Another Look at Effects of “Adults-Only” Age Restrictions on Housing Prices

Abstract

This study reexamines the price effects of age restrictions on housing prices. Our data cover a period when the housing market is taking a steep downturn. We argue that, when housing prices are falling, seniors are more likely to avoid investing in housing for at least two reasons. First, seniors are relatively more sensitive to their immediate equity loss than younger homeowners, mainly due to the limited remaining lifetime over which they can afford to wait; second, age-restriction acts as a luxury good, with seniors not willing to pay for reduction in neighborhood uncertainty, eliminating buyer demand for this segment of the population. If this “larger demand loss” outweighs the positive externality of the reduction in neighborhood uncertainty during the market downturn, we would observe that age-restrictions reduce property values. Using data from Broward County, Florida for the years of 2005–2007, we find a significant discount in residential condominium prices due to age-restrictions. In particular, we find that imposing age-restriction on properties decreases housing prices by 17.9% during the period May 2005 to April 2006, while the discount is worse, 22.7%, during the later period May 2006 to May 2007.

Appendix

Table 4 Multicollinearity diagnostics

When a regressor is nearly a linear combination of other regressors in the model, the estimates for a regression model may not be uniquely computed. This problem is called collinearity or multicollinearity. The primary concern is that as the degree of multicollinearity increases, the regression model estimates of the coefficients become unstable and the standard errors for the coefficients can be wildly inflated. There are many ways to detect multicollinearity. We use the most common approach to calculate the variance inflation factor (VIF) by following Belsley et al. (1980). Below is the summary of multicollinearity diagnostics:

Belsley et al. (1980) suggest that if the VIF is around 10 or less (or Tolerance is around 0.10 or higher), the multicollinearity issue should not be of a great concern. However, if the VIF is larger than 100 (or Tolerance is lower than 0.01), the estimates should have a fair amount of numerical error and the multicollinearity issue must be addressed. Since VIF numbers in our regression models are much less than 100 and around 10 or less, and the VIF number for agerest (the most important variable in our study) is only 1.16, we conclude that there is no multicollinearity issue in our regression models.