Seasonally Adjusted Annual Rate – SAAR Definition

A seasonally adjusted annual rate (SAAR) is a rate adjustment used for economic or business data, such as sales numbers or employment figures, that attempts to remove seasonal variations in the data. Most data is affected by the time of the year, and adjusting for the seasonality means that more accurate relative comparisons can be drawn between different time periods.

For example, the ice cream industry tends to have a large level of seasonality as it sells more ice cream in the summer than in the winter, and by using seasonally adjusted annual sale rates, the sales in the summer can be accurately compared to the sales in the winter. It is often used by analysts in the automobile industry to account for car sales.

Seasonal adjustment is a statistical technique designed to even out periodic swings in statistics or movements in supply and demand related to changing seasons. Seasonal adjustments provide a clearer view of nonseasonal changes in data that would otherwise be overshadowed by the seasonal differences.

To calculate SAAR, take the un-adjusted monthly estimate, divide by its seasonality factor, and multiply by 12.

Analysts start with a full year of data, and then they find the average number for each month or quarter. The ratio between the actual number and the average determines the seasonal factor for that time period. Imagine a business earns $144,000 over a course of a year and $20,000 in June. Its average monthly revenue is $12,000, making June's seasonality factor as follows:

$\$20,000/\$12,000=1.67$$20,000/$12,000=1.67

The following year, revenue during June climbs to $30,000. When divided by the seasonality factor, the result is $17,964, and when multiplied by 12, that makes the SAAR $215,568, indicating growth. Alternatively, SAAR can be calculated by taking the unadjusted quarterly estimate, dividing by its seasonality factor, and multiplying by four.

The seasonally adjusted annual rate (SAAR) helps with data comparisons in a number of ways. By adjusting the current month's sales for seasonality, a business can calculate its current SAAR and compare it to the previous year's sales to determine if sales are increasing or decreasing.

Similarly, if a person wants to determine if real estate prices are increasing in his area, he can look at the median prices in the current month or quarter, adjust those numbers for seasonal variations and convert them into SAARs which can be compared to numbers for the previous years. Without making these adjustments first, the analyst is not comparing apples with apples, and as a result, cannot make clear conclusions.

For example, homes tend to sell more quickly and at higher prices in the summer than in the winter. As a result, if a person compares summer real estate sales prices to median prices from the previous year, he may get a false impression that prices are rising. However, if he adjusts the initial data based on the season, he can see whether values are truly rising or just being momentarily increased by the warm weather.

While seasonally adjusted (SA) rates try to ameliorate differences between seasonal variations, non-seasonally adjusted (NSA) rates do not take into account seasonal ebbs and flows. Concerning a set of information, NSA data corresponds to the information's annual rate, while SA data corresponds to its SAAR.