Philip Kalikman is Assistant Professor of Finance at the Sy Syms School of Business at Yeshiva University in New York City. Dr. Kalikman researches mortgage default, housing markets, financial crises, and heterogeneous computational models of consumer behavior and economics. Dr. Kalikman received his MA, MPhil, and PhD in economics from Yale University, and his BA in pure mathematics from the University of Chicago.
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.
Please download the latest version at kalikman.com/default.
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.
Works in Progress
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.
Dr. Kalikman developed his interest in mortgage finance throughout several years as a quantitative researcher at a mortgage-focused hedge fund. He led a team of programmers and data scientists who worked extensively with loan-level, borrower-level, and property-level microdata to develop applied models of mortgage performance, housing market dynamics, and consumer credit availability. The team’s research was presented at academic and policy institutions such as the International Monetary Fund, the European Central Bank, the Santa Fe Institute, and the Federal Reserve Bank of New York.
Dr. Kalikman’s interest in economic policy developed further through his service as a consulting economic policy advisor to Secretary of State Hillary Clinton’s 2016 Presidential Campaign. He has advised other policymakers, including members of the U.S. Senate, and also served as the lead economist for a family office that specialized in financial regulation and fintech venture capital, working closely with a former Under Secretary of the Treasury and a former Comptroller of the Currency.
Dr. Kalikman previously taught mathematics, computer science, and robotics at the middle school through university levels.
Dr. Kalikman prefers to code in Julia and, until Julia’s package ecosystem matures, to perform data analysis in R. He has experience coding in C, C++, Haskell, Java, Mathematica, Matlab, Python, SQL, and Shell. He is an experienced terminal and git user, with obsessively configured Neovim and Atom installations. He typesets in , and created this website using Jekyll with a customized Minimal Mistakes theme.
Dr. Kalikman serves on the board of Students For Educational Justice, a youth-led, intergenerational organizing body that drives efforts for racial and educational justice and cultivates self-pride in young BIPOC (Black people, Indigenous people, and people of color). SEJ envisions a just education system and an equitable society in which all people understand the history of race in the United States and are actively committed to dismantling systemic racism and other forms of oppression. Black Lives Matter.
Dr. Kalikman additionally serves on the board of the New York Festival Of Song, New York City’s premier art song organization performing classical and modern repertoire, and as President of the Exeter Association of Greater New York, Phillips Exeter Academy’s largest and most active regional alumni association.
Dr. Kalikman enjoys cooking, reading, making and listening to music, dancing, travel, puzzles, hanging out with his little brother and sister, and wandering into new places to drink coffee and eat tacos in his beloved New York City.