The data set I studied for this week’s blog post consists of 2014 registered foreclosed properties. The information presented in a foreclosure is presented in three core subcategories: foreclosure information, property information, and management information. This essentially breaks down the foreclosure into when and where it occurred, who it effected, and what managing entity was in control of the property at that time.
This ontology makes most sense from the point of view of somebody analyzing foreclosure and property information by date, region, and manager — basically somebody who is studying trends and consistent factors within the world of foreclosures. Although more information can be gleaned from the data, it wouldn’t be sufficient in doing analysis’ on factors such as family income.
This data, first and foremost, shows the sheer number of foreclosures that can occur in a single year. More specifically, it can map between locations day-to-day to help shed light on what regional and national factors associated with the date might be occurring to increase the number of foreclosures. There are, however, conclusions that we would be able to draw with sufficient information. This additional information would come from additional data, which exists in general but must also be missing from this set.
If somebody wanted to look at socioeconomic standing and how it relates to foreclosure, the data would have to focus more on family information specifically, and tie it to highlighting data factors like family income, size, race and ethnicity, etc. This would drastically change the shape this data points to currently, and help researchers highlight a whole new subcategory of information regarding foreclosures.
If data collection was starting completely over, there are quite a few directions that would achieve academic analysis and a greater understanding of the information behind foreclosures. Like I discussed earlier, we could focus less on the date and pricing of foreclosed properties, and try to answer more humanities-based questions regarding the families directly impacted by these foreclosures. It can also be taken a step further, and help map different dates over the span of multiple years to track what political events and significant economic activity influence the rates and locations of these foreclosures. Overall, this data might help researchers grasp a better understanding of which factors are most prominent when causing foreclosures, and help construct analysis that would avoid increasing rates in the future — helping protect American families and homes. This gives me faith in data helping contribute to the improved lives of Americans.