Comments (1)
Great work so far. Next steps:
- Let's add intro text to explain each of these categories. You'll probably have to do some digging... for example, here's how flag_char_missing_critical_value is initially constructed. Please also clarify in the initial text any geographic specs (e.g., clarify that the stats are countywide), and any other constraints on the data (e.g., which years of sales are we using?)
- The current topline table ("Difficult Categories") is only on the sold properties. Let's split it into two sections, for two universes:
- Universe of all properties: N, median prior value, median current estimated FMV, median % change
- Universe of sold properties, ratio stats: N sales, median FMV, median sale price, ratio stats per challenge group
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Related Issues (20)
- Add workflow/process for tagging models with `run_type` HOT 1
- Add SHAP maps for location and proximity features
- Create aggregate maps using comps output HOT 2
- Refactor comps to calculate tree weights on a per-card basis HOT 1
- Add sales val run_id to res model outputs
- Add sale ratio column to desk review sheets HOT 1
- Flesh out characteristics in desk review workbooks
- Refactor `export` stage to use a config dict representing the workbook structure
- Comps check HOT 1
- Update `ingest` stage to use `noctua` `unload = TRUE` option HOT 3
- Improve modeling multi-cards
- Spike upgrading comps algorithm with taichi HOT 1
- Spike model rebuild in Python/polars
- Create explainer/diagrams for multi-PIN and multi-card aggregations
- Investigate merging the residential and condo model codebases
- Adjust `model_fetch_run()` to optionally fetch input data
- Update comps algorithm to save `instruno` in addition to `parid` HOT 1
- Generate historical model API workbooks
- Edit `dvc.yaml` to include files invoked as deps
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