Philip Kalikman
I am a University Assistant Professor of Real Estate, Finance, and Economics
at the University of Cambridge in Cambridge, UK.
I research several areas of real estate and finance including mortgage default
and prepayment, housing market dynamics, macroprudential policy, and financial
crises. I build and study structural, heterogeneous, computational, and
AI/ML models and am interested in the interactions among real estate,
regulation, and racial discrimination, and in how these affect equality and
opportunity. Prior to joining Cambridge, I was an Assistant Professor of
Finance and Real Estate at the Sy Syms School of Business at Yeshiva University
in New York City. I received my MA, MPhil, and PhD in
Economics from Yale University and my BA in pure Mathematics from the
University of Chicago.
I teach Fundamentals of Finance and Investments in the BA and
Corporate Finance, Real Estate Finance and Investments,
and Research Design in the MPhil.
I supervise Cambridge dissertations in the BA, MPhil, MSt, and PhD
programs, but please note with my apologies that
I am not currently accepting unsolicited PhD supervisions from outside the University.
I consult on real estate and finance matters for
fintech startups, venture capital firms,
hedge funds, and real estate firms, and serve as an expert witness
on related matters.
Prior to completing my PhD, I worked at a real estate hedge
fund, as a consulting economic policy advisor to Secretary of State Hillary
Clinton and members of the U.S. Senate, and in fintech venture capital with
a former U.S. Under Secretary of the Treasury and U.S. Comptroller of the
Currency.
I serve on and was the founding Treasurer of the Board
of Students for Educational Justice,
a youth-led community organization that has led efforts for
racial justice in Connecticut since 2017,
and serve as Treasurer of the Board of New York Festival of Song,
a performing arts organization in New York City.
I also serve on the Board of Land Economy at the University of Cambridge and
am a member of the Cambridge Real Estate Research Center, the Cambridge
Center for Science and Policy, the American Economics Association,
the American Finance Association,the American Real Estate and Urban Economics Association,
the Financial Management Association,
the American Real Estate Society, the Society for Financial Studies,
and the European Financial Association.
I like to cook, read,
make and listen to music, dance, travel, solve puzzles,
spend time with my little brother
and sister, and
conduct field research into the economics of coffee and taco consumption.
(Preliminary results are encouraging.)
Research
Underlined titles link to working and published papers; arrows reveal abstracts.
Research into the Global Financial Crisis finds
forgiving mortgage principal ineffective at stemming defaults;
authors argue that borrowers default
because of illiquidity, not strategically.
We argue the opposite.
Targeted forgiveness is effective.
And default is better explained
by quantifying how illiquidity and strategy interact.
We embed their interactions
in a computational heterogeneous structural model.
We introduce idiosyncratic default penalties:
differing penalties underpin borrowers' differing deviations
from pure-financial optimality.
We run the model with heterogeneous microdata,
estimating penalties from credit scores and payment histories.
Forgiving low-score, deep-underwater borrowers
would have eliminated nearly all their defaults,
with net gain for lenders.
We introduce a loan-level model of mortgage default with heterogeneity in
borrower characteristics and mortgage terms, including idiosyncratic
penalties for default. Borrowers’ penalties determine how closely their
behavior hews to the predictions of the double-trigger or strategic models.
The state space varies loan-to-loan based on all of the loan’s, borrower's,
property's, and neighborhood's idiosyncratic characteristics. We test the
model on a high-performance computing cluster against real data drawn from
linked databases with billions of observations of hundreds of simultaneous
attributes. The model predicts defaults out-of-sample, fits cross-sectional
characteristics of the distribution of mortgage performance, and classifies
likelihood of default with high accuracy and better than all known
benchmarks.
"Machine Learning and Mortgage Lending,"
with Ernest Miller III and William Kalikman
(2024 grant-funded work in progress).
Banks and fintech lenders increasingly rely on
computer-aided models in lending decisions.
Traditional models were interpretable:
decisions were based on observable factors,
such as whether a borrower’s credit score was above a threshold value,
and explainable in terms of
combinations of these factors.
By contrast, modern machine learning models are opaque and
non-interpretable.
Their opaqueness and reliance on historical data that is the artifact
of past racial discrimination means these new models risk
embedding and exacerbating such discrimination,
even if lenders do not intend to discriminate.
We aim to develop a framework for
interpreting machine learning mortgage lending
models and testing them for discrimination.
We will use Explainable Artificial Intelligence models to characterize what
features drive the decisions produced by calibrated ML lending models and
to develop a framework for black-box testing new models for discrimination.
We expect our findings to bear on the regulation of model use in
mortgage lending, and expect our framework to provide the means for
regulators to monitor and enforce compliance with anti-discrimination laws.
"Climate Change and Financial Assets: The Effects of Stormwater on Mortgage Default",
with Hai Long Duong et al. (2022)
We exploit a unique dataset with high-resolution data on adverse weather
to quantify the risks that climate change poses to mortgage-backed securities. Using a
difference-in-difference approach, we identify the effect of stormwater runoff
on mortgage default. Residential property owners inadequately self-insure
against flood risk from stormwater runoff, leading mortgages backed by
properties in areas exposed to increased runoff to go delinquent and default at
higher rates. Moderate storms, not just headline disasters, exacerbate
mortgage default. Losses are subsidized by borrowers in other geographies, but
discrimination and redlining considerations prevent the GSEs from correcting
the mispricing in loan-level price adjustments. Losses are projected to grow
substantially in the next several decades.
"Credit Availability Did Expand Before the Global Financial Crisis" (2016).
Scholars remain divided on whether the Global Financial Crisis was fueled by significantly looser
credit underwriting standards in the early 2000s, disagreeing not only about whether loose
standards caused the crisis, but even on whether standards were loose in that era. I examine three
different loan-level mortgage origination datasets in the US to try to disentangle why scholars
disagree on this question. I show that linking loans at the property-level, which is only possible for
some of those datasets, is necessary to see the higher origination loan-to-value ratios that obtained
before the crisis. Moreover, I show that leverage rose especially for less creditworthy borrowers,
many of whom would have been excluded entirely from getting a mortgage in times of tighter
credit. The simultaneous expansion in low-LTV financing diluted average leverage, obscuring
the reality that unprecedented cheap credit was available pre-Crisis.
"Endogenous Leverage and Credit in an Agent-Based Model of the Housing Market,"
with John Geanakoplos, Ravi Jagadeesan, Emily Dodwell, and Jesse Wang (2015).
Did house prices rise before the Global Financial Crisis because lenders
expanded credit? Or did lenders expand credit because of the economic forces
that led prices to rise? We model endogenous credit extension in a computational
agent-based model of the housing market. Profit-maximizing, rational,
forward-looking lenders charge interest rates to borrowers with different
loan-to-value ratios and different credit scores based on the lenders' estimates
of those borrowers' propensities to default. When lenders extrapolate future
default rates from prior history, then expanding house prices shield borrowers
from negative equity and fuel an artificially low level of defaults. Lenders
respond by loosening credit standards, which feeds back into borrowers' demand
for housing, fueling a fragile bubble. The bubble bursts when relatively few
initial defaults cascade, leading lenders to choke off further credit and
subsequent demand for housing to dry up. The model captures the time series and
distributional features of lending and house prices observed throughout the
Global Financial Crisis and provides a micro-foundation for studying how credit
availability fuels bubbles.
With Robert Axtell, Benjamin Conlee, Ernesto Carella, Doyne Farmer, John Geanakoplos,
Jon Goldstein, Matthew Hendrey, Peter Howitt, David Masad, and Nathan Palmer.
Published 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. 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
interest rates.
