Price-Related Differential (PRD)

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

PRD is the mean ratio divided by the mean ratio weighted by sale price. It is a measure of vertical equity in assessment. Vertical equity means that properties at different levels of the income distribution should be similarly assessed.

PRD centers slightly above 1 and has a generally accepted value of between 0.98 and 1.03, as defined in the IAAO Standard on Ratio Studies Section 9.2.7. Higher PRD values indicate regressivity in assessment.

Note

The IAAO recommends trimming outlier ratios before calculating PRD, as it is extremely sensitive to large outliers. PRD is being deprecated in favor of PRB and MKI, which are less sensitive to outliers and easier to interpret.

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 PRD of the inputs.

Return type:

float

Example:

# Calculate PRD:
import assesspy as ap

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

assesspy.prd_ci(estimate: list[int] | list[float] | Series, sale_price: list[int] | list[float] | Series, nboot: int = 1000, alpha: float = 0.05) → tuple[float, float]

Calculate the non-parametric bootstrap confidence interval for PRD.

See also:

boot_ci()

assesspy.prd_met(x: float) → bool

Check whether PRD meets IAAO standards (between 0.98 and 1.03, inclusive).

Parameters:

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

Returns:

A boolean value indicating whether the PRD meets IAAO standards.

Return type:

bool