Use worst case method for MKI / KI#19
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Damonamajor merged 12 commits intomasterfrom Sep 16, 2025
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…data/assessr into Use-worst-case-method-for-MKI-/-KI
jeancochrane
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Sep 12, 2025
| calc_gini <- function(assessed, sale_price) { | ||
| df <- data.frame(av = assessed, sp = sale_price) | ||
| df <- df[order(df$sp), ] | ||
| df <- df[order(df$sp, -df$av), ] |
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[Suggestion, optional] I think we should add the same comment here as assesspy, to make sure it's clear in both places:
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| df <- df[order(df$sp, -df$av), ] | |
| # This Gini coefficient algorithm is sensitive to the order of the input | |
| # observations: If multiple observations share the same sale price but have | |
| # different estimates, the output coefficients will be different depending | |
| # on which of the sales with identical prices gets ordered first in the | |
| # input dataframe. To ensure a stable sort order, Quintos recommends | |
| # sorting by ascending sale price and then by descending estimate to break | |
| # any ties. This produces "worst case" MKI/KI statistics, but ensures those | |
| # statistics are deterministic. See this issue for more discussion: | |
| # https://github.com/ccao-data/assesspy/issues/33#issuecomment-3180632954 | |
| df <- df[order(df$sp, -df$av), ] |
Damonamajor
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Sep 16, 2025
This reverts commit ba04058.
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This uses the worst case method for MKI / KI to ensure stability across different runs. This ordered by ascending sale price followed by decreasing assessed value.
Connects ccao-data/assesspy#33.