Download Data from the Bank of England Statistical Database • boe

An R package for downloading data from the Bank of England Statistical Database.

What is the Bank of England?

The Bank of England is the United Kingdom’s central bank. Founded in 1694, it is responsible for setting monetary policy (including Bank Rate), issuing banknotes, supervising the banking system, and maintaining financial stability. Its Monetary Policy Committee meets eight times a year to set the interest rate that ripples through every mortgage, savings account, and bond in the UK economy.

The Bank publishes thousands of statistical time series through its Interactive Statistical Database - covering interest rates, exchange rates, money and credit, gilt yields, and housing market indicators. This data underpins monetary policy analysis, financial research, and economic journalism in the UK.

How is this different from existing packages?

The bbk package on CRAN provides a single generic function for Bank of England data (bbk::boe_data()), but it is primarily a Bundesbank client - the Bank of England is one of seven central banks it covers, and its BoE support amounts to a raw API wrapper. You still need to know the series codes, and the output requires further processing.

This package is different. It is built specifically for the Bank of England and provides named, documented functions for the series people actually use - boe_bank_rate(), boe_mortgage_rates(), boe_yield_curve(), and so on. You don’t need to know that Bank Rate is IUDBEDR or that a 2-year fixed mortgage rate is IUMBV34. The package handles series codes, date formatting, caching, and error handling internally.

Beyond the IADB wrappers, it also ships:

boe_curve(): the full Anderson-Sleath fitted yield curves (nominal, real, implied inflation, OIS) at all maturities, parsed from the BoE’s published Excel archive.
boe_search() / boe_browse(): a built-in catalogue of wrapped series so you can find codes from R rather than the website.
A boe_tbl S3 class so every returned data frame carries provenance metadata (series codes, date range, frequency, fetch timestamp).

Why does this package exist?

The data is freely available, but using it programmatically requires knowing obscure series codes, constructing query URLs with a non-standard date format (DD/Mon/YYYY), parsing CSV responses with irregular date formats, and handling HTML error pages returned with HTTP 200 status codes. Every analyst who works with this data writes the same boilerplate.

This package replaces all of that with named functions that return clean data frames.

# Without this package
url <- paste0(
  "https://www.bankofengland.co.uk/boeapps/database/",
  "_iadb-fromshowcolumns.asp?csv.x=yes",
  "&SeriesCodes=IUDBEDR&UsingCodes=Y&CSVF=TN",
  "&Datefrom=01/Jan/2020&Dateto=01/Jan/2025"
)
raw <- read.csv(url)
# ... parse dates, rename columns, handle errors ...

# With this package
library(boe)
boe_bank_rate(from = "2020-01-01")

Installation

install.packages("boe")

# Or install the development version from GitHub
# install.packages("devtools")
devtools::install_github("charlescoverdale/boe")

Functions

Data access:

Function	Description	From	To
`boe_get()`	Fetch any series by BoE series code	Any	Present
`boe_bank_rate()`	Official Bank Rate (daily or monthly)	1975	Present
`boe_sonia()`	SONIA risk-free reference rate (daily, monthly, or annual)	1997	Present
`boe_yield_curve()`	Nominal and real gilt yields at 5yr, 10yr, 20yr maturities	1985	Present
`boe_curve()`	Full Anderson-Sleath fitted curves (nominal / real / inflation / OIS, spot or forward) at all maturities	Latest month	Present
`boe_exchange_rate()`	Daily sterling spot rates for 27 currencies	1975	Present
`boe_mortgage_rates()`	Quoted mortgage rates (2yr/3yr/5yr fixed, SVR)	1995	Present
`boe_mortgage_approvals()`	Monthly mortgage approvals for house purchase	1993	Present
`boe_consumer_credit()`	Consumer credit outstanding (total, cards, other)	1993	Present
`boe_money_supply()`	M4 broad money amounts outstanding	1982	Present

Monetary policy:

Function	Description	From	To
`boe_mpc_decisions()`	MPC rate-change events: date, new rate, change in bps, direction	1997	Present
`boe_mpc_votes()`	Full MPC voting record, one row per (meeting, member), with dissent flag	1997	Present
`boe_mpr_forecasts()`	Monetary Policy Report forecast paths (CPI inflation, GDP growth, GDP level, unemployment, Bank Rate)	2019	Present

Discovery:

Function	Description
`boe_series`	Exported catalogue of every wrapped series (code, title, category, frequency, unit, start date)
`boe_search()`	Keyword search over `boe_series`
`boe_browse()`	Filter `boe_series` by category or frequency
`list_exchange_rates()`	Currency codes available to `boe_exchange_rate()`

Cache:

Function	Description
`boe_cache_info()`	Report cache directory, file count, total size
`clear_cache()`	Delete locally cached data files

Examples

What is Bank Rate today?

library(boe)

# Bank Rate since 2000
br <- boe_bank_rate(from = "2000-01-01")
tail(br, 6)
#>         date rate_pct
#>   2026-02-26     3.75
#>   2026-02-27     3.75
#>   2026-03-02     3.75
#>   2026-03-03     3.75
#>   2026-03-04     3.75
#>   2026-03-05     3.75

How has sterling moved against other currencies?

# GBP/USD and GBP/EUR
fx <- boe_exchange_rate(c("USD", "EUR"), from = "2024-01-01", to = "2024-01-31")
head(fx, 6)
#>         date currency   rate
#>   2024-01-02      EUR 1.1536
#>   2024-01-03      EUR 1.1580
#>   2024-01-04      EUR 1.1591
#>   2024-01-05      EUR 1.1615
#>   2024-01-08      EUR 1.1623
#>   2024-01-09      EUR 1.1636

# See all 27 available currencies
list_exchange_rates()

What are gilt yields doing?

# 10-year nominal gilt yield
yc <- boe_yield_curve(from = "2024-01-01", to = "2024-01-31", maturity = "10yr")
head(yc, 5)
#>         date maturity yield_pct
#>   2024-01-02     10yr    3.7190
#>   2024-01-03     10yr    3.7638
#>   2024-01-04     10yr    3.8006
#>   2024-01-05     10yr    3.8398
#>   2024-01-08     10yr    3.8619

# Full curve: 5yr, 10yr, and 20yr
boe_yield_curve(from = "2024-01-01")

# Real yields
boe_yield_curve(from = "2024-01-01", type = "real", measure = "zero_coupon")

The full Anderson-Sleath fitted curve

For the complete yield curve at every published maturity (typically 0.5 years to 25 or 40 years, in 0.5-year steps), use boe_curve(). This parses the BoE’s published Excel archive and covers four curves: nominal gilt, real (index-linked) gilt, implied inflation (breakeven), and overnight index swap (OIS).

# Latest nominal spot curve at all maturities
nc <- boe_curve(curve = "nominal", measure = "spot")
head(nc, 6)
#>         date maturity_years rate_pct
#>   2026-04-01            0.5    3.95
#>   2026-04-01            1.0    4.10
#>   2026-04-01            1.5    4.13
#>   2026-04-01            2.0    4.15
#>   2026-04-01            2.5    4.16
#>   2026-04-01            3.0    4.17

# Implied inflation curve (breakeven inflation)
boe_curve(curve = "inflation", measure = "spot")

# OIS forward curve
boe_curve(curve = "ois", measure = "spot")

Requires the readxl package (loaded lazily). Reference: Anderson and Sleath (2001), New estimates of the UK real and nominal yield curves, Bank of England Working Paper No. 126.

What are mortgage rates right now?

# All mortgage rate types
mr <- boe_mortgage_rates(from = "2023-01-01")

# Latest rates (as of December 2024)
#>   2yr_fixed: 4.60%
#>   3yr_fixed: 4.48%
#>   5yr_fixed: 4.37%
#>   svr:       7.47%

How active is the housing market?

# Monthly mortgage approvals - a leading indicator of housing activity
ma <- boe_mortgage_approvals(from = "2019-01-01")
tail(ma, 6)
#>         date approvals
#>   2025-08-31     64588
#>   2025-09-30     65436
#>   2025-10-31     64634
#>   2025-11-30     64018
#>   2025-12-31     61007
#>   2026-01-31     59999

How much are households borrowing?

# Total consumer credit outstanding
cc <- boe_consumer_credit(type = "total", from = "2024-01-01")
tail(cc, 6)
#>         date  type amount_gbp_m
#>   2024-01-31 total       476154
#>   2024-02-29 total       479974
#>   2024-03-31 total       484269
#>   2024-04-30 total       490106
#>   2024-05-31 total       494904
#>   2024-06-30 total       498639

# Credit card debt only
boe_consumer_credit(type = "credit_card", from = "2024-01-01")

How much money is in the economy?

# M4 amounts outstanding
m4 <- boe_money_supply(from = "2024-01-01")
head(m4, 6)
#>         date amount_gbp_m
#>   2024-01-31      2986264
#>   2024-02-29      2999033
#>   2024-03-31      3025146
#>   2024-04-30      3030412
#>   2024-05-31      3028825
#>   2024-06-30      3044464   # ← £3 trillion

What is the risk-free rate?

# SONIA replaced LIBOR as the UK's benchmark interest rate
sonia <- boe_sonia(from = "2024-01-01", to = "2024-01-31")
head(sonia, 6)
#>         date rate_pct
#>   2024-01-02   5.1863
#>   2024-01-03   5.1863
#>   2024-01-04   5.1870
#>   2024-01-05   5.1869
#>   2024-01-08   5.1869
#>   2024-01-09   5.1867

# Monthly or annual average
boe_sonia(from = "2020-01-01", frequency = "monthly")

Fetching any series by code

# If you know the BoE series code, use boe_get() directly
# Series codes: https://www.bankofengland.co.uk/boeapps/database/

# Multiple series in one call - Bank Rate vs SONIA
boe_get(c("IUDBEDR", "IUDSOIA"), from = "2024-01-01", to = "2024-01-10")
#>          date    code  value
#>    2024-01-02 IUDBEDR 5.2500
#>    2024-01-03 IUDBEDR 5.2500
#>    2024-01-04 IUDBEDR 5.2500
#>    ...
#>    2024-01-02 IUDSOIA 5.1863
#>    2024-01-03 IUDSOIA 5.1863
#>    2024-01-04 IUDSOIA 5.1870
#>    ...

Tracking MPC decisions and votes

# Every Bank Rate change since 1997
decisions <- boe_mpc_decisions()
tail(decisions, 5)
#>         date new_rate_pct prev_rate_pct change_bps direction
#>   2024-08-01         5.00          5.25        -25       cut
#>   2024-11-07         4.75          5.00        -25       cut
#>   2025-02-06         4.50          4.75        -25       cut
#>   2025-08-07         4.25          4.50        -25       cut
#>   2026-02-05         4.00          4.25        -25       cut

# Full voting record: who dissented, and how
votes <- boe_mpc_votes()
recent_dissents <- subset(votes, dissent & date >= as.Date("2024-01-01"))
head(recent_dissents)

# How does Catherine L Mann vote?
mann <- subset(votes, member == "Catherine L Mann")
table(mann$dissent)

Forecasts from the Monetary Policy Report

# Latest CPI inflation projections (one row per publication x horizon)
cpi <- boe_mpr_forecasts(series = "cpi_inflation")
head(cpi)
#>         date horizon horizon_date        series value
#>   2026-02-01 2026 Q1   2026-01-01 cpi_inflation   2.7
#>   2026-02-01 2026 Q2   2026-04-01 cpi_inflation   2.6
#>   2026-02-01 2026 Q3   2026-07-01 cpi_inflation   2.5

# All five headline series for the most recent MPR
all <- boe_mpr_forecasts()
unique(all$series)
#>   [1] "bank_rate" "cpi_inflation" "gdp_growth" "gdp_level" "unemployment"

Requires the readxl package. Note: this targets the post-2025 MPR file format; older releases use a different archive layout.

Searching for a series

# Keyword search across the catalogue
boe_search("mortgage")

# Filter by category and frequency
boe_search(category = "interest_rates", frequency = "daily")

# Browse without a keyword
boe_browse(category = "exchange_rates")

# The full catalogue is exported as a data frame
head(boe_series)
table(boe_series$category)
#>     consumer_credit       exchange_rates       interest_rates
#>                   3                   27                   14
#>     monetary_aggregates    mortgage_market
#>                       2                  6

Provenance

Every result from a boe_*() function is a boe_tbl (a data frame with attached metadata). Printing shows a one-line provenance header, but it behaves like a normal data frame for everything else.

br <- boe_bank_rate(from = "2024-01-01", frequency = "monthly")
br
#> # BoE [boe_bank_rate]: 1 series [IUMABEDR] · 16 obs · 2024-01-01 to 2025-04-30 · freq=monthly
#>         date rate_pct
#>   2024-01-31     5.25
#>   2024-02-29     5.25
#>   ...

Caching

All downloads are cached locally in your user cache directory. Subsequent calls return the cached copy instantly - no network request is made.

# Inspect the cache (path, file count, size, range)
boe_cache_info()
#> BoE cache
#> * Path:  /Users/.../R/boe/cache
#> * Files: 12
#> * Size:  6.4 MB
#> * Range: 2026-04-12 09:14:02 to 2026-04-25 11:30:18

# Force a fresh download
boe_bank_rate(from = "2020-01-01", cache = FALSE)

# Remove files older than 7 days
clear_cache(max_age_days = 7)

# Remove all cached files
clear_cache()

This package is part of a suite of R packages for economic, financial, and policy data. They share a consistent interface (named functions, tidy data frames, local caching) and are designed to work together.