Purchase Intention is a very common measure constantly measured in many consumer behaviour studies, but the score derived from here always initiates a lot of discussion on the strength and how true is the measure to help it translate into actual purchase behaviour. As practicing market researchers we have always developed action norms on this score that are customised to industry sectors. But it is always better to explore and come with better “purchase probabilities” in the context of the sector. This paper attempts to build “purchase probabilities” in order to derive true “Purchase Intention Score” that can translate into actual purchase behaviour. It is felt that the reality sector can begin using these measures and develop their database to help make the scores as mirror scores for actual home purchase.
Keywords: Affordable housing, Purchase Intention, Attitude towards home purchase.
The Context:
There are at least 17 new entrants in the affordable housing space even as 14 existing lenders are significantly expanding their portfolios of low-cost home loans by broadening their distribution channels through direct sales and community-based loans.
The Indian home loan market consists of 76 housing finance companies and state-owned as well as private banks and all have started using technology to reach out to the lower end of the customer segment. There has also been a rate war to woo the customers.
But will it only be easy finance and housing projects that fall in the purview of size, income and affordability that will sell these affordable homes in these projects? The researcher team here believed that there is a need to build customer orientation and so aspirations of customers need to be taken on board. These aspirations could represent a robust purchase intention score that could translate the intention of purchase into actual purchase behaviour.
A survey was thus taken up in the city of Mumbai to understand the triggers of purchase.
The Survey set- up and findings:
The survey was set up in Mumbai since this city has huge housing shortage. The objective was to go beyond size, income and price/ EMI dynamic or affordability dynamic and find out aspirations and expectations of the customers. The quantitative survey was set up in 2016 among a defined target audience. The target audience had to be intended purchasers of the house. A small subset of respondents was second home purchasers. This subset was met to understand if triggers of purchasing homes are similar across time periods.
Random sampling was followed as the field protocol to identify the respondent. While random sampling for a complete interview clearly highlighted that every one in six respondents is wanting to buy a new home in Mumbai, when it came to allotting a time frame to such a decision only about 1% of such defined target audience really came forwarded and gave a time frame. Time frame to purchase intention is one such dimension which can help a marketer plan his persuasive selling activities because the potential target audience has the goal of home purchase.
1009 intending home purchasers were met in this research. These respondents wanted to buy a house but when asked to give a time frame to the decision only 1% wished to allot a time frame. The following table highlights the time dimension:
|
|
|
Income |
|
|
|
|
|
|
Total |
Less than ? 2.5 Lakhs |
? 2.5 - ? 5.0 Lakhs |
? 5.0 - ? 7.0 Lakhs |
? 7.0 - ? 10.0 Lakhs |
?10 Lakhs and above |
|
|
|
A |
B |
C |
D |
E |
|
BASE |
1009 |
217 |
408 |
190 |
136 |
58* |
|
(5) Definitely want to purchase in next six months |
0 |
0 |
0 |
1 |
0 |
0 |
|
|
|
|
|
|
|
|
|
(4) Probably will purchase in next six months |
1 |
0 |
2 |
1 |
2 |
0 |
|
|
|
|
|
|
|
|
|
(3) Not decided |
11 |
8 |
12 |
14 |
15 |
2 |
|
|
|
|
|
|
|
|
|
(2) Probably will not purchase in next six months |
6 |
6 |
8 |
7 |
1 |
0 |
|
|
|
e |
aE |
AE |
AE |
|
|
(1) Definitely will not purchase in next six months |
0 |
0 |
0 |
1 |
0 |
0 |
|
|
|
de |
DE |
DE |
|
|
|
|
|
|
|
|
|
|
|
DK/NA |
80 |
86 |
76 |
77 |
81 |
98 |
80% of respondents were not willing to give a response in this segment. This was especially intriguing because these respondents were from stable income households and yet they were not able to give a clear cut answer to purchase intention question. That these are respondents from a reasonable income group is observed through following facts that got revealed through this primary survey:
|
Figs in % |
Total |
|
BASE |
1009 |
|
Less than ? 2.5 Lakhs |
22 |
|
? 2.5-5.0 Lakhs |
40 |
|
? 5.0-7.0 Lakhs |
19 |
|
? 7.0-10 Lakhs |
13 |
|
? 10-15 Lakhs |
6 |
|
? 15-20 Lakhs |
0 |
78% of the sample population earns household income of ? 2.5 lakhs -15 lakhs. Although they are not from a very high income group segment there is a financial stability that is observed. What is therefore needed to arrive at a true or a more predictable purchase intention score that will give a purchase probability and reduce the uncertainty in the marketing/ business revenues is the marketers challenge.
This called for a discussion because only intentions do not translate into actual purchase is a reality. Thus it was important to know what else is reflecting their intention and would transform their decision of purchase. A set of triggers were developed using qualitative interviews among second time home purchasers. These were the triggers in case of their home purchase in addition to measurement of purchase intention. It is important to understand these triggers and their relationship with the purchase intention score, as they can give the very premise to develop a logical marketing plan to address the potential need of home purchase.
Thus the researcher team conducted advanced analysis to understand the mind-set of these set triggers and the respondents buy decision. This paper is an attempt to reduce this uncertainty around the purchase intention score. The team here uses stepwise regression model where purchase intention is a dependent variable and triggers of home purchase are independent variables.
Through a comprehensive qualitative survey a list of triggers that were generated as the key triggers of house purchase were elicited then validated through quantitative research. Following were the key parameters that were found as key triggers of home purchase even in the subset of respondents who had purchased home in the past:
|
Q3_4a_1 Proximity to my workplace |
|
Q3_4a_2 Availability of social amenities such as schools, hospitals |
|
Q3_4a_3 Good Investment Option |
|
Q3_4a_4 Good Product and Amenities in the house such as layout, facilities in the housing |
|
Q3_4a_5 Most affordable option |
|
Q3_4a_6 Was tired of living in a rented/relative/parent housing |
|
Q3_4a_7 Needed privacy after my marriage so bought my own house |
|
Q3_4a_8 Closer to spouses work place |
|
Q3_4a_9 Modern dwelling |
Stepwise regression was conducted. This model removed all the variables from the model barring Availability of social amenities such as schools, hospitals”.
|
Variables Entered |
Variables Removed |
|
|
(Q3_4a_2 Availability of social amenities such as schools, hospitals. |
- |
Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100). |
a. Dependent Variable: PI
Model Summary
|
Mode 1 |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Change Statistics |
||||
|
R Sauare Change |
F Change |
df1 |
df2 |
Sig. F Change |
|||||
|
1 |
.212a |
.045 |
.040 |
.678 |
.045 |
9.220 |
1 |
195 |
.003 |
a. a. Predictors: (Constant), Q3_4a_2 “Availability of social amenities such as schools, hospitals”
Correlations
|
|
|
PI |
Q3_4a _1 |
Q3_4a _2 |
Q3_4a _3 |
Q3_4a _4 |
Q3_4a _5 |
Q3_4a _6 |
Q3_4a _7 |
Q3_4a _8 |
Q3_4a _9 |
|
Pearson Correlation |
PI |
1.000 |
-.043 |
-.212 |
.043 |
-.049 |
-.033 |
-.115 |
.016 |
-.060 |
-.024 |
|
Q3_4a _1 |
-.043 |
1.000 |
-.113 |
.023 |
-.016 |
-.040 |
-.001 |
.365 |
.382 |
.199 |
|
|
Q3_4a _2 |
-.212 |
-.113 |
1.000 |
-.243 |
.354 |
.134 |
.273 |
.068 |
.101 |
.108 |
|
|
Q3_4a _3 |
.043 |
.023 |
-.243 |
1.000 |
-.141 |
-.150 |
.062 |
.055 |
-.104 |
-.139 |
|
|
Q3_4a _4 |
-.049 |
-.016 |
.354 |
-.141 |
1.000 |
.030 |
.303 |
-.056 |
.114 |
.096 |
|
|
Q3_4a _5 |
-.033 |
-.040 |
.134 |
-.150 |
.030 |
1.000 |
.046 |
.096 |
-.196 |
.164 |
|
|
Q3_4a _6 |
-.0115 |
-.001 |
.273 |
.062 |
.303 |
.046 |
1.000 |
-.068 |
.144 |
.218 |
|
|
Q3_4a _7 |
.016 |
.365 |
.068 |
.055 |
-.056 |
.096 |
-.068 |
1.000 |
.366 |
.407 |
|
|
Q3_4a _8 |
.060 |
.382 |
.101 |
-.104 |
.114 |
-.196 |
.144 |
.366 |
1.000 |
.369 |
|
|
Q3_4a _9 |
-.024 |
.199 |
.108 |
-.139 |
.096 |
.164 |
.218 |
.407 |
.369 |
1.000 |
The R-square is not high but there is a small effect that is happening and the model is significant so the suggestion is when these amenities are present and then the purchase intention question is administered, then the intention to purchase can be more predictable. Any model that can offer 5% predictability can be important and helps the marketer give different communication platforms to begin his persuasive selling strategies.
Conclusion:
Affordable housing concept at its very core is for bringing in stability to human life and build strong human
communities. These social amenities can further help the developers contribute to the stability of human life.
It is concluded that if social amenities such as schools, hospitals are made available then the purchase intention
score given by the respondent can be considered more robust. It is suggested that the purchase intention score with
the time frame may be asked after administering the concept of amenities the developer is planning to give to the
potential home buyers. Further, for every project such a concept test which details out these amenities is strongly
recommended to help make purchase intention score stronger and stronger.
References:
Authored by
Mr. Sudhir Gururaj Kulkarni
Research Scholar
Dr. Ms Vibha Bhilawadikar
The Pearl Collection Foundation
Dr. Nirmala S. Joshi
MET, Mumbai
©Mumbai Educational Trust, All rights reserved | Last Modified Date: Thursday, November 21st, 2024