Abstract
This paper investigates the impact of shocks in the unemployment rate on household formation. Prior research has shown that negative economic shocks reduce household formation, but does not inform how long the declines in household formation will persist. Using time series data from 1975 to 2011, we examine how households respond to unemployment rate shocks and estimate the length of time it takes for households to return to its original level in a vector autoregressive model. The results demonstrate that household formation falls in the quarter after unemployment increases, and that it can take up to 10 quarters to return its previous level. While this is a substantial length of time, one implication of these results is that even a permanent increase in the unemployment rate will not permanently affect housing formation in the long run.
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Notes
Mykyta and Macartney (2011) define a household as doubling up if it adds an adult that is not the householder, spouse or cohabiting partner of the householder.
Painter and Redfearn (2002) used homeownership rate, housing starts, house prices and mortgage rates as endogenous variable, and median income, unemployment and population as exogenous variables. Since the focus of the paper was to look at the changes in homeownership rates and housing starts in response to interest rate shocks, their model treated the interest rate as endogenous and the unemployment rate as exogenous.
The change in the number of households is likely to have a proportional relationship with the changes in the population and housing starts. Thus both the first difference of the population and housing starts (which is already in changes) are included in the model. All other variables are in levels.
During our estimation period, the data has been revised in 1982, 1993 and 2002. However, there is almost no difference between the revised and the non-revised monthly estimates in 1993, and thus we drop only the change in the number of household estimates in the first quarter of 1982 and 2003. The discontinuous line change in the number of household graph in Fig. 1 reflects this adjustment.
We can estimate the VECM model prior to the most recent revision. The results are qualitatively similar to the VAR results. The primary difference is that the impact of the unemployment rate on the number of households is greater and more persistent. Results of the Vector Error Correction Model and the Johnson tests are presented in Appendix Table 7 and 8.
We have also implemented the VAR after differencing the HPI and the unemployment rate. While the results do not show a noticeable difference from what is presented in this paper, it is more difficult to interpret the impulse response function for the change in the unemployment rate.
The AIC was first proposed by Akaike (1973). The AIC for a given model is the difference between the maximized log-likelihood (L) and the number of estimable parameters (K): AIC = −2log (L) + 2 K. The optimum number of lags is chosen at the minimum AIC value. The main advantage of the AIC is that it rewards goodness of fit through the likelihood ratio, while penalizes the increase in the number of variables.
We tested additional criteria to determine the number of lags. All other approaches also suggested that four lags were optimal except for the Schwarz Criterion, which suggested two lags to be optimum. We also estimate the model with two lags based on the Schwarz Criterion, and the results are invariant to the reduced number of lags.
The red dotted lines show the confidence interval at 95 % band.
Two lags are included based on AIC.
We have also tried a 3 standard deviation unemployment rate shock which is similar to the increase of unemployment rate in the current regression. Although the number of households drops by a greater magnitude, the result from impulse response function shows a similar pattern: the number of households returns back to its original level by the 12th quarter. We also tried to analyze the impact of unemployment rate in the four regions and among different age groups. However, since the number of households for the subsample could only be obtained annually, the estimates were inaccurate to gain any meaningful interpretation.
As of the fourth quarter in 2011, 11.1 million U.S. homeowners were underwater on their mortgages, accounting for 22.8 % of all residential properties with a mortgage. (Corelogic, 2011)
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Appendix
Appendix
Impulse response: change in the number of households from an unemployment rate shock: multivariate prior to 2007
Impulse response: change in the number of households from an unemployment rate shock 2 S.D. unemployment shock
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Choi, J.H., Painter, G. Housing Formation and Unemployment Rates: Evidence from 1975–2011. J Real Estate Finan Econ 50, 549–566 (2015). https://doi.org/10.1007/s11146-014-9487-7
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DOI: https://doi.org/10.1007/s11146-014-9487-7