InfoSparks provides many useful filters that help you pinpoint meaningful information to help you better guide your clients. One of these filters allows you to add an area and then select from a list of predetermined options like cities, towns, ZIP codes and subdivisions. The question that then often comes up is, “How are boundaries of areas defined in InfoSparks?” While your available options depend on your market, there are multiple factors that go into determining whether a listing falls within a certain boundary.
In most instances, the information is being pulled from listings with specified values. As an example, for a listing to be included in the stats for Subdivision A, Subdivision A would need to be included in the MLS data and then selected for that listing.
It’s important to note sometimes a custom area is created specifically for InfoSparks. These cases happen upon request from the MLS and relate to areas that are not readily definable within the MLS. However, these instances are unique.
Available Filter Options
The types of geography descriptions available to filter by may also vary. For instance, your MLS may show a field for subdivision, neighborhood, school district or another geography category. However, depending on how the field is set up in the MLS, the options may not be available as a filter. If the geography type can be selected as part of a predetermined list in the MLS, it will likely be included as an option in InfoSparks. However, if the field is set up as a free-text field in which users can enter any description, there is a greater possibility of too many variations. This makes it difficult to group the options for the purposes of statistical reporting.
The Role of Sample Size
When evaluating or relying on trend data, an important factor is the sample size — or the number of sales from which the trends are concluded. The smaller the sample size, the more susceptible the data are to skew. To be useful, you’ll want data specific enough to be relevant to a client without filtering down so much that the sample size limits the reliability of the trend data.
Better than filtering by all available criteria, select two or three of the most statistically significant variables (e.g., square footage, property type). Then, use a rolling time calculation that helps reduce seasonality and volatility of small datasets (e.g., Rolling 3 Month, Rolling 12 Month). With only a few minor adjustments to the report settings in InfoSparks, you’ll maximize the relevancy of data to your business while minimizing the risk of data skepticism.
Using InfoSparks’ geographical data, you can build a clearer picture for both your clients and your business efforts. But, this is just one of the insightful tools InfoSparks provides. Looking to learn more or answer a specific question? The InfoSparks User Manual & FAQ, available as part of your service, can provide even more in-depth information.
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