The Keys to the White House, developed by Allan Lichtman, is a system for predicting the winner of American presidential elections, based upon the theory of pragmatic voting. America’s electorate, according to this theory, chooses a president, not according to events of the campaign, but according to how well the party in control of the White House has governed the country. If the voters are content with the party in power, it gains four more years in the White House; if not, the challenging party prevails. Thus, the choice of a president does not turn on debates, advertising, speeches, endorsements, rallies, platforms, promises, or campaign tactics. Rather, presidential elections are primarily referenda on the performance of the party holding the White House.
Lichtman first developed the Keys system in 1981, in collaboration with Vladimir Keilis-Borok, founder of the International Institute of Earthquake Prediction Theory and Mathematical Geophysics. The methodology used in the development of the Keys is described in Keilis-Borok and Lichtman (1981) and Lichtman (2008, 2010a). As shown in Table 1, each of the thirteen keys is stated as a threshold condition that always favors the re-election of the party holding the White House. For example, Key 5 is phrased as “The economy is not in recession during the election campaign.” Each key can then be assessed as true or false prior to an upcoming election and the winner predicted according to a simple decision rule. Unlike other systems for predicting election results, the Keys do not assume a fixed relationship between election results and one more dependent variables, such as economic growth or presidential approval ratings. Rather predictions are based on an index comprised of the number of false or negative keys: When five or fewer keys are false, the incumbent party wins; when any six or more are false, the challenging party wins.
Key Topic Threshold condition 1 Party Mandate After the midterm elections, the incumbent party holds more seats inthe U.S. House of Representatives than it did after the previousmidterm elections. 2 Contest There is no serious contest for the incumbent-party nomination. 3 Incumbency The incumbent-party candidate is the sitting president. 4 Third party There is no significant third party or independent campaign. 5 Short-term economy The economy is not in recession during the election campaign. 6 Long-term economy Real per-capita economic growth during the term equals or exceeds mean growth during the previous two terms. 7 Policy change The incumbent administration effects major changes in national policy. 8 Social unrest There is no sustained social unrest during the term. 9 Scandal The incumbent administration is untainted by major scandal. 10 Foreign/military failure The incumbent administration suffers no major failure in foreign or military affairs. 11 Foreign/military success The incumbent administration achieves a major success in foreign or military affairs. 12 Incumbent charisma The incumbent-party candidate is charismatic or a national hero. 13 Challenger charisma The challenging-party candidate is not charismatic or a national hero.
In his 2020 forecast, Lichtman coded seven keys as false (red font color in Table 1). Thus, the Keys model predicts that the Democrats will in 2020. In order to translate this forecast into a forecast of the incumbent’s popular two-party vote (V), PollyVote uses the number of Keys coded as
True as the single predictor in a simple linear regression model that is estimated based on historical data from 1860 to 2012.
This approach was suggested in Armstrong & Cuzán (2006). The regression, updated through 2016, yielded the following vote equation:
V = 37.3 + 1.77 *
True = 37.3 + 1.77 * 6 = 47.9%
That is, the Keys model predicts Trump to gain 47.9% of the two-party popular vote (Biden: 52.1%).
Retrospectively, the keys model accounts for the outcome of every American presidential election since 1860, much longer than any other prediction system. Prospectively, the Keys to the White House has correctly forecast the winner of all eight presidential elections from 1984 to 2016, usually months or even years prior to Election Day.
Table 2 reports the first published predictions based on the Keys for the elections of 1984 through 2016.
Election Forecast date Source 1984 April 1982 “How to Bet in ’84”, Washingtonian Magazine, April 1982 1988 May 1988 “How to Bet in November”, Washingtonian Magazine, May 1988 1992 September 1992 “The Keys to the White House”, Montgomery Journal, September 14, 1992 1996 October 1996 “Who Will Be the Next President?”, Social Education, October 1996 2000 November 1999 “The Keys to Election 2000”, Social Education, November/December 1999. 2004 April 2003 “The Keys to the White House”, Montgomery Gazette, Apr. 25, 2003 2008 February 2006 “Forecast for 2008”, Foresight, Feb. 2006 2012 January 2010 “The Keys to the White House: A Preliminary Forecast for 2012”, International Journal of Information Systems and Social Change, 1(1), 31-43 2016 December 2015 Predicting 2016: What the “13 Keys to the White House” Say and Why Foreign Policy Will Decide the Next President September 2016 Trump is headed for a win, says professor who has predicted 30 years of presidential outcomes correctly 2020 August 2020
- Armstrong, J. S. & A. G. Cuzán (2006). Index methods for forecasting: An application to the American presidential elections, Foresight: The International Journal of Applied Forecasting, (2006, 3), 10-13.
- Keilis-Borok, V. I. & Lichtman, A. J. (1981). Pattern Recognition Applied to Presidential Elections in the United States, 1860-1980: The Role of Integral Social, Economic, and Political Traits, Proceedings of the National Academy of Sciences 78 (November 1981), 7230-7234.
- Lichtman, A. J. (2006). The Keys to the White House: Forecast for 2008, Foresight: The International Journal of Applied Forecasting, 3, (February 2006), 5-9.
- Lichtman, A. J. (2008). The Keys to the White House, 2008 Edition. Lanham, MD: Rowman & Littlefield.
- Lichtman, A. J. (2010). The Keys to the White House: A Preliminary Forecast for 2012” International Journal Of Information Systems & Social Change 1 (Jan.-March 2010).