Nevertheless I have been spending a bit carelessly recently (birthday week, what can a girl do?) and much of that cash went to unnecessary expenses like drinks, nights out, cabs home - you get the picture. Oh and there’s shoes. Gorgeous shoes. I blame Karen Millen

This is probably the point where I should say “It’s all going to change”. Well, I’ve tried that before and I’m not convinced it’s worked yet. I’m on it, wish me luck.

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Let’s start with a picture highlighting the key changes in our economy since the start of the credit crunch. The message is clear - petrol and food, two essential cost factors in nearly every household, have increased significantly in price over the last twelve months while the value of our homes has been eroded by approximately 4.4%.

With the rise in petrol prices come further cost increases in related fields such as energy or holidays (think airfares…). But did anyone ever stop to think that this has actually nothing to do with the credit crunch per se? Through an unfortunate coincidence we see a boom in commodities prices at the same time as our economy is already suffering from the aftermath of the subprime crisis - yet that doesn’t mean one caused the other.

Similarly, the food inflation we witnessed in the last couple of months originated in the commodities boom that saw prices in wheat and other agricultural produce reach heights of unprecedented nature. I agree that it has been rather extreme and that certain products seem to have been increasing at the rate of a penny a day, nevertheless that doesn’t automatically mean it’s a direct cause of the credit crisis.

More importantly I’m starting to wonder whether conditions like this couldn’t have been avoided if only people/businesses would have appropriately used hedging. Only today I read that South West Airlines still bought its fuel for $26 a barrel at a time when the market price had reached $80 (slightly old example, but it illustrates my point). How come the likes of Tesco’s, Sainsbury’s or Marks & Spencer’s didn’t come up with a clever idea like that? After all, hedging was introduced for companies to sell their risk in exchange for a small premium and stable input prices.

Before I rant even further, let’s move on to the last category in the summary picture: housing. If you have been reading this blog for longer than just a few days, you will know that I join forces with all the other people struggling to get their foot on the housing ladder and hence eagerly awaiting a double-digit drop in house prices. I totally emphasise with anyone who is worried about negative equity but if you are living in your house because it is your home then you have almost no reason to be overly worried. Hopefully nobody will be forcing you to sell any time soon, hence you can simply wait it out and I’m certain that we will see prices returning to their historic levels (with the only difference that hopefully a few more first-time buyers will have joined the ranks of home owners). And even if you are looking to sell and for whatever reason you cannot wait a few more months or a year until you do so, there are a one-hundred and one things you can do to enhance the value of your home.

In any case, my actual point was related to the graph below. After you got over the fact that house prices have officially been falling since April, have a closer look at the second graph with details of house prices over the last 10 years. Note that it charts the annual change in house prices - that means, as long as the graph runs above the 0 line, your home will have increased in value. Looking at your portfolio or pension account - how many investments can you quote that haven’t fallen in value once over the last 10 years? I doubt there will be many.

What I’m trying to say is that a house purchase has always been a good and solid investment with annual returns of anywhere up to nearly 30%. Now, for the first time in over 10 years we’ve seen a careful reversal of this trend and the world is in panic. As I said before, I totally emphasise with people worried about negative equity, especially as a house purchase is such a major investment, maybe the biggest one many of us will make in our life. However, that put aside, any investment bears the risk of losing as well as gaining in value. Why should property be different?

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Don’t get me wrong - I find this extremely exciting and interesting and I’m certainly going to write about it in the near future, but from a logical point of view it might make more sense to cover a few statistical concepts first.

This is why I will be changing the initial order of the Portfolio Returns series to the following:

1. Post : The Basics  (covered on June 28th)
Arithmetic mean (average loss, average gain), geometric mean, frequency distribution, maximum value, minimum value, positive # of years / months / weeks, negative # of years / months / weeks.

2. Post : Statistics (previously 3rd post)
Standard deviation, semi-variance (semi-deviation), downside variance and below-target probability.

3. Post : All About Interaction (previously 4th post)
Covariance: degree of variability of returns between two assets, correlation coefficient, units of annual return per unit of standard deviation, expected final value of $1.00 / £1.00.

4. Post : First lesson in Greek (previously 5th post)
Beta coefficient or an assets’ degree of responsiveness to market movements.

5. Post : Advanced Greek (previously 6th post)
Alpha or superior returns.

6. Post : More Jargon (previously 7th post)
Sharpe ratio.

7. Post : Risk (previously 2nd post)
VAR: value at risk, M-squared.

I think this order will prepare us nicely for the last topic which also turns out to be the most challenging and complex. The post on Statistics will follow shortly, stay tuned!

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While the code is by no means perfect (yet) it does the job nicely and I’m nevertheless pleased with the result. As long as it gets called properly, it converts your negative percentages into a value equivalent to (100 - x) / 100 where x is your percentage. For positive values, it does the conversion according to (100 + x) / 100. These conversions means that a decrease of 10% will be expressed as 0.9 and an increase of 10% as 1.1 which subsequently allows you to compute the geometric mean (which is exactly what it does).

Below is the VBA code you will need to utilise this functionality. Feel free to use it as you please but don’t worry if you have no clue how exactly to do that. I have included a step-by-step description of how to install and call it from your Excel worksheet underneath the code.

Function geometric(data As Variant)
     Dim vaData As Variant
     Dim rnData As Range
     Dim i As Long
     Dim j As Long
     Dim iblank As Long
     Dim jblank As Long
     Dim bblank As Boolean
     Set rnData = data
     vaData = rnData.Value
     temp = 1
     n = 0
     For j = 1 To UBound(vaData, 2)
          For i = 1 To UBound(vaData, 1)
               If vaData(i, j) <> Empty Then
                    vaData(i, j) = 1 + vaData(i, j)
                    ’ compute product for non-empty data cells
                    temp = temp * vaData(i, j)
                    ’ count number of items in list
                    n = n + 1
               End If
          Next i
     Next j
geometric = (temp ^ (1 / n)) - 1
End Function

Follow the steps below to make the above function available in your Excel worksheet:

1. In Excel, open the file you want to use the function in. Note: at the moment the function will only be available on a per-file basis. That means, if you’re planning to use it in more than one file, you will need to repeat this procedure for every single file. I’m working on coding a proper add-on that you simply install into Excel and hence make available to any new or existing file. I told you it was work in progress…
2. In the menu bar, next to the entry “File”, right-click on the Excel icon and select “View Code”
3. The Microsoft Visual Basic Editor opens and you stare at a blank document
4. In the menu bar, click on “Insert” followed by “Module”
5. Copy and paste the above code into that “new” blank space and hit “Save”
6. After you have saved the changes, click on “File” and select the entry “Close and return to Microsoft Excel”. This brings you back to where you started, and your function is ready to use.

Here is how you use the function:

1. Open the file in which you have installed the above function
2. Pick a cell that you want the result to contain and type “=geometric(”
3. Note the opening bracket !
4. With your mouse click and hold until you have highlighted the cell range that contains the numbers you want the geometric mean of. Alternatively, type the cell range yourself as A1:D4 where A1 should be replaced by the coordinates of your first cell and D4 should be replaced by the last cell’s identifier. At the moment, the geometric function only works with a cell range, not individual cells or numbers. I’m working on it…
5. Close the bracket by typing “)”
6. Hit ENTER to see the geometric mean of your highlighted cells

If you format the cell in question in such a way to convert your result back into a percentage (just right-click, choose Format - Number Format - Percentage), the result should make sense in the context of portfolio returns.

Note that the code above has been tested and verified in Excel 2002 and should work with any other Excel version(s) since. The only concern I have regards the compatibility with - you might guess it - Excel 2007. If anyone manages to run it under the new Office version, please let me know!

As usual, if you have questions or concerns, find a bug or just need a hand to get it to work, just give me a shout or leave a comment! I hope you will find it as useful as I did.

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I am now worse off than three months ago.

Despite three months’ salary and pension contributions my net worth is below the March level. I’m absolutely shocked and even a little unhappy since I enjoy seeing the progress bar increase (as opposed to decrease by nearly 2% this month)!

At least I have a pretty good idea what led to this fiasco: an orange coat, a digital piano and a trip to the Caribbean. In hindsight, would I commit these “sins” again? Maybe, yes and yes. The coat, I fully admit, was an impulse buy and a very expensive one at that. Yes I needed a coat, but no it didn’t have to be that one. On the other hand, I still love it as much as I did the minute I walked out of the shop with it and have so far not seen a single person (other than me) wearing it. In a city like London that’s pretty impressive

My piano purchase was an even larger expense than the coat and by no means an impulse buy. In fact, since leaving home I had told my parents I would take my piano with me as soon as I had finished University and had a permanent place to live. Unfortunately it turned out to be prohibitively expensive to ship an item like a piano from Germany to the UK. Hence I decided to get a digital piano in the meantime so that I could start playing (and practising!) again after having not touched a single key during my undergrad studies. The model I ended up getting was only half as much as the Yamaha Clavinova I had set my heart on previously and I got it for £100 less during an end-of-season sale. Regrets? None.

And finally, my holidays. One week on Grenada, a tiny little Caribbean island just north of Trinidad and Tobago. Flights and hotel together came to just over £400 and despite regular dinners out, numerous activities on the island and a day at a local spa, the holiday was definitely on the cheap side. Considering it was the Caribbean anyway Again, I don’t regret this trip at all. On the contrary, I would have happily stayed and travelled much further and for much longer than I was able to. The numerous once-in-a-lifetime memories made it worth every penny.

Regardless of whether I consider this money well spent, it’s time to stop. I am basically exactly where I was three months ago, so for the next quarter I will need to curb my spending in order to get my growth and progress back on track. Given that the house market is still in a pretty bad place, I probably won’t need my deposit money for at least another six months. But by then I definitely will need to have accumulated enough to make this (temporary!) backdrop in net worth unnoticeable.

If you have kept a close eye on my progress page you will notice that I redistributed some of my money between the various goals. I depleted my emergency fund in favour of allocating more money to the house deposit and I also shifted more money into the account intended to cover the outstanding bill I have with my parents. The latter is now fully funded, while my deposit has grown to 70% of my initial goal of £20,000.

Given that we’re halfway through the year, it’s once again time to have a look at how well my budget is working out. The good news is that my interest income reached 73% of my goal for 2008 by the end of June, indicating that my savings are working hard for me while I sleep This is even better news when you consider that this goal was revised upwards twice already this year: from £200 to £300 to its current value of £600. I decided to change it once more to £750, which is definitely fairly ambitious given that I received the interest from a fixed one-year term monthly saver account when it matured in June (hence half of that interest was technically already earned last year).

In the interest of brevity, I will only list the remaining changes to the budget (since I don’t deem them noteworthy enough to dedicate an entire paragraph to each):

• Utilities: new category with £350 since it took our utility provider more than 7 months to get our bill sorted and we hadn’t paid anything until they finally managed to get organised
• Landline: slightly up from £90 to £100 based on usage
• Mobile: down from £220 to £180 based on usage
• Groceries: slightly up from £1,400 to £1,500 - inflation is kicking in
• Dining out: down from £1,000 to £900 to accommodate the increase in food prices
• Clothes: up from £1,000 to £1,200 with the best intentions to undercut it
• Gifts: up from £750 to £1,000 (I’m too generous for my own good)
• Hairdresser / Manicure: up from £500 to £600
• Drinks: pooled with the Category “Clubbing” hence total down from £250 to £200
• Cinema: up from £50 to £100
• Health Insurance supplement: up from £60 to £65 - I blame the bad £/€ exchange rate
• Gym: Down from £360 to £150 because I cancelled my membership
• Contact lenses: Up from £200 to £500 as an eye infection forced me to change my prescription to the more expensive daily disposable lenses
• Life Insurance: Up from £900 to £950 due to £/€ exchange rate
• Broadband: Down from £100 to £90 as we are on a fixed subscription
• Charity: Up from £120 to £150
• Planes: Down from £900 to £800 as my one major holiday is already accounted for
• Holiday Accommodation: Up from £300 to £500 due to major naivety on my part (initially)

That’s all from me for now. Progress page and “Best Of” section have been updated as usual.

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Based on my outline, this is what I’m looking to cover in this post:

• arithmetic mean
• geometric mean
• frequency distribution / histograms
• maximum and minimum value
• positive / negative number of weeks / months / years

Arithmetic Mean

The arithmetic mean is more commonly referred to as the average (or just mean) and is calculated by summing all numbers of a list and subsequently dividing the result by the number of items in the list. Finding the average of three numbers A, B and C would involve forming the sum A + B + C and dividing by 3. It answers the question “If all quantities had the same value, what would that value have to be in order to achieve the same total?” and the calculation is relevant whenever the quantities concerned add together to produce a total. For instance, if you buy milk, bread and butter you will be expected to pay a total sum to cover the price of these three items and calculating the average would tell you what price a different product X would have to have so that you could buy three instances of X and end up paying the same amount.

The trouble with the simple average is that is greatly influenced by outliers, i.e. extreme values at either end of the spectrum. If you calculate the average of ten 5s and 1000 you will end up with (10 * 5 + 1000) / 11 = 95.45 which is unproportionately large and could give the impression (assuming the individual data points are not known) that the data is concentrated around values just short of 100. This problem is a simple example of skewed distributions where the mean of a data series doesn’t coincide with the median (the “middle value” if you were to list them all).

Finally, the arithmetic mean is not suitable for calculating stock performance since you’re looking for the product of the yearly performance, not the sum of those values. Consider an investment that loses 10% of its value in the first year and gains 30% in the second year. Now the annualised performance since you started investing is not [(-10%) + 30%]/2 = 10% but in fact 8.2% since the 30% increase only affects the 90% of your investment that’s left after the first year. To correctly calculate annualised performance rates like this we will have to use the geometric mean.

Excel Function: AVERAGE(number1, [number2], …)

Geometric Mean

The geometric mean is a value that gives an indication of the central tendency of a set of numbers (i.e. of a distribution) regardless of whether this distribution is normal or skewed. In other words, it shows the “typical value” of a list of values. Note that this is different from the most common value which is a mathematical value called “mode”. It answers the question “If all quantities had the same value, what would that value have to be in order to achieve the same product?” and hence is relevant whenever quantities multiply together to produce a product. The geometric mean is always less than or equal to the arithmetic mean.

To calculate the geometric mean you need to multiply all the values in your set and subsequently find a scientific calculator to take the nth root of the product where n is the number of numbers you previously multiplied. In the simplest case when calculating the geometric mean of two values A and B you would need to take the square root of A*B. Unfortunately, this also means that the geometric mean in its purest form can only be applied to positive values as (in conventional mathematics and without exploring imaginary values) you cannot take the root of a negative number.

While we all hope that our investments are going to increase year after year, we can’t simply assume this and happily apply the geometric mean without a little work first. To take negative growth into account, you will need to look at your percentage values from a slightly different angle. Let’s go back to our original example of a return of -10% in the first year and 30% in the second year. After the first year, only 90% of your original investment will remain - that is, your portfolio will stand at 0.9 instead of 1 (= 100%). In the second year, you’re more lucky and see a 30% growth of that remainder. If you would have had a fresh portfolio (i.e 100% = 1) you’d now be left with 130% of your money or 1.30. Since you only had 90% remaining we need to multiply 0.9 by 1.30 to get 1.17. This means that in relative terms to the amount you started with, your investment is worth 17% more after 2 years. To now calculate the annualised growth rate of your investment we need to take the square root of 1.17 which yields 1.0816 or a growth of 8.16% per annum.

The Excel function given below does not convert your percentages accordingly so the restriction with regards to positive values still applies. So far, I haven’t been able to find an in-built function that does the work for you which I find rather frustrating. It is extremely annoying to represent a 10% drop in my portfolio as 0.90 and if I don’t come across a more workable solution soon, I will probably end up writing a function myself If you know something that I don’t please let me know before I spent hours writing an Excel Add-In from scratch.

Excel Function: GEOMEAN(number1, [number2], …)

Frequency Distribution

If you’re a visual thinker like myself, you will appreciate that I often find it more helpful to look at a graph instead of raw numbers. An obvious choice in the case of portfolio returns is a histogram which is based on a frequency distribution. At this stage, I won’t dwell on this topic for long as I haven’t got sufficient data yet to experiment with histograms and the associated frequency distributions yet. As soon as that is the case, I will almost certainly give you a little more insight into this topic.

For the moment, imagine that you have two buckets in which you need to throw little cards that represent your portfolio performance - whether you’re looking at weekly or monthly returns or any other values is fairly irrelevant as long as you have enough data. Your task now is to separate the positive returns from the negative ones by throwing them into their respective buckets. Once you’re done with that, you can draw a simple bar graph with the number of items per basket representing the height of an individual bar (assuming one unit width per bar). Voila - there is your first histogram

If you imagine that you could have as many buckets as you like, then you only need to come up with a criteria to decide which bucket contains what data to create whatever histogram you like. You could distinguish between negative and positive values, determine 5% steps or pick any other classification you might find useful. The only rule is that categories must not overlap - i.e. there is always one unique bucket for every single piece of data and at no point would any value (whether in your sample or not) match two or more buckets.

The interesting thing about histograms is that it’s not technically the height of a bar that determines the value it represents (unlike in a simple bar chart where only the height matters) but it’s the area that is covered by it. Hence you could draw a bar that’s one inch wide and three inches high and that would represent the same value as a bar that’s three inches wide and one inch high. For this reason the bars of a histogram have to be adjacent with no gaps in between.

Because you are free to choose however many buckets you want to use when constructing a histogram (the official term for my made-up bucket is “bin”) much research has been done to determine the optimal number of bins as too small a number might hide valuable insights while too many bins render the diagram useless. The level of granularity is crucial. One formula that has been put forward is k = [(max(x) - min(x)) / bin width h] where k represents the optimal number of bins and max(x) and min(x) denote the maximum and minimum values of your data respectively. Whatever value you calculate, you should round up to the nearest full value and use this to create your categories.

Excel 2007 includes an Add-In that creates a histogram from your data if presented in the correct way (data values in one column, bin limits in second column) and hence extends the basic FREQUENCY function that simply returned an array of values. While I have Excel 2007, the data arrangement it requires to execute properly doesn’t work very well if you’re planning to have more than just a histogram graph in your worksheet. Further, from the previews I have seen of the functionality, the histogram looks wrong as there are in fact gaps between the bars (which violates the rules given above). Hence, once more I find myself wondering whether I should invest the time and effort to create something a little more elegant myself. Stay tuned

Maximum and Minimum Value

Since I assume you graduated from primary school long ago, I don’t really need to explain the meaning of maximum and minimum value to you. I simply include it for completeness’s sake and to create a central place for all important Excel functions

Excel Function: MAX(number1, [number2], …) and MIN(number1, [number2], …)

Positive and Negative Number of Weeks

This is an interesting little insight I came across in the book I was reading about Asset Allocation not long ago. While it doesn’t tell you that much per se, it gives you valuable perspective and a sense of patience when you’re tempted to give up your long-term investing for short-term speculation. If you can see that your investment is up most weeks / months / years and only down in value occassionally, you might find it easier to sit out the rough patch - rest assured that other (better) times will come.

To avoid having to count these items manually every time you update the performance, you can use the Excel function COUNTIF that looks at a list of values and only counts the instances that fit a criteria given as part of the formula. By using the criteria “>0″ you will end up with a count of all positive values, while “<0″ returns a total of negative occurences.

Excel Function: COUNTIF(range, criteria)

I hope you enjoyed this first introduction into maths and basic statistics as I very much look forward to researching the upcoming posts! If you have any questions or concerns, just leave a comment and either myself or another reader will surely be able to help.

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Anyway, as I promised in my last post (yes it’s been a while) I have had an idea about a series of posts that might be of interest to you, especially if you really really like numbers (like I do). While reallocating my pension across several funds, I kept wondering how to best measure how well (or badly) my choices were performing compared to the market in general. For a start I have split money fairly evenly across index funds and actively managed funds in a way that will hopefully allow me to compare apples with apples - i.e. simply speaking, for each asset class I have picked an index fund as well as an actively managed fund to compare their performance against each other.

Somehow, that didn’t seem good enough and so I did some research on what else I could do. As I said, I like Maths and numbers because they have an inherent logic and beauty… great, now I sound like a total geek. Or idiot. Your choice

Hence, here’s my line-up of posts that will hopefully appear throughout June and July (bear with me as I’m also going on holiday in three days). I’m not going to explain much (or anything) at this stage, because otherwise there’s not much point in writing separate posts about each. I simply hope you’ll be excited to read what’s coming up and all the things you might be able to learn soon(ish).

1. Post : The Basics
- arithmetic mean (average loss, average gain)
- geometric mean
- frequency distribution
- maximum value
- minimum value
- positive # of years / months / weeks
- negative # of years / months / weeks

2. Post : Risk
- VAR: value at risk
- M-squared

3. Post : Statistics
- standard deviation
- semi-variance (semi-deviation)
- downside variance
- below-target probability

4. Post : All About Interaction
- covariance: degree of variability of returns between two assets
- correlation coefficient
- units of annual return per unit of std. dev.
- expected final value of $1.00 / £1.00

5. Post : First lesson in Greek
- beta coefficient or an assets’ degree of responsiveness to market movements

6. Post : Advanced Greek
- alpha or superior returns

7. Post : More Jargon
- sharpe ratio

Some of these will be rather obvious, or aren’t even really mathematical but I thought they were nevertheless useful when analysing your portfolio. I’m going to try my best to explain even the more complex concepts in a straight-forward way that will make you (and me) understand why exactly a particular formula might in fact be useful.

Knowing myself, all of the above will eventually end up in a big spreadsheet, which I naturally will also make available to you so you don’t have to play with Excel for hours to get it all onto one page (surprisingly enough not everyone finds that sort of stuff fascinating).

I’m getting rather excited while writing this, so hopefully my energy won’t be wasted and you’ll enjoy it as well! Let me know if you think I’ve omitted anything absolutely obvious that shouldn’t be missing from a series of posts on portfolio calculations.

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Overall the May results aren’t too bad… an increase of 3.67% in net worth translated into a 1.5% step towards my next net worth goal. The stock market didn’t do too badly and hence both my pension and life insurance were up. Those developments, however, are masking the fact that I have dipped into my deposit savings to fund my piano purchase.

The good news is that it didn’t have too bad an effect on the “bottom line” but on the other hand it’s forcing me to live paycheck to paycheck at the minute. The reason is that I haven’t fully funded it using savings, but paid for it using my credit card (for those reward points!) - hence most of my salary that isn’t already allocated to fixed costs (rent, utilities etc) goes towards the credit card payment.

For the last two months it has always been enough (just!) to cover the total so I wasn’t forced to carry a balance (yet?). My aim now is to drastically reduce my spending to get back in a position where I have money left over from my previous paycheck to go towards the bill.

I guess the fact that I’ve just paid for my summer holiday won’t necessarily help with next month’s bill either… *blush* But hey, blame the British spring and the miserable weather it has brought with it ! Who doesn’t need a little bit of sunshine to compensate??

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Have you ever thought what to do with the old one? At some point the option of “I can give it to my sister/Mum/Dad/brother-in-law (delete as appropriate)” doesn’t really work anymore and the old handset will soon be forgotten. Why not get some free cash for recycling your phone instead?

The website envirofone.com will help you with exactly that. Head over there now to see what your old phone could still be worth! It’s simple, fast and absolutely no hassle at all. When you register with them, you will automatically receive a “trade pack” consisting of a delivery card and a jiffy bag for you to send your phone in. Once you agree to trade your phone for either cash or an Argos voucher (whose value will be slightly higher than the cash value you’d receive otherwise), all there is left for you to do is to put your (old!) phone into the envelope and drop it in the nearest letter box.

The envirofone website has a “My Account” section that lets you monitor the status of your trades. Once the trade is agreed, it will show up under the “View Trades” tab where the status will say “Awaiting Receipt”. You agree to send the phone within a time frame of 10 days at most and once you have done so the trade status will soon change to “Received” indicating that the envirofone team has received your phone and is currently testing whether it is in full working order.

The quote you got when the trade was initially submitted assumes the phone is functioning properly, but you’re still encouraged to send your phone even if it is not as you might receive up to 90% of the originally quoted value. If a fault is detected by the envirofone people, you will be contacted with a new (lower) quote which you can choose to accept or refuse. If you refuse to trade for the specified amount, the phone will be returned to you. If you decide to accept the lower offer (what else are you going to do with a faulty phone??), you will receive your cheque or Argos voucher in the post within 7 days.

My old phone has been received as of this morning, so I’m waiting for my voucher as we speak. I’ll keep you posted on how long it actually takes, but their overall process seems pretty streamlined and I expect only the best So if you possess one of the 80 million mobile phones that have been forgotten about in the UK, then I think it’s time for you to act…

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After I had been watching the Zopa site grow for quite some time, I decided that it was time to join the fun and have a go at it myself. With the house purchase one of my major short-term goals at the minute, I didn’t want to tie up a lot of capital for a long time, hence the amounts I’m allowing myself to use at Zopa are fairly small (£ 25 in total at the minute, increasing by about £ 10 a month). Nevertheless I figured I had seen enough to share my experience with the site so far…

Signing up: Ideally this should have been a fairly easy process, especially since I was intending to become a lender, not borrower. However, due to money laundering regulations Zopa is still required to verify your identity and address. Since we’ve moved to our current flat only a few months ago, this identity check could not be carried out online and I had to submit the usual proof of identity and address documentation. This is nothing unique to Zopa and I’ve encountered the same issue several times before with banks, credit card and loan companies. In the end it took only 2 days for them to process my documents and I could sign up successfully! Overall impression: good.

Customer Service: Apart from the registering process I’ve had several other encounters with the Customer Service department relating simple queries as well as a functionality problem at one point. The usual way of contacting them is by sending an email and the response time is always within the promised 1 - 2 business days. All requests were dealt with swiftly and the team is very helpful and always friendly. Overall impression: excellent.

Transferring money: There are three major ways you can transfer money into your Zopa account: Debit card by phone (for instant transferral) or online (for transferral within the same business day), standing order (similar to the way you’d set up the standing order for a savings account) or by bank transfer (longest of all methods as it takes about 3 days to reach your account). With either option you will receive a confirmation email when your funds reach the account and are ready to be used. To transfer money out, you will need to have your bank account confirmed with Zopa. To do that, you simply need to transfer £1 by bank transfer once for them to be able to verify the account belongs to you. At this stage, you cannot transfer the money in your Zopa account to anybody else but yourself. Overall impression: very good.

Lending in Zopa Markets: With Zopa you have two major lending options - Markets
or Listings. If you allocate your money to the Markets section, most of the work matching your lending offer with a borrower request will be done behind the scenes for you. You merely see your money moving between the stages of being offered (currently available), processing (matched to a borrower, loan verification in progress), lent out and late payments (hopefully very few in the latter category). To determine your rate of return you can either give Zopa your desired rate of return and the longest amount of time you’re willing to tie up your capital or you can fine tune your offer by indicating an exact rate of return per market segment. These segments are determined by the borrowers credit rating and the duration of the loan and range from A* for 12 months to C for 60 months. Zopa is helping you to offer realistic rates by quoting you a range of rates that other lenders are offering.

My experience with the Markets section is thoroughly positive. I’ve got all my lending offers at the higher end of the market range and yet I find that my available money is usually processing within a time span of about 2 days. I’ve only had one slight hiccup so far that was explained to me and hence resolved by the Customer Service team within 2 days (my Zopa account contained a little less than the shown £ 10 due to the Zopa fees being earmarked but not deducted every month). Overall impression: good

Lending on Zopa Listings: Zopa Listings are essentially an eBay-like reverse auction system where borrowers advertise their borrowing needs together with an explanation of their finances and lenders can quote how much they’d be willing to lend to this one borrower and at which rate. All quotes get aggregated throughout the duration of the listing. When the borrower’s desired loan amount has been reached (i.e. funding is at 100%), lenders can continue to quote and hence will start outbidding each other with lower rates. Eventually only the minimum number of lenders with the lowest rates will be kept in the listing and hence will be able to lend their money to the borrower. The advantage of the Listings is that you might be able to get a higher rate than you’ve quoted in the Markets section by bidding at the last minute - similarly to how you can get a bargain at eBay through sniping (or old-fashioned pressing of refresh and bidding on the last second).

I’ve only (actively) participated in one Listing so far which ended at 4.20am in the morning. I waited to submit my quote until half past midnight and went to bed hoping lots of people would have already done the same. By the time I submitted my quote, I was about 50 offers (out of 130) away from being excluded so I felt pretty safe and happy as I had a good impression of the borrower. Unfortunately I was out-bid just 15 minutes before the end of the Listing… In any case, the entire process was certainly exciting and I’m intending to look out for other Listings as soon as I fund my Zopa account with more money (waiting for the paycheck, anyone?). Overall impression: excellent

Total verdict: For me, Zopa turned out to be everything I expected and wanted it to be. Obviously I can’t really comment on the bad loan rate at this stage, but then I don’t think it is a major part of evaluating Zopa itself. Every lender can adjust the risk he or she is willing to take by only offering money in certain (high-quality) markets or reducing the term of the loan they’re happy to accept. I believe that people might be less likely to default on their loans when they know that they owe their money to individual people, not big face-less institutions - if you had the choice, whom would you pay back first? Your neighbour or the bank? I might be wrong, but this is what I would like to believe and Zopa’s low bad-loan quota might prove just that.

If you’re intrigued by the concept and would like to explore alternative ways of making money / earning a return on investments, I urge you to give this a go. Sign up here to get £30 when you start lending (minimum amount applies) and become part of the Zopa Community. Trust me, it’s fun!

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However, the “major purchase” is now sitting in my study in the form of a digital piano - the Yamaha P140S and I’m still convinced that (a) it was worth it and (b) I got a good price for it as it was reduced by more than £100 from its RRP and hence cheaper than any other quote I could find (including Internet shops…). I will probably have to use some of my savings in order to pay off the credit card I used for the purchase, but I’m hoping to reduce that to a minimum and instead fund it out of my regular salary by simply cutting back on other excess.

A few other things contributed to my negative net worth result this month: most notably a trip “up North” to Harrogate for my friend’s wedding which set me back about £300 in total (2 return tickets plus hotel). It was obviously more than worth it and I wouldn’t have wanted to miss it regardless of the expenses attached.

The only good thing to report is that my miscellaneous income is still rising, mainly due to a steady increase in interest income. My expectation for this year has been raised from £250 in January to about £600. Barclays have finally managed to open my cash ISA for this tax year. Luckily it’s only taken them about a month (*sarcasm*). But when I enquired about the progress of my application I was told (without having to ask) that interest would be credited to my account from the day the application was made - despite the cheque only being cashed 4 weeks later! This effectively means that I will have earned double interest on the initial amount I deposited…

That’s all from me for this month. I’m hoping to have a much better net worth result for May, even though I’m not 100% confident I’ll be able to achieve this as I won’t have much opportunity to increase my savings as a result of having to put all my income towards April’s purchases… Keep your fingers crossed!

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