Investor Column

How I Selected My Preferred P2P Lending Marketplaces – Part I

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

Perhaps an introduction is the best way of starting this blog post since it should explain my reasons and approach to Peer to Peer (P2P) and Peer to Business (P2B) lending.

I am a UK based independent professional engineer. An engineer in my discipline requires a love of detail, data and spreadsheets and being independent it is required that I run my own company so I understand basic accounting and number/data manipulation.

So why do I invest in P2P and P2B? In the past I have had Pension funds raided, Investment funds loosing capital due to stock market losses and fees, a mortgage endowment policy returning 1.9% over 25 years when a simple cash investment returned +9%, shares devalued by the UK government who then bought them out at the devalued rate … a long list of ‘professionally’ managed schemes that lost my money. With P2P and P2B I am in control, I either sink or swim based on my decisions.

I started lending at the start of 2012 with Zopa and to a lesser degree with Ratesetter but not before I had read as much as I could find regarding P2P and the various business models. Using on-line resources research into Company and Directors ‘histories’ followed, a process I continue to use before I start investing with a new platform. Risk and Taxation were the next topics I looked into.

Whilst projected default rates were available on  Zopa I took a pessimistic view and anticipated a higher rate of loss when I put together my first spreadsheet to log my transactions and real rate of return (I mainly use Excel with the XIRR function). My aim with Zopa was to diversify as quickly as possible so I quickly put together a large number of small loans whilst ensuring that I didn’t have ‘dead money’ waiting to be lent out. This strategy worked and my losses have so far turned out to be below the Zopa projected level. In recent years Zopa have changed the way monies are lent out and introduced a provision fund to cover bad debts (Ratesetter have always had a fund) and at the same time investors rates dropped (Zopa dictated the rate at which money was lent) so I decided with regret that Zopa was no longer for me and started to withdraw monies as they became available, a process that will continue for some years since I am still happy with the return from my remaining loans.

In the meantime my Ratesetter account quietly built up (the power of compounding interest) and I had started investing in Funding Circle (Sept 2012). I quickly found out that due diligence was required when investing in listed loans (I do not like automatic bidders, I will always manually invest/re-invest) and whilst time consuming it gives some reassurance that you are not investing blind. Whilst the returns I received (and still receive) from Funding Circle are above those I receive from Zopa and Ratesetter I have found the time taken checking companies can be disproportionate to the return if small loans are made. In spite of due diligence the defaults in my experience are higher and coupled with the current UK taxation system for individuals, defaults can hit your rate of return in a disproportionate way.*

It is for these reasons that I have in the last year started withdrawing cash from Funding Circle in the same manner I am taking with Zopa. In the meantime my Ratesetter account continued to build. Continue reading →

P2P Lending Experiences of a British Expat Living in the Eurozone

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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 →

Experiences with Setting up a Company in Estonia for the Purpose of Investing in Bondora P2P Lending

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This is an interview with Austrian investor Bernd R. about the experiences he made when he created a company in Estonia to benefit from the advantages that investing as business on Bondora brings. Note that these are his personal experiences and should not be construed to be investment or tax advice. The circumstances for other investors will be different and investors should seek tax advice by qualified and certified tax advisors.

How did you get the idea to setup a company in Estonia for your Bondora investments?

I read a lot about Estonia – its business friendly environment, simple tax system, huge start-up culture and the efforts to make administration processes available online.

Setting up an investment vehicle in Estonia would allow me to combine an uncomplicated taxation system with the advantages of a legal entity and all that at low costs.

What are the main advantages when investing as a company rather than an individual on Bondora?

There are several advantages.

  • The corporate tax rate in Estonia is 0%. Only dividends are taxed with 20%. This means that your retained profits will generate additional profit. Double taxation agreements with your home country protect you from being taxed twice and usually limit the total taxation to the tax rate for dividends of your country of residence.
  • In Austria interest income of private loans is treated in a different way than regular interest income (e.g. from a bank saving account). Interest income of classic bank saving product are taxed with a 25% flat rate, “private loans” fall under progressive taxation. On-top income of a full-time employee is easily taxed with 43% till 50%. So depending on the individual situation the tax savings can be up to 25%.
  • Provisions for bad debts or write-offs reduce the taxation basis.
  • Profits and Losses of different activities can be consolidated, e.g. losses generated with stock trading can be consolidated with your Bondora interest earnings and reduce the taxation basis.

How does the tax situation improve in your specific case?

I reduced the tax rate by 25% compared to my individual tax rate.

In addition I will generate more profit in absolute numbers due to untaxed retained earnings invested and at the same time reduce the taxation basis with bad debt provisions. The impact of these 2 factors depend on the future default- and interest rate of my Bondora portfolio.

To setup the Estonian OÜ you used a company formation service. Did that require you to travel to Estonia?

No, it was not necessary. A power of attorney does the job. Continue reading →

Decision Trees – Using The Available Data to Identify Lending Opportunities on Bondora – Part 3

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This is part 3 of a guest post by British Bondora investor ‘ParisinGOC’.

Read part 1 and part 2 first.

Investments Decisions using the Tree(s)

Using the Data

Using the output is as simple as looking at the visualisation to see how the Decision Tree splits down from the Root Node and comparing this with a Bondora loan application that I see as a potential target for investment. (Illustration 2 and Illustration 3) At the end of the set of branches that I follow dependant upon the data in the loan application, I end up at a “leaf node”- the end of the tree. (Illustration 4)  This node simply states how many previous loans match the one I am looking at, showing how many of the previous loans defaulted and how many have not.

I treat the Decision Tree as the first step in choosing whether to invest. If the performance record of previous loans like the one I am now considering suggest a default rate of 5% or less, I look further into the loan application. Continue reading →

Decision Trees – Using The Available Data to Identify Lending Opportunities on Bondora – Part 2

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This is part 2 of a guest post by British Bondora investor ‘ParisinGOC’.

Read part 1 first.

Data Mining the Bondora data.

The initial process.

To help understand the specific data cleansing that the Bondora Data Set needed, I first made use of the RapidMiner metadata view – a summary of all the attributes presented to the software – showing Attribute name, type, statistics (dependant on type, includes the least occurring and most occurring values, the modal value and the average value), Range (min, max, quantity of each value for polynominal and text attributes) and, most critically, “Missings” and “Role”.

“Role” is the name given by RapidMiner to the special attributes that are needed to allow certain operations. In my case, the Decision Tree module needed to know which Attribute was the “Target”, that is the attribute that is the focus of the analysis and to which the Decision Tree has to relate the other attributes in its processing.  My “Target” was the “Default” attribute – a “Binominal” (called as such by RapidMiner and meaning an attribute with just 2 values) attribute – 1 if the loan had defaulted, 0 if not.

“Missings” is easy – this is the number of times this attribute has no valid value. For example, my import of the raw Bondora input data has 150 attributes.  Only half of these attributes have no missing values.  The remainder have between 13 and 19132 rows with missing values from a data set of 20767 rows.

To know whether these “missings” would impact my analysis, I needed to get to know the data in more detail.

I knew that Bondora had started to offer loans in Finland in summer 2013 with Spain following in October of that year and Slovakia in the first half of 2014.

I therefore decided not to bother with any loan issued prior to 2013. Continue reading →

Decision Trees – Using The Available Data to Identify Lending Opportunities on Bondora – Part 1

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This is a guest post by British Bondora investor ‘ParisinGOC’.

Introduction

Financial institutions across the world have many ways of assessing whether a loan is worth making.  A simple search on the web reveals that many use Data Mining.  More specifically, “Decision Trees” are a particular tool within Data Mining that has been analysed and I quickly found at least 2 papers (Mining Interesting Rules in Bank Loans Data and Assessing Loan Risks: A Data Mining Case Study) amongst many pointing in this direction.

Having had some experience of Data Mining in a financial environment, I believed I could use these same techniques in my own P2P lending which, after over 12 months activity, I felt could be improved.

In this document, I explore the use of the freely available Data Mining Software “RapidMiner” and its Decision Tree capabilities when applied to the data available to investors from Bondora, a peer-to-peer (P2P) lending site.

Bondora

Bondora is a P2P lending site based in Estonia that “unites investors and borrowers from all corners of the world”, allowing investors to invest funds to satisfy advertised borrowing needs.

Fundamentally, Bondora also provides comprehensive data to investors, allowing detailed data downloads of the individual loans held by the investor, as well as data on every application made to Bondora (originally known as Isepankur) since the first application on 21st February, 2009.

It is the complete Bondora data set that I have used as the raw data for analysis as it is the best data available to find out which potential borrowers are the right match to the potential lenders.  Only if enough lenders feel that a loan application is worth investing in will the loan be fulfilled.  Self-selection is taking place in both elements of the loan fulfilment and this data is the result of that interaction.

Also shown in this data are some elements of loan performance post-drawdown.  Crucially, it shows those loans that subsequently defaulted (failed to make any payments for a period in excess of 60 days).  Although Bondora will chase the debt on behalf of the investor and have a track record of some success, there is no guarantee that the investment, or any part of it, will be returned.

Decision Trees

www.investopedia.com/terms/d/decision-tree.asp states: A schematic tree-shaped diagram used to determine a course of action or show a statistical probability.

In this case, I am using the data provided by Bondora on all its previous applications to reveal how the resulting loans that share similar characteristics have performed.

Specifically, I am using this data to show the percentage of those previous loans that have defaulted and using this to indicate how a similar, new application may perform should the application succeed in attracting enough investors.

In other words, I am using past performance data to show how future investments may perform – I feel sure I have seen this phrase somewhere before! Continue reading →