When viewing statistical graphs in InfoSparks, it is important to understand how the different drop down menus impact the data that is displayed:
The first drop down allows you to choose the type of graph you would like use to display your results:
The second drop down menu, the Timeline, allows you to choose how far back you look for stats. This menu will change depending on whether you choose Line or Bar (there are examples of each further down this article).
The last drop down, Data, allows you to choose the length of time/interval for each data point:
For our configuration, when working with Line graphs, you can set the Timeline to either 1, 3, 5 or 10 years, or select Max to go all the way back to 2003.
As an example, below is the the average sales price of all listings on the MLS since 2003, by month:
As you move your mouse cursor horizontally along the graph, it will show you the data from the corresponding plot point. So, in this image, the mouse cursor is at the March 2013 plot point, which shows an average sales price for the entire MLS of $293,771. Since we chose the maximum timeline duration and broke it down by month, there are a lot of data points being used to create a picture of this statistic.
If we chose a shorter timeline, say only going back 3 years, the graph is spread out more, with each data point taking up more space:
The peaks and valleys are a lot less dramatic, even though we are using the same data (we are just using the last three years of it, compared to the last 16 years in the first graph).
Another time interval we can use to plot our data is the Rolling option. We actually have three different choices here: 3, 6 or 12 month views. We will use the Rolling12 Month view, which means that each point on the line will show the average sales price for 12 months of data (that month plus the previous 11 months):
In this image, the March 2018 data point is plotted, revealing an average sales price of $334,330 which actually covers from March 2018 back to April 2017.
Using the Rolling view will smooth out the peaks and valleys even more than just shortening the timeline. This is a good tool to use when looking at data trends.
Switching the type of graph from Line to Bar gives us slightly different options in the Timeline and Data drop down menus. For the Timeline, we can only choose from 1, 2 or 3 years:
And for the Data, we still have the Monthly and Rolling 3/6/12 Months, with the addition of a Year to Date option:
A major difference between Line and Bar is that the Bar graph is designed to show the data from the most current complete reporting month as compared to the same month in the previous year (going back up to 3 years). It is important to note that this works off of the most current complete reporting month. The previous month is not considered complete until the 15th of the next month.
So, running this search in late September 2019, the most current complete reporting month is August 2019. Using the 3 Year timeline, we will get the average sales price figures for August 2017, 2018 and 2019. In the image below, since we are also using the Rolling 12 Months interval, each bar represents the average sales price from the previous September through that August (the 2017 figure, for instance, gives the average sales price from Sept. 2016 through August 2017):