Skip to contents

realestatebr: Brazilian Real Estate Data in R

realestatebr provides a unified interface to Brazilian real estate data. The package is organized by source, and each source holds one or more tables. Every table is returned as a tidy tibble.

Installation 

# Install the released version from CRAN
install.packages("realestatebr")

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

Quick Start

Two functions cover most of the package. get_dataset() retrieves data and list_datasets() lists what is available. get_dataset() takes a dataset name and an optional table. Without table, it returns the default table for that dataset.

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 wraps public data sources and returns each table in a tidy format. Datasets are updated weekly or monthly, depending on the source.

Dataset Source Tables
abecip ABECIP sbpe, units, cgi
abrainc ABRAINC / FIPE indicator, radar, leading
bcb_realestate Banco Central do Brasil accounting, application, indices, sources, units
bcb_series Banco Central do Brasil core, primary, secondary, tertiary, full
fgv_ibre FGV IBRE —
rppi FIPE/ZAP, IVG-R, IGMI-R, IQA, IQAIW, IVAR, SECOVI-SP sale, rent, all, fipezap, ivgr, igmi, iqa, iqaiw, ivar, secovi_sp
rppi_bis Bank for International Settlements selected, detailed_monthly, detailed_quarterly, detailed_annual, detailed_halfyearly
secovi SECOVI-SP condo, rent, launch, sale

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")

# Residential sale and rent prices in São Paulo
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 = scales::label_percent()) +
  labs(
    title = "São Paulo Property Price Index",
    x = NULL,
    y = "Year-on-year change",
    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(
    ref_area_name %in% c("Brazil", "United States", "Japan"),
    is_nominal == 0,
    unit == "index",
    date >= as.Date("2010-01-01")
  )

ggplot(bis_compare, aes(x = date, y = value, color = ref_area_name)) +
  geom_line(lwd = 0.8) +
  geom_hline(yintercept = 100) +
  labs(
    title = "Real Property Prices Across Countries",
    x = NULL,
    y = "Index (2010 = 100)",
    color = ""
  ) +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    palette.colour.discrete = c("#1E3A5F", "#4A90C2", "#2C7A7B")
  )

Learn More