The 2014 Registered Foreclosures Properties data compiled by the Office of Los Angeles Mayor Eric Garcetti showed the pattern and trend of properties foreclosures in Los Angeles. Some of the metadata describing the foreclosures include registered date, property type location and information about lender and property management. Since the data is collected when a foreclosure paperwork is filed, the dataset is mainly for government official record and to certain extend, for academic research.
As aforementioned, since the data is for official government record, the dataset is not informative because very few details about the buyers are collected. The dataset is meant for parties involved to verify whether a foreclosure has happened. As such, property managers or the lenders would find the dataset informative as they browse through the record to verify a foreclosure incidence. The names of the lenders and property managers are listed to ease this verification process. Additionally, lenders and property managers could source foreclosure data for their respective company form this dataset.
Government data is also sought after by researchers and think tanks. However, this dataset seems to be of little use for policy research. This dataset registered a sheer number of foreclosures in 2014. However, in order to examine foreclosure trend, researchers need data for a longer time span to construct time-series data. Moreover, more data on ethnicity, race, age and gender of the buyers are necessarily if researchers want to study which group of people is more vulnerable to foreclosures. In short, this dataset is not particularly helpful for researchers.
Foreclosure is not just another line of record in a dataset. It’s true that borrowers have their mortgages foreclosed due to financial difficulties. However, against the backdrop of America’s norms of uncompromising individual responsibility, these borrowers are often faulted for their bad spending habit or financial illiteracy that causes the foreclosure. Societal judgements and norms are what we can’t observe from the dataset. Equally unobservable is the potential predatory practice by lenders that at time, leads to foreclosure.
If I were given a chance to start over and design the ontology of the dataset, I would definitely factor in the demographics of the borrowers/buyers so as to make the dataset more useful for researchers. However, individual narratives are something that’s not practical to be captured through in this dataset. What we can include in the dataset might be the lending schemes that were offered to the borrowers who had their mortgages disclosed. With that, we might be able to detect potential fraud or predatory practice and take action to investigate and rectify.
Hi there,
I love your analysis of this data collection! I also wrote my post for the week about the 2014 foreclosures, and I completely agree that the dataset doesn’t accurately portray the whole narrative of the buyers whose houses were foreclosed. Perhaps it would be useful to have two separate datasets: one with the basic documentation of which houses were foreclosed, and one that relays more information regarding the buyers’ demographics.