Getting Started with Price Index Calculation using REPS

Introduction

The calculate_price_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 (Ishaak, Ouwehand, and Remøy 2024).

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) (Eurostat 2013). For (Rolling) Time Dummy we refer to Hill et al. (Hill et al. 2018, 2022).

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)
#>   period   price floor_area dist_trainstation neighbourhood_code
#> 1 2008Q1 1142226  127.41917       2.887992985                  E
#> 2 2008Q1  667664   88.70604       2.903955192                  D
#> 3 2008Q1  636207  107.26257       8.250659447                  B
#> 4 2008Q1  777841  112.65725       0.005760792                  E
#> 5 2008Q1  795527  108.08537       1.842145127                  E
#> 6 2008Q1  539206   97.87751       6.375981360                  D
#>   dummy_large_city
#> 1                0
#> 2                1
#> 3                1
#> 4                0
#> 5                0
#> 6                1

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_price_index()

The calculate_price_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)

Tbl_TD <- calculate_price_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)
#>   period    Index
#> 1 2008Q1 99.31408
#> 2 2008Q2 97.74089
#> 3 2008Q3 98.72017
#> 4 2008Q4 98.85271
#> 5 2009Q1 98.26102
#> 6 2009Q2 98.11607

multi_result <- calculate_price_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$fisher)
#>   period    Index
#> 1 2008Q1 99.61436
#> 2 2008Q2 98.33742
#> 3 2008Q3 98.98908
#> 4 2008Q4 98.02118
#> 5 2009Q1 98.49971
#> 6 2009Q2 98.26416
head(multi_result$paasche)
#>   period    Index
#> 1 2008Q1 99.84034
#> 2 2008Q2 98.44649
#> 3 2008Q3 98.88248
#> 4 2008Q4 98.23097
#> 5 2009Q1 98.55413
#> 6 2009Q2 98.17509
head(multi_result$laspeyres)
#>   period    Index
#> 1 2008Q1 99.38877
#> 2 2008Q2 98.22836
#> 3 2008Q3 99.09567
#> 4 2008Q4 97.81174
#> 5 2009Q1 98.44521
#> 6 2009Q2 98.35320

plot_price_index(multi_result)

Summary

The calculate_price_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.