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realestatebr aims to provide an unified interface to Brazilian real estate information, delivering data from different sources in a tidy format. The package is organized by source, with each source having multiple associated tables (datasets).
All datasets are returned as tibble objects. The package
is centered around a key function: get_dataset(name, table)
which retrieves any dataset by name.
# Install the development version from GitHub
# install.packages("remotes")
remotes::install_github("viniciusoike/realestatebr")There are two key functions in realestatebr:
get_dataset() and list_datasets(). The former
retrieves data, while the latter lists all available datasets.
get_dataset() expects a name and a (optional)
table argument. If no table is specified, the
function returns the first available table.
library(realestatebr)
# Discover available datasets
datasets <- list_datasets()
# Get specific table
sbpe <- get_dataset(name = "abecip", table = "sbpe")
# Get property price indices
fipezap <- get_dataset("rppi", "fipezap")The package acts as a convenient wrapper around public data sources.
All data is provided in a tidy format in a tibble object.
Datasets are updated weekly or monthly, depending on the source.
| Dataset | Source | Tables | Status |
|---|---|---|---|
abecip |
ABECIP | sbpe, units, cgi |
Active |
abrainc |
ABRAINC / FIPE | indicator, radar,
leading |
Active |
bcb_realestate |
Banco Central do Brasil | accounting, application,
indices, sources, units |
Active |
bcb_series |
Banco Central do Brasil | price, credit, production,
interest-rate, exchange,
government, real-estate |
Active |
fgv_ibre |
FGV IBRE | — | Active |
rppi |
FIPE/ZAP, IVGR, IGMI, IQA, IVAR, SECOVI-SP | sale, rent, fipezap,
ivgr, igmi, iqa,
iqaiw, ivar, secovi_sp |
Active |
rppi_bis |
Bank for International Settlements | selected, detailed_monthly,
detailed_quarterly |
Active |
secovi |
SECOVI-SP | condo, rent, launch,
sale |
Active |
The source parameter controls where data comes from.
# Auto (default): in-session memo -> GitHub release -> fresh download
data <- get_dataset("abecip")
# Pre-processed asset from the package's GitHub release
data <- get_dataset("abecip", source = "github")
# Fresh download from the original source
data <- get_dataset("abecip", source = "fresh")Repeated calls within one R session are served from an in-memory
memo. Use clear_session_cache() to drop the memo without
restarting R.
library(ggplot2)
library(realestatebr)
library(dplyr)
# Get FipeZap index
fipezap <- get_dataset("rppi", table = "fipezap")
# Brazil national index
rppi_spo <- fipezap |>
filter(
name_muni == "São Paulo",
market == "residential",
rooms == "total",
variable == "acum12m",
date >= as.Date("2019-01-01")
)
ggplot(rppi_spo, aes(x = date, y = value, color = rent_sale)) +
geom_line(lwd = 0.8) +
geom_hline(yintercept = 0) +
scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
scale_y_continuous(labels = seq(-0.05, 0.15, by = 0.05) * 100, ) +
labs(
title = "Brazil Property Price Index",
x = NULL,
y = "YoY chg. (%)",
color = ""
) +
theme_minimal() +
theme(
legend.position = "bottom",
palette.colour.discrete = c("#1E3A5F", "#4A90C2", "#2C7A7B")
)
# Get BIS international data
bis <- get_dataset("rppi_bis")
# Compare countries
bis_compare <- bis |>
filter(
reference_area %in% c("Brazil", "United States", "Japan"),
is_nominal == FALSE,
unit == "Index, 2010 = 100",
date >= as.Date("2010-01-01")
)
ggplot(bis_compare, aes(x = date, y = value, color = reference_area)) +
geom_line(lwd = 0.8) +
geom_hline(yintercept = 100) +
labs(
title = "Real Property Prices - International",
x = NULL,
y = "Index (2010 = 100)",
color = ""
) +
theme_minimal() +
theme(
legend.position = "bottom",
palette.colour.discrete = c("#1E3A5F", "#4A90C2", "#2C7A7B")
)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.