calculate_repricing: Calculate repricing index based on hedonic model (geometric...

In REPS: Hedonic and Multilateral Index Methods for Real Estate Price Statistics

Calculate repricing index based on hedonic model (geometric adjustment)

Description

For each pair of subsequent periods, this method compares the observed geometric mean price with the predicted mean price from a hedonic regression model. The ratio of these two values forms the basis of the repricing growth rate, which is then accumulated into an index.

Usage

calculate_repricing(
  dataset,
  period_variable,
  dependent_variable,
  numerical_variables,
  categorical_variables,
  reference_period = NULL,
  number_of_observations = FALSE,
  periods_in_year = 4
)

Arguments

dataset	a data frame containing the data
period_variable	character name of the time period variable
dependent_variable	character name of the dependent variable (e.g., sale price)
numerical_variables	character vector of numeric quality-determining variables
categorical_variables	character vector of categorical variables (including dummies)
reference_period	reference period (numeric or string) to normalize index to 100
number_of_observations	logical, if TRUE, adds number of observations column
periods_in_year	if month, then 12. If quarter, then 4, etc. (default = 4)

Value

a data.frame with columns: period, Index, (optionally number_of_observations)

Author(s)

Vivek Gajadhar, Farley Ishaak

REPS documentation built on March 16, 2026, 5:08 p.m.