Getting Started with Price Index Calculation using REPS"
In REPS: Hedonic and Multilateral Index Methods for Real Estate Price Statistics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(REPS)
data("data_constraxion")

Introduction

The calculate_hedonic_index() function is the central entry point in REPS for computing price indices using various hedonic-based methods. It supports six commonly used approaches:

Laspeyres – hedonic double imputation base-weighted index
Paasche – hedonic double imputation current-weighted index
Fisher – geometric average of Laspeyres and Paasche (both hedonic double imputation)
HMTS – Hedonic Multilateral Time Series re-estimation Splicing
Time Dummy – single-regression hedonic index using time dummies
Rolling Time Dummy – chained index based on overlapping time-dummy regressions
Repricing – quasi-repeat-sales method comparing observed vs predicted price changes

This vignette demonstrates how to apply each method using a consistent interface, making it easy to compare results across approaches.

The HMTS method implemented in REPS is a multilateral, time-series-based index that balances stability, limited revision, and early detection of turning points in the context of property price indices [@51c4602ed48c4adbb7b7d15176d2da7a].

For broader context and international guidelines on the compilation of property price indices, including traditional methods such as hedonic double imputation Laspeyres, Paasche, Fisher and Repricing, we refer to Eurostat's Handbook on Residential Property Price Indices (RPPIs) [@eurostat2013rppi]. For (Rolling) Time Dummy we refer to Hill et al. [@hill2018repricing; @hill2022rolling].

Required Data

Before running any calculations, ensure that your dataset is available and contains the necessary variables:

# Example dataset (you should already have this loaded)
head(data_constraxion)

The required variables include:

period_variable: the time period
dependent_variable: usually price
numerical_variables: e.g., floor_area
categorical_variables: e.g., neighbourhood_code

Typically, for some numerical variables you may want to apply a log transformation. For example, floor_area is often log-transformed to improve linearity, stabilize variance, and reduce the impact of extreme values. Log-transforming variables can help meet regression assumptions by making relationships between variables more linear and residuals more homoscedastic (constant variance).

Example of log-transforming floor_area:

dataset <- data_constraxion
dataset$floor_area <- log(dataset$floor_area)

Using `calculate_hedonic_index()`

The calculate_hedonic_index() function provides a unified interface for estimating hedonic price indices. You only need to specify the method via the method argument — the function handles the rest.

Supported methods:

"laspeyres": base-period weighted double imputation
"paasche": current-period weighted double imputation
"fisher": geometric average of Laspeyres and Paasche
"hmts": Hedonic Multilateral Time Series with re-estimation and splicing
"timedummy": single time-dummy regression (log-linear hedonic model with time factors)
"rolling_timedummy": chained index from overlapping time-dummy regressions
"repricing": compares observed vs predicted price growth in consecutive periods (quasi-repeat-sales)

Example: Single Index Method - Time Dummy

Tbl_TD <- calculate_hedonic_index(
  dataset = dataset,
  method = "timedummy",
  period_variable = "period",
  dependent_variable = "price",
  numerical_variables = c("floor_area", "dist_trainstation"),
  categorical_variables = c("dummy_large_city", "neighbourhood_code"),
  reference_period = 2015,
  number_of_observations = FALSE
)

head(Tbl_TD)

Example: Multiple Index Methods - Fisher, Paasche and Laspeyres

multi_result <- calculate_hedonic_index(
  dataset = dataset,
  method = c("fisher", "paasche", "laspeyres"),
  period_variable = "period",
  dependent_variable = "price",
  numerical_variables = c("floor_area", "dist_trainstation"),
  categorical_variables = c("dummy_large_city", "neighbourhood_code"),
  reference_period = 2015,
  number_of_observations = FALSE
)

head(multi_result$fisher)
head(multi_result$paasche)
head(multi_result$laspeyres)

Visualizing the Index

For quick and clear visualizations, the plot_price_index() utility function can be used to generate time-series plots of the calculated indices.

While we encourage users to create custom visualizations suited to their analytical needs, this built-in plotting function provides a convenient starting point for simple and consistent line plots.

plot_price_index(multi_result)

knitr::include_graphics("multi_index.png")

Summary

The calculate_hedonic_index() function streamlines access to multiple hedonic index methods via a consistent interface. This allows analysts to easily compare outputs and select the most appropriate method for their context.