Import Brazilian Real Estate Data into R.

realestatebr: Brazilian Real Estate Data in R

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.

Installation

# Install the development version from GitHub
# install.packages("remotes")
remotes::install_github("viniciusoike/realestatebr")

Quick Start

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")

Available Datasets

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.

Data Sources

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.

Example: Property Price Indices

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")
  )

International Comparison

# 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")
  )

Learn More

• Getting Started vignette
• Working with RPPI vignette.