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Real Estate and Housing Finance

Paper Session

Saturday, Jan. 4, 2025 10:15 AM - 12:15 PM (PST)

San Francisco Marriott Marquis, Yerba Buena Salon 12 & 13
Hosted By: American Finance Association & American Real Estate and Urban Economics Association
  • Nancy Wallace, University of California-Berkeley

Environmental health risks, property values and neighborhood composition

Marco Grotteria
,
London Business School
Jules Van Binsbergen
,
University of Pennsylvania
Joao Cocco
,
London Business School
S. lakshmi Naaraayanan
,
London Business School

Abstract

We quantify the impact of perceived environmental health risk on property values and
neighborhood composition using a widely-advertised re-classification of chemical carcinogenicity.
Focusing on plants that were previously emitting the chemicals, we estimate
a 2% decline in property values for houses closer to the plant relative to those farther
away. Our analysis also reveals a shift towards a higher presence of minority households
in houses closer to the plant. This evidence of granular changes in neighborhood composition
in the wake of a change in perceived environmental health risk informs the debate
on environmental justice and health inequities.

The Winner’s Curse in Housing Markets

Soon Hyeok Choi
,
Rochester Institute of Technology
Adam Nowak
,
West Virginia University
Patrick Smith
,
University of North Carolina-Charlotte
Alexei Tchistyi
,
Cornell University

Abstract

This paper tests for a winner’s curse in housing markets by examining the subsequent performance of bidding war transactions relative to non-bidding war transactions. We develop a model in which homeowners who purchase their house in a bidding war transaction experience lower annualized returns and a higher likelihood of mortgage default. Consistent with the model predictions, we find homebuyers who purchase their house in a bidding war experience 8.8pp lower total unlevered return compared to those not engaged in a bidding war and are 2.1pp more likely to default. These winner’s curse effects are more pronounced among socioeconomically vulnerable homebuyers.

Racial Differences in the Total Rate of Return on Owner-Occupied Housing

Rebecca Diamond
,
Stanford University
William Diamond
,
University of Pennsylvania

Abstract

We quantify racial differences in the total rate of return on owner-occupied housing from 1975-2021.
The total rate of return of buying a house equals the price appreciation plus the rental value of its
housing services, minus taxes and maintenance. To measure the total return, we develop a new method
to estimate the rental value of each owner-occupied house, using houses that switch between the rental
and owner-occupied market. We then use this method to predict the rental value of the entire owner-
occupied housing stock and find this prediction out-performs standard hedonic techniques. We document
across multiple datasets that Black homeowners earn a .5% percentage point higher rental yield on
housing than white homeowners, consistent with our method’s estimates. This gap largely explains why
minority homeowners earn .6% percentage point higher total returns on housing. Minority homeowners’
total returns are also more volatile and sensitive to the business cycle. These racial differences can be
fully explained by other observables, with household income differences playing the largest role. Our
findings are broadly consistent with a model with a more severe credit constraint for minorities, which
bids up rents, lowers house prices, and makes house prices sensitive to credi

Financial Technology and the 1990s Housing Boom

Stephanie Johnson
,
Rice University
Nitzan Tzur-Ilan
,
Federal Reserve Bank of Dallas

Abstract

The 1990s rollout of mortgage automated underwriting systems allowed for complex underwriting rules, cut processing time, and increased house prices. By comparing early systems with different characteristics, we separately estimate the effect of automation and the effect of new lending standards. We show that locations exposed to initial adopters of Freddie Mac’s Loan Prospector system experienced an early housing boom due to a switch to statistically-informed underwriting rules. Loan Prospector adoption increased the share of lending at high loan-to-income ratios by around 30 per cent. Using exposure to initial adopters of Fannie Mae’s Desktop Underwriter system (which initially did not apply new lending rules) we show that automation reduces loan processing time by up to one week but does not have a significant effect on house prices. House prices only grew differentially in these locations after Desktop Underwriter also started to apply new underwriting rules. Usage of both GSEs’ systems accelerated during the late 1990s refinancing boom, dramatically changing the underwriting rules U.S. lenders used. Applying our estimated price response to later adopters, we suggest that the rollout of new lending standards with the GSEs’ systems can explain a large share of U.S. house price growth between 1993 and 2002.

Discussant(s)
Lucas Davis
,
University of California-Berkeley
Alina Arefeva
,
University of Wisconsin-Madison
Bronson Argyle
,
Brigham Young University
Daniel Greenwald
,
New York University
JEL Classifications
  • G0 - General