WYOMING HOUSING AND HOME IMPROVEMENT MARKETS:
AN ECONOMIC INDICATOR

Examining the last six months of housing starts/building permits and home additions/improvements will indicate whether the Wyoming economy is improving, declining or stagnating. New construction building permits are often used as a leading economic indicator in order to forecast the general direction of the economy. Home improvements/additions should also be included with building permits as a leading economic indicator. The relationship between employment and consumption, wages in the Construction industry and housing costs and improvements illustrate the health of the economy. Previous employment and wages for the Construction industry affect current and future employment, but housing starts/additions are also directly correlated with employment.

Residential housing is considered the largest part of both personal consumption and investment expenditures in gross domestic product (GDP). Housing costs also make up the largest part of the consumer price index (CPI) and fluctuates rapidly with the demand for housing. Housing is considered a leading economic indicator because it makes up the largest part of the GDP and the CPI.

The April 1995 issue of Wyoming Labor Force Trends ("Unemployment Insurance Statistics" article) discusses the seasonal nature of the Construction industry and the correlation between separation rates (job loss) and unemployment insurance claims in the winter. As in all other industries, unemployment insurance is considered a cost of labor in the Construction industry due to federal law requirements. The scale of seasonal savings, however, occurs in the Construction industry due to fewer employees working in the winter months (see Figure 1). Looking at aggregated time series data for past employment and wage interaction in the Standard Industrial Classification (SIC) 15--Building Construction--from October 1991 to March 1995 helps to explain more about the variability in employment (see Figure 1). Employment and wages peak in the summer and drop to their lowest in the winter because of the seasonal nature of the construction industry.

However, in 1994 employment levels had a very large peak, while wages had no substantial peak due to increased hours and/or wage rates being bid up. The largest percentage of the total variation in employment is explained by wages. Table 1 shows that a regression equation or predictive model for employment and wages yields a coefficient of determination R-squared of .8649. The R-squared is used to determine the association between two or more variables. The closer R-squared is to one, the stronger the relationship between the level of employment and the level of wages. When R-squared equals one, and the level of one variable is known, the value of the other variable can be predicted with a high degree of certainty.

An important economic theory states that as employment increases, the production of goods or services also increases, but at a continuously diminishing or slowing rate. This is known as the law of diminishing returns to the labor input. On the principle that wages reflect the marginal productivity of labor (i.e., rate of change of labor), as employment increases, marginal productivity declines and wages fall. Given the level of employment, the age can therefore be fully determined. New firms entering the market will decrease wages and prices but increase housing quantity available and the number of employees. The 1994 data show a large peak in employment with wages having no peak. This indicates that employment should have decreased in 1995, which it did. Employers are willing to hire new workers as long as the wage can be justified by the added value of production (i.e., increase in profit). The volume of employment is determined by the volume of production. Employers hire enough workers at the existing wage to produce the goods they think they can sell at maximum profit. The level of output is directly determined and the level of employment is indirectly determined by the total amount that consumers, investors, and governments are able and prepared to spend on goods or housing. As increased consumption pushes employment toward the full employment level (where increases in employment will cause inflation), the marginal (i.e., the rate of change) product of labor falls and so does the real wage that employers are willing to pay. It is not possible for demand and output to increase in real terms indefinitely with prevailing prices increasing and causing inflation. Expenditures are attempted at volumes greater than the actual money value of output at present prices. This suggests that prices will rise. From the data presented, the value of housing is lower compared to the CPI increase or cost of living increase.

Housing starts are associated with total employment (as a proxy or substitute for population) and employment in the Construction industry (see Figure 2). In 1995, employment levels were at the same level as 1994. The 1994 real wage had no substantial seasonal peak, with considerably lower housing starts in 1995 as compared to 1994. Also, home additions/improvements and costs are directly correlated with housing starts and employment (see Figure 3). The number of home additions appears to be high when housing starts are low and vice versa. If housing starts and home additions are included, much more of the variability with employment can be explained with an R-squared of .9084 (see Table 1). Table 1 presents the average elasticity values along with the respective t-values. The higher the absolute value of the t-ratio, the more the variable influences employment. All t-ratios are statistically significant with a high degree of accuracy at a 95.0 percent confidence level in all the variables, meaning each variable influences employment and consumption at their R-squared value 95 times out of 100.

Elasticity is a unitless measure of the response change of a dependent variable (i.e., employment) to a change in an independent variable (i.e., housing starts). It can be interpreted as the percentage change in the dependent variable per percent change in the independent variable. The housing market is strongly inelastic with respect to the supply of housing. For instance, a 10.0 percent decrease in housing will decrease total employment by 2.4 percent or a 10.0 percent increase in housing costs will decrease total employment by 7.0 percent.

Looking at the addition and improvement side of housing also opens the door to look at closely related industries such as forestry, lumber, wood products, furniture, and building materials. The Construction industry influences the economy as a whole, although more so in some states than others. Looking at the last six months of 1995, April through September (see Figure 2), housing starts/additions and employment in Wyoming have experienced a slight decline from 1994. Housing starts were around 110 in 1995 and 1993 compared to 180 in 1994 and 100 in 1992. Home additions were around 200 permits in 1995 compared to 340 in 1994, 400 in 1993 and 1992. Does this mean that the Wyoming economy is improving, declining or stagnating? Using housing starts and home additions as economic indicators, it is stagnating, peaking out in 1994. Many other factors and/or variables will affect the Construction industry and can also be used as economic indicators in other research to verify these findings: demographic factors (i.e., age composition of the population, out and in population migration patterns), rental prices, wood prices, mortgage and interest rates.

Mike Evans is a Senior Economist supervising Bureau of Labor Statistics (BLS) programs at Research & Planning.

1 Department of Employment, Employment Resources Division, Research & Planning, Current Employment Statistics (CES).

2 Department of Employment, Employment Resources Division, Research & Planning, Covered Employment and Wages (ES-202).

3 Bureau of the Census, Building Permits Branch, Manufacturing and Construction Division.

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