map_listings | R Documentation |
This function returns data from an API that maps the most pertinent matches to a users input.
map_listings(
locations = NULL,
listing_type = "sale",
search_type = "city",
city_isolated = NULL,
county_isolated = NULL,
zipcode_isolated = NULL,
state_isolated = NULL,
street_isolated = NULL,
features = NULL,
only_open_houses = NULL,
neighborhood_isolated = NULL,
beds_min = NULL,
beds_max = NULL,
baths_min = NULL,
baths_max = NULL,
price_min = NULL,
price_max = NULL,
property_type = NULL,
sqft_min = NULL,
sqft_max = NULL,
acre_min = NULL,
acre_max = NULL,
age_min = NULL,
age_max = NULL,
days_on_market = NULL,
pending = NULL,
is_new_construction = NULL,
generate_new_cookies = F,
include_pending_contingency = TRUE
)
locations |
vector of locations |
listing_type |
Listing type
|
search_type |
search type options include
|
city_isolated |
if not |
county_isolated |
if not |
zipcode_isolated |
if not |
state_isolated |
if not |
street_isolated |
if not |
features |
if not |
only_open_houses |
if |
neighborhood_isolated |
if not |
beds_min |
if not |
beds_max |
if not |
baths_min |
if not |
baths_max |
if not |
price_min |
if not |
price_max |
if not |
property_type |
if not |
sqft_min |
if not |
sqft_max |
if not |
acre_min |
if not |
acre_max |
if not |
age_min |
if not |
age_max |
if not |
days_on_market |
if not |
pending |
if |
is_new_construction |
if |
generate_new_cookies |
generate new cookies |
include_pending_contingency |
if |
This function is faster than listings
but returns less detailed information.
a tibble
Other listing search:
dictionary_listing_features()
,
dictionary_property_types()
,
listing_counts()
,
listings()
## New Construction Waterfront actual mapped listings
library(dplyr)
library(realtR)
df_new_water <-
map_listings( locations = c("Miami Beach, FL", "Naples, FL"),
features = "Waterfront", is_new_construction = TRUE )
df_new_water %>%
glimpse()
df_new_water %>%
group_by(cityProperty, stateProperty, typeProperty) %>%
summarise( meanPSF = mean(priceListingPerSF, na.rm = T),
meanPrice = mean(priceListing, na.rm = T), countListings = n()) %>%
ungroup()
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