With InfoSparks’ line graphs, each point represents data for a certain period of time. When creating your graphs, you can choose which time frame you want your points to represent. Better yet, there are two types of time frames utilized in InfoSparks, Monthly and Rolling Months.
In this blog, we’ll cover an example of how using different time frames on your line graphs helps you analyze year-over-year trends.
In many markets, data in InfoSparks are available going back at least 10 years, making ample statistical insights readily accessible. And, when creating a chart or graph in InfoSparks, using time frames can make data fluctuations apparent. There are two types of time frames utilized in InfoSparks: Monthly and Rolling Months (Rolling 3, 6, 12 Months).
Each point on a line graph represents data for a certain period of time, determined by which setting you choose. Some example data points include Average Sale Price or New Listings. The points can be for a singular place in time such as May 2020, or they may contain a rolling average of data. Rolling means the data being used to calculate that monthly point value on your graph is always shifting and moving for each month. Rolling calculations essentially fit more time into each data point and are therefore better for trend analysis and small sample sizes of data.
As an example, COVID-19 has affected real estate data for this year. If you want to more accurately represent this year’s data compared to other years, using rolling data points will give you a less extreme difference. The below graph uses rolling six months data points. Using this time frame, each data point is equal to six months of activity. If we choose to look at May 2020 with this time frame, the InfoSparks system will go back six months from May and average the total from the six months before, meaning this data point represents the average number from December 2019 to May 2020.
When you use a rolling view, the graphs usually appear smoother than when you use monthly points because there is more sample data for each point on the line. This also gives you a more accurate look at the impact of COVID-19 over time than the monthly graph. In this sample MLS, it shows that the median number of new listings for a rolling six-month period in May 2020 was 20,774 fewer than in May 2019.
Below is the same graph using the monthly data point time frame. As you can see, the ebb and flow of the graph is more dramatic and bumpier as each data point represents a smaller amount of data. The impact of COVID-19 is not as prevalent on this chart as it shows that the median number of listings for the month of May 2020 was only 7,835 less than the median number for May 2019.
In all, using different time frames when looking at your data is helpful in understanding how data compares year over year. Using InfoSparks, there are countless calculations of data that you can use for trend analysis to share with your customers.
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