RESEARCH REPORT

A PRESENTATION OF RECENT LEARNINGS FROM USERS IN LIFE INSURANCE, RETIREMENT PLANNING AND REAL ESTATE

C O N T E N T S

W H O W E A R E

M E T H O D O L O G Y

F I N D I N G S : L I F E I N S U R A N C E

F I N D I N G S : R E T I R E M E N T

F I N D I N G S : R E A L E S T AT E

S I G N O F F

D I S C L A I M E R

45610141718

WHO WE ARE?

Personal finance is unique in that ones decisions can have a huge impact over a long period of time. A pricey house, an under-insured family or goals that one did not plan adequately for, can play out long after the purchase decision was made (or not), and hence deserves far more attention than it often gets.

BigDecisions.in helps retail users make smarter and more informed financial decisions by providing a free platform comprising informative videos, data backed financial calculators and other content that can be used as a starting point for decisions that have a big financial impact.

The current thought process centres around a product solution, rather than having the customer at the centre of it all. We help in re-arranging this thought process and bring focus back to the consumer. BigDecisions.in decouples the core decision of what should I do from what or whom should I buy from.

METHODOLOGYWe provide our users with a non-intrusive platform to access content and mathematical calculators for evaluating their financial decisions. Once we crossed the 50,000 user milestone, we decided to look closer at some numbers around peoples financial decision making.

Our starting point was ~60,000 users across our six financial calculators. We used algorithmic filters (listed below) to eliminate any invalid and test data from the analytical data set.

Records used for testing the tool Records that indicated non-use of the tool Records where any single user input field was incomplete/ empty or incorrectly entered Records prior to December 2013 (prior to achieving the desired traffic volume) Outlier data records

15,000 valid (complete and correct) records were identified. We selected data from three calculators, each providing a comprehensive understanding of certain aspects of financial decision making.

Life Insurance: ~3,000 unique users where data consists of :a. Demographics (age, gender, family size, dependents, marital status, location)b. Finances (income, expenses, debt/ loans, savings, employment status)c. Aspirations (target spend plans for education/ weddings)d. Expectations (salary increase, inflation, return on investments)

Retirement Planning: ~4,000 unique users where data consists of :a. Demographics (age, gender, family size, dependents, marital status, location)b. Expense categoriesc. Expectations (inflation by expense category, return on investments)

Real Estate - choose between buying & renting: ~4,000 unique users where data consists of :a. Rental information (rental value, inflation)b. Purchase information (area, price/ rate, financing, appreciation)c. Location

Data records were classified into categories along demographics, finances, aspirations and expectations. We extracted views based on the classifications above, which helped understand trends and correlations prior to publishing our insights.

RESEARCH FINDINGSLIFE INSURANCE

FINDINGS ON THE AGE GROUP OF 30-35 FOR DIFFERENT INCOME BANDS

FINDINGS ON THE AGE GROUP OF 35-40 FOR DIFFERENT INCOME BANDS

FINDINGS ON THE AGE GROUP OF 40-45 FOR DIFFERENT INCOME BANDS

INSURANCE NEEDED FOR EXPENSE COVER EXISTING INSURANCE

Rs.10-15 lakh/annum

` 25-30 lakh p.a

` 30-35 lakh p.a

` 35-40 lakh p.a

` 20-25 lakh p.a` 15-20 lakh p.a` 10-15 lakh p.a 87.6 lakh25.2 lakh

31.4 lakh 107.4 lakh

243.7 lakh45.5 lakh

160.3 lakh

168.2 lakh

314.7 lakh

17.1 lakh

54.9 lakh

40.5 lakh

INSURANCE NEEDED FOR EXPENSE COVER EXISTING INSURANCE

96.9 lakh

147.7 lakh

160.3 lakh

84.0 lakh

127.6 lakh

INSURANCE NEEDED FOR EXPENSE COVER EXISTING INSURANCE

Rs.10-15 lakh/annum101.8 lakh 25.7 lakh

37.8 lakh131.0 lakh

164.2 lakh 38.9 lakh

162.9 lakh

157.3 lakh

194.8 lakh

43.9 lakh

54.9 lakh

28.0 lakh

26.6 lakh

34.4 lakh

40.7 lakh

58.7 lakh

53.4 lakh

40.9 lakh111.0 lakh` 25-30 lakh p.a

` 30-35 lakh p.a

` 35-40 lakh p.a

` 20-25 lakh p.a` 15-20 lakh p.a` 10-15 lakh p.a

` 30-

35 lak

h p.a

` 25-3

0 lakh

p.a

` 20-2

5 lakh p

.a

` 15-20 la

kh p.a

` 10-15 lakh

p.a

` 35-

40 la

kh p.

a

KEY TAKEAWAYS

UNDER-INSURANCE HAS BEEN AND CONTINUES TO BE A CONCERN IN INDIA.

As per the IRDA annual report 2012, life insurance penetration (or ratio of premiums as a % of GDP) for India fell from 3.4% in 2011 to 3.17 in 2012. If you compared ratios of protection where you compare levels of sum assured (or actual amount of insurance) to GDP, the trends might be similar. Hence, the growth in insurance premium is lower than the growth in national GDP, said IRDA in its report.

If you compare the amount of existing life insurance against the additional life insurance required for the family, in the case of the main earners death, to manage expenses, you will find that across income and age bands, people have nowhere near enough life insurance as needed.

KEY TAKEAWAYS

Contrary to popular perception, its not that the relatively lower and middle-income groups have a greater problem of under-insurance. In fact, as incomes go up, the amount of existing insurance cover grows at a much slower pace than income growth indicating:

Higher income groups are likely to have higher expenses and, therefore, their families are at higher risk of having to make drastic lifestyle changes in the case of the chief earners death.

Even though premiums possibly increase (as evidenced by higher ticket size from distribution channels like private and foreign banks) with higher income levels, protection continues to remain a lower focus area and a larger portion of the premiums seem to be going into savings products with inadequate protection levels.

RESEARCH FINDINGSRETIREMENTConventional financial planning suggests taking your current expense levels and inflating them by the prevailing Consumer Price Index to arrive at expected expenses post retirement. The next step is to then arrive at a corpus or sum of money that you need to put aside that will provide for the given expenses. However, the bigdecisions inflation index or conflation as shown in the table below shows the more likely expected inflation levels in a given age band.

14years

23years

EXPECTEDCONSUMPTIONINFLATION

33years

30years

3.7%

3.5%

3.4%27years

18years

09years

3.5%

2.9%

2.8%

3.5%

YEARS TO RETIREMENT

40-45

35-40

30-35

25-30

20-25

45-50

50-55

AGE GROUP

The methodology used to compute this index was to look at 20 years of daily expense data of a given household, remove one-time expenses to arrive at how increase due to inflation, combined with reductions in consumption impact the effective inflation rate for a household. These results are meant to be indicative and may vary across different households.

KEY TAKEAWAYS

Indicates a much lower than expected inflation of expenses at retirement and, therefore, a lower amount of savings and investments will help the familys primary income earner to meet the goal.

Lets see how the 2 approaches differ for a 35 year old man who is the main income earner of a family of 4 people, expecting to retire at the age of 60 and whos current family expenses, including an EMI for their home (of ` 25,000), is ` 75,000 per month.

Conventional financial planning would suggest that the familys expenses would inflate @7% a year for the next 25 years, resulting in their expenses becoming a little over ` 4,00,000. Even if the family expects to earn a post-tax return of 8%, the primary income earner will need to plan to build a corpus of over ` 9 crore requiring him to save more than ` 90,000 per month.

Taking the BigDecisions.in index into account, the family can expect expenses to inflate only at 3.5% per annum for the next 25 years resulting in familys expenses being under ` 2 lakh per month. To prepare for this, the family will need a corpus of under ` 4.5 crore and a monthly saving of less than ` 45,000 per month.

46.9%

38.2%

24.7%

23.5%

20.8%

25.0%1,34,790

1,03,085

74,834

40,717

27,253

67,249

40-45

35-40

30-35

25-30

45-50

50-55

MONTHLY SAVINGS REQUIRED( in ` )

AGE

GROUPPERCENTAGE OF WORKING SPOUSES

THE NEED TO START EARLY

KEY TAKEAWAYS

Theres a sharp drop in the percentage of double income families after the main wage earner (the male head of the household in the case of our visitors) turns 35 from almost half of all visitors under 30, to only one fourth after 35. This impacts peoples lives in 3 ways:

The required savings to fund a comfortable retirement goes up dramatically post age 35, from about ` 27,000 to over ` 67,000 and it only increases with age.

A large number of people have only one income to save from.

Chances are, an increased family size that caused the spouse to stop working also results in higher monthly expenses.

THEREFORE, STARTING TO SAVE EARLY WHEN YOURE STILL DOUBLE INCOME WITH NO CHILDREN CANNOT BE OVER-EMPHASIZED !

RESEARCH FINDINGSREAL ESTATE

year

s to

bre

akev

en*

Mumbai Bangalore Delhi Chennai

cities

KEY TAKEAWAYS

Our users used BigDecisions.in to evaluate/ validate their house buying decisions compared to the possibility of renting. In these cases, people were unable to compare an under-construction propertys appreciation compared to ready property and this therefore, is only a straight comparison between buying or renting a given, ready to occupy property. Some key messages coming out:

People expect property appreciation rates in the next 5-10 years to be well within single digits and feel the doubling and even tripling every few years is a thing of the past. This is not surprising since the countrys GDP growth rates have come off our 8% and 9% highs to around the 5% mark.

At these appreciation rates, it could take anywhere from 9 to 12 years for a buying decision to make more financial sense than renting the same property.

4.12%

7.20%6.84%

9.26 years

3.93%

6.89% 6.77%

9.37 years

4.25%

7.70%7.08%

8.51 years

4.28%

6.72%6.47%

10.08 years

Rental Yield Rental Inflation Property Appreciation Years To Break Even*

1

2

3

4

5

6

7

8

9

10

11

12

13

* Period of stay required for buying to be the better decision.

Pune Hyderabad Gurgaon Kolkata

cities

While the sample size for users from smaller cities is low and therefore not shown in the table below, relative to the bigger cities people from these cities do expect a higher rate of property appreciation.

An important implication for the professional migrant population would be to seriously consider buying in cities other than which they live in at the moment. Take an example of someone from Surat or Trivandrum working in Gurgaon or Mumbai. Our findings suggest that people will benefit by renting in the city they work in (like Gurgaon in this example) while buying (at a lower per-square-foot rate) in their hometown (like Surat or Trivandrum in this example) and can expect to maximize their benefit by doing so.

3.64%

7.59% 7.45%

8.93 years

4.33%

6.78%6.55%

10.01 years

4.23%

8.32%

7.18%

8.42 years

4.37%

5.62%5.14%

12.38 years

Rental Yield Rental Inflation Property Appreciation Years To Break Even

* Period of stay required for buying to be the better decision.

TRULY SUCCESSFUL DECISION-MAKING RELIES ON A BALANCE BETWEEN DELIBERATE AND INSTINCTIVE THINKING.

- MALC OLM GLA DWELL

S I G N O F F

Our users seem to confirm a larger trend thats been playing out in the Indian context over the last decade. The central theme seems to be a never before seen level of dynamism in the financial and physical asset markets. Real estate delivered phenomenal returns for many users including the authors of this report. Yet, most of our users expect single digit appreciation in the coming decade making renting an apartment a better decision than buying one for many. The life insurance industry has seen lacklustre growth over the last few years and yet, protection levels, though well short of where they need to be have been climbing, if sales of online term insurance trends are anything to go by. Equity markets seemed to be going nowhere for several years until 2014, post which, theyve started climbing steeply again while gold has been steadily going the other way.

Our key learning has been that a one size fits all or expert advice thats applicable to everyone, was never more out of place. Outsourcing ones financial decisions entirely to others needs to be replaced by consumers taking greater ownership. This is especially necessary in the Indian context where consumers expect great advice but remain largely unwilling to pay for it. Simultaneously, there is a dislike for the strong product push culture that has pervaded asset product sellers because of commission driven sales.

BigDecisions aims to play a part in building a better ecosystem where consumers seek out solutions for themselves. It will help deepen the market for essential product categories and also help financial institutions and real estate providers focus on developing and delivering more of what consumers really need rather than pushing aggressively what they have.

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