House Prices

Data - Fred

Author

Info

LAST_COMPILE

LAST_COMPILE
2026-06-19

Last

date	Nobs
2026-05-01	8
2026-04-01	9

variable

variable	Variable	Nobs
CPIAUCSL	Consumer Price Index for All Urban Consumers: All Items in U.S. City Average	953
CPILFESL	Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average	833
CSUSHPINSA	S&P Cotality Case-Shiller U.S. National Home Price Index	615
MICH	University of Michigan: Inflation Expectation	580
CUSR0000SEHA	Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average	545
CUSR0000SAS2RS	Consumer Price Index for All Urban Consumers: Rent of Shelter in U.S. City Average	437
ASTMA	All Sectors; Total Mortgages; Asset, Level	322
CP00MI15EA20M086NEST	Harmonized Index of Consumer Prices: Total for Euro Area (20 Countries)	318
CP041MI15EA20M086NEST	Harmonized Index of Consumer Prices: Actual Rentals for Housing for Euro Area (20 Countries)	318
TOTNRGFOODEA20MI15XM	Harmonized Index of Consumer Prices: Overall Index Excluding Energy, Food, Alcohol, and Tobacco for Euro Area (20 Countries)	318
A255RD3Q086SBEA	Imports of goods (implicit price deflator)	317
DPCCRV1Q225SBEA	Personal Consumption Expenditures (PCE) Excluding Food and Energy (Chain-Type Price Index)	268
MSPUS	Median Sales Price of Houses Sold for the United States	253
USSTHPI	All-Transactions House Price Index for the United States	205
CDSP	Consumer Debt Service Payments as a Percent of Disposable Personal Income	184
MDSP	Mortgage Debt Service Payments as a Percent of Disposable Personal Income	184
TDSP	Household Debt Service Payments as a Percent of Disposable Personal Income	184
FPCPITOTLZGUSA	Inflation, consumer prices for the United States	65
FIXHAI	Housing Affordability Index (Fixed)	14

Housing Affordability Index

Code

housing %>%
  filter(variable %in% c("FIXHAI")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) + 
  theme_minimal() + xlab("") + ylab("Housing Affordability Index") +
  scale_x_date(breaks = "1 month",
               labels = date_format("%Y %b")) + 
  scale_y_log10(breaks = seq(10, 400, 2)) + 
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

Rents

inflation

12 months

English

Code

housing %>%
  filter(variable %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA",
                         "CP00MI15EA20M086NEST", "CPIAUCSL")) %>%
  #add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
  select(date, variable, value) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = value/lag(value, 12)-1) %>%
  filter(date >= as.Date("2012-01-01")) %>%
  ungroup %>%
  mutate(type = case_when(variable  %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA") ~ "Rents",
                          variable  %in% c("CP00MI15EA20M086NEST", "CPIAUCSL") ~ "All")) %>%
  mutate(country = case_when(variable %in% c("CP041MI15EA20M086NEST", "CP00MI15EA20M086NEST") ~ "Euro area (HICP)",
                              variable %in% c("CUSR0000SEHA", "CPIAUCSL") ~ "US (CPI)")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, linetype = type, color = country)) + 
  theme_minimal() + xlab("") + ylab("Inflation (%)") +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = scales::date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

French

All

Code

Sys.setlocale("LC_TIME", "fr_CA.UTF-8")

# [1] "fr_CA.UTF-8"

Code

housing %>%
  filter(variable %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA",
                         "CP00MI15EA20M086NEST", "CPIAUCSL")) %>%
  #add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
  select(date, variable, value) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = value/lag(value, 12)-1) %>%
  filter(date >= as.Date("2012-01-01")) %>%
  ungroup %>%
  mutate(type = case_when(variable  %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA") ~ "Loyers",
                          variable  %in% c("CP00MI15EA20M086NEST", "CPIAUCSL") ~ "Tous")) %>%
  mutate(country = case_when(variable %in% c("CP041MI15EA20M086NEST", "CP00MI15EA20M086NEST") ~ "Zone euro (IPCH)",
                              variable %in% c("CUSR0000SEHA", "CPIAUCSL") ~ "États-Unis (CPI)")) %>%
  mutate(country = factor(country, levels = c("Zone euro (IPCH)", "États-Unis (CPI)")),
         type = factor(type, levels = c("Tous", "Loyers"))) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, linetype = type, color = country)) + 
  theme_minimal() + xlab("") + ylab("Inflation (%)") +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = scales::date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  scale_linetype_manual(values = c("dashed", "solid")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Code

Sys.setlocale("LC_TIME", "en_CA.UTF-8")

# [1] "en_CA.UTF-8"

6 months

Code

housing %>%
  filter(variable %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA",
                         "CP00MI15EA20M086NEST", "CPIAUCSL",
                         "TOTNRGFOODEA20MI15XM", "CPILFESL")) %>%
  #add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
  select(date, variable, value) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = value/lag(value, 6)-1) %>%
  filter(date >= as.Date("2012-01-01")) %>%
  ungroup %>%
  mutate(type = case_when(variable  %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA") ~ "Rents",
                          variable  %in% c("CP00MI15EA20M086NEST", "CPIAUCSL") ~ "All",
                          variable  %in% c("TOTNRGFOODEA20MI15XM", "CPILFESL") ~ "Core")) %>%
  mutate(country = case_when(variable %in% c("CP041MI15EA20M086NEST", "CP00MI15EA20M086NEST", "TOTNRGFOODEA20MI15XM") ~ "Euro area (HICP)",
                              variable %in% c("CUSR0000SEHA", "CPIAUCSL", "CPILFESL") ~ "US (CPI)")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, linetype = type, color = country)) + 
  theme_minimal() + xlab("") + ylab("Inflation, 6 months (%)") +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = scales::date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

3 months

Code

housing %>%
  filter(variable %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA",
                         "CP00MI15EA20M086NEST", "CPIAUCSL",
                         "TOTNRGFOODEA20MI15XM", "CPILFESL")) %>%
  #add_row(date = as.Date("2023-10-01"), variable = "CP00MI15EA20M086NEST", value = 124.55) %>%
  select(date, variable, value) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = value/lag(value, 3)-1) %>%
  filter(date >= as.Date("2012-01-01")) %>%
  ungroup %>%
  mutate(type = case_when(variable  %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA") ~ "Rents",
                          variable  %in% c("CP00MI15EA20M086NEST", "CPIAUCSL") ~ "All",
                          variable  %in% c("TOTNRGFOODEA20MI15XM", "CPILFESL") ~ "Core")) %>%
  mutate(country = case_when(variable %in% c("CP041MI15EA20M086NEST", "CP00MI15EA20M086NEST", "TOTNRGFOODEA20MI15XM") ~ "Euro area (HICP)",
                              variable %in% c("CUSR0000SEHA", "CPIAUCSL", "CPILFESL") ~ "US (CPI)")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, linetype = type, color = country)) + 
  theme_minimal() + xlab("") + ylab("Inflation, 3 months (%)") +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = scales::date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) + 
  scale_color_manual(values = c("#003399", "#B22234")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Price index

Just housing

Code

housing %>%
  filter(variable %in% c("CP041MI15EA20M086NEST", "CUSR0000SEHA"),
         date >= as.Date("2020-01-01")) %>%
  select(date, variable, value) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  ungroup %>%
  mutate(Variable = case_when(variable == "CP041MI15EA20M086NEST" ~ "Euro area HICP Rents",
                              variable == "CUSR0000SEHA" ~ "US CPI Rents")) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = Variable)) + 
  theme_minimal() + xlab("") + ylab("Price Index") +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = scales::date_format("%Y")) + 
  scale_y_log10(breaks = seq(100, 200, 2)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Real House Prices

Growth

Code

housing %>%
  filter(variable %in% c("CPIAUCSL", "CSUSHPINSA"),
         month(date) == 1,
         date >= as.Date("1987-01-01")) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(CPIAUCSL = 100*(log(CPIAUCSL) - lag(log(CPIAUCSL))),
         CSUSHPINSA = 100*(log(CSUSHPINSA) - lag(log(CSUSHPINSA)))) %>%
  gather(variable, value, -date) %>%
  left_join(variable, by = "variable") %>%
  ggplot(.) + geom_line(aes(x = date, y = value / 100, color = Variable)) + 
  theme_minimal() + xlab("") + ylab("Inflation Rates (%)") +
  scale_x_date(breaks = seq(1870, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.4, 0.2),
        legend.title = element_blank())

Index

Code

housing %>%
  filter(variable %in% c("CPIAUCSL", "CSUSHPINSA"),
         date >= as.Date("2000-01-01")) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(`Nominal S&P/Case-Shiller Index` = CSUSHPINSA,
         CSUSHPINSA_real = CSUSHPINSA/CPIAUCSL,
         `Real S&P/Case-Shiller Index` = 100*CSUSHPINSA_real / CSUSHPINSA_real[1]) %>%
  select(date, `Nominal S&P/Case-Shiller Index`, `Real S&P/Case-Shiller Index`) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2006-10-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("S&P/Case-Shiller Index (Nov. 2006 Peak = 100)") +
  scale_x_date(breaks = seq(1870, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 400, 10)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

Median House Prices

All

Code

housing %>%
  filter(variable %in% c("MSPUS", "CPIAUCSL"),
         date >= as.Date("1965-01-01"),
         month(date) %in% c(1, 4, 7, 10)) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(`Nominal Median Sales Price of Houses` = MSPUS,
         MSPUS_real = MSPUS/CPIAUCSL,
         `Real Median Sales Price of Houses` = 100*MSPUS_real / MSPUS_real[1]) %>%
  select(date, `Nominal Median Sales Price of Houses`, `Real Median Sales Price of Houses`) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2007-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Median Sales Price of Houses (2007 = 100)") +
  scale_x_date(breaks = seq(1870, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 400, 10)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

1980-

Code

housing %>%
  filter(variable %in% c("MSPUS", "CPIAUCSL"),
         date >= as.Date("1980-01-01"),
         month(date) %in% c(1, 4, 7, 10)) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(`Nominal Median Sales Price of Houses` = MSPUS,
         MSPUS_real = MSPUS/CPIAUCSL,
         `Real Median Sales Price of Houses` = 100*MSPUS_real / MSPUS_real[1]) %>%
  select(date, `Nominal Median Sales Price of Houses`, `Real Median Sales Price of Houses`) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2007-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Median Sales Price of Houses (2007 = 100)") +
  scale_x_date(breaks = seq(1870, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 400, 10)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

1990-

Code

housing %>%
  filter(variable %in% c("MSPUS", "CPIAUCSL"),
         date >= as.Date("1990-01-01"),
         month(date) %in% c(1, 4, 7, 10)) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(`Nominal Median Sales Price of Houses` = MSPUS,
         MSPUS_real = MSPUS/CPIAUCSL,
         `Real Median Sales Price of Houses` = 100*MSPUS_real / MSPUS_real[1]) %>%
  select(date, `Nominal Median Sales Price of Houses`, `Real Median Sales Price of Houses`) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2007-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Median Sales Price of Houses (2007 = 100)") +
  scale_x_date(breaks = seq(1870, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 400, 10)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

2000-

Code

housing %>%
  filter(variable %in% c("MSPUS", "CPIAUCSL"),
         date >= as.Date("2000-01-01"),
         month(date) %in% c(1, 4, 7, 10)) %>%
  select(date, variable, value) %>%
  spread(variable, value) %>%
  mutate(`Nominal Median Sales Price of Houses` = MSPUS,
         MSPUS_real = MSPUS/CPIAUCSL,
         `Real Median Sales Price of Houses` = 100*MSPUS_real / MSPUS_real[1]) %>%
  select(date, `Nominal Median Sales Price of Houses`, `Real Median Sales Price of Houses`) %>%
  gather(variable, value, -date) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2007-01-01")]) %>%
  ggplot(.) + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Median Sales Price of Houses (2007 = 100)") +
  scale_x_date(breaks = seq(1870, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_log10(breaks = seq(10, 400, 10)) + 
  scale_color_manual(values = c("#2D68C4", "#F2A900", "#000000")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  geom_hline(yintercept = 100, linetype = "dashed",  color = "black")

Mortgage Debt Service Payments (% Disposable Personal Income)

All

Code

housing %>%
  filter(variable %in% c("MDSP")) %>%
  mutate(value = value / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1870, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 15, 0.2),
                     labels = scales::percent_format(accuracy = 0.1)) + 
  xlab("") + ylab("% of Disposable Income") + 
  geom_vline(xintercept = as.Date("2008-09-15"), linetype = "dashed", color = viridis(3)[2]) + 
    geom_rect(data = data_frame(start = as.Date("2004-06-01"), 
                                end = as.Date("2007-08-01")), 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = +Inf), 
              fill = viridis(4)[4], alpha = 0.2) +
    geom_vline(xintercept = as.Date("2004-06-01"), linetype = "dashed", color = viridis(4)[4]) + 
    geom_vline(xintercept = as.Date("2007-08-01"), linetype = "dashed", color = viridis(4)[4])

2000-

Code

housing %>%
  filter(variable %in% c("MDSP"),
         date >= as.Date("1994-01-01")) %>%
  mutate(value = value / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1870, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 15, 0.2),
                     labels = scales::percent_format(accuracy = 0.1)) + 
  xlab("") + ylab("Mortgage debt service payments (% of Disposable Income)") + 
  geom_vline(xintercept = as.Date("2008-09-15"), linetype = "dashed", color = viridis(3)[2]) + 
    geom_rect(data = data_frame(start = as.Date("2004-06-01"), 
                                end = as.Date("2007-08-01")), 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = +Inf), 
              fill = viridis(4)[4], alpha = 0.2) +
    geom_vline(xintercept = as.Date("2004-06-01"), linetype = "dashed", color = viridis(4)[4]) + 
    geom_vline(xintercept = as.Date("2007-08-01"), linetype = "dashed", color = viridis(4)[4])

2000-2012

New

Code

housing %>%
  filter(variable %in% c("MDSP")) %>%
  filter(date >= as.Date("2000-01-01"), 
         date <= as.Date("2012-01-01")) %>%
  mutate(value = value / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 15, 0.2),
                     labels = scales::percent_format(accuracy = 0.1)) + 
  xlab("") + ylab("% of Disposable Income") + 
  geom_vline(xintercept = as.Date("2008-09-15"), linetype = "dashed", color = viridis(3)[2]) + 
    geom_rect(data = data_frame(start = as.Date("2004-06-01"), 
                                end = as.Date("2007-08-01")), 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = +Inf), 
              fill = viridis(4)[4], alpha = 0.2) +
    geom_vline(xintercept = as.Date("2004-06-01"), linetype = "dashed", color = viridis(4)[4]) + 
    geom_vline(xintercept = as.Date("2007-08-01"), linetype = "dashed", color = viridis(4)[4])

old

Code

housing %>%
  filter(variable %in% c("MDSP")) %>%
  filter(date >= as.Date("2002-01-01"), 
         date <= as.Date("2012-01-01")) %>%
  mutate(value = value / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1870, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 15, 0.2),
                     labels = scales::percent_format(accuracy = 0.1)) + 
  xlab("") + ylab("Mortgage Debt Service Payments (% of Disp. Income)") + 
  geom_vline(xintercept = as.Date("2008-09-15"), linetype = "dashed", color = viridis(3)[2]) + 
    geom_rect(data = data_frame(start = as.Date("2004-06-01"), 
                                end = as.Date("2007-08-01")), 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = +Inf), 
              fill = viridis(4)[4], alpha = 0.2) +
    geom_vline(xintercept = as.Date("2004-06-01"), linetype = "dashed", color = viridis(4)[4]) + 
    geom_vline(xintercept = as.Date("2007-08-01"), linetype = "dashed", color = viridis(4)[4])