This week we put a lot into JUMA, the Justice Map, for the Atlanta Legal Aid Society. After adding various socioeconomic layers, a search box, and edit features, our contact with the Society was so impressed she wants us to do much more! We’re currently adding Zillow data along with additional data from the Legal Society. The Society has also told us about various shady (and legal) ways residents can easily lose their properties; ask us about them.
For our other project, estimating the number of residents who can qualify for the Anti-Displacement Tax Fund, we have split the work into two subprojects: estimating the income for residents, and forecasting property tax assessments. Income estimates are being generated from Census data, IRS data, and speaking with local residents. So far, we’ve determined that most residents in the affected areas should qualify, as the majority of resident incomes are generally much lower than the requirements, if they own their home. Right now, we are trying to figure out the home ownership rates by comparing owner addresses with parcel addresses.
For the tax assessment forecasts of homes in the neighborhoods with current beltline construction, we decided to use the known impacts of the completed beltline in the Old Fourth Ward neighborhood. A cluster analysis was performed on time series tax assessment data for 2200 homes in the Old Fourth Ward from 2005 to 2016. The time series were first differenced and scaled by the previous time-step tax assessment to create a new time series of the percent changes in value (to ensure comparative scales since there is a large range in home values). The cluster analysis resulted in 3 discrete clusters: (1) homes with a large increase in assessments after the beltline announcement in 2004, (2) homes with an increase after construction, and (3) homes that followed the more common recession and post-recession trend observed elsewhere. It’s interesting that the recession had less impact on the homes in this area compared with national trends; maybe the beltline provided some insulation. Random forests were then constructed to determine the most important home characteristics for each cluster. The distance to the beltline, the size of the land/home, and the nominal value were the most important features for classification, as we expected.
Now we need to group the homes in the West-si
de neighborhoods where the beltline is currently being built by those characteristics deemed important from the random forest results. These West-side groups will be matched with the Old Fourth Ward clusters and the West-side tax assessments forecasts will be modeled from the corresponding Old Fourth Ward trends. We’re also considering other models for comparison, such as random forests for clustering and recurrent neural networks for forecasting.
Still so much to do:)