Queuing model reveals mortgage rate and service time dynamics

Building on queuing theory, a new model of residential mortgage supply is developed and empirically validated.

It provides insights into how the stochastic arrival and sequential servicing of loan applications affect mortgage origination, offering closed-form predictions for lenders' optimal responses to changes in mortgage demand.

Using confidential Home Mortgage Disclosure Act (HMDA) data, the study estimates that a one standard deviation increase in mortgage demand raises mortgage rate spreads by 3 to 8 basis points, increases loan quantities by 20 to 32 percent, and extends application processing times by 3 to 5 days.

These findings highlight that operational congestion, driven by demand shocks, directly influences lenders' pricing power and causes processing delays, providing microeconomic evidence for capacity constraint channels previously observed at an aggregate level.

The study reveals that borrower price sensitivity significantly moderates the increase in lender pricing power during demand shocks.

Specifically, a one standard deviation increase in mortgage demand elasticity reduces spreads by 0.7 to 2.4 basis points.

This interaction means the net effect of a positive demand shock on mortgage spreads is a more contained 1.3 to 7.5 basis point increase.

The empirical results are robust across various specifications, with identification primarily driven by time-series rather than cross-sectional variation.

This suggests that macroeconomic factors, such as interest rate cycles, are key determinants of mortgage demand dynamics and influence lenders' pricing power.