How important are compulsory deposits, insurance, etc. to calculating Annual Percentage Rates (APRs)? I played around some more with the MFTransparency dataset on India I wrote my last post on to answer this question, and it seems the answer is, “it depends”.
The Pricing Data Report looks at APR including deposits and APR including insurance, but in addition to this, the web portal offers APR figures that include neither, and that include both. It then becomes a trivial exercise to figure out how much compulsory deposits and insurance contribute to APR.
Intuitively Speaking …
The graph below is shows what happens when you calculate APR by including neither of, one of or both of the two constituents – deposits and insurance:
The red and blue columnar bars were presented before; the grey one is kind of a baseline without neither deposits or insurance, and the green one includes both. The error bars are the maximum and minimum.
By seeing how the red and blue bars change compared to the grey, we get an intuitive sense of what is going on. For co-ops, there is a small change of about 1% from the base 23% when you include insurance, but when you include deposits, it shoots up to 35%. On the other hand, for for-profit public MFIs, there is no difference from the base value of ~30% when you include deposits, but on including insurance, this moves up about 3%.
More Formally …
Fortunately, the MFT site allows you to download all the data and come up with precise numbers for what the differentials with respect to deposits and insurance. The graph above is built off of columns A, B, C and D from the table below. Columns E and F give us the differentials.
This tells us that:
- Co-ops charge an additional 1000+ basis points on top of interest and fees by taking compulsory savings and deposits
- For profit public MFIs charge an additional 250+ basis points on top of interest and fees by charging for insurance
- Other types of MFIs charge between ~100 and ~250 additional basis points on top of interest and fees through insurance and deposits
How’s that for “it depends”?
This also goes to show why proper pricing is so important – if one were to look at the top line numbers reported for interest rates, one would very easily conclude that Co-ops were about 10% cheaper than other MFIs as a source of funds.
By the way, the “Unexplained” in the last column is essentially the difference between the kitchen-sink APR as reported by MFT, and the kitchen-sink APR that we get when we reconstruct it from its constituent parts. That all the numbers are less than 1% is a good sign – we’re not missing some big chunk of data when we do the differentials.
While there is a fair degree of divergence on what constitutes APR, it turns out that the averages for each type of organization is within a narrow band of 32.3% and 36.7%. Not surprising, since the Indian microfinance sector has many players and is increasingly saturated, leading to a leveling of the marketplace when it comes what a financial institution can charge a for a fairly similar service to a fairly similar market segment.
It would be interesting to see how the reported interest rates diverge from this APR, and hopefully that will be the topic of the next post.
Skin Your Own Cat
I’ll briefly outline how to get the data I used to pull together the tables and graphs above, since it’s a little involved:
- Go to: http://www.mftransparency.org/data/countries/in/data/
- Under Filter Graph Results on the right, select the kind of Calculation Method you want to use (there are four types – stick to one)
- Also under Filter Graph Results on the right, select the kind of Institution Type you want to use (there are six types)
- Make sure the Loan Size and Number of Clients boxes are empty (the site fills them up with some defaults – this may filter your results. Also, do not press “All” button under the graph – it reverts to some kind of default …)
- Hit Filter Graph
- From the page that comes up, select the table only, and paste it into an Excel worksheet As Text
- Go back to step 3, choose another kind of institution and follow steps to #6, pasting in a new worksheet
- Once all institution types are done, go back to step 2, choose another calculation method, then go through #3 to #7
- Once all calculation methods are done, throw in your formulae in Excel
Yes, you will have to pull in 24 feeds from the site to get all the data, so take care as you consolidate data. Wouldn’t it be nice if you could have one extra column for institution type, and then a column each for each of the types of APR calculations.. Probably wouldn’t fit on the website, but that being available even as a CSV would have made life so much easier.
I should also note that the numbers I got from the raw data are slightly different than the average, maximum and minimum values that are in the report, but I couldn’t tell you why.
MFTransparency does some awesome work surrounding pricing transparency of micro-loans. One of their latest initiatives involved pricing microfinance loans in India. The report is available here, and you can play around with the data they collected using a pretty nifty dashboard-like tool here.
Transparent pricing is necessary because the terms that come with relatively innocuous looking micro-loans can be quite involved. The Annual Percentage Rate (APR) is considered to be the “true” price of a loan, and is often used to make apples-to-apples comparisons between loan products. If all the loan involves is taking out a principal amount of say 10,000 [insert your favorite currency (YFC) here] at an interest rate of 24%, and is paid down in weekly installments using a declining balance method, its a fairly simple calculation.
However, a range of “features” are often tied to the product and this can make calculating APR a less than trivial task, not least because not all MFIs are equally diligent when it comes to properly disclosing them to their clients. Some examples of such “features” would be:
- Principal and interest payments being paid down on a straight-line schedule
- Paying over 52 weeks but in 50 installments, allowing 2 weeks for national holidays
- Having a compulsory savings amount of 25 YFCs, payable every week
- Having a compulsory insurance premium of 10 YFCs, payable every week
- Having an origination fee equal to 1.5% of initial principal balance of the loan
- First installment being due on the day the loan is disbursed
… and so on and so forth.
MFTransparency takes this smorgasbord of product offerings and harmonizes them into APRs so that we can compare all the MFIs they talked to on. Because the 82 MFIs sampled represent about 80% of both the gross loan outstanding and the number of borrowers, it’s a very nice representative sample of the Indian microfinance market.
The Answer Is …
Anyway, enough background info. The top-line number, the APR for Indian MFIs sampled, is:
32.78% .. or 32.61%
Yes, it’s perfectly fine to round that to 33%, and leave it at that.
If you’re wondering why there are two numbers, it turns out there is a “it depends” surrounding the supposedly-ultimate-harmonizer, APR, also.
According to MFT, there are four possible interpretations:
APR India (Int + Fees + Deposit) : Interest + Fees + Security Deposit APR (excluding insurance) : Interest + Fees APR (Int + Fees + Ins) : Interest + Fees + Insurance APR (including security deposit) : Interest + Fees + Insurance + Security Deposit
Throughout their report, they use the two that are bolded. The one called “APR India” that includes deposits such as compulsory savings but does not include insurance is the one used by India’s Microfinance Institutions Network (MFIN). The one called “APR” is the more “international” one used by MFT generally, and includes insurance but does not include deposits.
It makes a difference on which one you use, as you can see from the graph below:
(The information presented in this graph is pulled from two graphs/tables in the report – figures 17 and 18 on pages 29 and 30 respectively.)
The implication of using one or the other is as follows:
- If you include insurance but not deposits (APR), co-ops have a much lower average interest rate, compared to other types of MFIs.
- If you include deposits but not insurance (APR India), public for-profit MFIs have the lowest average interest rate, but all types are pretty much at par with one another.
This is a result of the fact that co-ops use member savings and other forms of compulsory deposits as a source of funds to a greater degree than their peers.
Sadly, the report does not present APR values when you include all four – interest, fees, insurance and security deposit. It is available on their website though, but one needs to use the filters to pull out numbers for one type of institutions and one type of APR at a time. (Unlike MiX, you can’t get a ginormous Excel dump that includes all the data, as far as I can tell.)
I’ll try to pull all that together soon; in the meantime, the answer is 33%, more or less.
In my last post, we saw how SKS’ write-offs levels went up from 0.29% in FY2008 to 0.60% in FY2009, and then up again to 0.86% in FY2010, and that PAR numbers followed a similar trend. Today, we’re going to look into this in more detail, and then ask why this might be an issue for SKS.
Tracking the increase
Before we go any further, let’s cross-check the numbers we got from MixMarket. Their numbers are generally very good, and allowing for reporting period disparities, differences in interpretation of similar terms, or differences in how something is bucketed, there is broad agreement in most cases with the numbers one gets from MFIs.
This is no different for SKS, which notes the following for it’s write-off figures in its Annual Report for 2009-2010:
(Source: Schedule 19, SKS Annual Report 2009-2010, p.g. 82)
Ok, SKS agrees that write-offs levels increased between FY2009 and FY2010. What’s their story for FY2008 to FY2009?
Turn to page 203 of the Red Herring, and you’ll learn that the increase in bad debt write offs was a whooping 2,205.8%!
Yes, that’s right, bad debt write-offs were up 22 times in FY2009, compared to FY 2008. Umm… what!?
And let me take this opportunity to say again why I am really happy about getting my hands on the Red Herring document – SKS has gone out of the way to provide an abundance of information, and it’s all very informative. There are two primary reasons that account for this, albeit partly:
- Under their provisioning policy, they used to write off 50% of the loans overdue between 25 to 50 weeks (or 6 to 12 months, roughly) in FY2008. In FY2009, they wrote off 100%, i.e. the entire amount. Makes sense that this increased write-off, since this change doubled the number of loans they had to bump off the books for that category of loans, all other things remaining the same.
- The portfolio grew during this period, to the tune of 81.5%. Again, makes sense that this increased write-offs – if you give out more loans, proportionately more loans will also have to be written-off, all other things remaining the same.
Here’s what’s interesting – once you normalize for these changes, (essentially deflating the 2,205.8% number by 100% and then by 81.5% again to account for the explanations) the write-offs still go up by approximately 600% … ! Gonna have to say it again – umm… what!?
Something is pushing up SKS’s bad loan count, and pushing it beyond what seems to be the average for Indian MFIs. As we saw in the previous post, the average write-off for the market was 0.52% in 2009; SKS’ figure was 0.73% for the corresponding period.
SKS needs to identify the drivers behind bad loans. I could hazard a couple of guesses:
- Quality of staff – any any given point, three-quarters of SKS’ staff has been around for less than year. This is partly because of phenomenal growth leading to continuous hirings, and partly because of high staff turnover.
- Characteristics of new markets – borrower demographics, existing competitors etc.
- Credit policy – particularly those related to due diligence on loan disbursal and procedures for loan recovery
- Resource focus on growth, as opposed to consolidation
Without looking at more detailed numbers or talking to SKS staff though, we’d be hard pressed to know which of these, if any, are key drivers of deteriorating portfolio quality.
Additional Mitigating Factors
Irrespective of the underlying drivers, there are two effects to keep in mind that make deteriorating portfolio quality a particularly pernicious issue for MFIs enjoying rapid growth:
- Lag Effect: Loan vintages (i.e. loans given out during the same period – say a month) do not display full delinquency levels until a few months after disbursement. Given that most SKS loans have a 50-week term, a short 3 month catch-up period would mean that ¾ of the portfolio are displaying full delinquency levels, while a 6 month one would imply that only ½ of them were doing so – assuming 0% portfolio growth. This also means that if, for some reason, the overall portfolio quality deteriorates, it will not manifest itself fully for a good couple of months.
- Dilution Effect: Of course, SKS’ portfolio is not growing at 0%. It grew 99% between FY2009 and FY2010, and according to the 2009-2010 Annual Report, has a CAGR of 150% over the last 4 years. Consider what this means for the lag effect. The earlier vintages that are now displaying full delinquency levels are essentially watered down, and the low or non-existent delinquency levels of newer vintages have a higher per-rupee weight. How much watering down happens is dependent on the vintage disbursement amounts, but in general, the dilution effect depresses the delinquency levels further.
Why is this an issue? Well, SKS cannot continue to grow at a CAGR of 150% for too many more years. The Indian MFI market is getting increasingly saturated, and the industry as a whole will need to slow down. As growth slows, the delinquency and write-off levels will catch up. If SKS does not anticipate this catch-up and prepare accordingly, investor confidence will take a blow, along with portfolio quality.
Vintage-level Analysis Has Some Answers
One can get a sense of what will happen in the future by literally seeing how loans in SKS’ portfolio age. Loans given out during the same period, usually a month, are said to be in the same vintage. I have not found any public information on SKS vintages, let alone their delinquency profiles. It’s generally not very helpful to guesstimate on this because such profiles can vary greatly between MFIs with similar top-level PAR and write-off numbers, and can differ substantially for different products even within the same MFI, so I’m not going to dive into a modeling exercise.
It’s not too difficult to figure out the lag and dilution effect though, if one has access to granular portfolio data. SKS would have to take each vintage, figure out its delinquency profile, derive historic trends by superposition, and account for differences based on product characteristics, branch office location or any other salient factors. It can then combine projected performance of existing vintages with internal growth targets, and see where the PAR and write-off numbers end up at.
Sure, the past is not necessarily the best predictor of the future, but given the purported out-of-the-box nature of SKS products in particular and microfinance products in general, and the short loan lifecycles that reveal profile changes relatively quickly, this should be a pretty useful exercise.
This is the first of a series of posts that will take a look at the numbers behind MFI operations. I find it to be quite an instructive exercise to wade through MFI data in the rare instances where they are available in any level of detail beyond mere institution-level aggregation, as presented in annual reports and the like.
We’ll first look at delinquencies and write-offs, and use the Indian MFI SKS as a case study of sorts. This is partly because the Red Herring released prior to its IPO provides a wealth of information that allows for more meaningful and in-depth analysis, and in general, affords a rare look at the inner workings of an institution enjoying prodigious growth while carving out a place in a rapidly evolving market. SKS is also the largest MFI in India, followed by Spandana and SHARE, and according to 2009 MixMarket data, serves about a fifth of the Indian MFI market. What we might glean from this is therefore relevant to a large chunk of the Indian microfinance market too.
PAR and Write-off as Measures of Delinquency
Well-run MFIs fastidiously maintain high portfolio quality. Many MFIs have primarily lent to women, who traditionally have excellent repayment rates. Many adhere to the Grameen group-lending model because they rely on peer support and pressure to enforce regular repayment habits. Very low delinquency and default rates have made this sector a favourite for investors, domestic and international.
Portfolio-at-Risk (PAR) is one of the standards of measuring delinquency for microfinance loan portfolios. It is defined as the total outstanding principal balance of loans with any amount of arrears due. Thus, if a $100 loan still has $45 in outstanding principal, and the $2 of that was due last week is not paid up, the PAR amount is noted as $45. In a way, PAR is the most conservative measure of how much loss the portfolio would suffer, since this is the maximum value that the MFI would lose from this loan if nothing more was ever paid back.
PAR is usually associated with a number of days count, where PAR30 would mean the total outstanding principal balance with any amount of arrears due for over 30 days, PAR60 for 60 days, and so on. Many MFIs will typically provision against 100% of the PAR120 amount, thus assuming that none of it can be recovered.
Once a loan is written off, it is no longer on the books of the MFI.
SKS Delinquency Profile
The unweighted averages for PAR30 and write-offs were 1.84% and 0.52% respectively for Indian MFIs with more than USD 10m in gross loan portfolio. When one considers the weighted averages, the corresponding figures are 0.59% and 0.54%. (The weight applied is principal outstanding.) We are interested in looking at the weighted average, by the way, because it gives us a sense of how every dollar (or rupee) in the portfolio is doing, on average, as opposed to every loan, which would be the case for the unweighted measure. Comparing the two PAR30s tells us that larger portfolios have much lower PAR amounts on average – this is quite interesting, and we’ll come back to why a little later.
This provides some context to PAR and write-off data for SKS for the last 5 years:
(Screenshot Source: MixMarket. Note that FY2010 denotes fiscal year ended Mar 31, 2010.)
Two things of interest jump out at me from this data:
- Write-offs levels went up from 0.29% in FY2008 to 0.60% in FY2009, and then up again to 0.86% in FY2010.
- The recent PAR30 and PAR90 rates are much less than the write-off rates.
Rising write-offs are an obvious issue. The PAR numbers also follow a similar trend. We’ll take a closer look at this in my next post.
Do you think it’s normal that the PAR numbers are less than write-offs, by the way? It’s quite an interesting phenomena, and we’ll focus on it on the third post of this series.