Steven Laufer

Equity Extraction and Mortgage Default

Using a property-level data set of houses in Los Angeles County, I estimate that 30% of the recent surge in mortgage defaults is attributable to early home-buyers who would not have defaulted had they not borrowed against the rising value of their homes during the boom. I develop and estimate a structural model capable of explaining the patterns of both equity extraction and default observed among this group of homeowners. In the model, most of these defaults are attributable to the high loan-to-value ratios generated by this additional borrowing combined with the expectation that house prices would continue to decline. Only 30% are the result of income shocks and liquidity constraints. I use this model to analyze a policy that limits the maximum size of cash-out refinances to 80% of the current house value. I find that this restriction would reduce house prices by 14% and defaults by 28%. Despite the reduced borrowing opportunities, the welfare gain from this policy for new homeowners is equivalent to 3.2% of consumption because of their ability to purchase houses at lower prices.

A More Timely House Price Index

Using listings data, we construct a new repeat-sales house price index that describes house values at the contract date when the price is determined rather than the closing date when the property is transferred. We showthat this difference in timing helps explain several puzzles about house prices, including their strong short-term serial correlation and their weak correlation with stock prices and macroeconomic news shocks. In addition, we showthat a variant of our index that relies exclusively on listings data for recent transactions accurately reveals trends in house prices several months before existing price indexes like Case-Shiller become available.

Search, Matching and Training

We estimate a partial and general equilibrium search model in which firms and workers choose how much time to invest in both general and match-specific human capital. To help identify the model parameters, we use NLSY data on worker training and we match moments that relate the incidence and timing of observed training episodes to outcomes such as wage growth and job-to-job transitions. We use our model to offer a novel interpretation of standard Mincer wage regressions in terms of search frictions and returns to training. Finally, we show how a minimum wage can reduce training opportunities and decrease the amount of human capital in the economy.

Search, Matching, and Training

We estimate a partial and general equilibrium search model in which firms and workers choose how much time to invest in both general and match-specific human capital. To help identify the model parameters, we use NLSY data on worker training and we match moments that relate the incidence and timing of observed training episodes to outcomes such as wage growth and job-to-job transitions. We use our model to offer a novel interpretation of standard Mincer wage regressions in terms of search frictions and returns to training. We use our model estimates to investigate the impact of minimum wages on training and outcomes, and find significant impacts on both of these dimensions for sufficiently high minimum wage values. (Copyright: Elsevier)