Yale (Robert J. Shiller)

Shiller long-run data — Stock prices, dividends, earnings, CPI and long rates since 1871 — the CAPE's home

When the question is valuation over decades rather than years — the cyclically adjusted price–earnings ratio, real prices and real dividends, the long rate — and you need the one series that goes back before the Fed existed.

equitiesvaluationratespricesUnited States

Asset classesequities · valuation · rates · prices

Frequencymonthly

History1871 → today

Limit · costNo key · one Excel file

KeyNo key

Formats · pull withxls — python, excel

LicenceFree for personal and academic use with attribution to Robert Shiller; not for redistribution.

RedistributeNo — link and pull, do not republish

Best for“Is the market expensive by the standards of 150 years? (CAPE)” · “Real stock returns and real rates over the very long run”

When to use it

Reach for it when…

Not for: Anything current-month (the file updates irregularly), daily data, non-US markets, earnings by company.

How to read it

Units, revisions, traps

Units. Monthly averages of daily closes for prices; earnings and dividends are trailing twelve months, interpolated; CAPE is price over the 10-year average of real earnings.

Revisions. Recent months change as earnings finalise; the history is stable.

The Data sheet has eight header rows; the date column is a decimal (1871.01 = January).
Prices are monthly averages, not month-end closes.
CAPE uses reported (GAAP) earnings; the 'total return CAPE' is a separate column.

Classic mistake: Selling because CAPE is 'above average' — it has been above its long-run mean for most of the last 30 years.

How to use it in your own work

Each recipe: Python · Excel · Sheets, with how to read the result. Python recipes run under pandas; recipe 1 is re-run by the weekly check where the source allows it.

How to use it in your own work

Copy the snippet, change the series id, read the result the way the footer says. Replace YOUR_…_KEY with your own key where one is needed.

Recipe 1CAPE since 1881

# pip install xlrd  (needed for .xls)
import pandas as pd
u = "http://www.econ.yale.edu/~shiller/data/ie_data.xls"
df = pd.read_excel(u, sheet_name="Data", skiprows=7)
df = df.rename(columns={df.columns[0]: "date", df.columns[12]: "CAPE"})
df = df[pd.to_numeric(df.date, errors="coerce").notna()]
df["year"] = df.date.astype(float)
print(df[["year", "CAPE"]].dropna().tail())

How to read the resultCAPE above 30 has happened three times: 1929, 2000 and the 2020s. It says little about the next year and a lot about the next ten.

Recipe 2Real total return, a century

import pandas as pd
df = pd.read_excel("http://www.econ.yale.edu/~shiller/data/ie_data.xls", sheet_name="Data", skiprows=7)
df = df.rename(columns={df.columns[0]: "date", df.columns[1]: "P", df.columns[2]: "D", df.columns[4]: "CPI"})
df = df[pd.to_numeric(df.date, errors="coerce").notna()].astype({"P": float, "D": float, "CPI": float})
real_p = df.P / df.CPI * df.CPI.iloc[-1]
print(real_p.iloc[[0, -1]])

How to read the resultReal price only; adding reinvested dividends (column C) roughly doubles the long-run growth rate.

Recipe 3The long rate since 1871

import pandas as pd
df = pd.read_excel("http://www.econ.yale.edu/~shiller/data/ie_data.xls", sheet_name="Data", skiprows=7)
df = df.rename(columns={df.columns[0]: "date", df.columns[5]: "GS10"})
df = df[pd.to_numeric(df.date, errors="coerce").notna()]
print(df[["date", "GS10"]].tail())

How to read the resultNominal 10-year yield; subtract the CPI's 12-month change for the real rate readers argue about.

Series → question map

The ids we use from Shiller long-run data, each with the question it answers. The catalog's compare view reads the concept tags behind these rows.

Series	Answers	Region	Concept
ie_data · CAPE	Is the market expensive by long-run standards?	United States	long_run_valuation
ie_data · P, D, E	Real prices, dividends and earnings since 1871	United States	long_run_valuation
ie_data · GS10	The long rate since 1871	United States	yield_10y
ie_data · CPI	US CPI since 1871	United States	cpi

Compare with

Same question, different source: Kenneth French Data Library, FRED, Stooq. The compare view lines up coverage, frequency, history and access side by side and lists what the combination makes possible.

Open compare: Shiller long-run data · Kenneth French Data Library · FRED →

Questions readers ask

Is the Shiller data free?

Yes for personal and academic use; do not redistribute the file.

How often is it updated?

Irregularly, roughly monthly; check the file's last row.

Why xls and not csv?

It has been an Excel file since the 1990s. pandas needs the xlrd package to read .xls.

Educational only — we explain, we never advise · snippet licence: public domain · corrections to [email protected], fixed within a day and logged in the changelog.