Nominal house prices data in Luxembourg

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(purrr)
library(tidyr)

Let’s load the datasets:

commune_level_data <- read.csv(
  "../datasets/house_prices_commune_level_data.csv"
)

country_level_data <- read.csv(
  "../datasets/house_prices_country_level_data.csv"
)

Let’s compute the Laspeyeres index for each commune:

get_laspeyeres <- function(dataset){

  which_dataset <- deparse(substitute(dataset))

  group_var <- if(grepl("commune", which_dataset)){
                quo(locality)
              } else {
                NULL
              }
  dataset %>%
    group_by(!!group_var) %>%
      mutate(p0 = ifelse(year == "2010", average_price_nominal_euros, NA)) %>%
      fill(p0, .direction = "down") %>%
      mutate(p0_m2 = ifelse(year == "2010", average_price_m2_nominal_euros, NA)) %>%
      fill(p0_m2, .direction = "down") %>%
      ungroup() %>%
      mutate(pl = average_price_nominal_euros/p0*100,
             pl_m2 = average_price_m2_nominal_euros/p0_m2*100)

}
commune_level_data <- get_laspeyeres(commune_level_data)
#commune_level_data <- commune_level_data %>%
#  group_by(locality) %>%
#  mutate(p0 = ifelse(year == "2010", average_price_nominal_euros, NA)) %>%
#  fill(p0, .direction = "down") %>%
#  mutate(p0_m2 = ifelse(year == "2010", average_price_m2_nominal_euros, NA)) %>%
#  fill(p0_m2, .direction = "down") %>%
#  ungroup() %>%
#  mutate(pl = average_price_nominal_euros/p0*100,
#         pl_m2 = average_price_m2_nominal_euros/p0_m2*100)

Let’s also compute it for the whole country:

country_level_data <- get_laspeyeres(country_level_data)
#country_level_data <- country_level_data %>%
#  mutate(p0 = ifelse(year == "2010", average_price_nominal_euros, NA)) %>%
#  fill(p0, .direction = "down") %>%
#  mutate(p0_m2 = ifelse(year == "2010", average_price_m2_nominal_euros, NA)) %>%
#  fill(p0_m2, .direction = "down") %>%
#  mutate(pl = average_price_nominal_euros/p0*100,
#         pl_m2 = average_price_m2_nominal_euros/p0_m2*100)

We are going to create a plot for 5 communes and compare the price evolution in the communes to the national price evolution. Let’s first list the communes:

communes <- c("Luxembourg",
              "Esch-sur-Alzette",
              "Mamer",
              "Schengen",
              "Wincrange")

make_plot <- function(commune){

  commune_data <- commune_level_data %>%
    filter(locality == commune)

  data_to_plot <- bind_rows(
    country_level_data,
    commune_data
  )

  ggplot(data_to_plot) +
    geom_line(aes(y = pl_m2,
                  x = year,
                  group = locality,
                  colour = locality))
}

res <- lapply(communes, function(x){

  knitr::knit_child(text = c(

    '\n',
    '## Plot for commune: `r x`',
    '\n',
    '```{r, echo = F}',
    'print(make_plot(x))',
    '```'

     ),
     envir = environment(),
     quiet = TRUE)

})

cat(unlist(res), sep = "\n")

Plot for commune: Luxembourg

Plot for commune: Esch-sur-Alzette

Plot for commune: Mamer

Plot for commune: Schengen

Plot for commune: Wincrange