Housing In Canada
Graphs of housing data in Canada using STATCAN data
Data
STATCAN Table: 18-10-0205-01 (New housing price index)
STATCAN Table: 34-10-0162-01 (Newly completed and unoccupied housing)
STATCAN Table: 36-10-0688-01 (Housing stock)
STATCAN Table: 11-10-0688-01 (Market income, government transfers, total income, income tax and after-tax income)
STATCAN Table: 34-10-0133-01 (Average rents)
Bank of Canada: Interest Rates
Prepare Data
# Prep data
myCaption <- "www.dblogr.com/ or derekmichaelwright.github.io/dblogr/ | Data: STATCAN"
myColors_M <- c("purple4", "darkgreen", "darkorange")
myColors_P <- c("steelblue","darkred","darkblue")
#
d1 <- read.csv("1810020501_databaseLoadingData.csv") %>%
select(Date=REF_DATE, Area=GEO, Measurement=New.housing.price.indexes,
Unit=UOM, Value=VALUE) %>%
separate(Date, c("Year", "Month"), sep = "-", remove = F) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"))
pp <- data.frame(Admin = factor(1:4),
Party = factor(c("PC", "LIB", "CPC", "LIB"), levels = c("PC", "LIB", "CPC")),
xmin = as.Date(c("1984-09-17", "1993-11-04", "2006-02-06", "2015-11-04")),
xmax = as.Date(c("1993-11-04", "2006-02-06", "2015-11-04", "2022-06-01")))
#
myAreas <- c("Census metropolitan areas and census agglomerations of 50,000 and over",
"Census metropolitan areas", "Census agglomerations 50,000 and over")
d2 <- read.csv("3410016201_databaseLoadingData.csv") %>%
select(Date=REF_DATE, Area=GEO, Measurement=Type.of.unit,
Unit=UOM, Value=VALUE) %>%
arrange(Area) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"),
Year = as.numeric(substr(Date, 1, 4)),
Area = factor(Area, levels = unique(c(myAreas, .$Area))))
#
myProvs <- c("Canada", "British Columbia", "Alberta", "Saskatchewan",
"Manitoba", "Ontario", "Quebec",
"Prince Edward Island", "New Brunswick",
"Nova Scotia", "Newfoundland and Labrador",
"Yukon", "Northwest Territories", "Nunavut")
myTypes <- c("Total dwelling type", "Single house", "Double house",
"Row house", "Apartment building", "Mobile house")
d3 <- read.csv("3610068801_databaseLoadingData.csv") %>%
select(Date=REF_DATE, Area=GEO, Sector=Institutional.sector, Private=Housing.Type,
Occupancy=Dwelling.Occupation, Type=Dwelling.type, Tenure=Tenure.type,
Unit=UOM, Value=VALUE) %>%
mutate(Date = as.Date(paste0(Date,"-01"), format = "%Y-%m-%d"),
Year = substr(Date, 1, 4),
Type = factor(Type, levels = myTypes),
Area = factor(Area, levels = myProvs))
#
d4 <- read.csv("1110019001_databaseLoadingData.csv") %>%
select(Year=REF_DATE, Area=GEO, Measurement=Income.concept,
Group=Economic.family.type, Unit=UOM, Value=VALUE) %>%
filter(Group == "Economic families and persons not in an economic family",
Measurement == "Median market income", Year >= 1986) %>%
mutate(Area = factor(Area, levels = unique(.$Area)),
Year = as.character(Year))
d4 <- d4 %>%
left_join(d4 %>% filter(Year == 1986) %>% select(Area, V1986=Value), by = "Area") %>%
mutate(Rate = 100 * (Value - V1986) / V1986) %>%
select(Year, Area, `Median market income`=Rate)
d5 <- d1 %>%
filter(Date >= "1986-01-01", Measurement == "Total (house and land)") %>%
select(Date, Year, Month, Area, Value)
d5 <- d5 %>%
left_join(d5 %>% filter(Date == "1986-01-01") %>% select(Area, V1986=Value), by = "Area") %>%
mutate(Rate = 100 * (Value - V1986) / V1986) %>%
select(Date, Year, Month, Area, `Housing price index`=Rate)
d6 <- left_join(d5, d4, by = c("Year", "Area")) %>%
filter(!is.na(`Median market income`), !is.na(`Housing price index`)) %>%
mutate(`Median market income` = ifelse(Month != "01", NA, `Median market income`)) %>%
gather(Measurement, Value, 5:6) %>% filter(!is.na(Value))
d7 <- read.csv("3410013301_databaseLoadingData.csv") %>%
select(Year=REF_DATE, Area=GEO, Structure.Type=Type.of.structure,
Unit.Type = Type.of.unit, Unit=UOM, Value=VALUE) %>%
filter(!is.na(Value), Value > 0)
d8 <- read.csv("bankofcanada_interestrates.csv", skip = 11) %>%
mutate(Date = as.Date(paste0(Date, "-01"))) %>%
rename(Value=2)House Prices
Canada
# Prep data
xx <- d1 %>% filter(Area == "Canada", Measurement == "Total (house and land)")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value)) +
geom_line(color = "darkred", alpha = 0.7, size = 1.5) +
scale_x_date(date_minor_breaks = "year") +
theme_agData(legend.position = "bottom") +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_01.png", mp, width = 6, height = 4)Regions
All Data
# Prep data
myAs <- c("Canada", "Ontario", "Quebec", "Prairie Region")
myCs <- c("darkred", "darkblue", "steelblue", "darkgreen")
xx <- d1 %>%
filter(Measurement == "Total (house and land)",
Area %in% myAs) %>%
mutate(Area = factor(Area, levels = myAs))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Area)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myCs) +
scale_x_date(date_minor_breaks = "year") +
#scale_x_date(breaks = as.Date(c("1985-01-01","1995-01-01","2005-01-01","2015-01-01")),
# date_labels = "%Y", date_minor_breaks = "year") +
theme_agData() +#axis.text.x = element_text(angle = 45, hjust = 1)
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_02.png", mp, width = 6, height = 4)> 2017
# Prep data
myAs <- c("Canada", "Ontario", "Quebec", "Prairie Region")
myCs <- c("darkred", "darkblue", "steelblue", "darkgreen")
xx <- d1 %>%
filter(Measurement == "Total (house and land)",
Area %in% myAs, Year > 2016) %>%
mutate(Area = factor(Area, levels = myAs))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Area)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myCs) +
scale_x_date(date_breaks = "year", date_labels = "%Y") +
theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_03.png", mp, width = 6, height = 4)Houses vs land
# Prep data
xx <- d1 %>%
filter(Area == "Canada", Measurement %in% c("House only", "Land only"))
myxmin <- min(xx$Value)
myxmax <- max(xx$Value)
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value, color = Measurement), alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_y_continuous(limits = c(myxmin,myxmax)) +
theme_agData(legend.position = "bottom") +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_04.png", mp, width = 6, height = 4)All Data
# Prep data
xx <- d1 %>% filter(Measurement == "Total (house and land)")
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value), size = 1.5, alpha = 0.7) +
facet_wrap(Area ~ ., ncol = 5) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Canada", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_05.png", mp, width = 16, height = 16)Saskatchewan
# Prep data
xx <- d1 %>%
filter(grepl("Saskatchewan", Area),
Measurement %in% c("House only", "Land only"))
# Plot
mp <- ggplot(xx) +
geom_rect(data = pp, alpha = 0.3, ymin = -Inf, ymax = Inf,
aes(xmin = xmin, xmax = xmax, fill = Party)) +
geom_line(aes(x = Date, y = Value, color = Measurement),
alpha = 0.7, size = 1.5) +
facet_grid(. ~ Area) +
scale_color_manual(name = NULL, values = myColors_M) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "House prices in Saskatchewan", x = NULL,
y = "Index (2016-12 = 100)", caption = myCaption)
ggsave("canada_housing_1_06.png", mp, width = 8, height = 4)Percent Change
Canada
# Prep data
xx <- d1 %>% filter(Area == "Canada", Measurement == "Total (house and land)")
for(i in 1:nrow(pp)) {
pp$price1[i] <- xx$Value[xx$Date == paste0(substr(pp$xmin[i],1,7),"-01")]
pp$price2[i] <- xx$Value[xx$Date == paste0(substr(pp$xmax[i],1,7),"-01")]
pp$PercentChange[i] <- (100 * pp$price2[i] / pp$price1[i]) - 100
}
# Plot
mp <- ggplot(pp, aes(x = Admin, y = PercentChange, fill = Party)) +
geom_col(color = "black", alpha = 0.7) +
scale_x_discrete(labels = paste(pp$xmin, pp$xmax, sep = "\n")) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Canada - Change in House Prices", x = NULL,
y = "Percent Change", caption = myCaption)
ggsave("canada_housing_1_07.png", mp, width = 5, height = 4)Regina, Saskatchewan
# Prep data
xx <- d1 %>% filter(Area == "Regina, Saskatchewan",
Measurement == "Total (house and land)")
for(i in 1:nrow(pp)) {
pp$price1[i] <- xx$Value[xx$Date == paste0(substr(pp$xmin[i],1,7),"-01")]
pp$price2[i] <- xx$Value[xx$Date == paste0(substr(pp$xmax[i],1,7),"-01")]
pp$PercentChange[i] <- (100 * pp$price2[i] / pp$price1[i]) - 100
}
# Plot
mp <- ggplot(pp, aes(x = Admin, y = PercentChange, fill = Party)) +
geom_col(color = "black", alpha = 0.7) +
scale_x_discrete(labels = paste(pp$xmin, pp$xmax, sep = "\n")) +
scale_fill_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Regina, Saskatchewan - Change in House Prices",
y = "Percent Change", x = NULL, caption = myCaption)
ggsave("canada_housing_1_08.png", mp, width = 5, height = 4)Home Builds
Canada
Monthly
# Prep data
xx <- d2 %>%
filter(Area %in% myAreas[1], Measurement == "Total units")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value)) +
geom_col(position = "dodge", alpha = 0.8, fill = "darkgreen") +
scale_x_date(date_breaks = "1 year", date_labels = "%Y",
date_minor_breaks = "1 year",) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_01.png", mp, width = 7, height = 5)Yearly
# Prep data
xx <- d2 %>%
filter(Area %in% myAreas[1], Measurement == "Total units") %>%
group_by(Year, Area) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value)) +
geom_col(position = "dodge", color = "black",
alpha = 0.7, fill = "darkgreen") +
scale_x_continuous(breaks = 1992:max(xx$Year)) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_02.png", mp, width = 7, height = 5)Houses vs Appartments
# Prep data
xx <- d2 %>%
filter(Area %in% myAreas[1], Measurement != "Total units") %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 1992:max(xx$Year),
minor_breaks = 1992:max(xx$Year)) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_03.png", mp, width = 6, height = 4)
# Prep data
xx <- xx %>% filter(Year >= 2005)
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, color = Measurement)) +
geom_line(alpha = 0.7, size = 1.5) +
scale_color_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 2005:max(xx$Year),
minor_breaks = 2005:max(xx$Year)) +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_04.png", mp, width = 6, height = 4)
# Prep data
xx <- xx %>% filter(Year >= 2018)
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value, fill = Measurement)) +
geom_col(position = "dodge", color = "black", alpha = 0.7) +
scale_fill_manual(name = NULL, values = myColors_P) +
scale_x_continuous(breaks = 2005:max(xx$Year)) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units Built in Canada", subtitle = myAreas[1],
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_05.png", mp, width = 6, height = 5)Cities
All Cities
# Prep data
xx <- d2 %>%
filter(Area != myAreas[1], Measurement != "Total units")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Measurement)) +
geom_line(alpha = 0.7) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 6) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units Built Per Month in Canada",
y = "Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_06.png", mp, width = 20, height = 15)Van + Tor + Cal + Reg
# Prep data
myCities <- c("Vancouver, British Columbia", "Toronto, Ontario",
"Calgary, Alberta", "Regina, Saskatchewan")
xx <- d2 %>%
filter(Area %in% myCities, Measurement != "Total units") %>%
mutate(Area = factor(Area, levels = myCities)) %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000, color = Measurement)) +
geom_line(alpha = 0.7) +
expand_limits(y = 0) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 2) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(subtitle = "Housing Units Built Per Year",
y = "Thousand Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_07.png", mp, width = 6, height = 4)British Columbia
# Prep data
myCities <- c("Vancouver, British Columbia", "Victoria, British Columbia",
"Kelowna, British Columbia", "Abbotsford-Mission, British Columbia")
xx <- d2 %>%
filter(Area %in% myCities, Measurement != "Total units") %>%
mutate(Area = factor(Area, levels = myCities)) %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000, color = Measurement)) +
geom_line(alpha = 0.7) +
expand_limits(y = 0) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 2) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(subtitle = "Housing Units Built Per Year",
y = "Thousand Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_08.png", mp, width = 6, height = 4)Prairie
# Prep data
myCities <- c("Calgary, Alberta", "Edmonton, Alberta", "Medicine Hat, Alberta",
"Regina, Saskatchewan", "Saskatoon, Saskatchewan",
"Winnipeg, Manitoba")
xx <- d2 %>%
filter(Area %in% myCities, Measurement != "Total units") %>%
mutate(Area = factor(Area, levels = myCities)) %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000, color = Measurement)) +
geom_line(alpha = 0.7) +
expand_limits(y = 0) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 2) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(subtitle = "Housing Units Built Per Year",
y = "Thousand Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_09.png", mp, width = 6, height = 6)Eastern Canada
# Prep data
myCities <- c("Toronto, Ontario", "Ottawa-Gatineau, Ontario/Quebec",
"Windsor, Ontario", "London, Ontario",
"Montréal, Quebec", "Québec, Quebec")
xx <- d2 %>%
filter(Area %in% myCities, Measurement != "Total units") %>%
mutate(Area = factor(Area, levels = myCities)) %>%
group_by(Year, Area, Measurement) %>%
summarise(Value = sum(Value))
# Plot
mp <- ggplot(xx, aes(x = Year, y = Value / 1000, color = Measurement)) +
geom_line(alpha = 0.7) +
expand_limits(y = 0) +
facet_wrap(Area ~ ., scales = "free_y", ncol = 2) +
scale_color_manual(name = NULL, values = myColors_P) +
theme_agData(legend.position = "bottom") +
labs(subtitle = "Housing Units Built Per Year",
y = "Thousand Units Built", x = NULL, caption = myCaption)
ggsave("canada_housing_2_10.png", mp, width = 6, height = 6)Occupancy
# Prep data
xx <- d3 %>%
filter(Area == "Canada", Sector == "Total economy",
Private == "Total housing type", Tenure == "Total tenure type",
Occupancy == "Private dwellings unoccupied")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000)) +
geom_col(alpha = 0.7, size = 1.5, fill = "darkgreen") +
facet_wrap(Type ~ ., scales = "free_y") +
expand_limits(y = 0) +
theme_agData() +
labs(title = "Unoccupied Housing Units in Canada",
y = "Thousand Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_01.png", mp, width = 8, height = 6)
# Prep data
xx <- d3 %>%
filter(Sector == "Total economy", Type == "Total dwelling type",
Private == "Total housing type", Tenure == "Total tenure type",
Occupancy == "Private dwellings unoccupied")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000)) +
geom_col(alpha = 0.7, size = 1.5, fill = "darkgreen") +
facet_wrap(Area ~ ., scales = "free_y") +
expand_limits(y = 0) +
theme_agData() +
labs(title = "Unoccupied Housing Units in Canada",
y = "Thousand Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_02.png", mp, width = 12, height = 8)Tenure
# Prep data
xx <- d3 %>%
filter(Area == "Canada", Sector == "Total economy", Type == "Total dwelling type",
Private == "Total housing type", Tenure %in% c("Owner", "Renter"),
Occupancy == "Total private dwellings") %>%
mutate(Tenure = factor(Tenure, levels = c("Renter", "Owner")))
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value / 1000000, fill = Tenure)) +
geom_col(alpha = 0.7, color = "black") +
scale_fill_manual(name = NULL, values = c("steelblue", "darkgreen")) +
theme_agData(legend.position = "bottom") +
labs(title = "Housing Units in Canada",
y = "Million Units", x = NULL, caption = myCaption)
ggsave("canada_housing_3_03.png", mp, width = 6, height = 4)Housing vs Income
# Prep data
xx <- d6 %>% filter(Area == "Canada")
# Plot
mp <- ggplot(xx, aes(x = Date, y = Value, color = Measurement)) +
geom_line(size = 1.5, alpha = 0.7) +
scale_color_manual(name = NULL, values = c("darkred", "darkgreen")) +
scale_x_date(date_breaks = "1 year", date_minor_breaks = "1 year", date_labels = "%Y") +
theme_agData(legend.position = "bottom",
axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(title = "Canada", subtitle = "Housing Price vs. Income",
y = "Percent Change", x = NULL, caption = myCaption)
ggsave("canada_housing_4_01.png", mp, width = 6, height = 4)