The Global Housing Bubble is Losing Air

While Miami and Tokyo continue to heat up, the primary global trend is a significant cooling of major hubs. Former ‘Bubble Risk’ leaders like Toronto, Frankfurt, and Hong Kong have seen their risk scores halved since 2020.

MakeoverMonday
2026
A #MakeoverMonday redesign analyzing UBS Global Real Estate Bubble Index data (2020-2025). This dual-panel visualization reveals a surprising story: while Miami dominates headlines, the broader trend shows major financial hubs like Toronto, Frankfurt, and Hong Kong experiencing dramatic bubble deflation.
Author

Steven Ponce

Published

January 12, 2026

Original

The original visualization comes from The Biggest Housing Bubble Risks Globally

Original visualization

Makeover

Figure 1: Two-panel visualization comparing housing bubble risk trajectories for 9 global cities from 2020 to 2025. Left panel: Slope chart showing dramatic divergence—Toronto, Hong Kong, and Munich fell from 1.8 to below 1.0 (teal lines), while Miami rose from 0.5 to 1.7 (grey lines). Right panel: Scatter plot of current risk score versus 5-year change, with point size reflecting magnitude of change. Cooling markets cluster in the lower-left quadrant; Hong Kong shows the most significant decline (-1.3 points).

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
    tidyverse, janitor, skimr, scales, ggtext, showtext, glue,
    patchwork, ggrepel          # Interpreted String Literals
)
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 12,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|

### |- Current data (2025) ----
housing_bubble_raw <- readxl::read_excel(
   here::here("data/MakeoverMonday/2026/Housing bubbles.xlsx")) |>
  clean_names()

### |- Historical data (2020-2025) from UBS reports ----

# Source: UBS Global Real Estate Bubble Index reports 2020-2025
# https://www.ubs.com/global/en/wealth-management/insights/2024/global-real-estate-bubble-index.html
# Note: Historical data compiled from annual UBS reports

historical_raw <- tribble(
  ~city, ~year_2020, ~year_2021, ~year_2022, ~year_2023, ~year_2024, ~year_2025,
  "Miami", 0.5, 0.8, 1.4, 1.8, 1.8, 1.7,
  "Tokyo", 0.7, 0.7, 0.6, 0.9, 1.2, 1.6,
  "Zurich", 1.5, 1.8, 1.7, 1.7, 1.5, 1.6,
  "Los Angeles", 0.7, 1.0, 1.2, 1.2, 1.2, 1.1,
  "Toronto", 1.8, 2.0, 2.2, 1.2, 0.9, 0.8,
  "Frankfurt", 1.5, 2.2, 2.2, 1.1, 0.8, 0.8,
  "Munich", 1.8, 2.3, 2.0, 1.1, 0.9, 0.6,
  "Hong Kong", 1.8, 1.7, 1.7, 1.2, 0.7, 0.5,
  "Vancouver", 1.0, 1.6, 1.7, 1.1, 0.9, 0.8
)
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(housing_bubble_raw)
glimpse(historical_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

slope_data <- historical_raw |>
  mutate(
    change     = year_2025 - year_2020,
    direction  = if_else(change < 0, "Cooling", "Rising"),
    label_2020 = sprintf("%s  %.1f", city, year_2020),
    label_2025 = sprintf("%s  %.1f", city, year_2025)
  ) |>
  arrange(desc(year_2025)) |>
  mutate(city = factor(city, levels = unique(city)))
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(
  palette = list(
    cooling    = "#0D7377",
    rising     = "#C4C4C4"
  )
)

### |-  Main titles ----
title_text <- "The Global Housing Bubble is Losing Air"

subtitle_text <- str_glue(
  "While Miami and Tokyo continue to heat up, the primary global trend is a **significant cooling** of major hubs.<br>",
  "Former 'Bubble Risk' leaders like ",
  "<span style='color:{colors$palette$cooling};'>**Toronto, Frankfurt, and Hong Kong**</span>",
  " have seen their risk scores halved since 2020."
)

caption_text <- create_mm_caption(
  mm_year = 2026, mm_week = 02,
  source_text = str_glue(
    "UBS Global Real Estate Bubble Index 2025"
  )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # # Text styling
    plot.title = element_text(
      size = rel(1.5), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "right",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),
    axis.text.y = element_markdown(
      size = rel(0.85), family = fonts$subtitle,
      color = "gray40"
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |-  p1: slope chart ----
p1 <-
ggplot(slope_data) +
  # Geoms
  geom_segment(
    aes(
      x = 1, xend = 2,
      y = year_2020, yend = year_2025,
      color = direction, alpha = direction, linewidth = direction
    )
  ) +
  geom_point(aes(x = 1, y = year_2020, color = direction, alpha = direction), size = 2) +
  geom_point(aes(x = 2, y = year_2025, color = direction, alpha = direction), size = 3) +
  geom_text_repel(
    aes(x = 1, y = year_2020, label = label_2020),
    direction = "y",
    hjust = 1,
    nudge_x = -0.15,
    size = 3.5,
    segment.color = NA,
    family = fonts$text,
    color = colors$text     
  ) +
  geom_text_repel(
    aes(x = 2, y = year_2025, label = label_2025, color = direction),
    direction = "y",
    hjust = 0,
    nudge_x = 0.15,
    size = 3.8,
    fontface = "bold",
    segment.color = NA,
    family = fonts$text,
    seed = 123
  ) +
  # Scales
  scale_color_manual(values = c("Cooling" = colors$palette$cooling, "Rising" = colors$palette$rising)) +
  scale_alpha_manual(values = c("Cooling" = 1, "Rising" = 0.4)) +
  scale_linewidth_manual(values = c("Cooling" = 1.2, "Rising" = 0.5)) +
  scale_x_continuous(
    limits = c(0.4, 2.6),
    breaks = c(1, 2),
    labels = c("2020", "2025")
  ) +
  # Labs
  labs(subtitle = "Bubble Risk Index: 5-Year Trajectory", x = NULL, y = NULL) +
  # Theme
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.y = element_blank(),
    axis.text.y = element_blank(),
    legend.position = "none"
    )

### |-  p2: quadrant chart ----
p2 <-
ggplot(slope_data, aes(x = year_2025, y = change)) +
  # Geoms
  geom_hline(yintercept = 0, color = "grey80", linetype = "dashed") +
  geom_vline(xintercept = 1, color = "grey80", linetype = "dashed") +
  geom_point(aes(color = direction, alpha = direction, size = abs(change))) +
  geom_text_repel(
    aes(label = city, color = direction),
    size = 3.5,
    fontface = "bold",
    box.padding = 0.5, 
    seed = 123
  ) +
  # Scales
  scale_color_manual(values = c("Cooling" = colors$palette$cooling, "Rising" = colors$palette$rising)) +
  scale_alpha_manual(values = c("Cooling" = 1, "Rising" = 0.4)) +
  scale_size(range = c(2.5, 8)) +
  coord_cartesian(xlim = c(0, 2)) +
  # Labs
  labs(
    subtitle = "Current Risk vs. Velocity of Change",
    x = "Risk Score in 2025",
    y = "5-Year Net Change"
  ) +
  # Theme
  theme(
    legend.position = "none",
    panel.grid.major = element_line(color = "grey95")
  )

### |-  combined plot ----
combined_plot <- p1 + p2 +
  plot_layout(widths = c(1.15, 1)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(2.4),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.15,
        margin = margin(t = 5, b = 10)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.8),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.88),
        lineheight = 1.5,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.55),
        family = fonts$subtitle,
        color = colors$caption,
        hjust = 0,
        lineheight = 1.4,
        margin = margin(t = 20, b = 5)
      ),
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 12, 
  height = 8
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      ggrepel_0.9.6   patchwork_1.3.0 glue_1.8.0     
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         gifski_1.32.0-1    fansi_1.0.6        pkgconfig_2.0.3   
[13] ggplotify_0.1.2    readxl_1.4.3       lifecycle_1.0.4    compiler_4.4.0    
[17] farver_2.1.2       munsell_0.5.1      repr_1.1.7         codetools_0.2-20  
[21] snakecase_0.11.1   htmltools_0.5.8.1  yaml_2.3.10        pillar_1.9.0      
[25] camcorder_0.1.0    magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1  
[29] digest_0.6.37      stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1        
[33] rprojroot_2.0.4    fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1  
[37] cli_3.6.4          magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4        
[41] withr_3.0.2        timechange_0.3.0   rmarkdown_2.29     cellranger_1.1.0  
[45] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[49] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[53] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[57] jsonlite_1.8.9     R6_2.5.1           fs_1.6.5           systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2026_02.qmd.

For the full repository, click here.

10. References

11. Custom Functions Documentation

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

  • fonts.R: setup_fonts(), get_font_families() - Font management with showtext
  • social_icons.R: create_social_caption() - Generates formatted social media captions
  • image_utils.R: save_plot() - Consistent plot saving with naming conventions
  • base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils

Back to top

Citation

BibTeX citation:
@online{ponce2026,
  author = {Ponce, Steven},
  title = {The {Global} {Housing} {Bubble} Is {Losing} {Air}},
  date = {2026-01-12},
  url = {https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_02.html},
  langid = {en}
}
For attribution, please cite this work as:
Ponce, Steven. 2026. “The Global Housing Bubble Is Losing Air.” January 12. https://stevenponce.netlify.app/data_visualizations/MakeoverMonday/2026/mm_2026_02.html.