Price-Related Bias (PRB)#

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

PRB is an index of vertical equity that quantifies the relationship between ratios and estimated values as a percentage. In concrete terms, a PRB of 0.02 indicates that, on average, ratios increase by 2% whenever the estimated values increase by 100 percent.

PRB is centered around 0 and has a generally accepted value of between -0.05 and 0.05, as defined in the IAAO Standard on Ratio Studies Section 9.2.7. Higher PRB values indicate progressivity in assessment, while negative values indicate regressivity.

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 PRB of the inputs.

Return type:

float

Example:

# Calculate PRB:
import assesspy as ap

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

assesspy.prb_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 closed-form confidence interval for PRB. Unlike COD and PRB, this does not use bootstrapping.

See also:: boot_ci()

assesspy.prb_met(x: float) → bool#

Check whether PRB meets IAAO standards (between -0.05 and 0.05, inclusive).

Parameters:: x (float) – A single float value containing the PRB.
Returns:: A boolean value indicating whether the PRB meets IAAO standards.
Return type:: bool