Housing Data - Zillow Research - zillow

Data - Zillow

Author

François Geerolf


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

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

(ref:unemp2007) Mortgage Debt Per Capita, 2007-Q4, FRB

Code 
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")

(ref:unemp2007)

(ref:opioid-county) Opioid Consumption by County

Code 
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")

(ref:opioid-county)

California

(ref:unemp2007-CA) Mortgage Debt Per Capita, 2007-Q4, FRB

Code 
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)

(ref:unemp2007-CA)

(ref:zillow-sq-feet-CA) Price per Square Feet, 2007-Q4, FRB

Code 
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)

(ref:zillow-sq-feet-CA)

County Map - Dynamic

County Map

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

Code 
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

Code 
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

Code 
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

(ref:CBSA-level) CBSA Level

Code 
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")

(ref:CBSA-level)

(ref:CBSA-elasticity-FL) CBSA Level Elasticity, Florida

Code 
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)

(ref:CBSA-elasticity-FL)

(ref:CBSA-elasticity-CA) CBSA Level Elasticity, California

Code 
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)

(ref:CBSA-elasticity-CA)

(ref:CBSA-rentshare-FL) CBSA Level Rent Share (%)

Code 
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)

(ref:CBSA-rentshare-FL)

(ref:CBSA-level-CA) CBSA Level (California)

Code 
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)

(ref:CBSA-level-CA)

CBSA Map - Dynamic

Code 
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

Code 
us <- map_data("state")
map_state <- read_parquet(here::here("data", "maps", "map_state.parquet"))
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))

(ref:states) Top 10% Per Cent Share.

Code 
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")

(ref:states)

County Map

CBSA Map

State Maps

Code 
us <- map_data("state")

Median Household Income

(ref:state-per-capita-income) Median Household Income by State

Code 
# choroplethr's df_state_demographics no longer ships a per_capita_income
# column (current CRAN version 5.0.1 only has region/population/
# median_hh_income) -- using median household income instead, relabeled.
ggplot() +
  geom_map(data = df_state_demographics, map = map_state,
           aes(fill = median_hh_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 = "Median Household \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))

(ref:state-per-capita-income)

Zidar (2019): Share of High Income Workers

(ref:Zidar2019-share-high) Zidar (2019) Share of High Income

Code 
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))

(ref:Zidar2019-share-high)

County Maps

Median Household Income

(ref:county-per-capita-income) County Median Household Income

Code 
# df_county_demographics has the same trimmed schema as df_state_demographics
# (region/population/median_hh_income only) -- using median household income
# instead of per_capita_income, relabeled.
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_hh_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 = "Median Household \nIncome",
                     labels = dollar_format(accuracy = 1),
                     values = c(0, 0.2, 0.3, 0.4, 1)) +
  theme(legend.position = c(0.87, 0.1))

(ref:county-per-capita-income)