The Primary Market and the Construction Cycle • realestatebr

The primary market covers newly built homes sold by developers. This article tracks the development cycle, from launches to sales and deliveries, and the business environment around it using three datasets

abrainc for national primary market indicators from the ABRAINC-FIPE panel of developers,
secovi for the São Paulo market,
fgv_ibre for construction costs and business sentiment.

library(realestatebr)
library(dplyr)
library(ggplot2)

The code below defines a common theme for the plots in this article. It is entirely optional and can be omitted.

color_palette <- c(
  "#1E3A5F",
  "#DD6B20",
  "#2C7A7B",
  "#D69E2E",
  "#805AD5",
  "#C53030"
)

theme_series <- function() {
  theme_minimal(base_size = 10) +
    theme(
      plot.title = element_text(size = 16),
      panel.grid.minor = element_blank(),
      panel.grid.major.x = element_blank(),
      axis.line.x = element_line(color = "gray10", linewidth = 0.5),
      axis.ticks.x = element_line(color = "gray10", linewidth = 0.5),
      axis.title.x = element_blank(),
      legend.position = "bottom",
      palette.color.discrete = color_palette
    )
}

Launches and sales

The indicator table from abrainc reports monthly launches, sales, deliveries, cancellations, and supply for a panel of large developers. The series are volatile month to month, so the plot below uses values accumulated over twelve months.

abrainc <- get_dataset("abrainc", table = "indicator")

glimpse(abrainc)
#> Rows: 3,504
#> Columns: 6
#> $ date           <date> 2014-01-01, 2014-01-01, 2014-01-01, 2014-01-01, 2014-0…
#> $ year           <dbl> 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2014, 2…
#> $ category       <chr> "new_units", "new_units", "new_units", "new_units", "so…
#> $ variable       <chr> "total", "market_rate", "social_housing", "other", "tot…
#> $ value          <dbl> 1726.0000, NA, NA, NA, 4232.0000, NA, NA, NA, 6409.0000…
#> $ variable_label <chr> "Total", "Market-rate Development", "Social Housing (MC…

cycle <- abrainc |>
  filter(
    category %in% c("new_units", "sold"),
    variable == "total"
  ) |>
  mutate(
    units_12m = zoo::rollsumr(value, k = 12, fill = NA) / 1e3,
    category_label = if_else(category == "new_units", "Launches", "Sales"),
    .by = category
  )

ggplot(filter(cycle, !is.na(units_12m)), aes(date, units_12m)) +
  geom_line(aes(color = category_label), lwd = 0.8) +
  scale_color_manual(values = color_palette[c(1, 2)]) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  labs(
    title = "Launches and Sales in the Primary Market",
    subtitle = "Units accumulated in 12 months, ABRAINC-FIPE panel",
    y = "Units (thousand)",
    color = NULL
  ) +
  theme_series()

Market segments

The same indicators are split between market-rate developments and social housing (the MCMV program and its successors). Social housing accounts for a large and fairly stable share of total sales.

segments <- abrainc |>
  filter(
    category == "sold",
    variable %in% c("market_rate", "social_housing")
  ) |>
  mutate(
    units_12m = zoo::rollsumr(value, k = 12, fill = NA) / 1e3,
    .by = variable
  )

ggplot(filter(segments, !is.na(units_12m)), aes(date, units_12m)) +
  geom_area(aes(fill = variable_label), alpha = 0.9) +
  scale_fill_manual(values = color_palette[c(1, 2)]) +
  scale_x_date(date_breaks = "1 year", date_labels = "%Y") +
  scale_y_continuous(expand = expansion(mult = c(0, 0.05))) +
  labs(
    title = "Sales by Market Segment",
    subtitle = "Units accumulated in 12 months",
    y = "Units (thousand)",
    fill = NULL
  ) +
  theme_series()

Business conditions

The radar table condenses the business environment into standardized 0-10 scores across four dimensions, macroeconomy, credit, demand, and the real estate sector. The plot shows one representative indicator from each dimension, smoothed with a six-month moving average.

radar <- get_dataset("abrainc", table = "radar")

radar_sel <- radar |>
  filter(
    variable %in% c("confidence", "finance_condition", "employment", "input_costs"),
    date >= as.Date("2012-01-01")
  )

ggplot(radar_sel, aes(date, ma6)) +
  geom_line(color = color_palette[1], lwd = 0.7) +
  geom_hline(yintercept = 5, linetype = 2, color = "gray40") +
  facet_wrap(vars(variable_label)) +
  scale_x_date(date_breaks = "4 years", date_labels = "%Y") +
  labs(
    title = "ABRAINC-FIPE Radar",
    subtitle = "Standardized scores (0-10), 6-month moving average",
    y = "Score"
  ) +
  theme_series()

A leading indicator

Building permits anticipate construction activity. The leading table compiles permits in the city of São Paulo into a leading indicator of real estate activity.

leading <- get_dataset("abrainc", table = "leading")

leading_index <- leading |>
  filter(
    variable == "leading_index",
    zone == "Total",
    date >= as.Date("2010-01-01")
  )

ggplot(leading_index, aes(date, value)) +
  geom_line(color = color_palette[1], lwd = 0.7) +
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  labs(
    title = "Real Estate Leading Indicator",
    subtitle = "Based on building permits in São Paulo (100 = Dec/2000)",
    y = "Index"
  ) +
  theme_series()

São Paulo in focus

SECOVI-SP, the housing syndicate of São Paulo, reports complementary indicators for the largest market in the country. The launch and sale tables track new supply and sales in the city.

secovi <- get_dataset("secovi")

glimpse(secovi)
#> Rows: 9,608
#> Columns: 5
#> $ date     <date> 2006-01-01, 2006-02-01, 2006-03-01, 2006-04-01, 2006-05-01, …
#> $ category <chr> "condo", "condo", "condo", "condo", "condo", "condo", "condo"…
#> $ variable <chr> "default_condominio", "default_condominio", "default_condomin…
#> $ name     <chr> "acao_por_falta_de_pagamento", "acao_por_falta_de_pagamento",…
#> $ value    <dbl> 1216, 1249, 1699, 1482, 1562, 1511, 1577, 1720, 1376, 1270, 1…

sp_market <- secovi |>
  filter(
    variable %in% c("launches", "sales"),
    name == "unidades"
  ) |>
  mutate(
    units_12m = zoo::rollsumr(value, k = 12, fill = NA) / 1e3,
    variable_label = if_else(variable == "launches", "Launches", "Sales"),
    .by = variable
  )

ggplot(filter(sp_market, !is.na(units_12m)), aes(date, units_12m)) +
  geom_line(aes(color = variable_label), lwd = 0.8) +
  scale_color_manual(values = color_palette[c(1, 2)]) +
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  labs(
    title = "Launches and Sales in São Paulo",
    subtitle = "Units accumulated in 12 months, SECOVI-SP",
    y = "Units (thousand)",
    color = NULL
  ) +
  theme_series()

Construction costs and sentiment

The fgv_ibre dataset gathers FGV indicators for the construction sector. INCC measures construction costs; the IC-CST index measures the confidence of construction firms.

fgv <- get_dataset("fgv_ibre")

glimpse(fgv)
#> Rows: 3,118
#> Columns: 7
#> $ date            <date> 1996-07-01, 1996-07-01, 1996-07-01, 1996-07-01, 1996-…
#> $ name_simplified <chr> "incc_brasil_di", "incc_brasil_10", "incc_brasil", "in…
#> $ value           <dbl> 149.095, 147.979, 149.224, 1.520, 149.445, 149.044, 15…
#> $ name_series     <chr> "INCC - Brasil - DI", "INCC - Brasil-10", "INCC - Bras…
#> $ code_series     <dbl> 1464783, 1464331, 1465235, 1000370, 1464783, 1464331, …
#> $ unit            <chr> "Índice", "Índice", "Índice", "Percentual", "Índice", …
#> $ source          <chr> "FGV-INCC", "FGV-INCC", "FGV-INCC", "FGV-INCC", "FGV-I…

The plot below combines cost pressure and sentiment. Cost shocks, such as the one in 2021, typically depress confidence with a lag.

incc <- fgv |>
  filter(name_simplified == "incc") |>
  mutate(
    acum12m = (zoo::rollapplyr(
      1 + value / 100, width = 12, FUN = prod, fill = NA
    ) - 1) * 100
  )

confidence <- fgv |>
  filter(name_simplified == "ic_cst")

fgv_plot <- bind_rows(
  list(
    "INCC (% accumulated in 12 months)" = select(incc, date, value = acum12m),
    "Construction confidence (IC-CST)" = select(confidence, date, value)
  ),
  .id = "series"
)

ggplot(filter(fgv_plot, !is.na(value)), aes(date, value)) +
  geom_line(color = color_palette[1], lwd = 0.7) +
  facet_wrap(vars(series), ncol = 1, scales = "free_y") +
  scale_x_date(date_breaks = "2 years", date_labels = "%Y") +
  labs(
    title = "Construction Costs and Sentiment",
    y = NULL
  ) +
  theme_series()

Learn more

The column-level documentation of each dataset used here is available in the help topics ?abrainc, ?secovi, and ?fgv_ibre. For housing credit, see the article on housing credit; for price indices, see vignette("working-with-rppi").