~/data/

Datasets

id	Geography	Nobs	html
City_MedianValuePerSqft_AllHomes	City	8049470	html
City_PriceToRentRatio_AllHomes	City	2347688	html
County_MedianRentalPrice_AllHomes	County	54654	html
County_MedianRentalPricePerSqft_AllHomes	County	56716	html
County_MedianValuePerSqft_AllHomes	County	589123	html
County_PriceToRentRatio_AllHomes	County	240760	html
Metro_MedianRentalPrice_AllHomes	Metro	50760	html
Metro_ZORI_AllHomesPlusMultifamily_SSA	Metro	8766	html
Zip_ZORI_AllHomesPlusMultifamily_SSA	Zip	254960	html

Other Datasets

Definitions

Site. html
Econ Data API. html

Home types

All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
Condo/Co-op: Condominium and co-operative homes.
Multifamily 2+ units: Units in buildings with 5 or more housing units, that are not condominiums or co-ops.
Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Inventory and Sales

For-Sale Inventory: The count of unique listings that were active at any time in a given month.
Newly Pending Listings: The count of listings that changed from for-sale to pending status on Zillow.com in a given time period.
Days to Pending: How long it takes homes in a region to change to pending status on Zillow.com after first being shown as for sale. The reported figure indicates the number of days (mean or median) that it took for homes that went pending during the week being reported, to go pending. This differs from the old “Days on Zillow” metric in that it excludes the in-contract period before a home sells.
Median List Price: The median price at which homes across various geographies were listed.
Median Sale Price: The median price at which homes across various geographies were sold.
Share of Listings With a Price Cut: The number of unique properties with a list price at the end of the month that’s less than the list price at the beginning of the month, divided by the number of unique properties with an active listing at some point during the month.
Price Cuts: The mean and median price cut for listings in a given region during a given time period, expressed as both dollars ($) and as a percentage (%) of list price.

County Map

dataraw_county_long %>%
  filter(variable == "UNR", date == as.Date("2007-10-01")) %>%
  select(county_code, value) %>%
  mutate(value = value/100) %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = "county_code") %>%
  right_join(map_county, by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(labels = scales::percent_format(accuracy = 1),
                       na.value = "white",
                       breaks = c(0, 0.05, 0.10, 0.15, 0.20),
                       values = c(0, 0.1, 0.2, 0.3, 1)) +
  theme_void() +
  theme(legend.position = c(0.9, 0.2)) + labs(fill = "Unemp.\nRate")

Figure 1: Mortgage Debt Per Capita, 2007-Q4, FRB

mme_percap_county %>%
  select(county_code = fips, value) %>%
  filter(value <= 500) %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = "county_code") %>%
  right_join(map_county, by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(labels = scales::dollar_format(accuracy = 1, prefix = ""),
                       na.value = "white",
                       breaks = c(-100,  0, 100, 200, 300, 400, 500),
                       values = c(0, 0.1, 0.2, 0.3, 1)) +
  theme_void() +
  theme(legend.position = c(0.9, 0.2)) + labs(fill = "Opioid\nConsumption")

Figure 2: Opioid Consumption by County

California

dataraw_county_long %>%
  filter(variable == "UNR", date == as.Date("2007-10-01")) %>%
  select(county_code, value) %>%
  mutate(value = value/100) %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = "county_code") %>%
  right_join(map_county %>%
               filter(region == "california"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(labels = scales::percent_format(accuracy = 1),
                       na.value = "white",
                       breaks = c(0, 0.05, 0.10, 0.15, 0.20),
                       values = c(0, 0.1, 0.2, 0.3, 1)) +
  theme_void() +
  theme(legend.position = c(0.8, 0.8)) + 
  labs(fill = "Unemp.\nRate") + coord_fixed(ratio = 1)

Figure 3: Mortgage Debt Per Capita, 2007-Q4, FRB

dataraw_county_long %>%
  filter(variable == "HOUSE_zillow_sqfeet", date == as.Date("2007-10-01")) %>%
  select(county_code, value) %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = "county_code") %>%
  right_join(map_county %>%
               filter(region == "california"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(labels = scales::dollar_format(accuracy = 1),
                       na.value = "white",
                       breaks = c(0, 200, 300, 400, 500, 600),
                       values = c(0, 0.2, 0.4, 0.6, 0.8, 1)) +
  theme_void() +
  theme(legend.position = c(0.8, 0.8)) + 
  labs(fill = "Price Per\nSq Feet") + coord_fixed(ratio = 1)

Figure 4: Price per Square Feet, 2007-Q4, FRB

County Map - Dynamic

County Map

The list of maps data is: https://code.highcharts.com/mapdata/

dataraw_county_long %>%
  filter(variable == "UNR", date == as.Date("2007-10-01")) %>%
  select(county_code, value) %>%
  mutate(county_code = str_pad(county_code, 5, pad = "0")) %>%
  hcmap("countries/us/us-all-all", data = .,
        name = "Unemployment", value = "value", joinBy = c("fips", "county_code"),
        borderColor = "transparent", valueSuffix = "%") %>%
  hc_colorAxis(dataClasses = color_classes(c(seq(0, 10, by = 2), 50))) %>% 
  hc_legend(layout = "vertical", align = "right",
            floating = TRUE, valueDecimals = 0, valueSuffix = "%") %>%
  hc_mapNavigation(enabled = TRUE)

California

dataraw_county_long %>%
  filter(variable == "UNR", date == as.Date("2007-10-01")) %>%
  select(county_code, value) %>%
  mutate(county_code = str_pad(county_code, 5, pad = "0")) %>%
  hcmap("countries/us/us-ca-all", data = .,
        name = "Unemployment", value = "value", joinBy = c("fips", "county_code"),
        borderColor = "transparent") %>%
  hc_colorAxis(dataClasses = color_classes(c(seq(0, 10, by = 2), 50))) %>% 
  hc_legend(layout = "vertical", align = "right",
            floating = TRUE, valueDecimals = 0, valueSuffix = "%")

United Kingdom

cities <- data.frame(
  name = c("London", "Birmingham", "Glasgow", "Liverpool"),
  lat = c(51.507222, 52.483056, 55.858, 53.4),
  lon = c(-0.1275, -1.893611, -4.259, -3),
  z = c(1, 2, 3, 2)
)

hcmap("countries/gb/gb-all", showInLegend = FALSE) %>%
  hc_add_series(data = cities, type = "mapbubble", name = "Cities", maxSize = '10%') %>%
  hc_mapNavigation(enabled = TRUE)

CBSA Map

CBSA-level

cbsa_nodate %>%
  select(cbsa_code, value = elasticity) %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = c("county_code")) %>%
  right_join(map_county,
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "")) +
  theme_void() +
  theme(legend.position = c(0.9, 0.2)) + 
  labs(fill = "CBSA\nElasticity")

Figure 5: CBSA Level

cbsa_nodate %>%
  select(cbsa_code, value = elasticity) %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = c("county_code")) %>%
  right_join(map_county %>%
               filter(region == "florida"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 0.1, prefix = "")) +
  theme_void() +
  theme(legend.position = c(0.2, 0.4)) + 
  labs(fill = "CBSA\nElasticity") + 
  coord_fixed(ratio = 1)

Figure 6: CBSA Level Elasticity, Florida

cbsa_nodate %>%
  select(cbsa_code, value = elasticity) %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = c("county_code")) %>%
  right_join(map_county %>%
               filter(region == "california"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 0.1, prefix = "")) +
  theme_void() +
  theme(legend.position = c(0.05, 0.25)) + 
  labs(fill = "CBSA\nElasticity") + 
  coord_fixed(ratio = 1)

Figure 7: CBSA Level Elasticity, California

cbsa %>%
  filter(date == as.Date("2017-01-01")) %>%
  mutate(rent_share = median_rent*12/medincome) %>%
  select(cbsa_code, value = rent_share) %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = c("county_code")) %>%
  right_join(map_county %>%
               filter(region == "florida"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1, prefix = "")) +
  theme_void() +
  theme(legend.position = c(0.2, 0.4)) + 
  labs(fill = "Rent\nShare") + 
  coord_fixed(ratio = 1)

Figure 8: CBSA Level Rent Share (%)

cbsa %>%
  filter(date == as.Date("2015-01-01")) %>%
  mutate(rent_share = median_gross_rent*12/medincome) %>%
  select(cbsa_code, value = rent_share) %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  left_join(county_code_name %>%
              select(county_code, subregion = county_name3, region = state_name3),
            by = c("county_code")) %>%
  right_join(map_county %>%
               filter(region == "california"),
             by = c("region", "subregion")) %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1, prefix = "")) +
  theme_void() +
  theme(legend.position = c(0.05, 0.4)) + 
  labs(fill = "Rent\nShare") + 
  coord_fixed(ratio = 1)

Figure 9: CBSA Level (California)

CBSA Map - Dynamic

cbsa_nodate %>%
  left_join(county_to_cbsa %>%
              select(county_code, cbsa_code),
            by = "cbsa_code") %>%
  select(county_code, value = elasticity) %>%
  mutate(county_code = str_pad(county_code, 5, pad = "0"),
         value = value %>% round(digits = 1)) %>%
  hcmap("countries/us/us-all-all", data = .,
        name = "Elasticity", value = "value", joinBy = c("fips", "county_code"),
        borderColor = "transparent") %>%
  hc_colorAxis(dataClasses = color_classes(c(seq(0, 10, by = 1), 15))) %>% 
  hc_legend(layout = "vertical", align = "right",
            floating = TRUE, valueDecimals = 0) %>%
  hc_mapNavigation(enabled = TRUE)

State Map

us <- map_data("state")
load_data("maps/map_state.RData")

ggplot() +
  geom_map(data = sh_top10_state_fig2b %>%
             left_join(fips_statenames_xwalk_short, by = "state") %>%
             mutate(mean_s_stateig10 = mean_s_stateig10 /100), 
           map = map_state, 
           aes(fill = mean_s_stateig10, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region),
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Top 10% \nShare",
                     labels = percent_format(accuracy = 1),
                     values = c(0, 0.2, 0.4, 0.5, 1)) +
  theme_map() + 
  theme(legend.position = c(0.87, 0.1))

sh_top10_state_fig2b %>%
  left_join(fips_statenames_xwalk_short, by = "state") %>%
  mutate(value = mean_s_stateig10 /100) %>%
  right_join(map_state, by = "region") %>%
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = value), colour = alpha("black", 1/2), size = 0.2)  + 
  scale_fill_viridis_c(labels = scales::percent_format(accuracy = 1),
                       breaks = c(0.05, 0.08, 0.10, 0.12, 0.15),
                       values = c(0, 0.2, 0.4, 0.5, 1)) +
  theme_void() +
  theme(legend.position = c(0.9, 0.2)) + labs(fill = "Top 10%\nShare")

Figure 10: Top 10% Per Cent Share.

County Map

CBSA Map

State Maps

us <- map_data("state")

Per Capita Income

ggplot() +
  geom_map(data = df_state_demographics, map = map_state, 
           aes(fill = per_capita_income, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Per Capita \nIncome",
                     labels = dollar_format(accuracy = 1),
                     values = c(0, 0.2, 0.3, 0.4, 1)) +
  theme_map() + theme(legend.position = c(0.87, 0.1))

Figure 11: Per Capita Income by State

Median Rent

ggplot() +
  geom_map(data = df_state_demographics, map = map_state, 
           aes(fill = median_rent, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Median Rent",
                     labels = dollar_format(accuracy = 1),
                     values = c(0, 0.2, 0.3, 0.4, 1)) +
  theme_map() + theme(legend.position = c(0.87, 0.1))

Figure 12: State Median Rent

Hispanic Share

ggplot() +
  geom_map(data = df_state_demographics, map = map_state, 
           aes(fill = percent_hispanic / 100, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Hispanic \nShare",
                     labels = percent_format(accuracy = 1),
                     values = c(0, 0.2, 0.3, 0.4, 1)) +
  theme_map() + theme(legend.position = c(0.87, 0.1))

Figure 13: State Percentage of Hispanics

Median Age

ggplot() +
  geom_map(data = df_state_demographics, map = map_state, 
           aes(fill = median_age, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Median Age",
                     labels = comma,
                     values = c(0, 0.2, 0.3, 0.5, 0.7, 1)) +
  theme_map() + theme(legend.position = c(0.87, 0.1))

Figure 14: State Median Age

Zidar (2019): Share of High Income Workers

ggplot() +
  geom_map(data = sh_top10_state_fig2b %>%
             left_join(fips_statenames_xwalk_short, by = "state") %>%
             mutate(mean_s_stateig10 = mean_s_stateig10 /100), map = map_state, 
           aes(fill = mean_s_stateig10, map_id = region),
           color = "white", size = 0.15) +
  geom_map(data = map_state, map = map_state, 
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Top 10% \nShare",
                     labels = percent_format(accuracy = 1),
                     values = c(0, 0.2, 0.4, 0.5, 1)) +
  theme_map() + theme(legend.position = c(0.87, 0.1))

Figure 15: Zidar (2019) Share of High Income

County Maps

Per Capita Income

df_county_demographics %>%
  left_join(county.regions, by = "region") %>%
  rename(fips = region, subregion = county.name) %>%
  left_join(us, by = "subregion") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = per_capita_income)) + 
  geom_polygon() + coord_map() +
  theme_map() +
  geom_map(data = map_state, map = map_state,
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Per Capita \nIncome",
                     labels = dollar_format(accuracy = 1),
                     values = c(0, 0.2, 0.3, 0.4, 1)) +
  theme(legend.position = c(0.87, 0.1))

Figure 16: County Median Rent

Median Rent

df_county_demographics %>%
  left_join(county.regions, by = "region") %>%
  rename(fips = region, subregion = county.name) %>%
  left_join(us, by = "subregion") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = median_rent)) + 
  geom_polygon() + coord_map() +
  theme_map() +
  geom_map(data = map_state, map = map_state,
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Monthly Rent",
                     labels = scales::dollar,
                     breaks = c(200, 400, 800, 1200, 1600),
                     values = c(0, 0.2, 0.4, 0.5, 1)) +
  theme(legend.position = c(0.87, 0.1))

Figure 17: County Median Rent

Hispanic Share

df_county_demographics %>%
  left_join(county.regions, by = "region") %>%
  rename(fips = region, subregion = county.name) %>%
  left_join(us, by = "subregion") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = percent_hispanic/100)) + 
  geom_polygon() + coord_map() +
  theme_map() +
  geom_map(data = map_state, map = map_state,
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Hispanic \nShare",
                     labels = percent_format(accuracy = 1),
                     values = c(0, 0.1, 0.2, 0.3, 0.5, 1)) +
  theme(legend.position = c(0.87, 0.1))

Figure 18: County Percentage of Hispanics

Median Age

df_county_demographics %>%
  left_join(county.regions, by = "region") %>%
  rename(fips = region, subregion = county.name) %>%
  left_join(us, by = "subregion") %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = median_age)) + 
  geom_polygon() + coord_map() +
  theme_map() +
  geom_map(data = map_state, map = map_state,
           aes(long, lat, map_id = region), 
           color = "#2b2b2b", fill = NA, size = 0.20) +
  scale_fill_viridis(name = "Median Age",
                     labels = comma,
                     values = c(0, 0.2, 0.3, 0.5, 0.7, 1)) +
  theme(legend.position = c(0.87, 0.1))

Figure 19: County Median Age

Housing Data - Zillow Research

Data

François Geerolf

Datasets

Other Datasets

Definitions

Home types

Inventory and Sales

County Map

California

County Map - Dynamic

County Map

California

United Kingdom

CBSA Map

CBSA-level

CBSA Map - Dynamic

State Map

County Map

CBSA Map

State Maps

Per Capita Income

Median Rent

Median Age

County Maps

Per Capita Income

Median Rent

Median Age