Investment in housing is a critical determinant of the direction of the economy. Investment in housing and TCS would be expected to decrease whenever consumer confidence falls, but housing investment would also be expected to fall as TCS rises whenever LTIRs rise. These events would be leading indicators of a recession. Since interest rates could rise unpredictably due to unanticipated inflation, the forecast horizon was restricted to two years. With a two-year forecast, the reliability of the findings increases.
The remainder of the paper is arranged as follows: Part 2. documents the data on housing starts, TCS, and LTIR for the past eight years; Part 3. presents the economic theory of the Keynesian investment function, on which the forecast is based; Part 4. presents the forecast of housing starts for the years 1999 and 2000; Part 5. discusses the economic importance of the forecast; Part 6. addresses effects on economic policy.
The CPI monthly data was taken from the Bureau of Labor Statistics. The monthly data for housing starts, LTIR, STIR, TCS, and GDP were taken from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED). The FRED variables used for the forecast are GS30 (30 year government securities interest rate), PCEC92 (real personal consumption expenditures), and HOUST (housing starts) and are all real variables. PCEC92 is represented in billions of chained 1992 dollars and both PCEC92 and HOUST are seasonally adjusted at annual rates (SAAR). LTIR is taken from the secondary market and is the thirty-year Treasury bond rate with a constant maturity, and is not seasonally adjusted. Little or no seasonal variation is present in long-term interest rates. The primary source of all of this data is the United States Department of Commerce, the Bureau of Economic Analysis, and the Federal Reserve Board of Governors.
Regressions on the forecast data were performed on two parameters. In the first regression, housing starts is compared to TCS and LTIR for the months between January 1990 and December 1998. A regression was then run with the right hand side variables lagged two years comparing housing starts to TCS and LTIR between the months of January 1992 and December 1998.
Housing starts is used as the forecast target since it is a leading
indicator of economic performance. As discussed above several variables
were considered. However, only LTIR and TCS had strong explanatory power
for housing starts, according to t-statistics and adjusted R-square. TCS
is used as a proxy for GDP. Intuitively, GDP or TCS should be significant
in forecasting housing starts since consumer spending responds to interest
rates through the loanable funds market and reflects consumer confidence.
LTIR should be inversely related to housing starts. When LTIR decreases,
the amount of housing starts should normally increase. Including both interest
rates and TCS as right hand side variables allows a more accurate forecast
of housing starts.
This is a simple model that will be used to forecast housing starts. I is investment that consumers commit to buying new houses. The function shows that either variable i or Y influences housing starts (I). The interest rate, (i) is LTIR, the long-term interest rate. Y is TCS, total consumption spending. The Keynesian Investment Function becomes:
Each variable can affect housing starts independently of the other variable. If LTIR continues to decrease, consumer buying power increases. Rising consumer incomes and confidence promote more purchases of new houses (E-mail Trends, 1999). Housing starts can also be an independent function of TCS. As TCS increases, the number of houses people can afford to buy also increases, because the income of one household may come from the consumption spending of another. TCS has increased due to the amount of increased income that American consumers have realized over the last several years. Increased incomes lead to higher levels of consumption at any level of GDP.
A few limitations of this paper should be noted. First, TCS is very volatile and depends highly on other economic factors, such as the unemployment rate and interest rates, which are not captured in the analysis attempted here. Another shortcoming is that LTIRs can be altered anytime the Federal Reserve Board decides interest rates need to be raised to combat inflation.
Without lagging the right-hand-side data, regression results are (with t-statistics in parentheses):
First, the t-statistics on the two-year lag are better than the unlagged regression. Next, the adjusted R-square for the two-year lagged regression is 61%, which means that there is only 39% variation in housing starts that may be explained by another variable. The Adjusted R-Square on the unlagged investment function was only slightly better (70%). However, the t-statistics for the unlagged regression did not produce as good results as the lagged regression. Forecast housing starts calculated from this equation are given in Table 1.
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November-98
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December-98
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January-99
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February-99
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March-99
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April-99
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May-99
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June-99
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July-99
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August-99
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September-99
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October-99
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November-99
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December-99
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January-2000
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February-2000
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March-2000
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April-2000
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May-2000
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June-2000
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July-2000
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August-2000
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September-2000
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October-2000
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November-2000
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December-2000
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The forecast reveals no sign of the economy slowing down. Over the next
two years, interest rates will most likely stay at their low levels since
inflationary pressure has not increased. Furthermore, TCS will remain high
due to an increase in buying power because of low interest rates, reduced
unemployment (Email Trends, 1998), and an average increase in salaries.
If this forecast turns out to be true, the economy is in for a prosperous two years. With interest rates low, consumers can afford to purchase more items including homes. An increase in the MPC provides an abundant amount of employment opportunities for U.S. citizens.
Even though higher wage demands are affecting businesses, many companies have learned to accomplish more tasks with fewer workers. Additionally, businesses are operating with increased profits in comparison to their past performance. What has led to a decrease in unemployment is the changing demand for businesses to provide more personal services to consumers. Even if the economy does not turn out the way the forecast predicts, the worst that is likely to happen is that unemployment may rise back up to its traditional standard of five to six percent.
Interest rates should stay the same over the next two years. If the
Federal Reserve Board finds it necessary to raise the interest rates due
to inflation, the Board should be cautious about raising the interest rates
too high. As it has been stated, an increase in interest rates will increase
unemployment, decrease consumer spending and inherently decrease the amount
of housing starts.
E-Mail Trends in Labor and Employment, "It Was a Golden Year" http://epfnet.org/et980116.htm/, Jan. 16, 1998.
Federal Reserve Bank of St. Louis, Federal Reserve Economic Data (FRED), http://www.stls.frb.org.fred/.
Federal Reserve Board of Governors, Gross Domestic Product Seasonally Adjusted Annual Rate, http://www.bog.frb.fed.us/.
Keynes, John Maynard, The General Theory of Employment Interest and Money, New York: Harcourt Brace and World, 1936.
U.S. Census Bureau, Economic Briefing Room, http://www.census.gov/indicator/www/housing.html/.