Quiet Trouble
Introduction:
The Topic and the Data:
The Topic:
This presentation will build on my previous investigation of troubled housing in Montgomery County (which was gathered by the Department of Housing and Community Affairs). This investigation aims to use the much more expansive data gathered from resolved and unresolved complaints made to the Office of Landlord-Tenant Affairs(within DHCA) to see if there is a connection between the complaints and the Department of Housing and Community Affairs findings.
The Data:
The DHCA and their investigators gathered the troubled housing data directly, while the OLTA “Complaints” data is a mix. For example, the “complaints” data is likely geotagged by the complainant and varies in accuracy. DHCA likely gathered the “Complaints” data almost entirely from the submissions of the “Landlord-Tenant Complaint Form,” found on DHCA’s pages. Importantly, these two datasets do not appear to have been linked, and the categories differ. However, both the “Troubled housing” dataset and the “Complaints” dataset have added location data, and it is by the location data and the ZIP code that I have decided to join these two datasets.
The variables I will be using are the number of complaints from tenants and landlords in relation to time. I will also look closer at possible correlations between the location of the mold, infestations, and eviction complaints from the “Complaints” data against the percentages of mold found, infestations, and severity ratings found in the “Troubled housing data.”
My motivation for taking a closer look at this datasset is partially out of an interest in two datasets that are surprisingly rich and have been underinvestigated (among the least downloaded datasets on DataMontgomery), but also becasue I believe tha decent housing/surroundings should be universal as it is essential for a flourishing life.
Tenants and Landlords:
Landlord-Tenant complaints vs Troubled Housing
Suspicous Absence: Complaints Data and the Troubled housing dataset
Explainer
MAP: The leaflet plot to the right displays, in blue, the location of troubled housing, in green the locations of complaints made by tenants and in red locations of complaints made by landlords.
The two blue markers point to two notable locations. Firstly, the housing complex known as “The Enclave” which was the subject of my last project and a good point of comparison. Secondly, “Kings View Apartments”, the place in Montgomery County with the highest proportion of landlord to tenant complaints, by at least a magnitude.
These markers also highlight a key challenge with this project, which was that none of the locations actually mapped onto each other directly from the troubled housing dataset to the complaints dataset. It is therefore not guaranteed that there were any complaints from locations named in the “troubled housing” data, which would be fairly unlikely. There are however one glaring absence from the complaints dataset, namely the area around “The Enclave”.
RIGHT:
Below we have two graphs, both graphing the relationship between time and complaints.
In the first graph we see how the process length has ballooned for certain cases, and how the backlog is periodically purged. It is also fairly easy to see how the pandemic may have led to an increase in this backlog.
In the second graph, we see how the number of complaints have in both cases remained fairly stable. However the number of tenant cases appear to be experiencing more month to month variance. I would be very interesting in hearing ideas for why that may be happening.
Wait Times
Wait times
Landlord complaints are stable, Tenant complaints merit investigation:
