Improve time-based features and handling

[dfsnow]

While I think the model's overall time-trending is performing pretty well, it never hurts to test some tweaks/improvements. One thing we can do to check the model's performance/understanding of time is look at the errors for each fold from rolling-origin CV. To improve performance, we can:

• Try adding more complex features (lagged predictors, lagged outcome, etc.). See some threads on this.
• Test creating a separate index to use as a model feature
  • Calculate the index by area (Census tract, neighborhood, etc.)
  • Allows you to use a dedicated time series model for better/more performant forecasting
  • Helps get overall price trends correct in the main model
• We can also revisit something we tried in the past: weighting more recent data using a decay function