A Look At My Current Bondora Portfolio

In October 2012 I started to invest into p2p lending at Bondora. I periodically blog about my experiences – you can read my update from Dec. 2015 here. Over the total time I did deposit 14,000 Euro and withdrew 13,380 Euro.  So as you see I cashed out an amount almost equal to the amounts I deposited. The good news is that I still own 705 loan parts with an outstanding principal of 10,362 Euro at an average interest rate of 23.74%. Of these 6,355 Euro are in current loans, 1,004 Euro in overdue loans and 3,003 Euro in 60+ days overdue loans. The reason that I still have such a large loan book despite cashing out nearly as much as I paid in, is that I reinvested nearly all interest and principal repayments from 2012 till 2015.

Bondora shows a net return of 24.6% for my portfolio. In my own calculations, using XIRR in Excel, assuming that 30% of my 60+days overdue and 15% of my overdue loans will not be recovered, my ROI calculations result in 17.0% return.

Let’s look how my remaining portfolio is distributed by several criteria

Bondora portfolio by country

Chart 1: My portfolio by country

Bondora Portfolio by rating

Chart 2: My portfolio by rating

Bondora portfolio ditribution by loan purpose

Chart 3: My portfolio by loan purpose

Recent developments

A lot has changed in the past four months. With the introduction of new regulation in Estonia, Bondora now prefunds all loans and also keeps a stake in the loans (‘skin in the game‘). Manual bidding on loans is not as straightforward as previously because now investors can make bids, which are not binding until allocation happens. This leads to situations were say 155% of the loan amount has been bid for, but the allocation has not happened yet, because some of the bidding investors have not enough cash in their account to match their bids and those bids that are sufficiently funded don’t add up to 100%. Furthermore Bondora gives bid preference to bids with larger amounts. If at allocation time bids with enough cash add up to more than 100%, then the bids for higher amounts will succeed, while the smaller amount bids will be rejected.

Continue reading

Year End Review of My Bondora Portfolio

In October 2012 I started p2p lending at Bondora. Since the start I did deposit 14,000 Euro. Since then I periodically wrote on my experiences – you can read my last review published in August here. In the 4 months since then, many changes happened at Bondora: New portfolio manager, new dashboard view, new collection procedures, a first version of an API in production, no more transaction fees for buying and selling on the secondary market and Bondora now allows investors to sell and buy defaulted loan parts (60+days overdue). I used this change to sell my overdue Slovakian loans at 87% discount.  I also sold my remaining Spanish loan parts. I did not have much of either of these in my portfolio anyway.

I also reduced my overall portfolio position at Bondora and transferred 11,022 Euro back to my bank account. I managed to sell many of my Estonian loans at a premium. My perception is that the liquidity on the secondary market increased after the transaction fees were removed. Still it is hard to effectively navigate on the secondary market.

I find it hard to price 60+days overdue loans I want to sell on the secondary market. The site Bondpicking.com has charts with recent transactions for defaulted loan sales on the secondary market that can help as guidance.

Currently I still have loan parts with the value of 11,428 Euro outstanding principal. Of these 1,441 Euro are in loans that are overdue and 2,076 Euro are in loans that are 60+ days overdue.

At the moment I have 280 Euro cash available.

My Bondora Portfolio 12/2015
Chart 1: Screenshot of account balance

My interim conclusions after 3 years

I am very satisfied with my Bondora investment. Of the 14,000 Euro investment I already withdrew over 11,000 Euro, meaning my remaining risk (concerning the initial investment) is very limited and I still have a huge portfolio of loans. Currently Bondora shows my ROI to be 25.0%. In my own calculations, using XIRR in Excel, assuming that 30% of my 60+days overdue and 15% of my overdue loans will not be recovered, my ROI calculations result in 16.3%. Even in a very pessimistic scenario adding up only withdrawals (approx 11,000 Euro), current loans (about 8,200 Euro) and cash (280 Euro), the initial 14.000 Euro grew in three years to a value of 19,500 Euro. Continue reading

Current State of My Bondora Portfolio & Announced Major Upcoming Changes

In October 2012 I started p2p lending at Bondora. Since then I periodically wrote on my experiences – you can read my last review published in April here. Since the start I did deposit 14,000 Euro (approx. 15,900 US$). My portfolio is very diversified. Most loan parts I hold are for loan terms between 36 and 60 months. Together the loans add up to 21,895 Euro outstanding principal. Loans in the value of 2,683 Euro are overdue, meaning they (partly) missed one or two repayments. 3,175 Euro principal is stuck in loans that are more than 60 days late. I already received 15,202 Euro in repaid principal back – this figure includes loans Bondora cancelled before payout. I reinvested all repayments.

Bondora Investment 08/2015
Chart 1: Screenshot of loan status

At the moment I have 0 Euro in bids in open market listings and 987 Euro cash available.

Bondora Balance of my Portfolio
Chart 2: Screenshot of account balance

Return on Invest

Currently Isepankur shows my ROI to be 26.76%. In my own calculations, using XIRR in Excel, assuming that 30% of my 60+days overdue and 15% of my overdue loans will not be recovered, my ROI calculations result in 21.8%. Continue reading

Bondora Investments Using Decision Trees – Review of Progress – Part 6

This is part 5 of a series of guest posts by British Bondora p2p lending investor ‘ParisinGOC’. Please read part 1, part 2,  part 3 and part 4 and part 5 first.

Plan Your Change And Change Your Plan!

As stated in the previous article (see part 1-3) and revealed in the graphs of performance, I started using the Decision Trees in response to the rapid rise in defaults in my portfolio. Except for very small numbers of “opportunistic” purchases, I have maintained a strict discipline on purchase in order to ensure that my progress could be monitored and assessed. As my confidence has grown, I have modified this discipline to take advantage of the Bondora environment to achieve the demanding personal goals I had set myself when I first started. These included only purchasing Loan parts that should accrue 50% interest over the forecast life of the loan – i.e. should turn 5 Euro into 7.5 Euro over the original loan period.

Since early June, I have modified this discipline further and now purchase loans that, whilst still meeting my overarching rule of looking for 5% to 7% historical default levels, do not have a high enough interest rate to meet my earlier profitability goal. I intend to try and sell these loan parts on the Secondary Market with a short-term profit goal, after Purchase/Sale costs.

This further leg of my overall strategy is still in its infancy, but the results from my use of Decision Trees in my initial selection of Loan Applications suggest I am buying the best performing loans available. This means that should other investors not share this view, I will at least be left with Loan Parts that will perform well for me for the time I hold them.

Given the latest changes at Bondora mentioned earlier, if I can only acquire “good” (as defined by the Decision Tree analysis) from the Secondary Market, it may be that this buy-to-sell tactic may not be possible into the future.

Tree development

Tree Analysis

In the previous article (see part 1-3) on the construction of the Decision Trees, I explained how I had made adjustments to the overall analysis process to give more weight to factors such as “Total Income” in the actual Decision Tree analysis. I have kept the included data under constant Review and have added a few further fields to the analysis process, in particular the field showing the “Total Monthly Income/ New Repayment”. As stated in the first article, this needed to be modified from an infinitely variable value into 20 ranges, each of equal numbers of samples.

I mention this particular field as, since January 2015, it appears as an important feature in both the Estonia and Finland Trees and continues to appear more often in these Trees.

Volume and confidence

It is a fact that Estonia has been the largest market for Bondora from its days as Isepankur. In simple volume terms, the data I use (from 1/1/2013) shows that Estonia accounts for c.50% of the total loans, with Finland and Spain making up about 25% each. Slovakia is simply no longer mentioned in polite, Bondora society, so I will pretend it never happened!

Whilst it is true that Estonia has a lower historical default rate, in the dataset that I use, defaults do occur and are presently running at around 11.986% (1009 out of 8418), compared with exactly 18% (576 out of 3200) for Finland and 27.059% (1022 out of 3777) for Spain.

The above figures carry several implications as follows:

The Estonian Tree is fairly static with few changes at the highest levels. Estonian Loans within Bondora bring with them a richness in the data, by which I mean that the original Credit Scores are well represented across the Loan Applications compared to Finland and Spain, which are almost entirely populated with examples with a Credit Score of “1000”. What this means for Estonia is that the Decision Tree neatly shows that the Bondora Credit Score is relatively accurate, with higher numbers of defaults at lower Credit Scores. Thus it is that the historical record shows that Loan Applications with a Credit Score of “1000” (the highest and most sought after) make for good hunting when searching for segments having a default rate of less than 5%. Indeed, it is not uncommon for the Decision Trees to reveal segments of 50+ examples with NO defaults over the last 2.5 years.
Finland and Spain however, with very few historical Loan Applications with a Credit Score of anything other than “1000” combined with a default rate 50% and over 100% higher respectively than Estonia AND volumes less than half that of Estonia, provide pitifully few obvious segments with a sub-5% default rate AND sufficient numbers of examples to support anything like the confidence levels of Estonia.

I believe that the lack of richness in the Finnish and Spanish data is revealed in the overall structure of the different Trees.

Estonia

The top-most branch in the Estonian Tree is based upon the Employment Status of Estonian Applicants. This represents 5 different values: Full Employment (c.90%), Entrepeneur (c.4%), Self-Employed, Retired and, finally, Partially Employed (these last at c.2%).

The Credit Score generally appears at the 2nd, 3rd or 4th level below this and, as stated above, provides a firm “fault line” between >5% and <5% default rates in most of the segmentation below these levels.
As noted earlier, for those in Full Employment initially Income and latterly the ratio of cost to income (which I refer to subsequently as “Affordability”) is the next most significant differentiator followed by Credit Score with the paths exhibiting differing significant data elements somewhat below this level.

A strange (in my eyes) feature of what I call “Affordability” that appears in the Estonian Tree for those in Full Employment is an apparent truth that the more someone can afford to cover the cost of the loan, the less likely they actually do so and the more likely it is that default will occur! 17.333% (65 out of 375) of those in Full Employment who appear to be most able to afford their loans go on to default whereas only 6.54% (24 out of 367) of those in Full Employment showing the lowest affordability have defaulted. So it seems that, in Estonia, the higher the ability to pay, the less likely this is to occur!

Finland

The lack of richness in the Credit Scores provided by Bondora for Finnish (and Spanish) Loan Applicants is revealed, as the Credit Score is the primary determinant at the top level. This is, however an almost totally useless determinant as just over 98% (just under 98% for Spain) of all Finnish Loan Applications carry a Credit Score of “1000”. Below this level, Employment Status is the prime determinant, as in Estonia, but there any resemblance ends as lacking the Credit Score and with lower overall volumes and there is no common thread to the analysis.

Latterly the ratio of cost to income (what I have termed “Affordability”) has crept in at lower levels but there is no pattern to be discerned and the Tree has not settled down to any pattern at the lower levels with changes occurring at all iterations.

Such are the problems with low volumes and high default rates that I have changed the parameters for the Decision Trees for Finland and Spain to force the analysis to work with higher volumes in the nodes and leafs (end points) in an attempt to increase confidence levels. This has the unfortunate side effect of there being few leafs with a sub-5% default rate, the notable exception being a leaf of 23 examples with a 0% default rate.

Spain

As noted above, Spain shares with Finland the feature of Credit Score and Employment Status being the top 2 levels but for Spanish Loan Applicants in Full Employment, the number of Dependants appears to be the most important factor and has remained so for over 6 months of analysis. This data element does appear occasionally in both other trees, but only at much lower levels.

Other than this notable difference, the overriding feature of the Spanish Decision Tree is the lack of leafs showing a sub-5% default rate. Even where sub-5% default rates can be found, there are so few examples in the set with little in the way of trend or discernable pattern to support confidence at any instinctive level.

The best sub-5% default rate is a leaf of 21 examples, being 4.75%, for fully employed, divorced people with 1 dependant living in Pre-Furnished property! All other leafs with a sub-5% default rate are based on less than 10 examples. Many are only single examples.

A competent statistician (which I am not!) may be able to pry some hidden gems from this Tree, but I fear not.

Conclusion

The Decision Trees themselves, whilst changing over time, now appear to have settled down and changes that occur do so at finer levels of granularity with only occasional changes in the overall structure of any particular tree.

The numbers of samples (the complete Bondora dataset) entering the process have now reached the level where the Trees for Finland and Spain required modification of the actual Decision Tree analysis (known as an “ID3” tree) to increase the sample sizes at the lowest level. This has increased my confidence in the output even though the levels of default are so high that identifying sub-5% default levels leave me rejecting many more Loan Applications than I actually invest in.

My initial, restricted purchasing at the start of my new strategy has opened out over the course of period under review. After an initial period where my cash reserves grew to over 25% of my initial investment at Bondora, I am now confidently pursuing new avenues of activity with a view to maximising my returns within the opportunities suggested by the Decision Tree analysis.

This success in using manual selection of investment opportunities comes in the face of constant change at Bondora, change that is trying to move the investment process towards a passive, easy-to-use activity – an understandable business logic.

I take some comfort that my total efforts to date (which include aggressive management of non-performing loans) appear to be returning better than average results. In conclusion, I believe that my change from instinct- to numbers-lead investing has improved my portfolio performance when measured by this admittedly coarse scale of default level. Furthermore, this process has allowed me to start to take a wider view of the opportunities available on the Bondora platform and I hope to be steering my returns back to the levels that initially drew me to this platform.
In terms of the performance over the past 9 months, I experience severely reduced default levels going forward compared to those that triggered my realisation that a new investment strategy had to be formulated. I am now seeing levels similar to those last observed almost 2 years ago, on purchasing volumes approximately double those from that time. I will be the first to admit that the loans purchased over the last 9 months have yet to “mature” to the level of those from nearly 2 years ago, but I have a renewed confidence in the future performance of my portfolio at Bondora.

P2P-Banking.com thanks the author for sharing his experiences and strategy in detail.

Back in March an investor from Luxembourgh wrote an article sharing his experiences in applying machine learning to peer-to-peer lending at Bondora.

Bondora Investments Using Decision Trees – Review of Progress – Part 5

This is part 5 of a series of guest posts by British Bondora p2p lending investor ‘ParisinGOC’. Please read part 1, part 2,  part 3 and part 4 first.

The Management of Change

As mentioned in my earlier article on the construction of the decision Trees, my responsibilities when employed (yes, dear reader, I am now retired) included the successful proposal to create new teams to conduct Data Mining and produce and disseminate Metrics relating to the research activities. As on many other occasions, I was then charged with making my assertions real by staffing and then running said teams to realise the benefits I had stated should arise.

As part of my (rapid) learning in these activities, I came to understand the need to maintain processes until solid analysis could isolate and support changes. So in this review period, for those elements under my control, I have maintained certain actions within set parameters until I felt I could justify a change and then have maintained that changed process until the next time the data supported a further change.

Changes I Controlled

Given that my need to change my selection process was as a direct of seeing my money rapidly disappear (!) I limited my ongoing expenditure to the minimum purchase (5 Euros) allowed by Bondora and only made 1 purchase per selected Loan Application.

This continued throughout October 2014, when I felt that the downward trend in parts falling behind with payments was established and likely to continue. From the beginning of November 2014 onwards I increased the number of parts of any single loan application I would buy to 2, still of 5 Euros each. Note that for some application types with, for example, a higher (between 5% to 7%) indicated historical failure rate, or a very high (above 45%) interest rate; I still limited my purchasing to 1 part of 5 Euros.

This Purchasing policy remained in place until the beginning of April 2015 when my increasing confidence in the selection process, my increasing cash reserve and other factors described below, meant I felt able to increase the value of purchases (to include 10 Euro parts if I felt an application was sufficiently strong) and increased the number parts purchased of any particular loan. This latter element in particular allowed me to take advantage of events outside of my control that offered opportunities that had not previously existed, explained later in this article.

Errors in my Process
In the period October 2014 to the end of the year, I was updating the Trees twice a month. There was no detailed timetable, but the Trees did exhibit a greater degree of change in this time than was later the case. It was during the first update in December, week 51 of 2014, I noticed that the previous Tree had been built using corrupted data. It was only later in the review period that I noticed that this period – from weeks 48 to 50 inclusive – exhibited the last “spike” in defaults.

From the next update onwards (31st December 2014) I implemented a more rigorous update procedure and restricted the updates to 1 at the end of each month. I felt that this may enable changes in the Tree Structures to be more visible and so attract my attention to these changes and validate the process that had generate them, thus avoiding process errors. The fact that the datasets provided by Bondora were subject change without notice (and did so often) was an additional factor in the decision to have fewer, more rigorous build events.

I worried that fewer updates to the Trees would lead to out-of-date trees and more In Debt and Defaulting loan parts, but this has not become apparent either in daily use or this review process.
I have noticed that the Decision Trees are not static and do change over time. Sometimes – rarely – these changes occur at a high level and are very noticeable. However, the Trees have changed in a subtle way at lower, more compartmentalised levels. This is discussed later in this article.

Changes I could not Control

Whilst I have tried to maintain a tight control over my activity since starting to use the Decision Trees to guide my loan selection, there is the overall Bondora environment over which I have no control. As noted in the previous article (see part 1-3), Bondora is a dynamic environment and changes, whilst usually signalled in advance, cannot usually be planned for and just have to be accommodated when the reality of the change becomes apparent. Where possible I have noted the changes that have occurred. As part of this review, I have gone back over the last 9 months activity to try and relate these changes and how I believe they have, or may have, affected my results.

Portfolio Manager
The Portfolio Manager in place up to the end of 2014 was an automated, parameter-driven mechanism to allow investors to automatically invest in loans that meet the criteria set by the investor. From the start of 2015, Bondora made major changes to the Portfolio Manager, preceded by allocating a “Risk Segment” (running from low to high risk) to each Loan Application.

Whilst a Loan Application retained the previous Credit Score and associated Credit Group (essentially an income-related grading), these no longer played a part in the new Portfolio Manager, which no longer allowed Loan Selection by any criteria other than the new “Risk Segment”. Probably the most contentious element of the new Portfolio Manager was the loss of selection by Country. The use of Country was a critical element in the previous automated selection process for most ( if not all) investors, and its loss was not well received on the official forum.

In terms of my process of Decision Tree analysis, this changed nothing. All the previous data was still present and some new data was added about the New Risk Segment and the process associated with it. I have considered adding the new Risk Segment data to the Decision Tree analysis, but decided against this primarily as its introduction, occurring as it did some 3 months into my experiment, had the potential to dramatically alter the structure of the Decision Trees, creating a possible disconnect at this point.

A secondary reason in my decision was the fact that this data was itself the result of an analysis conducted by Bondora and for which there is no detailed discussion or publication showing how it has been arrived at. Whilst I am not surprised at the decision not to publish what is, after all, company confidential data, the output – a legend consisting of a 1- or 2-letter classification – is not an independently verifiable fact, it is merely the output from an analysis and shares this feature with my own Decision Trees.

The major difference between this and the Decision Tree output I have is the context that is provided by a full Decision Tree to those who wish to use it. IMHO, the discerning viewer can decide from the context of a complete Decision Tree whether the end point of a particular branching of the tree indeed describes a trend or is just a convenient mathematical activity that segregates the data, but reveals no trend. I offer the snapshot of Self Employment from the Decision Tree for Estonia as an example of this added value.

Decision Tree View Estonia Bondora

 

To me, the bigger picture describes a trend suggesting that the longer the applicant has been in the same employment, the less likely a default will occur. It also shows that the Decision Tree has found that those in the same employment for over 5 years can be further segregated by age, with all defaults occurring in a single age range (45 to 51). Furthermore, the sample size of the >5 years employment is 51 and the defaults, which all occur in the noted age group, amount to just 2 examples – a 4% default rate on the set of 51 as a whole. Is this further segregation a guide to investment or just a “Clump” in a larger data set? In the words of the immortal Clint Eastwood “You’ve gotta ask yourself one question: “Do I feel lucky?Well, do ya, punk?.

Application Process

In last half of February 2015, Bondora introduced changes to the application process designed to allow applications to be assessed by Investors before all data had been collected and, where applicable, validated.

This had no immediate effect on the Decision Tree analysis, but did require minor amendments to the process. Many applications were taking up to 5 or even 6 attempts before they became fully acceptable and finally funded. Many of these rejections took place after funding was in place. They were then cancelled and re-submitted with updated data. It was important that such applications did not get counted as “Previous Applications”. This field does appear in some lower levels in a Decision Tree and therefore new data cleaning activities (explained in the previous article) had to be introduced into the process.

Server Capacity Issues at Bondora

Around the 2nd week in March, 2015, the servers at Bondora ran into capacity issues. This affected both the ability of the applicant to apply for loans and for investors to lend.

Aggregated effect of Bondora changes

Concurrent with the introduction of the changed application process and the server capacity problems, it is apparent from a chart provided by Peerlan that the new Portfolio Manager’s ability to fund loans collapsed, effectively to zero.

Portfolio Manager Funding from Peerlan - 2015-06-23 snapshot

When Bondora fixed their capacity problems, the mix of Loan Applications becoming available to manual investors had changed dramatically. Whilst this had no effect on the use of Decision Trees to select loans, it meant that many more loans became available to manual bidders. Many of these loans were Estonian, historically considered to be of higher quality.

This availability of more loans of potentially higher quality is reflected in my activity by the highest level of loan part purchases seen since the start of my use of Decision Trees. This higher number of purchases occurred even with the restrictions I had placed on myself regarding the level of purchases per Loan Application, mentioned earlier.

As I write this review, the new Portfolio Manager process has again changed, this time to run more often, with a target of running effectively all the time. This new process appears to have a dramatic effect during the 16th July, reducing opportunities for manual bidding on new Loan Applications essentially to zero, as the new Portfolio Manager process swept up all new listings.

New Loan Applications have appeared again the next day and a close reading of the Bondora “Guide to Investing” FAQ suggests that Loans that fail to be filled immediately should appear out of the back of the new process and become available to manual investing and this appears to be the case. This occurrence and the availability of loans on the Secondary Market (at a premium in most cases), leaves me feeling that my work to date has not been in vain. Time will tell!

Flip forward to the final part 6.

Bondora Investments Using Decision Trees – Review of Progress – Part 4

This is part 4 of a series of guest posts by British Bondora p2p lending investor ‘ParisinGOC’. In part 1, part 2 and part 3 published in December 2014 you could read how he used the data to built decision trees to identify lending opportunities. Now you can read how that strategy worked out.

Introduction

In August 2014, I realised my portfolio of P2P loans at Bondora was not performing as I would wish. There was an urgent need to change the way I selected loans in which to invest the money I had at my disposal. My search for a better way of selecting loans lead me to use Decision Trees to analyse the loan data available from Bondora using “RapidMiner” – software available to download for free.

It is now over 6 months since I described my original work to construct the Trees. This follow-up article chronicles what I believe is the success of my efforts to date whilst also describing the multiple factors, both within and beyond my control, that mean that, whilst I feel very comfortable with the progress made to date, others may feel that I have just been lucky!

The journey since I created my first Decision Tree and started to make purchasing decisions based almost totally on their outputs has been one of constant change. Detailing the changes to elements over which I have no control has shown me how they contribute to what I believe is success as much as my own efforts to improve the selection processes. Describing the change in the Decision Trees as well as their use in the dynamic Bondora environment has left me feeling that, without constant monitoring and review of both the process of creating the Trees as well as their use, it may still be very easy to snatch defeat from the jaws of victory.

Key to ensuring the veracity of my protestations of success has been the maintenance of a consistent approach to my selection and lending process. To this end, I will describe those changes to my process that I can control and explain how and why such changes have taken place. In short, I have maintained a restricted buying policy, investing only the minimum amount (5 Euros) at any one time and, latterly, only buying a maximum of 2 loan parts (of 5 Euros each) in any one loan, depending on the outputs from the Decision Trees and my own mood at the moment of purchase.
I realise that this last phrase is not at all scientific, but the fact that my Portfolio of c.12000 Euros was not performing as expected was for me, a non-trivial affair and some emotional response has to be accommodated.

I have already stated that I believe my efforts have been successful. This is based on the fact that the rate of default (Once a loan principal has been overdue for 60+ days, it is labelled as “defaulted” – Bondora FAQ) in my portfolio has returned to historical, pre-2014 levels. Up to this time, even though I had come to realise that I needed to actively manage my portfolio, my selection of loans was done almost entirely using the “Portfolio Manager” – an automated, parameter-driven purchasing function provided by Bondora and supplemented by instinctual analysis of the descriptions of the Loan Applications available to invest in.

Simple Chart - Held Loans and Defaults

 

Looking at the simple chart of Held Parts/Defaults, the number of defaults in held loans rose significantly over the summer of 2014, coinciding with a big increase in both the number and value of investments on my part. Referring to the same chart, it can be seen that, even though the number of investments remains close to summer 2014 levels, my defaults have fallen to the numbers experienced earlier, at much lower volumes.

With my new-found confidence that I have a process for selection and management that appears to be sound, I have started to increase the volume of Loan Parts purchased so that the value is now approaching Summer 2014 levels of investment.

Progress to date

Graphical representation of Progress

I will use a more detailed graph showing the volume of Loan Parts purchased, those subsequently sold, those “Overdue” and those in default (still held by me as well as sold) to hopefully illustrate the performance of my selection and management processes. Continue reading

Update: Current Status of my Bondora Portfolio

In October 2012 I started p2p lending at Bondora. Since then I periodically wrote on my experiences – you can read my last review here. Since the start I did deposit 14,000 Euro (approx. 15,600 US$). My portfolio is very diversified. Most loan parts I hold are for loan terms between 36 and 60 months. Together the loans add up to 20,616 Euro outstanding principal. Loans in the value of 2,397 Euro are overdue, meaning they (partly) missed one or two repayments. 2,623 Euro principal is stuck in loans that are more than 60 days late. I already received 13,261 Euro in repaid principal back – this figures includes loans Bondora cancelled before payout. I reinvested all repayments.

Bondora Investments 04/15
Chart 1: Screenshot of loan status

At the moment I have 0 Euro in bids in open market listings and 741 Euro cash available, which is rather high but it will take only 2 to 3 loans that match my investment criteria to allocate the money.

Bondora 04/15
Chart 2: Screenshot of account balance

Return on Invest

Currently Isepankur shows my ROI to be 27.22%. In my own calculations, using XIRR in Excel, assuming that 30% of my 60+days overdue and 15% of my overdue loans will not be recovered, my ROI calculations result in 19.6%. Continue reading

P2P Lending Experiences of a British Expat Living in the Eurozone

This is a guest post by British investor ‘JamesFrance‘.

Since retiring and leaving the UK to live in a warmer dryer part of Europe, I fortunately found myself able to live on less than my income, so had the problem of how to best manage these savings, which I wanted to protect from inflation and if possible achieve a positive return on by some type of short term investment. Unfortunately I never found a British savings account which would accept money from non residents, so I was obliged to accept a very low interest rate from my existing UK bank. I do have other long term investments so was prepared to take some risk to achieve a better return.

I had seen articles in the British press about Peer to Peer lending, which tended to refer to the big three, Zopa, Ratesetter and Funding Circle, none of which were prepared to allow a non resident to open an account, so I soon forgot about that as a possibility.   In August 2013 I read that another P2P business lending platform, Thincats, was joining the P2P finance association. I decided to look at their website and was surprised to learn that they could accept non resident investors.

Thincats is really for those with larger amounts to invest, having a minimum bid of 1000 GBP per loan, so it is difficult to achieve adequate diversification for relatively small sums without using their syndicates, which I didn’t find interesting, so I took the plunge and made 10 loans.   Needing 1000 GBP per loan meant that after that it took me some time to accumulate enough for my next bid, so I had the problem of uninvested money not earning until my next loan drew down.   I also found that some loans were repaid early which was reducing my returns because of the drawdown delays.   I think this would be an ideal platform for those with large amounts to invest, as they have a good flow of loans, there is plenty of information about the borrowing companies and once their new website is launched the process should be much easier.   A minimum 25 GBP fee for selling a loan on the secondary market makes it expensive to sell smaller amounts, which means that after several repayments a sale would not be economic.

By this time I was finding other possibilities with the help of websites such as P2P-Banking.com, where I read about isePankur in Estonia, which has an English language version and seemed ideal for any spare Euros languishing in my Euro account and only earning a secure 1% interest. isePankur now renamed Bondora, has been quite exciting to invest through as there have been many changes to the auto bidding system since I started there in September 2013, so just as I became used to the way my chioices were working out, it was all change so I had to start again to think of a good strategy.   They have been expanding rapidly and now issue personal loans in 4 European markets.   The defaut rates for their Spanish and Slovakian loans have been very high, so I have been avoiding those areas since that became apparent, which means time consuming manual investment because the auto bid system no longer allows choice of country.   I do not sell overdue loans on the secondary market, so my returns on the platform will be completely dependent on the eventual recovery of the defaulted loans, which will only become apparent after a few years.   The interest rates are high so I have accepted the level of risk involved. Continue reading

P2P Lending Marketplace Bondora Fuels European Expansion Plans

Bondora LogoP2P lending service Bondora, headquartered in Tallinn, announced that they raised 5M US$ Series A round led by Valinor Management to fuel further expansion plans for cross-border lending in Europe. Richard Fay and Ragnar Meitern also invested. Bondora was the first p2p lending service doing cross-border lending for retail investors. Bondora is currently facilitating loans to borrowers in Estonia, Spain, Finland and Slovakia from investors in all European countries. Bondora states that investments on the marketplace have consistently yielded premium returns to investors while simultaneously delivering competitive rates to borrowers through efficiency and lower interest rate spread.

Uniting European markets under the roof of a single platform creates a huge opportunity given the size of the population in the continent and the volume of outstanding debt. Thus, Eurozone countries alone account for 340 million people and EUR 1.1 trillion in outstanding consumer credit debt, a market equivalent to US. Lending to borrowers in markets that are independently relatively small (even Germany, the largest economy in Europe is only approximately twice the size of California in terms of GDP) allows earning premium returns due to lack of competition among traditional lenders.

Pärtel Tomberg, CEO and co-founder of Bondora, said he hoped the cash infusion from Valinor Management, the hedge fund run by David Gallo, will allow his company to build the more complex infrastructure needed to make more cross-border loans. ‘The goal is really to become a global market,’ Pärtel Tomberg said in an interview. ‘There are no precedents in the world on many of the things we want to do.’

The company also wants to attract institutional lenders from the US.

A possible mid-term competitor might be Lending Club. But Lending Club said in the investor conference call on Tuesday that they will focus on the US market and will not use the capital raised in their December IPO on international expansion plans in the near future. Renauld Laplanche is however monitoring international developments in the market: ‘We’ll see what model is really the winning model in any particular geography.’ Continue reading

Bondora Ratings Create a Single Eurozone Lending Marketplace

This is a guest post by Pärtel Tomberg, CEO of Bondora.

This month we started the roll-out of Bondora Ratings, a loan application rating system based on the proprietary credit scoring model, with the aim to bring better predictability and consistency of the returns for investors active on Bondora platform.

Our initial aim for developing a credit scoring model was to bring the best banking practices to peer lending. Banks might have failed at providing people with affordable credits, but they have done a few things right, such as developing strong credit scoring models, that have helped them generate premium returns for their investors. With Bondora Ratings we are bringing credit scoring model to individual investors so they could maximize their returns in the same way banks do.

Being a platform that facilitates the exchange between lenders and borrowers, we believe it is our responsibility to bring the best practices, such as credit scoring, to peer lending; thus, making it an effective, efficient and mutually beneficial process for both parties. Eventually, Bondora Ratings will allow credible borrowers get a better rate for a loan, while investors will receive a predictable return level.

On top of bringing banking practices to peer lending, we saw a need for a simple, transparent and unified way to represent the risks and potential returns associated with a particular loan. Until recently, our investors used their own sophisticated models to evaluate the risks and plan their investments at Bondora. We have supported the initiative by offering a wide range of filters and providing extensive data export sets, and we will continue to support those investors in the future by providing a Trading API.

However, as the platform grows, we see an increasing inflow of investors that do not have a need or desire to engage into extensive number crunching. The historic performance of peer lending platforms, and Bondora in particular, indicates that peer lending provides premium returns compared to other assets classes and investors want a simple and easy way to earn those premium returns. Continue reading