Search What is often not widely understood is that businesses can select a sample of projects (e.g., from multiple takeaway outlets) to look at in detail and extrapolate the findings to the total project population. For example, if a company reported gross capital of expenditure of £10m on a number of projects and found from a sample representing £1m of that expenditure that £750,000 (or 75%) qualified for capital allowances then the total claim for the business would be for £7.5m (or £10m x 75%).

Sampling can, therefore, save businesses a significant amount of time and money in claim analysis without compromising results but its use comes with strict conditions.

### Statistically Relevant

Businesses that wish to utilise sampling as a basis of claim must ensure that the approach and results are statistically relevant which means: -

1. The initial projects sampled must be demonstrably random, representative of the population as a whole and ideally discussed or agreed with HMRC in advance.

2. The sample size adopted for analysis must be statistically acceptable which creates a link to the results for each claim (in general, the closer the results, the smaller the sample needed to satisfy (3) below).

3. The ‘qualifying proportion’ of the sample size is statistically accurate (in general, this means a 95% ‘Confidence Interval’).

HMRC do not provide hard and fast rules on the methods of sampling so businesses must be prepared to set out their statistical methodology throughout all stages of their claim. An initial sample of 15 projects is normally acceptable but it can be more depending on the circumstances and results (a recent sample analysis of c.40, which has now been agreed with HMRC and its Revenue’s Analysis and Research team, was needed for a population of many hundreds).

If you cannot draw on a sufficiently large sample for your initial project study, sampling as an approach is unlikely to be suitable for you.

### What are Confidence Intervals?

When looking at a sample drawn from a larger population you need a measurement of how accurate your estimate of qualifying proportion is. The measurement used is a Confidence Interval, which gives you both a range in which the actual qualifying proportion is likely to fall, and a measure of how confident we are in that range. The more confident you wish to be (measured as a percentage, such as 95% or 99%), the wider the possible range will be. To interpret a 95% Confidence Interval, think of it as a 1 in 20 chance that the true qualifying proportion falls outside of the given range.

If the gross capital expenditure for a claim does not exceed £25 million HMRC will normally require the capital allowances qualifying proportions derived from a sample are within a 95% confidence interval of plus or minus (+/-) 5% of the gross expenditure. In other words, if the estimated qualifying proportion across expenditure is 40%, they would expect true value to lie within the range of 35% to 45% of the gross expenditure.

If the gross capital expenditure for a claim is more than £25 million HMRC will normally require the capital allowances qualifying proportions derived from a sample to be within a 95% confidence interval of plus or minus (+/-) 5% of the qualifying proportion. In other words, if the estimated qualifying proportion of gross expenditure is 60% the range would be from 57% to 63% (5% of 60% is 3%, so the range would be +/- 3% of the estimated qualifying proportion of 60%).

A typical calculation for the confidence interval/population mean is as follows: Sample Mean

The qualifying proportion for each case is calculated by dividing qualifying expenditure by gross expenditure. This will give a percentage. The mean (average) qualifying proportion is the average of these percentages across the whole sample (i.e., the sum of all proportions divided by the number of properties in the sample).

Standard Error of the Mean

If we took lots of different samples from the population and calculated the mean of each of them, the standard deviation of these means is called the Standard Error of the Mean (SE). Its purpose here is to enable us to estimate the chance that our sample mean is much bigger or smaller than the population mean. The smaller the SE, the more confident we can be that our sample mean is close to the population mean. It is used in the calculation of Confidence Intervals (as above). The SE is dependent on the size of the sample and its standard deviation, it is calculated by dividing the sample standard deviation by the square root of the sample size.

Normal Distribution

Where Z is a statistic from what is called the Normal Distribution, which is used for making statistical inferences. Z is dependent on the required confidence level. (In the case where the sample size is below 30, we use a t Distribution, which is more accurate for small sample sizes).

### Maintaining a Sampling Percentage Agreement

HMRC will not normally agree to the utilisation of a percentage in advance of the expenditure being incurred which means a historical agreement cannot automatically roll forward.

This is perfectly reasonable, since there is no way of knowing that the profile of planned expenditure will not change, and that the basis of the original sampling methodology will still be relevant. However, this does mean any original analysis is redundant.

Construction projects can often overlap accounting periods and when a business collates a portfolio of projects over several years it is often possible to demonstrate statistical validity and relevance with only minor adjustments and/or analysis in later years.

A material change in the nature of the works or business goals can result in the need for a more in-depth sample analysis and this must be kept under continual review.

### Exceptional Projects

Sampling is not generally appropriate for property acquisitions (due to the complexities in valuation, high levels of statutory restrictions and results variance). They are also not suitable for atypical projects (e.g., that residential conversion in redundant commercial space to extract alternate asset value) and such projects need to be identified and excluded.

At Furasta we have significant experience of working with property, finance, and asset teams to get the best capital allowances approach in place and if you would like to learn more about statistical based sampling, we will be delighted to hear from you.

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