Crowdfunding – Dutch investors – where to go?!

This is a guest post by Dutch lawyer Coen Barneveld Binkhuysen (see full bio at the end of the article)

Crowdfunding is growing exponentially in the Netherlands. Although the Dutch market has not yet reached the astronomical levels of the United States and the United Kingdom, many people have heard about the phenomenon and are intrigued by this potential alternative investment opportunity. While the Dutch market speaks a lot about crowdfunding, it is less familiar with the term p2p-lending (it is commonly available though). As this article covers investments in loans, convertible subordinated loans and equity, I will use the general term crowdfunding instead of p2p-lending.

In the first 6 months of 2015, almost 50 million Euro was raised via crowdfunding, which is double the amount raised in 2014. There are over 80 crowdfunding platforms active in the Netherlands, which makes it difficult for potential investors to gain an overview of the viable available investment opportunities. This article provides a general overview of the most important platforms active in the Dutch market. Furthermore, I will discuss some relevant topics in relation to crowdfunding, such as: diversification options, costs, default risks, cash flow, types of investment and the added value of a properly managed crowdfunding platform.

Overview investment options

In general, crowdfunding platforms in the Netherlands offer the option to invest in loans, subordinated convertible loans and equity (besides donations and the purchase of products). Each of these different investment options has benefits and drawbacks in terms of cash flow, risk and the potential upside can vary significantly:

Loans provide a direct cash flow to the investor as loans are usually repaid in monthly instalments. Loans only have a limited potential upside, maximized at the offered interest rate. Due to the monthly repayments, the risk decreases every month. Most crowdfunding platforms determine the interest rate based on the envisaged risk. As far as I am aware, there are no platforms active in the Netherlands that provide the option to “bid” on loans in auctions.

Convertible subordinated loans (also called convertibles) are considered to entail more risk than normal loans as convertibles are subordinated to (normal) loans and other claims. Investors generally expect a higher return in exchange for a higher risk. Instead of offering a higher interest rate, companies issuing convertibles via crowdfunding offer the option to convert these loans into certificates of shares.[1] The option to convert may be restricted by certain conditions such as (i) a specific period in which conversion must take place and/or (ii) the condition that a sophisticated investor invests at least amount “X” during the term of the loan. For an investor it is important to identify any conversion conditions that may apply. If the loan is not converted into certificates of shares during its term, the investor will receive the principal plus interest payments at the end of the term of the loan. These investments might not be interesting for investors looking for a steady cash flow, but they can be interesting for those who want to have a shot at a serious return.

Equity is normally being offered in the form of certificates of shares (equal to the convertibles described above). Again, investing in equity does not create a steady cash flow for the investor. The terms and conditions related to the certificates of shares may (and normally will) restrict the option to sell them. Therefore, investors are expected to wait for the moment the entire company is being sold to an investor, which can take a long time. Investing in equity might only be interesting for investors looking for long-term investments. Then again, these investments do have the largest potential upside as the investor will profit from every increase in value once the company is being sold.

Balancing risks

Each investor takes, or at least should take, the risk of default into account, especially when investing in high-risk companies such as start-ups. Business cases of start-ups have not yet been properly tested and most do not, or hardly have, any financial buffers. Should the financed company go bankrupt, practice shows that only in rare cases (only part of) the loan can be recovered. Normally, preferred creditors such as banks and the tax authorities will receive the benefit of all assets left in the company and there is nothing left for others. Some platforms try to reduce the risk by requesting a personal guarantee of the entrepreneur, but this is of little use if the person does not have any assets.

The actual difference between investments in loans, convertibles and equity from a risk perspective is small. Investors having certificates of shares have a larger potential upside than the holders of loans. One could say that investors almost bear the same risk, but with different potential upsides. In my opinion the most important reasons to choose for normal loans are the fixed term and monthly repayments. If you are not in a hurry to make a profit and are going for the highest potential return, convertibles and equity might be a more interesting option.

Overview largest platforms in the Netherlands

After selecting the preferred investment instrument, it is important to select one or more of the available crowdfunding platforms. Without aiming to be complete, I list the largest and most active platforms active in the Netherlands below:

nl-geldvoorelkaarGeldvoorelkaar.nl is the national market leader and funded over 825 projects, with a total sum of over 66,000,000 Euro. The platform focusses on p2p-lending and only provides investors the opportunity to invest in loans. Interest rates range from 4% to 9% depending on the risk score determined by Geldvoorelkaar.nl. All loans are being repaid in monthly instalments as of the first month. By investing in projects via this platform, it is fairly easy to generate a decent cash flow. Up to now, 3.5% of my investments on the platform have defaulted. As the principal of one of the defaulted projects was almost fully paid back, my average ROI still accounts for about 6.5% per year. The other defaulted project was probably a case of bankruptcy fraud, which I expect to happen more often in the future. The platform opens several dozen new projects every week, which creates sufficient opportunities to diversify your portfolio and reinvest your money. An investor must pay a fee equal to 0.3% * loan duration (in years) * invested amount (which amount will be refunded if the project defaults).

nl-oneplanetcrowdOneplanetcrowd claims to be Europe’s leading sustainable crowdfunding platform. Since launching in 2012 it raised over € 6 million in funding for more than 100 projects. Oneplanetcrowd operates in Germany and the Netherlands and is planning to open in other European countries soon. It provides investors the option to invest in loans and convertibles (apart from donations and presale options) and offers some of the most interesting investment opportunities, such as Snappcar and Wakawaka Power. Various projects offer the opportunity to co-invest with sophisticated venture capital firms as these firms invest simultaneously with the crowdfunding campaign. In my opinion, this is a huge advantage for investors as VCs tend to do a thorough due diligence before choosing to invest. The platform only allows companies with a sustainable philosophy to start a campaign on the platform. Their goal is to provide high quality investments with a decent return to investors. Although this is a good niche market, the strategy makes diversification opportunities fairly difficult. Investors do not pay a fee on Oneplanetcrowd.

nl-other

KapitaalOpMaat and Collin Crowdfund are some of the main competitors of Geldvoorelkaar.nl as these platforms focus solely on loans with loan periods ranging from 6 up to 120 months and interest rates of 5.5% up to 9% depending on the calculated risk. Almost 6.5 million Euro and 13 million Euro have been funded via these platforms, respectively. Investors on KapitaalOpMaat pay a one-time transaction fee of 0.9% and a yearly fee of 0.85% on Collin Crowdfunding. Both platforms provide discounts to investors investing more than certain thresholds.

Bondora is a European platform offering the opportunity to invest in loans on a European level. Although this is by far the most sophisticated (international) platform available to Dutch investors, its presence is fairly unknown to most Dutch investors. Already more than 35 million Euro has been financed via Bondora. Investors are allowed to choose their own investments on the primary market, but most loans are filled in advance by a bot. Therefore, it will be necessary to invest automatically via the provided bot in order to obtain sufficient loans. This enables the investor to invest in literally thousands of loans differing in purpose, country and risk. All loans are repaid in monthly instalments on a virtual account. Bondora also offers the option to purchase/sell investments to other investors on its secondary market (with a premium/discount) against a fee of 1.5%. Investors do not pay any fees on the primary market. Although Bondora claims an average ROI of 18.75%, many investors complain about the large number of defaults. As the minimum investment is only 5 Euro, the threshold is low.

Symbid is one of the established Dutch crowdfunding platforms and focuses on equity (certificates of shares) and loans. Although Symbid seems to suggest that already more than 300 million Euro has been invested via their crowdfunding platform, the actual amount funded by the crowd is closer to 6 million Euro. One of the advantages of Symbid is that it offers the option to sell your equity to other investors on the platform. 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

Queue Up for P2P Lending!

When was the last time you stood in a long line outside your bank branch, patiently waiting to deposit money into your savings account? Imagining a scene like that seems ridiculous at a time with near-zero interest rates in an increasingly large number of developed countries.

But there where you would least expect it, in the Fintech world of fast-moving bits, some startups actually are imposing measures to throttle influx of investor money in order to balance it with borrower demand. Welcome to p2p lending (short for peer-to-peer lending). The sector is experiencing tremendous growth rates. With attractive yields for investors some platforms struggle to acquire new borrowers fast enough for loan demand to match the ever-rising available investor demand.

One challenging factor is deeply ingrained in the business model of p2p lending marketplaces: once a new investor is onboarded and found the product satisfactory, he is most likely to stay a customer for years to come and reinvest repayments received and maybe the interest also. On the other hand the majority of borrowers are one-time customers. They take out a loan typically just once. While it may take years for the borrower to repay that loan, in most instances there is no repeat business for the marketplaces. So the marketplaces have to constantly fire on all marketing cylinders to win new borrowers in order to keep up and grow loan origination volume.

This has sparked some outside of the box thinking, e.g. the partnership of Ratesetter with CommuterClub to win their loan volume, which is in fact mostly repeat business.

Winning investors has been relatively easy for many of the p2p lending services in the recent past. Investors are attracted typically through press articles or word of mouth. One UK CEO told me he never spent a marketing penny ever to acquire investors.

But what happens on the marketplace, when there are so many investors waiting to invest their money in loans, but loans are in short supply?

  • If the marketplace does nothing or little to steer it, then those investors that react the fastest, when new loans are available, will be able to bid and invest their money. This is the situation e.g. on Prosper, Lending Club and Saving Stream.
  • The marketplace has some kind of queuing mechanism. This is typically coupled with an auto-bid functionality. Examples of this are Zopa, Ratesetter and Bondora.
  • The investors are competing during an auction period by underbidding each other through lower interest rates. Examples of p2p lending services with this model are Funding Circle, Rebuilding Society and Investly.
  • The marketplace can lower overall interest rates to attract more borrowers while the resulting lower yields slow investor money influx.

The UK p2p lending sector is eagerly awaiting the sector to become eligible for the new ISA wrapper. Inclusion into the popular tax-efficient wrapper will attract an avalanche of new investor money to the platforms.

“That’s going to be a challenge for the industry,” said Giles Andrews, CEO of Zopa. “Once the dates are worked out, the industry will need to plan for that together, and we may have to do something we have never done before, which is to limit the supply of money. It’s not good to have people’s money lying around [awaiting new borrowers] or to lower standards of borrowers.”[1]

So there is some speculation that UK p2p lending services could impose temporary limits on new investments.

The investor viewpoint

The aim of the investor is to lend the deposited money easy and speedy into those loans that match his selected criteria/risk appetite. Idle cash earns no interest and will impact yields achieved (aka cash drag).

For the retail investor none of the above mentioned mechanisms are ideal. The “fastest bidder wins” scenario means he would either have to sit in front of the computer most of the time or be lucky to be logged in just as new loans arrive. The queuing mechanisms are disliked as they can prove to be very slow in lending out the funds and can be perceived as nontransparent (see the lengthy and numerous forum discussions on the Zopa queuing mechanism). Underbidding in auctions does provide the chance to lend fast, but at the risk of setting the interest rate too low and this requires a strategy and can also be time consuming. 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

How I Selected My Preferred P2P Lending Marketplaces – Part II

This is part II of a guest post by British investor ‘Pete’. Read part I first.

The number P2P / P2B platforms in the UK has increased quite quickly over the past few years and I have currently settled on 3 further UK platforms that suit my needs and I strongly believe will be with us long term. In saying this I am not in possession of any privileged information and I am not by inference making any adverse comment about other platforms.

In alphabetical order

Ablrate

One of the new platforms (launched July 2014) that I have chosen to invest in and so far I have had a very positive experience. Specialising in secured Aircraft leasing and Plant and Machinery I have had the chance to diversify into a market that I knew little about before I started on my ‘due diligence’. The market may be new to me but there is a wealth of responsive experience behind Ablrate and coupled with a website update and promised increasing flow of loans I anticipate that my exposure with Ablrate will continue to grow. One interesting ‘innovation’ available on certain loans is ‘Instant Returns’. With long draw down times on some loans the potential for ‘dead money’ is large, instant returns circumvents this issue.

Assetz Capital

I have been investing with Assetz Capital since the second quarter of 2013 and have built up a diversified £ five digit portfolio of secured loans which continues to grow1. As with Ablrate there is a good, responsive and experienced team behind the web site, something that has become more than apparent when dealing with the occasional distressed loans that we must all expect when investing. Assetz Capital have big plans for expansion (they have already grown considerably since I started investing) and a relatively recent change to the way loan parts are bought has removed a very large percentage of the ‘dead money’ scenario that many of us early adopters experienced, not universally liked, I for one view it as a very positive move that has helped to push up my return on investment. I look forward to new opportunities this year.

1 I do not invest by choice in the provision fund protected ‘Green Energy Income Account’ preferring to take on the risk in return for a slightly higher returns.

Wellesley & Co

Again I was one of the early adopters and took advantage of some very attractive introductory rates that were offered. The loan and repayment terms suited my needs perfectly for tax planning purposes. Since then the rates have unsurprisingly been lowered and whilst Wellesley & Co have expanded rapidly and their range of investments on offer has expanded I find myself already invested in those areas with other platforms so I am running full term with my current investments whilst keeping an eye open on what is on offer.

Bondora

I also invest in one non UK platform, Bondora. This would probably be regarded as the ‘odd one out’ in my list of platforms. Far more volatile than the other platforms that I invest in Bondora has expanded rapidly since I started investing in the second quarter of 2013. I have experienced several changes to the platform, some which I have liked and several that I have not. I have experienced new markets being opened up and some eye watering rates of default in these new markets. That said and in spite of the treatment of defaults by the UK tax man and the strengthening of the Pound against the Euro (@16% since I started investing) my return after tax has remained positive. I spend more time on this relatively small percentage of my total investments to keep the returns positive than I do on any of the others. 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