One problem with interpreting data over time is that many data exhibits movements which recur during the same period each year. This is why it is normal to report in the news seasonally adjusted data figures to reveal the underlying trend in the market. The visualization above shows both seasonally and not seasonally adjusted existing home sales in the past three years. Compare the month-to-month percentage change for both types of home sales.
Taking a closer look at the percentage change from the previous month, it looks that the largest gaps between the two types occur in January and March. For instance, in January 2016, seasonally adjusted home sales increased 0.4% from December while not seasonally adjusted sales decreased 30.7%. This shows that once the seasonal influence is removed from the data, existing home sales actually increased in January. The fact that holiday celebrations diminish home shopping in November and December that leads to fewer sales in January is one of the reasons which make seasonally adjusted figures different than not seasonally adjusted data, and with the help of seasonally adjusted data, market analysts and policy makers can avoid overreacting to seasonal fluctuations in the data. Seasoned REALTORS®, however, will know that their business normally slows down at the end and beginning of the year for seasonal reasons and will adjust their planning accordingly while new REALTORS® will benefit from reviewing trends in the unadjusted data to learn about typical busy and less busy seasons.
In contrast with January and March, these gaps between adjusted and unadjusted data are small in October and February. It seems that there is not any seasonal component in the data series in these two months. Thus, both datasets exhibit the same movements in these months.