Models -> Mixed

Keys to the White House

  • uses 13 true or false statements to predict the election winner
  • strong track record since 1984
  • can be translated to quite accurate popular vote forecasts

Theory of retrospective voting

The Keys to the White House, crafted by Allan Lichtman, operates on the theory of retrospective voting. This means that the American electorate selects a president based not on campaign events, but on the performance of the party in control of the White House.

If voters approve, the incumbent party secures another term; if dissatisfied, the challenging party triumphs. In essence, presidential elections become referenda on the incumbent party’s governance rather than battlegrounds for debates, advertising, or campaign tactics.


Lichtman first developed the Keys system in 1981, in collaboration with Vladimir Keilis-Borok.

The 13 Keys

Each of the thirteen keys is stated as a threshold condition that always favors the re-election of the party holding the White House.

Coding of the Keys

Each key can then be assessed as true or false prior to an upcoming election. The model then uses a simple decision rule to predict the election winner: 

When five or fewer keys are false, the incumbent party wins; when any six or more are false, the challenging party wins.

Forecast accuracy
of the Keys to the White House

The Keys model is often cited as having a perfect record in correctly predicting each U.S. presidential election since 1984. This is not correct. In 2000, Lichtman coded five keys as false for the incumbent Democratic party. Given today’s implementation of the model, this would have implied a forecast that Democratic candidate Al Gore should have won the electoral college, which he didn’t (Gore did win the popular vote, however). Hence, the model’s track record in picking the electoral college winner since 1984 is a 9 out of 10.

The trouble with such an approach, or any other producing binary predictions, is that landslides such as 1964, 1972, and 1984 are easy to predict, and so supply almost no information relevant to training a model. Tie elections such as 1960, 1968, and 2000 are so close that a model should get no more credit for predicting the winner than it would for predicting a coin flip.

Gelman, Hullman, Wlezien and Morris (2023)

Either way, counting correct predictions for binary (yes/no) forecasts is a questionable validation of a forecasting model. A better way to judge the quality of the Keys model is thus to translate the binary forecasts into forecasts of the popular vote, as suggested by Armstrong & Cuzán (2006). In order to translate Lichtman’s coding into a forecast of the incumbent’s popular two-party vote (V), Armstrong & Cuzán (2006) used the number of Keys favoring the incumbent (i.e., keys coded as True) as the single predictor in a simple linear regression model estimated based on historical data back to 1860. This model does quite well, with an average error of roughly two points. This is particularly notable given that the forecasts were made with very long lead time, much longer than most other available models.