The key to starting to take control of your finances is to first understand your outgoings.
Before you can work out how much you can comfortably invest, you need to understand your monthly income versus your monthly outgoings.
In addition to this, by understanding your monthly expenses you can get a good picture of areas in which you may be able to cut back your spending and realize some monthly savings.
For the past year I have tracked my spending with Wally a spending tracking app for iOS.
By pulling the last 12 months of data we are able to see a very nuanced breakdown of all of spending by category, date, and amount.
As we can see in the table to the left here, the majority of spending was spent on housing costs, with the vast majority of this being rent.
After housing costs are taken in to account, the next biggest tranche of spending was on food & drink, with a substantial budget north of six and a half thousand dollars a year!
This data can perhaps better be visualized in the chart here.
Here we can see quite clearly the large chunk of annual spending that is allocated to household costs, such as rent and utilities.
The combined spending under the Food & Drink category still comes in at less than one quarter of all spending, and less than one third of the amount spent on combined housing costs.
If we average the above spending out over 12 months, we can get a rough monthly average of spending per category.
As we can see here, monthly cost of living is just over three thousand dollars per month.
These figures are not entirely accurate as some larger expenses occur in single months and are thus not amortized over the year, for example the $195 monthly cost of transport would actually be less on a per month basis, but far higher in the months in which I took flights to Europe or Asia.
If we take a look at the average deviation, we can see that each months actual spending is subject to an average deviation of $549, or 17.95% which gives an average monthly spending range of almost eleven hundred dollars, between $2,507 and $3,605.
If we drill down into the subcategories of spending we can see a much granular break down of the individual areas.
One thing that immediately stands out is the spending on rent. The rent cost totals just over eighteen thousand dollars.
In addition to the flights category, we can also see some other categories of spending that do not properly amortize.
For example; the events category is shows as $5 per month in budget, but the reality is 2-3 entries at an individual cost of $20 or more.
We can also see some of the benefits of living in the Middle East; the monthly spend on gas for example is eight dollars.
Some expenses can be hard to categorize. As we can see in the table there is an amount of $646 assigned to “Taxes” despite living in a tax free jurisdiction.
The figures filed under taxes are banking and transfer charges incurred such as annual credit card fees, ATM transaction fees, and currency exchange costs.
The monthly breakdown of $74 per month on “Electronics” is especially tricky as the actual costs recorded was a single transaction of $884 on an Xbox One console in February.
On the other side, there are some costs that are monthly and the average monthly cost are very close. The best example of this would be the “Electricity” cost which actually covers both electricity and water and is paid monthly in arrears. The monthly average cost of $101 is actually pretty close to the cost each month.
The huge amount of spend on rent means that in any data visualization the other subcategories are severely reduced, as we can see in the attached graph.
The visualization here is quite impressive, however the large amount of rental payments mean that all other expenses are pushed together into a small area making them much more difficult to distinguish.
In order to get a better view on the smaller categories, we will omit the rental category from all following visualizations.
Here we can see the breakdown of spending by subcategory, with “Rent” removed.
This gives a much better view of the spread of subcategories and highlights a few key areas.
For example, we can see that “Drinks” accounts for 15.5% of the remaining spending, followed by “Dining Out” at 13%. As a single male with no spouse or offspring, it is to be expected that these subcategories are among the highest percentages of discretionary spending.
The same data can be expressed in a bar chart as follows:
Displaying the data in a bar chart helps to visualize the difference between categories and give some idea to difference in the volumes of spending by subcategory.
an interesting comparison can be drawn here between the largest subcategory “Drinks” $2,793 and the smallest, “Parking” $6.
We can also see from this view that there are many subcategories that float around the $500 – $1,000 mark, including “Groceries”, “Medical”, and “Internet”.
If we take a look at the volume of entries we can see that over the year there was a total of 770 individual transactions recorded. this gives an average of 2.10 transactions per day.
The total annual spend of $36,672 taken over the 365 day period gives a nice, round, average of $100 per day when expressed to the nearest dollar.
The total of $36,672 when taken over the 770 transactions gives an average transaction value of $48 when expressed to the nearest dollar, however both the average transaction and average spend per day have very, very, large values of standard deviation.
Finally, if we take the 770 entries and plot them onto a cumulative graph over time, the results look as follows:
What is particularly interesting in this view is that you can very clearly see the monthly spike in spending at the time that the rental payments are recorded.
It is also possible in this view to identify large purchases for example a spike before Christmas shows the date of booking a return flight to Europe, and a spike in February shows the date of the Xbox One purchase.
Aside from the repetitive spikes we can also see a slower ramp up in the course of the month.
It is also clear to see that levels of spending in the latter half of the year have been much more temperate than in the first half of the year in which there were more sporadic entries of high value.
It is also possible to see from this graph that the first two quarters ran at very close to $10k per quarter, whilst the latter two quarters came in at close to $8k per quarter. This is likely due to an intentional efforts made to reduce spending in the second half of the year, based on the feedback gained by recording spending in the first half.