Modified Kakwani Index

assesspy.mki(estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series) → float

The Modified Kakwani Index (MKI) is a Gini-based measure to test for vertical equity in assessment. It first orders properties by sale price (ascending), then calculates the Gini coefficient for sale values and estimated values (while remaining ordered by sale price). The Modified Kakwani Index is the ratio between the coefficients: $Gini of Estimated Values / Gini of Sale Prices$.

For the Modified Kakwani Index:

MKI < 1 is regressive MKI = 1 is vertical equity MKI > 1 is progressive

Parameters:

• estimate (Array-like numeric values) – A list or pd.Series of estimated values. Must be the same length as sale_price.

• sale_price (Array-like numeric values) – A list or pd.Series of sale prices. Must be the same length as estimate.

Returns:

A single float value containing the MKI of the inputs.

Return type:

float

Example:

# Calculate MKI:
import assesspy as ap

ap.mki(ap.ccao_sample().estimate, ap.ccao_sample().sale_price)

assesspy.mki_met(x: float) → bool

Check whether MKI meets the recommendations outlined by Quintos (between 0.95 and 1.05, inclusive).

Parameters:

x (float) – A single float value containing the MKI.

Returns:

A boolean value indicating whether the MKI meets Quintos’ recommendations.

Return type:

bool