Philip Kalikman’s Research

Mortgage Default: A Heterogeneous-Agent Model.

Mortgage default has been a primary cause, consequence, and concern of financial crises, including in the Great Recession of 2008 and in the unfolding Covid-19 crisis. Understanding which borrowers will default, and which would not default if offered particular modifications, is essential to designing effective crisis mitigation policy.

I introduce a loan-level model of mortgage default with heterogeneity in borrower characteristics and mortgage terms. The model generalizes existing models, embedding the strategic and double-trigger models as special cases in a family unified by an idiosyncratic, non-pecuniary penalty for default. The model fits not only the aggregate level of defaults but also cross-sectional characteristics of the distribution of mortgage performance throughout the financial crisis.

The model’s structural specification and its support for loan- and borrower-level heterogeneity enable investigating policies that exploit heterogeneity in the population of borrowers. I show that the main government mortgage modification programs employed after the financial crisis could have been substantially improved through such policies, in particular by offering principal forgiveness to underwater borrowers who were ex ante identifiable as more likely to default. More generally, the model may be used to reveal which borrowers should receive what type of modifications during the present Covid-19 crisis.

An Agent-Based Model of the Housing Market Bubble in Metropolitan Washington DC. (With Robert Axtell, Benjamin Conlee, Ernesto Carella, Doyne Farmer, John Geanakoplos, Jon Goldstein, Matthew Hendrey, Peter Howitt, David Masad, and Nathan Palmer)

We develop a computational model of a regional housing market. Over a million distinct agents buy, sell, and rent houses according to different behavior rules, which depend on demographic, financial, and housing stock characteristics we estimate using data in the Washington, D.C. metropolitan area from 1997 – 2009. We use both individual record-level matching and statistical inference on several dozen disparate datasets to simulate a single joint distribution of household characteristics. Households’ transactions endogenously generate a housing market bubble and crash that resembles the observed history not only in the timing and magnitude of the boom and bust in home prices, but also in other aggregate dynamics such as time-on-market, homeownership rate, and vacancy rate and in distributional characteristics such as house prices across tiers of building quality and loan performance across bands of credit quality. We use the model to study the drivers of the bubble. We show that low risk-free interest rates do not generate a house price bubble when credit availability is restricted, whereas loose credit contributes to a bubble even without low risk-free rates.

A prior draft of this paper appears in Housing markets and the macroeconomy: challenges for monetary policy and financial stability—a conference by Deutsche Bundesbank, the German Research Foundation (DFG) and the International Monetary Fund.

Cheap Credit: Leverage In the Subprime Mortgage Crisis.

I examine loan-level mortgage origination data in the US throughout the boom and bust period from 1998 – 2008. Existing literature has alternately pointed to loose credit as a driver of the rise in house prices leading up to the 2007-8 US Financial Crisis, or argued that mortgage leverage did not rise significantly in the run-up to the crisis and therefore could not have been a significant driver of the rise in house prices. I compare three different mortgage origination datasets to investigate the discrepancy. Linking loans at the property level shows that loan-to-value ratios at origination did increase before the crisis. Leverage rose especially for less creditworthy borrowers, many of whom would have been excluded from getting a mortgage at all in times of tighter credit. The origination data are consistent with the view that loose credit supply fueled the bubble.