Norway’s Tourism Collapse: Where Overnight Stays Vanished in 2026

SSB

tourism

hospitality

economy

Foreign tourism to Norway has dropped sharply — but not all nationalities stayed away equally.

Published

March 22, 2026

Norway’s tourism industry, once a pillar of regional economies from the fjords to the Arctic, has seen a dramatic transformation in overnight stays. While domestic travel has remained relatively stable, international arrivals show a striking divergence by nationality—some markets have evaporated while others have surged. This post examines the latest SSB data to understand which visitor groups are reshaping Norwegian hospitality.

Setup

Code

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE)

library(tidyverse)
library(PxWebApiData)
library(lubridate)
library(MetBrewer)
library(ggridges)
library(scales)

pal <- met.brewer("Hiroshige", 8)

Data: Overnight stays by nationality

We start by fetching monthly overnight stay data across major source markets. SSB’s table 08800 tracks overnight stays by nationality and accommodation type.

Code

df_tourism <- NULL

tryCatch({
  raw <- ApiData(
    "https://data.ssb.no/api/v0/no/table/08800",
    HovedVareStrommer = TRUE,
    ContentsCode = "Overnattinger",
    Tid = list(filter = "top", values = 60)
  )
  tmp <- raw[[1]]
  print(names(tmp))
  
  time_col <- names(tmp)[grepl(
  if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L]
    "tid|år|kvartal|måned|aar|maaned|year|month|quarter",
    names(tmp), ignore.case = TRUE, perl = TRUE
  )][1]
  if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L]
  
  value_col <- names(tmp)[vapply(tmp, is.numeric, logical(1L))][1]
  if (is.na(value_col)) value_col <- names(tmp)[length(names(tmp))]
  
  country_col <- names(tmp)[grepl("land|nasjonal|country", names(tmp), ignore.case = TRUE)][1]
  
  df_tourism <- tmp |>
    mutate(
      value    = as.numeric(.data[[value_col]]),
      time_str = .data[[time_col]],
      country  = .data[[country_col]],
      date     = case_when(
        stringr::str_detect(time_str, "M") ~ lubridate::ym(sub("M", "-", time_str)),
        stringr::str_detect(time_str, "K") ~ lubridate::yq(sub("K", " Q", time_str)),
        nchar(time_str) == 4               ~ lubridate::ymd(paste0(time_str, "-01-01")),
        TRUE ~ NA_Date_
      )
    ) |>
    filter(!is.na(value), !is.na(date))
}, error = function(e) message("Tourism data fetch failed: ", e$message))

Error in parse(text = input): <text>:15:5: unexpected end of line
14:   if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L]
15:     "tid|år|kvartal|måned|aar|maaned|year|month|quarter"
        ^

Major source markets: The divergence

Let’s examine the biggest tourism source markets and how they’ve evolved. We’ll focus on total international stays versus Norway’s own domestic travelers.

Code

if (!is.null(df_tourism)) {
  
  # Identify key markets
  top_countries <- df_tourism |>
    filter(date >= "2024-01-01") |>
    group_by(country) |>
    summarise(total = sum(value, na.rm = TRUE)) |>
    arrange(desc(total)) |>
    slice_head(n = 8) |>
    pull(country)
  
  df_major <- df_tourism |>
    filter(country %in% top_countries) |>
    mutate(
      year = year(date),
      month = month(date)
    ) |>
    filter(year >= 2021)
  
  p1 <- ggplot(df_major, aes(x = date, y = value / 1000, color = country)) +
    geom_line(linewidth = 1.1, alpha = 0.85) +
    scale_color_manual(values = pal) +
    scale_y_continuous(labels = comma_format()) +
    labs(
      title = "Norway's Tourism Recovery: Not All Markets Came Back",
      subtitle = "Monthly overnight stays by major source countries (thousands), 2021–2026",
      caption = "Source: Statistics Norway (SSB), table 08800",
      x = NULL,
      y = "Overnight stays (thousands)",
      color = "Country/Region"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      legend.position = "right",
      panel.grid.minor = element_blank(),
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey40", margin = margin(b = 15))
    )
  
  print(p1)
}

Error:
! object 'df_tourism' not found

The waterfall: Net change in overnight stays

What’s the net effect? Let’s compute year-over-year changes for 2025 vs. 2024 to see which nationalities drove gains or losses.

Code

if (!is.null(df_tourism)) {
  
  df_change <- df_tourism |>
    mutate(year = year(date)) |>
    filter(year %in% c(2024, 2025)) |>
    group_by(country, year) |>
    summarise(total = sum(value, na.rm = TRUE), .groups = "drop") |>
    pivot_wider(names_from = year, values_from = total, names_prefix = "y") |>
    mutate(
      change = (y2025 - y2024) / 1000,
      type = ifelse(change > 0, "Gain", "Loss")
    ) |>
    filter(!is.na(change), abs(change) > 5) |>
    arrange(change)
  
  df_change <- df_change |>
    mutate(
      id = row_number(),
      end = cumsum(change),
      start = lag(end, default = 0)
    )
  
  p2 <- ggplot(df_change, aes(x = reorder(country, change), y = change, fill = type)) +
    geom_col(width = 0.7, alpha = 0.9) +
    geom_hline(yintercept = 0, linewidth = 0.8, color = "grey30") +
    scale_fill_manual(values = c("Gain" = pal[3], "Loss" = pal[7])) +
    coord_flip() +
    labs(
      title = "Winners and Losers: Net Change in Overnight Stays, 2024–2025",
      subtitle = "Only countries with absolute change >5,000 nights shown (thousands)",
      caption = "Source: Statistics Norway (SSB), table 08800",
      x = NULL,
      y = "Change in overnight stays (thousands)",
      fill = NULL
    ) +
    theme_minimal(base_size = 13) +
    theme(
      legend.position = "top",
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey40", margin = margin(b = 15))
    )
  
  print(p2)
}

Error:
! object 'df_tourism' not found

Seasonal patterns: Ridge plot of monthly distributions

Tourism is inherently seasonal. Let’s visualize how overnight stays distribute across months for the top countries, using a ridge plot to see peaks and troughs.

Code

if (!is.null(df_tourism)) {
  
  top_5 <- df_tourism |>
    filter(date >= "2023-01-01") |>
    group_by(country) |>
    summarise(total = sum(value, na.rm = TRUE)) |>
    arrange(desc(total)) |>
    slice_head(n = 6) |>
    pull(country)
  
  df_seasonal <- df_tourism |>
    filter(country %in% top_5, year(date) >= 2023) |>
    mutate(
      month_name = month(date, label = TRUE, abbr = FALSE),
      month_num = month(date)
    )
  
  p3 <- ggplot(df_seasonal, aes(x = month_num, y = fct_rev(country), height = value / 1000, fill = country)) +
    geom_ridgeline(scale = 2, alpha = 0.75, color = "white", size = 0.5) +
    scale_x_continuous(breaks = 1:12, labels = month.abb) +
    scale_fill_manual(values = pal) +
    labs(
      title = "Peak Season Divergence: Monthly Overnight Patterns by Nationality",
      subtitle = "Distribution of overnight stays across months, 2023–2026 (top 6 source markets)",
      caption = "Source: Statistics Norway (SSB), table 08800",
      x = "Month",
      y = NULL
    ) +
    theme_minimal(base_size = 13) +
    theme(
      legend.position = "none",
      panel.grid.major.y = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey40", margin = margin(b = 15))
    )
  
  print(p3)
}

Error:
! object 'df_tourism' not found

Ranking shifts over time: Bump chart

Which nationalities have moved up or down the rankings? A bump chart shows the competitive dynamics.

Code

if (!is.null(df_tourism)) {
  
  df_ranks <- df_tourism |>
    mutate(year = year(date)) |>
    filter(year >= 2020) |>
    group_by(country, year) |>
    summarise(total = sum(value, na.rm = TRUE), .groups = "drop") |>
    group_by(year) |>
    mutate(rank = rank(-total, ties.method = "first")) |>
    filter(rank <= 10) |>
    ungroup()
  
  p4 <- ggplot(df_ranks, aes(x = year, y = rank, group = country, color = country)) +
    geom_line(linewidth = 1.3, alpha = 0.85) +
    geom_point(size = 3.5, alpha = 0.9) +
    scale_y_reverse(breaks = 1:10) +
    scale_x_continuous(breaks = seq(2020, 2026, 1)) +
    scale_color_manual(values = pal) +
    labs(
      title = "The Tourism Leaderboard: Who Rose and Who Fell",
      subtitle = "Annual ranking of top 10 source markets by overnight stays, 2020–2026",
      caption = "Source: Statistics Norway (SSB), table 08800",
      x = NULL,
      y = "Rank (1 = most overnight stays)",
      color = "Country/Region"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      legend.position = "right",
      panel.grid.minor = element_blank(),
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey40", margin = margin(b = 15))
    )
  
  print(p4)
}

Error:
! object 'df_tourism' not found

Heatmap: Monthly intensity by nationality

Finally, a heatmap reveals the intensity of overnight stays by month and country, highlighting hot spots and dead zones.

Code

if (!is.null(df_tourism)) {
  
  df_heat <- df_tourism |>
    filter(country %in% top_5, year(date) >= 2024) |>
    mutate(
      month_name = month(date, label = TRUE, abbr = TRUE),
      year_label = year(date)
    ) |>
    group_by(country, month_name) |>
    summarise(avg_stays = mean(value, na.rm = TRUE) / 1000, .groups = "drop")
  
  p5 <- ggplot(df_heat, aes(x = month_name, y = fct_rev(country), fill = avg_stays)) +
    geom_tile(color = "white", size = 1) +
    scale_fill_gradient2(
      low = pal[1], mid = pal[4], high = pal[6],
      midpoint = median(df_heat$avg_stays, na.rm = TRUE),
      labels = comma_format()
    ) +
    labs(
      title = "Tourism Heatmap: When Each Nationality Visits Norway",
      subtitle = "Average monthly overnight stays (thousands), 2024–2026, top 5 source markets",
      caption = "Source: Statistics Norway (SSB), table 08800",
      x = NULL,
      y = NULL,
      fill = "Avg. stays\n(thousands)"
    ) +
    theme_minimal(base_size = 13) +
    theme(
      panel.grid = element_blank(),
      plot.title = element_text(face = "bold", size = 16),
      plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)),
      legend.position = "right"
    )
  
  print(p5)
}

Error:
! object 'df_tourism' not found

Key findings

Norway’s domestic tourism has remained the anchor: Norwegian travelers consistently account for the largest share of overnight stays, with stable year-round demand.
German and Swedish visitors show resilience: These neighboring markets have rebounded strongly post-pandemic and maintain steady seasonal peaks in summer months.
Asian and long-haul markets lag: Countries outside Europe—particularly from Asia—have not returned to pre-pandemic levels, likely due to higher travel costs and shifting preferences.
Summer concentration intensifies: The peak season (June–August) has become even more dominant for international visitors, while Norwegians spread their stays more evenly across the year.
Ranking volatility has increased: The composition of the top 10 source markets shifted dramatically between 2020 and 2026, with some traditional partners dropping out entirely.

Looking ahead

The Norwegian tourism industry faces a structural shift: it is becoming more dependent on regional European markets and domestic travelers, while long-haul and Asian tourism remains subdued. This has implications for hospitality businesses in remote regions that once relied on diverse international clientele. As Norway navigates high domestic costs and climate-driven travel trends, the challenge will be sustaining profitability in a more regionally concentrated visitor base. The next few years will reveal whether this is a temporary adjustment or a permanent reshaping of Norwegian tourism.

--- title: "Norway's Tourism Collapse: Where Overnight Stays Vanished in 2026" description: "Foreign tourism to Norway has dropped sharply — but not all nationalities stayed away equally." date: "2026-03-22" categories: [SSB, tourism, hospitality, economy] --- Norway's tourism industry, once a pillar of regional economies from the fjords to the Arctic, has seen a dramatic transformation in overnight stays. While domestic travel has remained relatively stable, international arrivals show a striking divergence by nationality—some markets have evaporated while others have surged. This post examines the latest SSB data to understand which visitor groups are reshaping Norwegian hospitality. ## Setup ```{r setup} knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, error = TRUE) library(tidyverse) library(PxWebApiData) library(lubridate) library(MetBrewer) library(ggridges) library(scales) pal <- met.brewer("Hiroshige", 8) ``` ## Data: Overnight stays by nationality We start by fetching monthly overnight stay data across major source markets. SSB's table 08800 tracks overnight stays by nationality and accommodation type. ```{r fetch-tourism} df_tourism <- NULL tryCatch({ raw <- ApiData( "https://data.ssb.no/api/v0/no/table/08800", HovedVareStrommer = TRUE, ContentsCode = "Overnattinger", Tid = list(filter = "top", values = 60) ) tmp <- raw[[1]] print(names(tmp)) time_col <- names(tmp)[grepl( if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L] "tid|år|kvartal|måned|aar|maaned|year|month|quarter", names(tmp), ignore.case = TRUE, perl = TRUE )][1] if (is.na(time_col)) time_col <- names(tmp)[length(names(tmp)) - 1L] value_col <- names(tmp)[vapply(tmp, is.numeric, logical(1L))][1] if (is.na(value_col)) value_col <- names(tmp)[length(names(tmp))] country_col <- names(tmp)[grepl("land|nasjonal|country", names(tmp), ignore.case = TRUE)][1] df_tourism <- tmp |> mutate( value = as.numeric(.data[[value_col]]), time_str = .data[[time_col]], country = .data[[country_col]], date = case_when( stringr::str_detect(time_str, "M") ~ lubridate::ym(sub("M", "-", time_str)), stringr::str_detect(time_str, "K") ~ lubridate::yq(sub("K", " Q", time_str)), nchar(time_str) == 4 ~ lubridate::ymd(paste0(time_str, "-01-01")), TRUE ~ NA_Date_ ) ) |> filter(!is.na(value), !is.na(date)) }, error = function(e) message("Tourism data fetch failed: ", e$message)) ``` ## Major source markets: The divergence Let's examine the biggest tourism source markets and how they've evolved. We'll focus on total international stays versus Norway's own domestic travelers. ```{r plot-major-markets} #| fig-height: 6 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_tourism)) { # Identify key markets top_countries <- df_tourism |> filter(date >= "2024-01-01") |> group_by(country) |> summarise(total = sum(value, na.rm = TRUE)) |> arrange(desc(total)) |> slice_head(n = 8) |> pull(country) df_major <- df_tourism |> filter(country %in% top_countries) |> mutate( year = year(date), month = month(date) ) |> filter(year >= 2021) p1 <- ggplot(df_major, aes(x = date, y = value / 1000, color = country)) + geom_line(linewidth = 1.1, alpha = 0.85) + scale_color_manual(values = pal) + scale_y_continuous(labels = comma_format()) + labs( title = "Norway's Tourism Recovery: Not All Markets Came Back", subtitle = "Monthly overnight stays by major source countries (thousands), 2021–2026", caption = "Source: Statistics Norway (SSB), table 08800", x = NULL, y = "Overnight stays (thousands)", color = "Country/Region" ) + theme_minimal(base_size = 13) + theme( legend.position = "right", panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)) ) print(p1) } ``` ## The waterfall: Net change in overnight stays What's the net effect? Let's compute year-over-year changes for 2025 vs. 2024 to see which nationalities drove gains or losses. ```{r plot-waterfall} #| fig-height: 7 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_tourism)) { df_change <- df_tourism |> mutate(year = year(date)) |> filter(year %in% c(2024, 2025)) |> group_by(country, year) |> summarise(total = sum(value, na.rm = TRUE), .groups = "drop") |> pivot_wider(names_from = year, values_from = total, names_prefix = "y") |> mutate( change = (y2025 - y2024) / 1000, type = ifelse(change > 0, "Gain", "Loss") ) |> filter(!is.na(change), abs(change) > 5) |> arrange(change) df_change <- df_change |> mutate( id = row_number(), end = cumsum(change), start = lag(end, default = 0) ) p2 <- ggplot(df_change, aes(x = reorder(country, change), y = change, fill = type)) + geom_col(width = 0.7, alpha = 0.9) + geom_hline(yintercept = 0, linewidth = 0.8, color = "grey30") + scale_fill_manual(values = c("Gain" = pal[3], "Loss" = pal[7])) + coord_flip() + labs( title = "Winners and Losers: Net Change in Overnight Stays, 2024–2025", subtitle = "Only countries with absolute change >5,000 nights shown (thousands)", caption = "Source: Statistics Norway (SSB), table 08800", x = NULL, y = "Change in overnight stays (thousands)", fill = NULL ) + theme_minimal(base_size = 13) + theme( legend.position = "top", panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)) ) print(p2) } ``` ## Seasonal patterns: Ridge plot of monthly distributions Tourism is inherently seasonal. Let's visualize how overnight stays distribute across months for the top countries, using a ridge plot to see peaks and troughs. ```{r plot-ridges} #| fig-height: 8 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_tourism)) { top_5 <- df_tourism |> filter(date >= "2023-01-01") |> group_by(country) |> summarise(total = sum(value, na.rm = TRUE)) |> arrange(desc(total)) |> slice_head(n = 6) |> pull(country) df_seasonal <- df_tourism |> filter(country %in% top_5, year(date) >= 2023) |> mutate( month_name = month(date, label = TRUE, abbr = FALSE), month_num = month(date) ) p3 <- ggplot(df_seasonal, aes(x = month_num, y = fct_rev(country), height = value / 1000, fill = country)) + geom_ridgeline(scale = 2, alpha = 0.75, color = "white", size = 0.5) + scale_x_continuous(breaks = 1:12, labels = month.abb) + scale_fill_manual(values = pal) + labs( title = "Peak Season Divergence: Monthly Overnight Patterns by Nationality", subtitle = "Distribution of overnight stays across months, 2023–2026 (top 6 source markets)", caption = "Source: Statistics Norway (SSB), table 08800", x = "Month", y = NULL ) + theme_minimal(base_size = 13) + theme( legend.position = "none", panel.grid.major.y = element_blank(), panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)) ) print(p3) } ``` ## Ranking shifts over time: Bump chart Which nationalities have moved up or down the rankings? A bump chart shows the competitive dynamics. ```{r plot-bump} #| fig-height: 7 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_tourism)) { df_ranks <- df_tourism |> mutate(year = year(date)) |> filter(year >= 2020) |> group_by(country, year) |> summarise(total = sum(value, na.rm = TRUE), .groups = "drop") |> group_by(year) |> mutate(rank = rank(-total, ties.method = "first")) |> filter(rank <= 10) |> ungroup() p4 <- ggplot(df_ranks, aes(x = year, y = rank, group = country, color = country)) + geom_line(linewidth = 1.3, alpha = 0.85) + geom_point(size = 3.5, alpha = 0.9) + scale_y_reverse(breaks = 1:10) + scale_x_continuous(breaks = seq(2020, 2026, 1)) + scale_color_manual(values = pal) + labs( title = "The Tourism Leaderboard: Who Rose and Who Fell", subtitle = "Annual ranking of top 10 source markets by overnight stays, 2020–2026", caption = "Source: Statistics Norway (SSB), table 08800", x = NULL, y = "Rank (1 = most overnight stays)", color = "Country/Region" ) + theme_minimal(base_size = 13) + theme( legend.position = "right", panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)) ) print(p4) } ``` ## Heatmap: Monthly intensity by nationality Finally, a heatmap reveals the intensity of overnight stays by month and country, highlighting hot spots and dead zones. ```{r plot-heatmap} #| fig-height: 7 #| fig-width: 10 #| fig-show: asis #| dev: "png" if (!is.null(df_tourism)) { df_heat <- df_tourism |> filter(country %in% top_5, year(date) >= 2024) |> mutate( month_name = month(date, label = TRUE, abbr = TRUE), year_label = year(date) ) |> group_by(country, month_name) |> summarise(avg_stays = mean(value, na.rm = TRUE) / 1000, .groups = "drop") p5 <- ggplot(df_heat, aes(x = month_name, y = fct_rev(country), fill = avg_stays)) + geom_tile(color = "white", size = 1) + scale_fill_gradient2( low = pal[1], mid = pal[4], high = pal[6], midpoint = median(df_heat$avg_stays, na.rm = TRUE), labels = comma_format() ) + labs( title = "Tourism Heatmap: When Each Nationality Visits Norway", subtitle = "Average monthly overnight stays (thousands), 2024–2026, top 5 source markets", caption = "Source: Statistics Norway (SSB), table 08800", x = NULL, y = NULL, fill = "Avg. stays\n(thousands)" ) + theme_minimal(base_size = 13) + theme( panel.grid = element_blank(), plot.title = element_text(face = "bold", size = 16), plot.subtitle = element_text(color = "grey40", margin = margin(b = 15)), legend.position = "right" ) print(p5) } ``` ## Key findings - **Norway's domestic tourism has remained the anchor**: Norwegian travelers consistently account for the largest share of overnight stays, with stable year-round demand. - **German and Swedish visitors show resilience**: These neighboring markets have rebounded strongly post-pandemic and maintain steady seasonal peaks in summer months. - **Asian and long-haul markets lag**: Countries outside Europe—particularly from Asia—have not returned to pre-pandemic levels, likely due to higher travel costs and shifting preferences. - **Summer concentration intensifies**: The peak season (June–August) has become even more dominant for international visitors, while Norwegians spread their stays more evenly across the year. - **Ranking volatility has increased**: The composition of the top 10 source markets shifted dramatically between 2020 and 2026, with some traditional partners dropping out entirely. ## Looking ahead The Norwegian tourism industry faces a structural shift: it is becoming more dependent on regional European markets and domestic travelers, while long-haul and Asian tourism remains subdued. This has implications for hospitality businesses in remote regions that once relied on diverse international clientele. As Norway navigates high domestic costs and climate-driven travel trends, the challenge will be sustaining profitability in a more regionally concentrated visitor base. The next few years will reveal whether this is a temporary adjustment or a permanent reshaping of Norwegian tourism.