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Fix incorrect param name #9

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merged 6 commits into from
Mar 20, 2024
Merged

Fix incorrect param name #9

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5f957d6
Fix incorrect param name
wagnerlmichael Mar 19, 2024
ffcb45d
Try to reduce query size
wagnerlmichael Mar 19, 2024
11c3a78
Change year calls in rest of doc
wagnerlmichael Mar 19, 2024
adfe19b
Adjust joins
wagnerlmichael Mar 20, 2024
5cdcf4e
Test query size
wagnerlmichael Mar 20, 2024
8e4b4f0
Fix ratio figure
wagnerlmichael Mar 20, 2024
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8 changes: 4 additions & 4 deletions vignettes/example-ratio-study.Rmd
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Expand Up @@ -23,7 +23,7 @@ In general, there are four important statistics produced in sales ratio studies,
| **COD** | 5 - 15 | How often properties with the *same* sale price receive the same predicted market value. Lower CODs indicate more fairness between similarly priced properties. |
| **PRD** | .98 - 1.03 | How often properties with *different* sale prices receive the proportionately different predicted market values. Lower PRDs indicate more fairness between low and high-priced properties. |
| **PRB** | -.05 - .05 | PRB is a different approach to measuring fairness across homes with different sale prices. |
| **Median Ratio** | .095 - 1.05 | The median ratio measures whether the most common ratios accurately reflect sale prices. |
| **Median Ratio** | .95 - 1.05 | The median ratio measures whether the most common ratios accurately reflect sale prices. |
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This looks like it was a typo.

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Nice catch!

| MKI | .95 - 1.05 | Measures the difference in inequality between assessed valuations and sale prices. |
| Sales Chasing (E.4) | $\le$ 5% | Measures the degree to which the statistics above are *true* reflections of the quality of assessments. |

Expand Down Expand Up @@ -68,7 +68,7 @@ library(stringr)
assessments <- read_json(
paste0(
"https://datacatalog.cookcountyil.gov/resource/uzyt-m557.json?",
"$where=starts_with(class,'2')&year=2020&$limit=100000"
"$where=starts_with(class,'2')&tax_year=2020&$limit=100000"
),
simplifyVector = TRUE
)
Expand Down Expand Up @@ -149,10 +149,10 @@ library(knitr)
# values AND sales
combined <- inner_join(
assessments %>%
select(pin, year, township_name, mailed_tot, certified_tot, board_tot),
select(pin, tax_year, township_name, mailed_tot, certified_tot, board_tot),
sales %>%
select(pin, year, sale_price, is_multisale),
by = c("pin", "year")
by = c("pin", "tax_year" = "year")
) %>%
filter(township_name %in% c("New Trier", "Palatine"))

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