase decision on contrary to that other demographic parameters of the customer does not shown significant relationship with purchasing of house in Pune.

Keywords: Real estate, demographic parameters, Consumer behaviour

Full Text:

Impact of Customers Demographic Characteristics during buying homes.

Introduction:

As universal population levels continue to rise, the housing shortage in many developing countries has reached critical levels (Morel, 2001). Real estate is one of the most important things to citizens, so "the house purchase decision of them can change their life" (Wells, 1993). The house purchase decisions are different from other business decisions due to "the innate, durable and long-term characteristics of real estate". It is a highly differentiated product with "each specific site unique and fixed in location"(Kinnard, 1968).

In India at the end of the 10th five year plan the overall shortage has been estimated at close to 25 million dwelling units. Depending on household income, different housing schemes i.e. high income groups (HIG), MIG, LIG and EWS are categorized. The cost of housing scheme mainly depends on the carpet area and costs that permit repayment of home loans in monthly instalments not exceeding 30% to 40% of the monthly income of the buyer (Menon M P, 2009).

Real estate is refers to things that are not moveable such as land and improvements permanently attached to the land for example house or any other building (Messah, 2011). Consumer Behaviour helps us understand the buying tendencies and spending patterns of consumers. Personal Factors play an important role in affecting consumer buying behaviour. Consumer behaviour is the study of individuals, groups, or organizations in the selecting, purchasing, using, and disposing of goods and services to satisfy needs and desires. Consumer behaviour examines not only what behaviour consumer exhibit but also the reasons for those behaviours (Gibler and Nelson, 1998).Customer decision making is one of the most important areas of customer behaviour and it requires gathering a lot of regarding information (Bettman et al., 1998 & Simonson et al., 2001). Demography is the study of human population and its distribution such as age, gender, income, education, density, occupation etc (Brandstetter, 2004). The factors that influence purchasing intention towards property shows significant relationship with the purchasing intention towards property which are personality, knowledge, social class and reference group(Md. Idham et. al, 2013).In addition to the idiosyncratic characteristics of the customer, his/her personal situation and environmental factors, the role of feelings, experience, subconscious factors, needs and goals should also be taken into consideration. The marketer studies these variables to understand changing needs of consumers. "Demographic" characteristics of customers are internal factors related to decision making (Mateja & Irena, 2009). "Demographic" characteristics consist of age (Yalch & Spangenberg, 1990), education (Gattiker et al., 2000), income level (Dawson et al., 1990), gender (Zhang et al., 2007) which are factors influenced on the "purchase intention" of customer. Particularly, "gender" has significantly influence on the financial feature of the house (Sengul et al., 2010). It is also confirmed that there is a significant difference in real estate buying decisions to "age" and "gender", and not to "educational levels" and "marital status" (Haddad et al., 2011).

There have been many published academic research about customer house purchase with variety of both developed and developing countries. However, "the demographic characteristics play a very significant role in house purchase decision". The purpose of this study is to identify what factors have impact on house purchase decision of customers and examine how these factors influence their decision of buying house in Pune City.

Methodology

This study describes the characteristics of real estate customers in Pune. The characteristics are checked with type of House they have. The population and sampling design also has scientific and statistical methods. The total population of the Pune is approximately 35 lakh. A sample of 272 respondents has been collected from Pune area using simple random sampling method.The variables namely gender, marital status, Educational Qualification, Income and No. of dependents have been measured using quantitative values. Variable 'Gender' has two categories female and male. Variable 'marital status' has two categories single and married. Variable 'Educational Qualification' had three categories: Graduate, Graduate and Post Graduate. Variable 'No. of dependent' had : no dependent, 1-2 dependents, 3-4 dependent and more than 4 dependents categories. Variable 'individual monthly income' had: less than Rs.25000, Rs.25001-50000, Rs.50001-Rs.200000, and Rs.200000 above categories. Variable 'House Structure' was measured with 1BHK, 2BHK, 3BHK, A row house, A Bungalow and other category.To address the research question a structured questionnaire was prepared. The hypotheses were formulated after a pilot study and are statistically tested. Hypotheses were formulated as:

Hypothesis 1:

H0: Purchasing house in Pune is independent of the gender of the customer

H1: Purchasing house in Pune is not independent of the gender of the customer.

Hypothesis 2:

H0: Purchasing house in Pune is independent of the Marital Status of the customer

H1: Purchasing house in Pune is not independent of the Marital Status of the customer. Hypothesis 3:

H0: Purchasing house in Pune is independent of the Educational Qualification of the customer

H1: Purchasing house in Pune is not independent of the Educational Qualification of the customer.

Hypothesis 4:

H0: Purchasing house in Pune is independent of the Monthly Income of the customer

H1: Purchasing house in Pune is not independent of the Monthly Income of the customer

DATA ANALYSIS

Table 1 shows that 51.5% Male and 48.5% Female have been involved in the study. Themale participants were more than female by very less frequency of 8. Table 2 shows that 28.3 % were single and 71.7 % were married who were involved in the study meaning that the frequency of married participants was more than single or unmarried persons. Table 3 shows that Post graduates were more in number than graduates and under graduates.Table 4 shows that Less than 25000 Rs. category people were 24.6% percent and 25001 - 50000 Rs. category people were half of the total sample.

Table 1. Gender of the Customer

	Frequency	Percent	Valid Percent	Cumulative Percent
Male	140	51.5	51.5	51.5
Female	132	48.5	48.5	100
Total	272	100	100

Table 2. Marital Status of the Customer

	Frequency	Percent	Valid Percent	Cumulative Percent
Single	77	28.3	28.3	28.3
Married	195	71.7	71.7	100
Total	272	100	100

Table 3. Education Background of the Customer

	Frequency	Percent	Valid Percent	Cumulative Percent
Undergraduate	31	11.4	11.4	11.4
Graduate	81	29.8	29.8	41.2
Post Graduate	160	58.8	58.8	100
Total	272	100	100

Table 4. Individual Monthly Income of the Customer

	Frequency	Percent	Valid Percent	Cumulative Percent
< Rs. 25000	67	24.6	24.6	24.6
Rs. 25001-50000	136	50.0	50.0	74.6
Rs. 50001- 2 Lakh	69	25.4	25.4	100
Total	272	100	100

HYPOTHESES TESTING

Hypothesis 1

Table 5. Gender of the Customer and House Structure Crosstabulation

Gender of Customer	House Structure				Total
	1BHK	2BHK	3BHK	A row-house
Male	14	43	63	20	140
Female	16	27	53	36	132
Total	30	70	116	56	272

Hypothesis 2

Table 6. Marital Status of the Customer and House Structure Crosstabulation

Marital Status of Customer	House Structure				Total
	1BHK	2BHK	3BHK	A row-house
Single	12	20	29	16	77
Married	18	50	87	40	195
Total	30	70	116	56	272

Hypothesis 3

Table 7. Education Background of the Customer and House Structure Crosstabulation

Educational Background of Customer	House Structure				Total
	1BHK	2BHK	3BHK	A row-house
Undergraduate	5	12	9	5	31
Graduate	13	24	31	13	81
Post Graduate	12	34	76	38	160
Total	30	70	116	56	272

Hypothesis 4

Table 8. Individual Monthly Income of the Customer and House Structure Crosstabulation

Individual Monthly Income	House Structure				Total
	1BHK	2BHK	3BHK	A row-house
< Rs. 25000	7	16	28	9	60
Rs. 25001-50000	23	35	56	39	153
Rs. 50001- 2Lakh		19	32	8	59
Total	30	70	116	56	272

Table 9. Pearson Chi-Square values of hypotheses

Pearson Chi-Square	Value	df	Asymp Sig
Hypotheses 1	8.996	3	0.029
Hypotheses 2	2.651	3	0.449
Hypotheses 3	12.626	6	0.049
Hypotheses 4	17.727	6	0.007

FINDINGS

While testing of hypothesis 1, the Pearson chi-square value is 8.996 with 3 df and p value is .029 has been shown. Since .029 < 0.05 so the null hypothesis is rejected and H1 is accepted. In testing of hypothesis 2, the Pearson chi - square value is 2.651 with 3 df and p value is 0.449 has been shown. Since 0.449 > 0.05 so failed to reject null. In testing of hypothesis 3, the Pearson chi - square value is 12.626 with 6 df and p value is 0.049 has been shown. Since .049 < 0.05 so the null hypothesis is rejected and H1 is accepted. In testing of hypothesis 4, the Pearson chi - square value is 17.72 with 6 df and p value is .007 has been shown. Since .007 < 0.05 so the null hypothesis is rejected and H1 is accepted.

As per hypothesis1 testing result it can be seen from the collected sample that the Purchasing house in Pune is not independent of the gender of the customer. The gender of the customer and their inclination towards purchasing house has been found in relation with each other. As per hypothesis2 testing result it can be seen from the collected sample that the Purchasing house in Pune is independent of the Marital Status of the customer. The Marital Status of the customer and their inclination towards purchasing house has not been found in relation with each other.As per hypothesis3 testing result it can be seen from the collected sample that the Purchasing house in Pune is not independent of the Educational Qualification of the customer. The Educational Qualification of the customer and their inclination towards purchasing house has been found in relation with each other.As per hypothesis4 testing result it can be seen from the collected sample that Purchasing house in Pune is not independent of the Monthly Income of the customer. The Monthly Income of the customer and their inclination towards purchasing house has been found in relation with each other.

CONCLUSION

Income is not the only parameter which affects the buying house in Pune. There are parameters like gender, Educational Qualification of the customer which affects the purchase decision. The empirical study shows that the gender is an important parameter for analysis for real estate business in Pune. Marital Status of the customer does not have any significant relationship with purchasing house. A single person may have big dreams so he or she may purchase a bigger size home. The person who is married may purchase a small house like 1BHK by adjusting with his lavish life style. There is relationship between Educational Qualification of the person and the type of house he or she purchases. There is a relationship between the monthly individual income and the type of houses in Pune.

References

1. Bettman, J. R., Luce, M. F., and Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187-217.
2. Brandstetter, M. C. G. O., (2004). Análise do comportamento dos clientes do mercado imobiliário com ênfase na mobilidade, escolha e satisfação residenciais. Doctoral Thesis. Federal University of Santa Catarina, Brazil.
3. Dawson, S., Bloch, P., & Ridgway, N. (1990). Shopping motives, emotional states and retail outcomes. Journal of Retailing, 66, 408-427.
4. Gattiker, U. E., Perlusz, S., & Bohmann, K. (2000). Using the internet for B2B activities: A review and future directions for research, internet research. Electronic Networking Applications and Policy, 10, 126-140.
5. Gibler K.M., and Nelson S.L., (1998). Consumer Behavior Applications to Real Estate. Journal of Real Estate Practice and Education. 6 (1), pp.63-83
6. Haddad, M., Judeh, M., & Haddad, S. (2011). Factors affecting buying behavior of an apartment and empirical investigation in Amman, Jordan. Applied Sciences, Engineering and Technology, 3(3), 234-239.
7. Kinnard, W. N. (1968). Reducing uncertainty in real estate decisions. The Real Estate Appraiser, 34(7), 10-16.
8. Mateja, K., & Irena, V. (2009). A strategic household purchase: Consumer house buying behavior. Managing Global Transitions, 7(1), 75-96.
9. Messa, O.B., and Kiggie, A. M., (2011). Factors Influencing Real Estate Property Prices A Survey of Real Estates in Meru Municipality, Kenya. Journal of Economics and Sustainable Development. 2(4):34-54.
10. Mohamad, I., Mohamad, R., Raihan, I., Nur, S. H. A., Idris, O., and Zainab, A., (2013). Purchasing Intention towards Real Estate Development in Setia Alam, Shah Alam:Evidence from Malaysia. International Journal of Business, Humanities and Technology. 3(6): 66-75.
11. Morel, J. C., Mesbah, A., Oggero, M., and Walker, P. (2001). Building houses with local materials: Means to drastically reduce the environmental impact of constructions. Building Environment, 36, 1119-1126.
12. Sengul, H., Yasemin, O., & Eda, P. (2010). The assessment of the housing in the theory of Maslow's hierarchy of needs. European Journal of Social Sciences, 16(2), 214-219.
13. Simonson, I., Z. Carmon, R. Dhar, A. Drolet, and S.M. Nowlis. 2001. Consumer research: On search of identity. Annual Review of Psychology 52:249-75.
14. Wells, W. D. (1993). Discovery-oriented consumer research. Journal of Consumer Research 19 (4): 489-504.
15. Yalch, R., & Spangenberg, E. (1990). Effects of store music on shopping behavior. The Journal of Consumer Marketing, 7(2), 55-63.
16. Zhang, X., Prybutok, V., & Strutton, D. (2007). Modeling influences on impulse purchasing behavior during online marketing transation. Journal of Marketing Theory and Practice, 15, 78-89.

References:

References

1. Bettman, J. R., Luce, M. F., and Payne, J. W. (1998). Constructive consumer choice processes. Journal of Consumer Research, 25(3), 187-217.
2. Brandstetter, M. C. G. O., (2004). Análise do comportamento dos clientes do mercado imobiliário com ênfase na mobilidade, escolha e satisfação residenciais. Doctoral Thesis. Federal University of Santa Catarina, Brazil.
3. Dawson, S., Bloch, P., & Ridgway, N. (1990). Shopping motives, emotional states and retail outcomes. Journal of Retailing, 66, 408-427.
4. Gattiker, U. E., Perlusz, S., & Bohmann, K. (2000). Using the internet for B2B activities: A review and future directions for research, internet research. Electronic Networking Applications and Policy, 10, 126-140.
5. Gibler K.M., and Nelson S.L., (1998). Consumer Behavior Applications to Real Estate. Journal of Real Estate Practice and Education. 6 (1), pp.63-83
6. Haddad, M., Judeh, M., & Haddad, S. (2011). Factors affecting buying behavior of an apartment and empirical investigation in Amman, Jordan. Applied Sciences, Engineering and Technology, 3(3), 234-239.
7. Kinnard, W. N. (1968). Reducing uncertainty in real estate decisions. The Real Estate Appraiser, 34(7), 10-16.
8. Mateja, K., & Irena, V. (2009). A strategic household purchase: Consumer house buying behavior. Managing Global Transitions, 7(1), 75-96.
9. Messa, O.B., and Kiggie, A. M., (2011). Factors Influencing Real Estate Property Prices A Survey of Real Estates in Meru Municipality, Kenya. Journal of Economics and Sustainable Development. 2(4):34-54.
10. Mohamad, I., Mohamad, R., Raihan, I., Nur, S. H. A., Idris, O., and Zainab, A., (2013). Purchasing Intention towards Real Estate Development in Setia Alam, Shah Alam:Evidence from Malaysia. International Journal of Business, Humanities and Technology. 3(6): 66-75.
11. Morel, J. C., Mesbah, A., Oggero, M., and Walker, P. (2001). Building houses with local materials: Means to drastically reduce the environmental impact of constructions. Building Environment, 36, 1119-1126.
12. Sengul, H., Yasemin, O., & Eda, P. (2010). The assessment of the housing in the theory of Maslow's hierarchy of needs. European Journal of Social Sciences, 16(2), 214-219.
13. Simonson, I., Z. Carmon, R. Dhar, A. Drolet, and S.M. Nowlis. 2001. Consumer research: On search of identity. Annual Review of Psychology 52:249-75.
14. Wells, W. D. (1993). Discovery-oriented consumer research. Journal of Consumer Research 19 (4): 489-504.
15. Yalch, R., & Spangenberg, E. (1990). Effects of store music on shopping behavior. The Journal of Consumer Marketing, 7(2), 55-63.
16. Zhang, X., Prybutok, V., & Strutton, D. (2007). Modeling influences on impulse purchasing behavior during online marketing transation. Journal of Marketing Theory and Practice, 15, 78-89.