Informational Advantages of Lending Locally
Kristle Romero Cortés *Abstract
Banks decrease loan originations and retention in potentially overheated housing markets when lending locally. This within bank finding holds even after addressing possible endogeneity concerns using historical branch networks and M&A
activity as instrumental variables. For every 1 percent increase in home prices
from 2002 to 2006, banks originated one less loan locally. Bank deposits may convey valuable market insights. Loans sold to Freddie Mac from banks with a local
presence exhibit higher default rates, demonstrating superior bank knowledge andexploring a new channel for adverse selection. Banks exited precisely the markets
that experienced price reversals.
Keywords: Mortgage Lending, House Price Growth, Loan Performance, Deposit Share, Branch Banking
JEL Classification: G21* Email: kristle.cortes@unsw.edu.au. This paper was previously circulated as "Did Local Lenders
Forecast the Bust? Evidence from the Real Estate Market." I would like to thank Edith S. Hotchkiss, Josh
Lerner, Jun (QJ) Qian, Hassan Tehranian and especially, Philip E. Strahan. I would also like to thankAndra Ghent (WFA discussant), Tom Engsted (EFA discussant), Steven Laufer (HULM discussant), Andrew Paciorek (ASSA discussant), Issac Pan (ANU FIRN banking discussant) and seminar participantsat the University of Miami, Federal Reserve Bank of New York, University of Michigan, Federal Boardof Reserves, American University, IESE, University of British Columbia, Washington University of St.
Louis, Federal Reserve Bank of Cleveland, Suffolk University, the Deutsche Bundesbank, Boston College’s Finance brown bag seminar, Washington University’s Corporate Finance Poster Session, the 2012
WFA meetings in Las Vegas, the 2012 EFA meetings in Copenhagen, the 2013 Federal Reserve Bank ofSaint-Louis and University of Wisconsin Housing Urban Labor Macro (HULM) Conference, the 2014
AREUEA meetings in Philadelphia, Massey University, the University of New South Wales and 2022
ANU FIRN Banking conference, Nova School of Business and Economics, and SFI UZH for their helpfulcomments. Lastly, I would like to thank Amir Sufi, Elena Loutskina and representatives at Zillow.com
for their assistance with the data and Mandeep Singh for superb research assistance.

1

Introduction

In the modern mortgage market, banks lend locally and non-locally. Banks originate
many of their mortgages in areas without branches. I show that this makes a substantial difference to outcomes. Banks make better long-run lending decisions with a local
presence in the market in which they are lending.
From 2002 to 2006, there was an unprecedented increase in lending in the US residential real estate market, but there is scant discussion of smart money in the literature.
I use 2002-2006 as a laboratory because home price deviations from fundamentals were
extreme. I find evidence within bank, that banks behave differently when lending locally in response to high home price appreciation. This finding explores a new channel
for adverse selection in loan securitization because the locally lent loans that were soldto Freddie Mac performed worse ex-post.
I define a lender to be Lending Locally if it has a branch in the county where theproperty is located for which the loan is being originated. I hypothesize that lenders
lending locally react differently when prices are out of line with fundamentals. Lenders
with a branch know their own deposit activity and are privy to soft information about
the local economy. By this definition, any bank can lend locally if it has a branch. I
show that a lender with a local presence behaves differently than — that same lender— does when it doesn’t have a local presence. For example, in counties where Bank ofAmerica has branches, I consider them to lend locally and I show they exit overheatedmarkets. However, in areas where Bank of America lends but does not have a branch,
I get different results. Typically, when a bank is lending to an area without a branch,those applications are submitted online and serviced centrally.
Branches serve as proxies for loan officers. Although the exact location of loan
officers is not known, they typically are dedicated to one or two bank branches ina specific region. In conversations with loan officers, they comment that while theywork with their borrowers, they are aware of the local economic conditions as well.
Regarding real estate, they know the agents and the housing stock and may have even1seen the same home recently appraised. While their incentives align with loan volume,
they have reputational concerns for the long-term performance of their approvals andwant to do a good job.
In my first set of results, I find that banks lent less locally in response to increasinghome prices when controlling for a myriad of economic factors and mortgage demand.
This result suggests that banks behaved differently when they had a local presencein the market in which they were lending. Importantly, this result is within bank, so
the same bank is held fixed. The effect is driven by the bank having a branch in the
county where the property is located. This result is new to the literature and begins
to unpack the assertion that banks have information about their local markets. I use
a cross sectional analysis because the mortgage default crisis provides a particularly
dramatic setting to study the knowledge of lenders with a local presence. I argue thatbanks are intentionally exiting overvalued markets. I control for credit demand using
the application for mortgages at each bank in 2002. On average, for every one percent
increase in house prices, banks originated two fewer loans locally.
A potential channel for the transmission of information in the local economy is captured by bank deposits. Recent papers (Drechsler et al. (2017),Yang (2022), Iyer et al.
(2023)) document the importance of deposits and how deposits can convey information about local market conditions. I use the deposit share of a lender to capture theintensity of information banks with branches could have about a region. On the extensive margin, the deposit share results replicate that of having a branch. On the intensive margin, having a greater deposit share impacts loan originations only as house
prices appreciate. This suggests that banks are responding to greater knowledge about
the area’s true reflection of house prices supported by fundamentals. The premise isthat by having a greater exposure to the region via bank deposits, banks are able tounderstand the local economic conditions.
Admittedly, the branch network in 2002 could be endogenous to the lending decision during the run-up to the crisis and potential home price behavior since banks
may select more enticing areas to locate their branches. So, I use two instruments for2the branch network in 2002, the first is the historical branch network defined by thebranch network in 1994, and the second is a branch network captured by bank mergers and acquisitions (M&A). The results continue to support the findings that banks
slowed their lending in overheated areas where they had a branch, and in fact, themagnitude is the same on average for every one percent increase in house prices, banks
originated two fewer loans locally.
I then separate the local loans into loans sold by the lenders and loans the lender retains on its balance sheet to determine if locally lent loans are sold more. Purnanandam(2011) and Mian & Sufi (2009) describe how securitization during this period playeda large part in the role of credit supply and the increase in housing prices. If lenders
lending locally understand that the housing market is overheating, then fewer locallymade loans should be held after origination since the lender would incur losses if theborrower were to default.
I capture this by looking directly at the change in the proportion of loans retained. The
measure is the retention rate of the newly originated loans, and the change reflects bankbehavior only since they decide the proportion of retained loans. Unlike the origination
decision that aptly needs to control credit demand, banks’ decision to change theirsecuritization pattern speaks to their desire to reduce exposure to the market. It is, infact, the case that banks retain fewer loans from high home price appreciation areas
when they had a branch in the region.
I find local lending was securitized more in home high-price appreciation areas.
This evidence suggests that lenders lending locally understand when prices exceed
the home’s value and exit the market to safeguard their portfolios against future mortgage defaults. A one percent increase in home prices decreases local retention by 33
thousand dollars. 1
So, when banks did lend locally, they decreased the proportion of loans they re1

I look at securitization rates within the sub-market of local loans. I do not differentiate between
prime and subprime loans. Though I recognize the potential for different behavior across loan pools,
previous studies have shown that concentrated lenders tend to lend to riskier borrowers. Agarwal et al.
(2012) has an excellent discussion of adverse selection in mortgage securitization regarding pre-payment
versus default risk for further information on subprime lending.

3

tained on their balance sheets. Naturally, it is interesting to know what became ofthose loans. The loan performance data provided by Freddie Mac Single Family Home
Loans shows a life of the loan after origination. With these data, I can track the performance of locally lent loans.
The results show that loans sold to Freddie Mac from lenders with a branch in the
county where the loan originated spent more time "delinquent." Not only do they default more, but they default more quickly than non-locally made loans. This suggeststhat while banks are better informed about their market, the information advantages
can result in moral hazard since they may not be willing to retain the risk.
Home prices did not behave the same across all regions of the U.S. during this period. As is widely understood, certain areas experience large swings in prices, whereas
others only increased, and others barely moved at all. I explore these regional differences by measuring how much home prices rose from 2002-2006 and then subsequently fell from 2006-2009.
I create a peak to trough measure that captures if an area saw large price movements
during both the boom and the bust periods. I then group these areas into terciles andexplore the local lending behavior across the different regions.
The results show that the decrease in local lending was concentrated in areas thatexperienced a significant price increase and subsequent fall, such as Las Vegas. In somezip codes, which saw an increase in home prices but no noticeable crash, or in areas,
where home price growth remained relatively flat, local lending did not decrease. Thisevidence suggests that lenders with a local presence exited precisely the right markets.
The loan officers who make these lending decisions work and live in the area andwant to see it flourish. Imprudent lending that could result in loan defaults or foreclosures would negatively impact the area. For example, Campbell et al. (2011) finds that
foreclosure at a distance of 0.05 miles lowers the price of a house by about 1%.
My results suggest that lenders with branches in the counties where they madetheir loans take the time to understand more completely the true value of the housing
market via local economic information. Understanding the information conveyed by4their bank deposits and by operating in an area enables them to secure returns on theirinvestments. It also highlights the need for bank counterparties to consider how a loan
is originated.
These relationship between originations, securitization, house prices, and loan performance suggests that lenders lending locally demonstrate smart banking.

2

Literature Review

The literature has largely emphasized how banks "follow the market" without taking
caution, and that housing price appreciation makes them more tolerant of risk. Yet, theimportance of information in financial contracting cannot be overstated. For lenders tomake loans, they must first decide if the conditions of the loans are favorable. Studying
lending behavior across institutions is useful because the supply of credit and financial
intermediation is heavily attributed to helping an economy grow. 2
I use the branch network of a lender to proxy for the lender’s ability to gather
information about the local economy. Adelino et al. (2019) show informed sellers in themortgage market can signal loan quality and my paper explains how sellers become
better informed, i.e., by having a physical presence in the market.
Bank deposits can convey information to a bank about their local area by reflecting
the economic activity and financial health of the community. Higher deposit levels may
indicate a prosperous local economy, while declining deposits could signal economic
challenges. Drechsler et al. (2017) demonstrate the importance of bank deposits for
lenders and how monetary policy changes can affect this funding channel. Yang (2022)
argues that deposits from local consumers convey private information about the local
credit market, which helps banks in information-sensitive lending. Iyer et al. (2023)
build on this premise and show that banks can detect regional economic downturns,
so, a useful measure to appreciate the local economic condition is to closely follow
bank deposit rates.
2

See: Demirgüç-Kunt & Maksimovic (1998); Jayaratne & Strahan (1996); King & Levine (1993);
Levine et al. (2000); Paravisini (2008); R. G. Rajan & Zingales (1998); Rice & Strahan (2010) among others.

5

My paper contributes to the literature by showing that banks have the ability tolearn information about the market beyond borrower behavior, and this knowledge increases
the asymmetric information for their potential trade partners, such as Freddie Mac.
Recent literature documents the advantages of having a physical presence. Bai &Massa (2021) show how important information is not easily replaced by other information sources and how certain information originates mainly from physical interaction.
Along these lines, both the size of the institution and the distance from borrowers havebeen suggested in prior literature as important factors in the institutions’ willingness
to make loans because of their ability to stay informed. 3
Loan officers work at a specific branch, or a few branches, within a very concentrated area, so they know when home prices are supported by fundamentals, such asincreased economic opportunities in the area or new school districts. Gao et al. (2023)
find that managers’ past experiences with mortgage approval and pricing significantly
influence their subsequent lending standards.
Loan officers can also see when home prices rise for other reasons, such as theappraisal process. Fout et al. (2022) show how appraisal values affect financing costs
is directly determined by the loan officer approval decision. Carvalho et al. (2022)show how loan officers’ personal experiences influence their lending decisions, andBushman et al. (2021) show how that influence can affect loan contract design andperformance.
Puri & Iyer (2012) find that stronger relationships between banks and depositors
can help mitigate bank runs. These results make sense because soft information produced over time is non-transferable. My measure allows small and large banks to beclassified as "lending locally" as long as they have a branch in the county where they
are making loans. It is an important distinction because I am quantifying lending locally,
not just "local lenders". Borrowers can form bonds with branch employees regardlessof the size of the bank.
3
Agarwal & Hauswald (2010); Coval & Moskowitz (2001); DeYoung et al. (1999); Ergungor (2010);
Kroszner & Rajan (1994); Morse (2011); Lerner (1995).

6

Some studies suggest the supply of credit played a part in the housing crisis. 4 While
my paper is not about the housing crisis, that time provides a nice laboratory to study
because the swings in home prices and bank behaviors were extreme. Over time, mortgage lending practices loosen, and the average borrower’s leverage increases, which
subsequently leads to more borrowers defaulting on their loans. 5 Some regions experience an increase in mortgage originations, even while relative income growth decreases. Lenders differ in their abilities and willingness to invest in information gathering about the default risk of their borrowers. 6 Gurun et al. (2016) show that subprime lenders marketed mortgages differently across borrowers. Adelino et al. (2016)
show not all lending was driven by subprime borrowers. Those differences in lending
strategies lead to divergence among lenders in how informed they are about their loan
portfolios.
R. Rajan & Ramcharan (2015) show that, in fact, credit expansions across lending
types can lead to economic growth. If the supply of credit contributed to the housingcrisis, it is important to determine how lenders behaved differently when they could
readily access information about the market compared to when they could not. My
findings suggest that lenders with information about the local economy exited areas
with the worst housing price declines.

3

Data Description

In this paper, I merge together three main datasets to identify local lending. Residential mortgage applications are merged with both the location of the lender’s branch
network and the respective home prices in the local area. The ability to match thelender is what allows for a clean definition of local lending. Further data is used tocapture bank mergers, loan performance and hard information observables. Summary
statistics of measures used are reported in Table (1).
4LaCour-Little et al. (2011); Favara & Giannetti (2014); Gerardi et al. (2007); Keys et al. (2010); Loutskina & Strahan (2009); Mayer & Pence (2008); Mian & Sufi (2009).
5
Agarwal & Wang (2009); Loutskina (2011); Loutskina & Strahan (2011); Mian et al. (2015); U. Rajan
et al. (2010).
6
Stiglitz & Weiss (1981).

7

3.1

Residential Mortgages

The mortgage loans’ data are from the Home Mortgage Disclosure Act (HMDA) database.
HMDA reports information on mortgages originated by both depository institutions
and independent mortgage companies. This study focuses on depository institutions,which, conditional on the annually adjusted threshold of assets, must report information about all loan applications. HMDA provides information on loan application
characteristics, such as application status and the loan amount for any reporting institution. The HMDA lender identifiers are from the Avery File constructed by Robert
Avery at the Federal Reserve Board. 7 The HMDA sample corresponds to the years
2002 and 2006. I construct a cross-section from this time period to highlight the behavior of banks during a setting in which home prices arguably diverged highly fromfundamentals.
I keep observations where the field ‘action taken’ is either approved, approved not accepted, or denied. The final sample corresponds to observations in which an applicant isapplying for a conventional loan for purchasing a single-family owner-occupied home,
and the mortgage loan is secured by first lien. 8
In the next step, I aggregate HMDA data to the county-bank-year level to estimate
variables of interest. Specifically, I estimate the total number of applications received,
originated, and held by a bank in a county-year. I also calculate the aggregate loan
amounts corresponding to applications received, originated, and held by a bank in acounty-year. Lastly, I estimate income and loan-to-income ratio of a bank’s representative (average) borrower in a county-year. 9
An important note is that this paper focuses on loan originations and not loan refinance applications because the bank itself partakes in the valuation process for a mortgage refinance. Whereas the mortgage application for loan origination is dictated by
7
I access the Avery File from Neil Bhutta’s website. The Avery File matches a subsidiary of a bankto the parent institution. A reporting entity part of a bank holding company is matched to the holding
company’s largest bank.
8
The property type, and lien status information is available in HMDA from 2004 onwards.
9
Self-reported variables, such as an applicant’s income, in HMDA are problematic to use (Favara &Imbs, 2015); therefore, I work with the average income and average loan-to-income ratio.

8the purchase price, which is set by the buyer and seller specifically, and the market asa whole.

3.2

Bank Branch Locations

To identify a bank’s branch presence in a county, I use Summary of Deposits (SoD) datafrom the Federal Depository Insurance Corporation (FDIC). The SoD is an annual survey of branch office deposits for all FDIC-insured institutions. The dataset provides
complete information about the geographic location of a branch. The data correspondto June end of each year. I use SoD data for the years 1994 and 2002 to determine thelocal presence of a lender.

3.3

Home Prices

The zip code-year level median home price data are from the Zillow Home Value Index. These data are available from 1996 onwards. Zillow provides monthly seasonallyadjusted pricing data for a typical home at the zip code level. 10 To match a zip codewith county FIPs, I source a zip code-county matched file from the Missouri Census
Data Center (MCDC). There are instances in which multiple counties share a zip code.
In those cases, I allocate a multi-county zip code to all counties sharing that zip code.
This study uses Zillow data for the years 2002, 2006, and 2009.

3.4

Bank Mergers

As previously mentioned, banks chose their branch network. This decision makes theirbranch locations endogenous to other bank decisions, such as lending. To address
this endogeneity and make causal claims, I use instrumental variables. One of the
instruments is based on the merger and acquisition activity (M&A) of the bank.
The M&A data are from the Federal Reserve Bank of Chicago. These data provide information on banks involved in M&A activities since 1976. This study uses
10

Mian & Sufi (2009) find that Zillow Home Price Index correlates positively with the Fiserv’s Case
Shiller Weiss index.

9data corresponding to non-failing banks involved in M&A activity in which the nonsurvivor transfers its assets to the survivor, and non-survivors charter is discontinued.
All mergers (or acquisitions) that take place between July of year t-1 and June of year t
are allocated to year t. This step ensures consistency of M&A data with the reporting
frequency (June end each year) of Summary of Deposits data. The sample consists of
5,266 mergers between 1994 and 2002.
Figure A.1 presents the intuition behind the mergers and acquisitions instrument.
It shows how the newly acquired branch network instruments for the original branch
network, and in the case of multiple acquisitions, how the final branch network is
defined.

3.5

Loan Default Data

After identifying that local lending behavior differs in response to high home pricegrowth, a natural variable of interest is the outcome of the loans that, in fact, are originated. To capture loan performance, I use a recently made available data set fromFreddie Mac, a prominent Government Sponsored Entity (GSE). For all loans that originate in 2006 and eventually are sold to Freddie Mac, I source their origination andmonthly performance data from the Single Family Loan-Level Data set. 11
This dataset results from Freddie Mac’s efforts to increase transparency and help investors build more accurate credit performance models. In addition to providing information characteristics of a loan contract at its origination and over its tenure, Freddie
Mac’s single-family loan-level data set includes information on a contract’s geography.
Specifically, the dataset provides information on a property’s state and the first three
digits of a property’s 5-digit zip code.
11

Fannie Mae reports a similar data set however, while the Emergency Home Finance Act 1970:
FHLMC or “Freddie Mac” allowed Fannie Mae to purchase conventional mortgages and allowed Freddie Mac to purchase FHA and VA mortgages, they do tend to still focus on their original lines of businessand my study focuses on traditional loans without additional government regulations.

10

4

Research Design and Results

4.1

Econometric Specification

The main research design uses a cross-sectional analysis of the data and is done withinbank. There are two reasons to use this design. The first is that the research question
is about local lending. All banks can have a local presence via their branch location,
regardless of other institutional details such as size. The second is the period from 2002
to 2006, which marks a time with home price appreciation where realized home prices,arguably, deviated from home price fundamentals.
I run a linear regression model that tests for the effect of a local presence on important lending decisions such as loan origination and retention.
Outcomecb2006 =β1 (Lending Locallycb2002 × Home Value Growthc2006 )(1)+ β2 Lending Locallycb2002 + Γ′ Xcb2006 + Bankb + Countyc + ucbt .

The subscripts b and c represent a bank, and county, respectively. Lending Locally is adichotomous variable that equals 1 if a bank has a branch in a county in 2002. Home
Value Growth is the average growth in the zip code level median home value index
between 2002 and 2006 in a county. Control variables that include important factors
such as borrower characteristics are included as well. Bank fixed effects are used acrossall models to identify exactly the difference between banks’ behavior with and withouta bank branch. County fixed effects are used to capture specifics of the local market atthe time of lending. The key coefficient β1 is identified because banks lend to borrowers
in multiple counties, and borrowers in a county borrow from multiple banks

11

4.2

Results

4.2.1

Lending Locally and Loan Origination

The starting point of this analysis is the negative relationship between lending locallyand home price growth. Figure (1) plots the difference in home value growth and theaverage logarithm of the number of mortgage originations between lending type, local
and non-local (within bank) in 2006. Figure (1) presents a plot of average mortgage
applications originated growth, and average home value growth, within bank by thelending type (local or non-local) in 2006. Both figures show from the raw data thetendency for local lending to decrease in high home prices appreciation areas.
Column 1 in Panel A, Table (2) summarizes the main result of the within-lenderanalysis for loan origination in which the "Outcome" from Equation 1 is the growth rateof the number of applications originated from 2002 to 2006. Column 2 adds Application
Market Share to capture the demand for credit at the lenders with bank branches in thelocal area. Column 3 adds borrower characteristics to account for borrower income
and borrower loan-to-income ratios. The results remain consistent across the models
that local lending decreased in response to home price appreciation during the run-up
to the crisis.
The coefficient from the interaction of Lending Locally and median home value inColumn 3 of Panel A in Table (2) is -1.25. The summary statistics in Table (1) report that,on average, a bank originated 53 loans per year in a given county between 2002 and2006. So, a one percent change in home price led to one fewer (-0.625=-1.25/100*53)
loan originations when a bank has a branch in the county where the property is located.
On average, home prices rose roughly 9 percent annually, suggesting that roughly
36 fewer home loans were originated locally in each county from 2002 to 2006.
Panel B of Table (2) changes the dependent variable to the growth rate of the volume
of loans originated from 2002 to 2006. Column 3 shows the results for the full model,
and a one percent change in home price led to, on average, 250 thousand dollars lessin local lending.

12The results show that lenders behave differently when they have a local presence.
Lending Locally is one if the bank has a branch in the county where the property is located, and Equation 1 includes lender fixed effects, so within the same lender, mortgage
origination behavior differed in response to home price appreciation.
This evidence further supports the view that physical presence matters to accesslocal information. The mechanism by which soft information is transferred into thelending decisions is, by definition, difficult to measure; however, the evidence consistently demonstrates that a bank branch is important to lending decisions.
4.2.2

Potential Channels

I argue that banks with a physical presence in the market where they originate loans
possess superior market knowledge. Lenders with a physical presence can glean information for their own interaction with the economy but another way lenders can bebetter informed is by paying attention to their deposit activity. The idea is that banks
would see movements in their deposits and could appreciate the economic wellbeing
in an area with this indicator. Bank deposits serve as a valuable source of insight forbanks regarding their local region, offering a glimpse into the economic vibrancy andfiscal well-being of the community. Elevated deposit volumes often signal a thriving
local economy, potentially indicating robust economic activity.
Because both Deposit Share and Home Value Growth are continuous variables, it isuseful to present their interaction in a picture. Figures (3a) and (3b) show a declining
relationship between bank lending defined by either number of applications or application volume. The point with the lowest loan growth, shown in the bottom right-handcorner, is when house price appreciation is high and Deposit Share is also high. Holding home price growth constant, there is a downward slope in lending as Deposit Shareincreases.
Table (3) captures the relationship between a lender’s Deposit Share, house prices
and lending. Panels A and B, Columns 1-3 replicate the findings in the baseline regression and show that having a greater share of deposits (potentially greater access13

to information) in an area is negatively associated with loan origination in areas withhigh home price appreciation.
Table (3) Column 4 studies the effect, however, when an area experiences below
median house price growth. The results from Column 4 show that there is no effectfrom the interaction term. Contrast this with Column 5, which reports areas with above
median house price growth, where the effect is strongly pronounced.
Comparative statics helps unpack the results since both variables of interest arecontinuous. The interpretation is that, holding the home price growth level constant,a bank with Deposit Share level of 20% originates approximately 11 less loans but thatsame bank with Deposit Share of 80% originates 45 less applications. The effect primarily comes from how easily a bank can extract information about their local market.
Which is what is defined by having a higher Deposit Share. Similar logic applies to theloan volume numbers. For the same bank, as they move from 20% to 80% Deposit Share,
they originate roughly $50,000 less.
If there is no noticeable changes in deposit growth in areas with high home priceappreciation, this may signal to banks that the house prices are not substantiated withreal economic growth. Banks would also see real time fluctuations that could be missedwith aggregate measures that are less granular and only available with a time lag. 12
4.2.3
4.2.3.1

Endogeneity Concerns
Instrumental Variables Based on 1994 Branch Network

I use two instrumental variables to address the endogeneity concerns of a bank’s branch
network in 2002. The first instrument is the bank’s branch network in 1994. Branches
networks are notoriously sticky. The correlation of the branch network in 1994 and2002 is 0.56. Table (4) shows a strong first stage supporting instrumental design. The
methodology is similar to that of (Mian & Sufi, 2009), who exploit the share of subprime
borrowers as of 1996 for analysis during the housing boom and (Gilje et al., 2016), who
use pre-shale-boom branch locations. While it is true that banks chose their histori12

Publicly available data on deposits from the FDIC SoD is annual, so banks would have a better
understanding than what annual aggregates would represent.

14

cal network as well, the historical network meets the exclusion restriction because thelikelihood that lenders chose those locations based on potential lending decisions in
the future — conditional on home price growth — is unlikely.
Table (5) replicates Table (2) by instrumenting Lending Locally by the Lending Locallyusing the 1994 Branch Network. The results consistently show that local lending declines
with home price appreciation. Panel A details the results for loan applications, and the
IV approach matches the OLS model that increasing home prices results in two fewer
loans being originated.
Panel B details the results for loan volume, and the results are even stronger, showing a million to a million-and-a-half dollar decrease in local lending.

4.2.3.2

Instrumental Variable Based on M&A Activity

The second instrument is the bank branch network resulting from a recent merger
and acquisition. In this method, the acquiring bank’s branch network from 2002 isinstead instrumented by the non-surviving bank’s branch networks from mergers between 1994-2001 (inclusive). Table (A.1) shows a strong first stage too. This method ispotentially prone to endogeneity concerns since the purchasing bank bought the nonsurviving bank for a reason, and the depositor footprint would be attractive, so I also
use the branches that are "incidental" to the purchase as these branches do not makeup the main portfolio of the non-surviving bank. This approach is similar to that of
Buchak & Jørring (2022).
Table (A.2) replicates Table (2) but this time instrumenting Lending Locally by theLending Locally using the M&A Branch Network.
Similar to the IV approach using the historical branch network, when instrumenting the branch network in 2002 with a branch network based on M&A Activity, theresults match the OLS results. The main findings remain, local lending decreases, andthe economic magnitudes are similar.
Taken together, the two IV approaches suggest that while bank branches are endogenous, they are a reliable proxy for more informed decision-making.

15

4.2.4

Lending Locally and Securitization

Prior literature shows the increased reliance on securitization during the housing boom.
The decision to hold loans on a bank’s balance sheets, rather than originate and distribute, is arguably even more pertinent to the bank than the decision to originate theloan.
I measure the securitization practice at a lender by taking the first difference of the
proportion of loans originated and retained in 2002 and 2006. For example, if Bank A
retains 40 percent of its loans in 2002, and 20 percent in 2006, then the change wouldbe -20.
Table (6) summarizes the results of loan securitization. In Panel A the "Outcome" inEquation 1 is the Change in the Proportion of Loans Held and measured by the number ofapplications. Panel B shows the results by loan volume. Banks reduce both the number
and volume of loans held.
The decision to reduce the amount retained is wholly made by the bank itself without compounding effects of market competition or borrower demand. The securitization process is often considered in aggregate, as the sum of the loans is sold off rather
than individual mortgages. A one percent increase in home prices results in local retention decreasing by 33 thousand dollars.
4.2.5

Loan Performance

When banks did lend locally in high home price appreciation areas, they also securitized more. Table (7) reports the results of the linear regression model that tests for theeffect of a local presence on loan performance of loans that were originated in 2006 andsold to Freddie Mac. As with previous models, bank fixed effects are included so theresults come from the presence of a bank branch and are within lender.

Loan Performance Metricizcb =β1 Lending Locallycb2002 + Bankb + zip codez + uizcb .
(2)16

Loan performance metrics measure loan level (subscript i) outcomes capturing default
behavior. Table (7) Panel A reports the results of a loan performance first by capturing if the loan has spent time "delinquent," as measured by being more than 28 days
past due. Column 1 focuses on the time period from 2006-2013, capturing the worst
years of the housing and financial crisis. When a loan was made by a bank that hada branch in the county where the property was located, it was more likely to spend
time "delinquent." Loan defaults rose during this period however, on average, loan default is uncommon. The results are both statistically and economically significant. The
default rate from 2006-2013 in the Single Home Freddie Mac data is 1.6%. Column 1reports the coefficient is 0.2% which explains roughly 10% of loan defaults. Column 2
extends this analysis to cover 2006-2018. The statistical significance increases, and themagnitude of the coefficient remains approximately the same.
Panel B reports the "Time to Delinquent." Both Columns 1 and 2 show that loans
default faster when made locally. Both models include borrower credit score as a control.
The evidence suggests that banks are informed about their lending portfolio andsecuritize loans with a higher incidence of default, demonstrating moral hazard. This
result is new to the literature and shows that bank trading partners need to consider
how a loan is originated. When banks have a local presence in the markets in which
they are lending, they are privy to better information, which puts the counterparty, inthis case Freddie Mac, at an informational disadvantage.
4.2.6

Lending Locally in markets with large peak and trough movements

The literature is rife with work studying the Great Recession. However, there is noevidence of "smart money." Real estate markets are particularly reliant on location.
While Equation 1 includes zip code fixed effects, Tables (2) through (7) treat the US
similarly. During the run-up to the housing crisis, certain regions of the US experienced
large increases in house prices and then experienced a subsequent crash (Las Vegas),17however certain zip codes in other markets saw home prices rise but not fall, and yetothers, saw hardly any home price appreciation. Important to note is that lenders havevery localized knowledge as not all zip codes in a given geographical area behaved thesame.
To account for this, I create a measure named Peak-Trough Ratio. The ratio is thegrowth in median home prices from 2006 to 2009, divided by the growth in medianhome prices from 2002 to 2006. For example, in zip code 89031, located in Las Vegas,
home prices rose from roughly $175 thousand in 2002 to $310 thousand in 2006, only
then to fall to $148 thousand in 2009. The ratio is -0.68. Contrast this with zip code
2118, in Boston, where home prices rose from $370 thousand in 2002 to $470 thousandin 2006, and continued to rise to $527 thousand in 2009. There the ratio is 0.43.
After calculating the ratios at the zip code level, I group the measure into terciles
so that similar price patterns are identified regardless of their geographical region.
Admittedly this is a forward-looking measure I could construct with perfect hindsight.
I update the main regression model and use the Peak-Trough Ratio to replace theMedian Home Value Growth.

Outcomecb2006 =β1 (Lending Locallycb2002 × Peak-Trough Ratioc )(3)′+ β2 Lending Locallycb2002 + Γ Xcb2006 + Bankb + Countyc + ucbt .

Column 1 of Table (8) shows the results for local lending are completely driven by theareas that experience a large increase followed by a large decrease in prices. Home
prices fell more in areas that experienced less local lending from 2002 to 2006. Confirming the information based explanation, these results suggest that local lending decreased with the expectation that home prices deviated from fundamentals and would
eventually experience a market correction.
Panel B shows the results for loan volume. Column 1 shows the results are concentrated again in the Bottom Tercile. The coefficient of -0.55 means that if in Las Vegas, for18example, home prices had instead fallen ten percent less in 2009, banks would have lent
locally roughly $2 thousand dollars more for each percentage increase in home prices
from 2002-2006.
Since the economic effect is in thousands of dollars for panel B, a 10 percent increasein 2006 median home price in Las Vegas suggests that local banks lend 1.2 million
dollars less. In contrast, a 10 percent increase in 2006 median home price in Boston
suggests that local banks lent 1.7 million dollars more.
Both results show banks behave differently when lending with a physical presence
based on the local knowledge of the market.

5

Conclusion

Banks trade on information, and the findings in this paper show that banks lending locally behave differently in response to potential market mispricing. Previous literatureshows that bank branch networks serve multiple purposes, mainly with better client
access. Importantly, this paper shows that banks have information about their markets,
not just their borrowers.
The ramifications for bank lending speak directly to the collateral underlying banklending decisions and their ability to price their lending products properly. In thecase of real estate, better information allows for more prudent lending overall but also
shows how banks are willing to trade potential short-term gains for longer-term stability and profits. Additionally, when banks are in better lending positions, their counterparts can pay the price, as is shown by the default behavior of securitized loans. As
more lending decisions are impacted by the introduction of technology etc. it will be
an important facet of the loan to know how a loan was originated, and to what degree
the process was automated.
Future research on the long-lasting effects of introducing financial technology, regulation on competition and bank diversification, and bank branch closures will beparamount to maintaining the informational advantage of lending locally.

19

References
Adelino, M., Gerardi, K., & Hartman-Glaser, B. (2019). Are lemons sold first? Dynamic
signaling in the mortgage market. Journal of Financial Economics, 132(1), 1-25.
Adelino, M., Schoar, A., & Severino, F. (2016). Loan originations and defaults in themortgage crisis: The role of the middle class. Review of Financial Studies, 29(7), 16351670.
Agarwal, S., Chang, Y., & Yavas, A. (2012). Adverse selection in mortgage securitization. Journal of Financial Economics, 105(3), 640-660.
Agarwal, S., & Hauswald, R. (2010). Distance and private information in lending.
Review of Financial Studies, 23(7), 2757-2788.
Agarwal, S., & Wang, F. H. (2009). Perverse incentives at the banks? Evidence from anatural experiment. Federal Reserve Bank of Chicago Working Paper WP 2009-08.
Bai, J., & Massa, M. (2021). Is Human-Interaction-based Information Substitutable?
https://ssrn.com/abstract=3783970.
Buchak, G., & Jørring, A. (2022). Do Mortgage Lenders Compete Locally? Implications
for Credit Access. https://ssrn.com/abstract=3762250.
Bushman, R., Gao, J., Martin, X., & Pacelli, J. (2021). The influence of loan officers on
loan contract design and performance. Journal of Accounting and Economics, 71(2-3),
101384.
Campbell, J. Y., Giglio, S., & Pathak, P. (2011). Forced sales and house prices. American
Economic Review, 101(5), 2108-31.
Carvalho, D. R., Gao, J., & Ma, P.
(2022).
Loan spreads and credit cycles: The role of lenders’ personal economic experiences. Available at SSRN:
https://ssrn.com/abstract=3404368.
Coval, J. D., & Moskowitz, T. J. (2001). The geography of investment: Informed trading
and asset prices. Journal of Political Economy, 109(4), 811-841.
Demirgüç-Kunt, A., & Maksimovic, V. (1998). Law, finance, and firm growth. Journalof Finance, 53(6), 2107-2137.
DeYoung, R., Goldberg, L. G., & White, L. J. (1999). Youth, adolescence, and maturityof banks: Credit availability to small business in an era of banking consolidation.
Journal of Banking & Finance, 23(2-4), 463-492.
Drechsler, I., Savov, A., & Schnabl, P. (2017). The deposits channel of monetary policy.
Quarterly Journal of Economics, 132(4), 1819-1876.
Ergungor, O. E. (2010). Bank branch presence and access to credit in low- to moderateincome neighborhoods. Journal of Money, Credit and Banking, 42(7), 1321-1349.
Favara, G., & Giannetti, M. (2014). Mortgage concentration, foreclosures and house
prices. Unpublished manuscript.
20Favara, G., & Imbs, J. (2015). Credit supply and the price of housing. American Economic
Review, 105(3), 958-92.
Fout, H., Mota, N., & Rosenblatt, E. (2022). When appraisers go low, contracts golower: The impact of expert opinions on transaction prices. Journal of Real Estate
Finance and Economics, 65(3), 451-491.
Gao, J., Wu, Y., & Zhang, W. (2023). Decentralized banking in mortgage market: Evidence from branch manager’s past experience. https://ssrn.com/abstract=4329311.
Gerardi, K., Shapiro, A. H., & Willen, P. (2007). Subprime outcomes: Risky
mortgages, homeownership experiences, and foreclosures. Available at SSRN:
https://ssrn.com/abstract=1073182.
Gilje, E. P., Loutskina, E., & Strahan, P. E. (2016). Exporting liquidity: Branch banking
and financial integraton. Journal of Finance, 71(3), 1159-1184.
Gurun, U. G., Matvos, G., & Seru, A. (2016). Selling expensive mortgages. Journal of
Finance, 71(5), 2371-2416.
Iyer, R., Kundu, S., & Paltalidis, N.
(2023).
coal mine:
Bank liquidity shortages and local
https://papers.ssrn.com/sol3/papers.cfm?abstracti d = 4247519.

Canaryeconomic

in theactivity.

Jayaratne, J., & Strahan, P. E. (1996). The finance-growth nexus: Evidence from bankbranch deregulation. Quarterly Journal of Economics, 111(3), 639-670.
Keys, B. J., Mukherjee, T., Seru, A., & Vig, V. (2010). Did securitization lead to lax
screening? Evidence from subprime loans. Quarterly Journal of Economics, 125(1),307-362.
King, R. G., & Levine, R. (1993). Finance, entrepreneurship and growth. Journal of
Monetary Economics, 32(3), 513-542.
Kroszner, R. S., & Rajan, R. G. (1994). Is the Glass-Steagall Act justified? A study of the
US experience with universal banking before 1933. American Economic Review, 84(4),810-832.
LaCour-Little, M., Calhoun, C. A., & Yu, W. (2011). What role did piggyback lending
play in the housing bubble and mortgage collapse? Journal of Housing Economics,
20(2), 81-100.
Lerner, J. (1995). Venture capitalists and the oversight of private firms. Journal of
Finance, 50(1), 301-318.
Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31-77.
Loutskina, E. (2011). The role of securitization in bank liquidity and funding management. Journal of Financial Economics, 100(3), 663-684.
Loutskina, E., & Strahan, P. E. (2009). Securitization and the declining impact of bankfinance on loan supply: Evidence from mortgage originations. Journal of Finance,64(2), 861-889.
21

Loutskina, E., & Strahan, P. E. (2011). Informed and uninformed investment in housing: The downside of diversification. Review of Financial Studies, 24(5), 1447-1480.
Mayer, C. J., & Pence, K. (2008). Subprime mortgages: what, where, and to whom? (Tech.
Rep.). National Bureau of Economic Research.
Mian, A., & Sufi, A. (2009). The consequences of mortgage credit expansion: Evidence
from the US mortgage default crisis. Quarterly Journal of Economics, 124(4), 1449-1496.
Mian, A., Sufi, A., & Trebbi, F. (2015). Foreclosures, house prices, and the real economy.
Journal of Finance, 70(6), 2587-2634.
Morse, A. (2011). Payday lenders: Heroes or villains? Journal of Financial Economics,
102(1), 28-44.
Paravisini, D. (2008). Local bank financial constraints and firm access to external finance. Journal of Finance, 63(5), 2161-2193.
Puri, M., & Iyer, R. (2012). Understanding bank runs: The importance of depositorbank relationships and networks. American Economic Review, 102(4), 1414-1445.
Purnanandam, A. (2011). Originate-to-distribute model and the subprime mortgage
crisis. Review of Financial Studies, 24(6), 1881-1915.
Rajan, R., & Ramcharan, R. (2015). The anatomy of a credit crisis: The boom and bust in
farm land prices in the united states in the 1920s. American Economic Review, 105(4),1439-77.
Rajan, R. G., & Zingales, L. (1998). Financial dependence and growth. American Economic Review, 559-586.
Rajan, U., Seru, A., & Vig, V. (2010). Statistical default models and incentives. American
Economic Review, 100(2), 506-10.
Rice, T., & Strahan, P. E. (2010). Does credit competition affect small-firm finance?
Journal of Finance, 65(3), 861-889.
Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393-410.
Yang, J. (2022). Deposit-lending synergies: Evidence from chinese students at us universities. Journal of Financial and Quantitative Analysis, 57(5), 1960-1986.

22

23
Depositscb2002
Depositsb 2002

Deposit Sharecb2002

A dichotomous variable that equals 1 (0 otherwise) if bank b has at least one branch in county c in 2002.

Lending Locallycb2002

P
Home Value Growthz2009
Median Home Valuez2009 −Median Home Valuez2006
where Home Value Growthz2009 = N1z∈cHome Value Growthz2006
Median Home Valuez2006
c

Median Home Valuez2006 −Median Home Valuez2002
1 P
where Nc represents number of zipcodes (z) in county cz∈cNcMedian Home Valuez2002

Home Value Growthc2006

Peak-Trough Ratio

Loan age at the time Delinquent flag takes on the value of 1 for the first time.

Time To Delinquency

Average Loan to Income Ratiocb2006 −Average Loan to Income Ratiocb2002|Application Status = Approved and OriginatedAverage Loan to Income Ratiocb2002

A dichotomous variable that equals 1 (0 otherwise) if a loan is at least 28 days past due the date of installment reported by the servicers to Freddie Mac.

Delinquent

Loan to Income Growth

Loan Volumecb2006 |Application Status = Approved, Originated, and Held
Loan Volumecb2002 |Application Status = Approved, Originated, and Held
− Loan Volume
Loan Volumecb2006 |Application Status = Approved and Originatedcb2002 |Application Status = Approved and OriginatedChange in Proportion of Loan Volume Heldcb2006

Average Applicant Incomecb2006 −Average Applicant Incomecb2002|Application Status = Approved and OriginatedAverage Applicant Incomecb2002

Number of Applicationscb2006 |Application Status = Approved, Originated, and Held
Number of Applicationscb2002 |Application Status = Approved, Originated, and Held
− Number of Applications
Number of Applicationscb2006 |Application Status = Approved and Originatedcb2002 |Application Status = Approved and OriginatedChange in Proportion of Applications Heldcb2006

Applicant Income Growth

Loan Volumecb2006 −Loan Volumecb2002|Application Status = Approved and Originated
Loan Volumecb2002

Loan Volume Growthcb2006

Number of Applicationscb2002| Application Status = Approved and OriginatedNumber of Applicationsc2002

Number of Applicationscb2006 −Number of Applicationscb2002|Application Status = Approved and OriginatedNumber of Applicationscb2002

Applications Originated Growthcb2006

Application Market ShareDefinition

Variable Name

This table presents the definitions of key variables used in various analyses in this study. The subscripts b, c, z represent bank, county, and zip code, respectively.

Definitions of Key Variables

24

27,95427,95427,95427,95427,95427,95427,95427,954
519,599
8,447
519,599
9,546
27,95427,95427,95427,95427,95427,95427,95427,95427,95427,954Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share (in %)Applicant Income Growth
Loan to Income Growth
Peak-Trough Ratio
Lending Locally IV - 1994 Branch Network (1 = Yes, 0 = No)
Lending Locally IV - M&A Branch Network (1 = Yes, 0 = No)
Deposit Share in 2002
Deposit Share in 1994

N

Applications OriginatedApplications Originated Growth
Loan Volume (Level)
Loan Volume Growth
Proportion of Applications Held (Level)
Change in Proportion of Number of Applications Held
Proportion of Loan Volume Held (Level)
Change in Proportion of Loan Volume Held
Delinquent (2006-2013)
Time to Delinquency (2006-2013)
Delinquent (2006-2018)
Time to Delinquency (2006-2018)

Variable

0.341
0.306
8.583
0.320
0.390
-0.203
0.128
0.129
0.076
0.056

52.811
1.081
8,887.244
2.589
0.412
-0.0780.406-0.0620.016
67.1340.018
74.196

Mean

0.254
0.461
21.020
0.742
0.810
0.608
0.335
0.000
0.222
0.000

223.389
3.445
50,519.570
7.300
0.391
0.364
0.395
0.371
0.126
11.5750.134
18.456

SD

0.104
0.000
0.005
-0.349-0.210-0.594
0.000
0.000
0.000
0.000

1.000
-0.722100.000-0.6470.000-0.5480.000-0.5660.000
52.0000.000
54.000

p10

0.156
0.000
0.019
-0.100-0.007-0.360
0.000
0.000
0.000
0.000

2.000
-0.500245.000-0.2720.000-0.1990.000-0.1690.000
59.0000.000
62.000

p25

0.255
0.000
0.176
0.163
0.202
-0.140
0.000
0.000
0.000
0.000

7.000
0.000
875.000
0.447
0.318
0.000
0.282
0.000
0.000
67.0000.000
71.000

p50

0.483
1.000
3.226
0.513
0.513
0.063
0.000
0.000
0.003
0.000

30.0001.000
3,790.000
2.078
0.829
0.029
0.830
0.054
0.000
76.0000.000
83.000

p75

0.755
1.000
28.947
1.084
1.043
0.278
1.000
1.000
0.212
0.036

102.000
3.313
14,532.000
6.367
1.000
0.288
1.000
0.319
0.000
83.0000.000
98.000p90

This table presents summary statistics of key variables used in various analyses in this study. Applications Originated Growth equals growth in the number of loan
originations in 2006 relative to 2002. Loan Volume Growth equals growth in the aggregate amount of loans originated in 2006 relative to 2002. Change in Proportionof Number of Applications Held equals the change in the proportion of the number of loan originations held by a bank on its balance sheet in 2006 relative to 2002.
Change in Proportion of Loan Volume Held equals the change in the proportion of originated loan volume held by a bank on its balance sheet between 2006 and2002. Lending Locally is a dichotomous variable that equals 1 (0 otherwise) if a bank has a branch in a county in 2002. Median Home Value Growth is growth in the
zip-code level median home value index between 2002 and 2006. Application Market Share equals a bank’s share of the number of loan originations in a countyin 2002. Applicant Income Growth equals growth in the income of a bank’s representative borrower, for whom a loan originates, between 2002 and 2006, andLoan-to-Income Growth is defined analogously for loan-to-income ratio. Lending Locally IV - 1994 Branch Network is a dichotomous variable that equals 1 if a bankhas a branch in a county in 1994. Lending Locally IV - M&A Branch Network is a dichotomous variable that equals 1 for all branches that bank A gains due to its
acquisition of bank B and equals 0 otherwise.

Table 1: Summary Statistics

Table 2: Within Lender Growth in Loan Originations and Loan Volume: Baseline
This table reports baseline results for the following within-lender OLS regression specification:
Outcomecb2006 =β1 (Lending Locallycb2002 × Home Value Growthc2006 )+β2 Lending Locallycb2002 + β3 Home Value Growthc2006 +Γ′ Xcb2006 + Bankb + Countyc + ucbt .
The subscripts b, and c represent a bank, and county, respectively. In Panel A, the dependent variable,
Applications Originated Growthcb2006 , equals growth in the number of loan originations in 2006 relative to
2002. In Panel B, the dependent variable is Loan Volume Growthcb2006 , which equals growth in the aggregate amount of loans originated in 2006 relative to 2002. Lending Locallycb2002 is a dichotomous variable
that equals 1 (0 otherwise) if a bank has a branch in a county in 2002. Home Value Growthc2006 is theaverage of the growth in the zip code level median home value index between 2002 and 2006. Xcb2006
is a vector of control variables. Application Market Sharecbt equals a bank’s share of the number of loan
originations in a county in 2002. Applicant Income Growthcbt equals growth in the income of a bank’s representative borrower, for whom a loan originates, between 2002 and 2006, and Loan-to-Income Growthcbt
is defined analogously for loan-to-income ratio. The standard errors are clustered by county and bank,and t-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
(1)

(2)

(3)

-1.165***(-3.061)-0.726***(-5.918)-1.234***(-3.262)-0.565***(-4.152)-1.228***(-6.746)-1.253***(-3.232)-0.617***(-4.902)-1.209***(-6.233)
0.126***(2.986)
0.239***(3.873)

0.272

0.273

0.277

-2.226***(-2.612)-1.411***(-3.589)-2.408***(-2.804)-0.991**(-2.264)-3.197***(-6.962)-2.839***(-3.365)-0.592*(-1.913)-2.012***(-4.390)2.359***(13.033)
2.769***(9.731)

0.286

0.286

0.286

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Panel A: Dependent Variable - Applications Originated Growth
Lending Locally × Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

Panel B: Dependent Variable - Loan Volume Growth
Lending Locally × Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

N
County FE
Bank FESE Clustered by25

Table 3: Within Lender Growth in Loan Originations and Loan Volume: Deposit ShareThis table reports baseline results under an alternative specification based on deposit share. The
subscripts b, and c represent a bank, and county, respectively. In Panel A, the dependent variable,
Applications Originated Growthcb2006 , equals growth in the number of loan originations in 2006 relative
to 2002. In Panel B, the dependent variable is Loan Volume Growthcb2006 , which equals growth in the agb2002,
gregate amount of loans originated in 2006 relative to 2002. The Deposit Sharecb2002 is defined as DDcb2002
where D represents bank deposits. Home Value Growthc2006 is the average growth in the zip code level
median home value index between 2002 and 2006. Application Market Sharecbt equals a bank’s share of
the number of loan originations in a county in 2002. Applicant Income Growthcbt equals growth in theincome of a bank’s representative borrower, for whom a loan originates, between 2002 and 2006, andLoan-to-Income Growthcbt is defined analogously for loan-to-income ratio. The standard errors are clustered by county and bank, and t-statistics are presented in parentheses. Significance levels: * p < 0.10, **p < 0.05, *** p < 0.01.
(1)

(2)

(3)

-0.687*(-1.823)-0.719***(-4.287)-0.735**(-2.034)
0.732***(3.785)-2.681***(-12.988)-0.673*(-1.774)
0.718***(3.483)-2.712***(-12.448)
0.153***(3.326)
0.258***(3.886)

0.261

0.264

0.267

-2.514**(-2.507)-1.259***(-3.020)-2.625***(-2.725)
2.063***(4.159)-6.140***(-12.792)-2.709***(-2.840)
2.202***(4.401)-4.751***(-9.760)
2.397***(12.795)
2.796***(9.542)

0.292

0.296

0.378

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Panel A: Dependent Variable - Applications Originated Growth
Deposit Share in 2002×Home Value Growth
Deposit Share in 2002
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2
Panel B: Dependent Variable - Loan Volume Growth
Deposit Share in 2002×Home Value Growth
Deposit Share in 2002
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

N
County FE
Bank FESE Clustered by26

Table 4: First Stage of Instrumental Variables Regression Analysis - 1994 Branch Network
This table presents the first-stage results for the instrumental variable regression analysis of the withinbank differential effect of Lending Locally the historical branch network in 1994. The instrumented
variable, Lending Locallycb2002 , is a dichotomous variable that equals 1 (0 otherwise) if a bank has abranch in a county in 2002. The instrument, Lending Locallycb1994 , is a dichotomous variable that equals
1 (0 otherwise) if a bank has a branch in a county in 1994. The standard errors are clustered by county
and bank, and t-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p <
0.01.
(1)

(2)

(3)

Dependent Variable - Lending Locally in 2002 (1 = Yes, 0 = No)
Lending Locally1994 (1 = Yes, 0 = No)
Lending Locally1994 x Median Home Value Growth

0.647***(24.817)-0.043(-0.686)0.539***(18.876)-0.011(-0.169)
0.541***(19.393)0.529***(18.018)0.004(0.055)0.524***(18.679)-0.025***(-5.075)-0.021***(-2.839)

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Application Market Share
Applicant Income Growth
Loan to Income Growth

N
County FE
Bank FESE Clustered by27

Table 5: Instrumental Variables Regression Analysis - 1994 Branch Network
This table presents baseline results under the Instrumental Variables Approach. In Panel A, the dependent variable, Applications Originated Growthcb2006 , equals growth in the number of loan originations in 2006 relative to 2002. In Panel B, the dependent variable is Loan Volume Growthcb2006 , whichequals growth in the aggregate amount of loans originated in 2006 relative to 2002. Lending Locallycb2002
is a dichotomous variable that equals 1 (0 otherwise) if a bank has a branch in a county in 2002.
Home Value Growthc2006 is the average growth in the zip code level median home value index between
2002 and 2006. The instrument for Lending Locallycb2002 is a dichotomous variable that equals 1 if a bankhas a branch in a county in 1994. Application Market Sharecbt equals a bank’s share of the number of loan
originations in a county in 2002. Applicant Income Growthcbt equals growth in the income of a bank’s representative borrower, for whom a loan originates, between 2002 and 2006, and Loan-to-Income Growthcbt
is defined analogously for loan-to-income ratio. The standard errors are clustered by county and bank,and t-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

(1)

(2)

(3)

-1.560**(-2.512)-0.573***(-3.316)-1.671***(-2.646)-0.079(-0.399)-1.601***(-6.899)-1.738***(-2.650)-0.135(-0.658)-1.555***(-6.313)0.136***(3.138)0.247***(3.900)

0.017

0.018

0.023

-3.411***(-2.734)-1.033***(-2.798)-3.691***(-2.936)
0.223(0.514)-4.067***(-8.133)-4.322***(-3.277)
0.417(1.044)-2.583***(-4.861)
2.375***(13.066)
2.783***(9.649)

0.015

0.016

0.128

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Panel A: Dependent Variable - Applications Originated Growth
Lending Locally×Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

Panel B: Dependent Variable - Loan Volume Growth
Lending Locally×Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

N
County FE
Bank FESE Clustered by
Instrument

Lending Locally based on 1994 Branch Network

28

Table 6: Within Lender Change in Proportion of Applications and Loan Volume Held
This table presents results for the within-bank differential effect of Lending Locally on the proportionof held loan applications and corresponding aggregate held loan volume. In Panel A, the dependent
variable, Change in Proportion of Application Heldcb2006 , equals the change in the proportion of the number
of applications held by a bank on its balance sheet between 2006 and 2002. In Panel B, the dependent
variable is Held Loan Volume Growthcb2006 equals the change in the amount of the loans held by a bankon its balance sheet between 2006 and 2002. Lending Locallycb2002 is a dichotomous variable that equals 1(0 otherwise) if a bank has a branch in a county in 2002. Home Value Growthc2006 is the average growth in
the zip code level median home value index between 2002 and 2006. The standard errors are clusteredby county and bank, and t-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p <0.05, *** p < 0.01.
(1)
Panel A: Dependent Variable - Change in Proportion of Number of Applications Held
Lending Locally×Home Value Growth-0.071**(-2.300)-0.003(-0.239)

Lending Locally (1 = Yes, 0 = No)
R2

0.463

Panel B: Dependent Variable - Change in Proportion of Loan Volume Held
Lending Locally×Home Value Growth-0.071**(-2.288)0.007(0.539)Lending Locally (1 = Yes, 0 = No)
R2

0.443

N
County FE
Bank FESE Clustered by

27,954✓✓
Bank and County

29

Table 7: Freddie Mac - Loan Level Data Analysis
The results in this table are based on the loan level data from Freddie Mac’s Loan Level Database.
Specifically, the results in this table are based on all loans that originate in 2006 and are subsequently
sold to Freddie Mac by the originating Lender. In Panel A, the dependent variable is Delinquent, whichequals 1 (0 otherwise) if a loan is past due for at least 28 days and equals 0 otherwise. In Panel B, thedependent variable is Time To Delinquency | Delinquent that equals the loan age (in months) at the time
when Delinquent flag takes on the value of 1 for the first time during a loan’s tenure. The independent
variable, Lending Locally, is a dichotomous variable that equals 1 (0 otherwise) if a lender has a branch ina county. The Time fixed effects in Panel B correspond to the year-month in which Deliquent flag takes on
the value of 1 for the first time during a loan’s tenure. All specifications include borrower-level creditscore as a control variable. The standard errors are clustered by zip code, and t-statistics are presented
in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
(1)

(2)

Panel A: Dependent Variable - Delinquent (1 = Yes, 0 = No)
Lending Locally (1=Yes, 0 = No)

0.002**(2.452)0.002***(2.981)R2N
Bank FE
Zipcode FE
SE Clustered byTime Period

0.040
519,599
Yes
Yes
Zipcode2006-2013

0.042
519,599
Yes
Yes
Zipcode2006-2018

Panel B: Dependent Variable - Time To Delinquency | Delinquent
Lending Locally (1=Yes, 0 = No)-0.455**(-2.452)-0.547***(-3.341)

R2
N
Bank FE
Zipcode FE
Time FESE Clustered byTime Period

0.920
8,447
✓
✓
✓
Zipcode2006-2013

0.969
9,546
✓
✓
✓
Zipcode2006-201830

Table 8: Peak-Trough Markets - Terciles-Based Regression Analysis
This table presents the results of the analysis based on the Peak-Trough Markets. In Panel A, the dependent variable, Applications Originated Growthcb2006 , equals growth in the number of loan originations in 2006 relative to 2002. In Panel B, the dependent variable is Loan Volume Growthcb2006 , whichequals growth in the aggregate amount of loans originated in 2006 relative to 2002. Lending Locallycb2002
is a dichotomous variable that equals 1 (0 otherwise) if a bank has a branch in a county in 2002.
Value Growthc2009
Peak-Trough Ratioc2006 equals Home
Home Value Growthc2006 , where Home Value Growthc2009 is average growth in
the zip code level median home value index between 2006 and 2009, and Home Value Growthc2006 is theaverage growth in the zip code level median home value index between 2002 and 2006. In columns(1), (2), and (3), the results are based on a sub-sample corresponding to the bottom, middle, and top
tercile, respectively, of the Peak-Trough Ratio. The standard errors are clustered by county and bank, andt-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
(1)

(2)

(3)

Peak-Trough Ratio Tercile:
BottomMiddleTop-0.153**(-2.063)-1.245***(-4.779)-0.960(-1.061)-1.533***(-7.013)-0.015(-0.245)-1.000***(-6.143)

0.310

0.287

0.251

-0.327**(-2.106)-2.489***(-4.016)-2.671(-1.381)-3.023***(-6.956)-0.101(-0.835)-1.937***(-7.256)

0.335

0.326

0.257

9,816
✓
✓
Bank and County

9,758
✓
✓
Bank and County

9,551
✓
✓
Bank and County

Panel A: Dependent Variable - Applications Originated Growth
Lending Locally×Peak-Trough Ratio
Lending Locally (1 = Yes, 0 = No)
R2

Panel B: Dependent Variable - Loan Volume Growth
Lending Locally×Peak-Trough Ratio
Lending Locally (1 = Yes, 0 = No)
R2NCounty FE
Bank FESE Clustered by

31

-1

Difference in Ln(Loan Volume): Non-Local v Local

Difference in Ln(Number of Originations): Non Local v Local

-1.5

-2

-1.5

-2

-2.5
-2.5
-.3

-.2

-.1

0

.1

-.3

Home Value Growth

-.2

-.1

0

.1

Home Value Growth

Figure 1: The figure presents a binned scatterplot showing an association between home value
growth in 2006 and the difference in the average logarithm of mortgage originations (left panel)and loan volume (right panel) across lending types: local and non-local (within bank). The local
lending corresponds to counties where a bank has at least one branch. The non-local lending
corresponds to counties where a bank does not have a branch.

32

Non-Local Lending: Number of Orignations

Local Lending: Number of Orignations

3.5

3

2.5

2
-.4

-.3

-.2

-.1

0

.1

3

2.5

2

1.5
-.4

-.3

Home Value Growth

Non-Local Lending: Loan Volume

Local Lending: Loan Volume

8.5

8

7.5
-.4

-.3

-.2

-.1

-.2

-.1

0

.1

0

.1

Home Value Growth

0

.1

8
7.5
7
6.5
6
5.5
-.4

Home Value Growth

-.3

-.2

-.1

Home Value Growth

Figure 2: This figure shows how local and non-local lending relate to the growth of homevalues in 2006. The left panel depicts the association between home value growth, and thelogarithm of one plus total number of originations (top left) and the logarithm of one plus totalloan volume (bottom left) in counties within banks’ branch networks (local lending). Analogous binned scatterplots in the right panel correspond to counties outside banks’ branch networks (non-local lending).

33

(a)

(b)

Figure 3: This figure presents margins plots of regression results with Applications OriginatedGrowth and Loan Volume Growth in 2006 relative to 2002 as dependent variables in Panel (a)and (b), respectively. Deposit Share is defined as the total deposits of a county-bank as a fractionof the total deposits of a bank in 2002. Home Growth equals the average growth in the zip codelevel median home value index between 2002 and 2006 in a given county.

34

Internet Appendix

Informational Advantages of Lending Locally

Kristle Romero Cortés
UNSW Business School, Sydney, Australia

Figure A.1: The figure presents the intuition behind the Mergers and Acquisitions based instrumental variable used in Tables A.1 and A.2. Let {Acquiror} (bottom circle), {Bank A} (topleft circle), and {Bank B} (top right circle) represent the set of counties in which Acquiror bank,
Bank A, and Bank B, respectively, has branch presence. Say, Acquiror bank buys Bank A in year
t and Bank B in t′ , where 1994 ≤ t, t′ ≤ 2001. In 2002, the total branch network of Acquiror bankis represented by {Acquiror}∪{Bank A}∪{Bank B} and instrumented by {Bank A}∪{Bank B}.

1

Table A.1: First Stage of Instrumental Variables Regression Analysis - M&A Activity Based
Branch Network
This table presents the first-stage results for the instrumental variable regression analysis of thewithin-bank differential effect of Lending Locally using M&A Activity. The instrumented variable,
Lending Locallycb2002 , is a dichotomous variable that equals 1 (0 otherwise) if a bank has a branch ina county in 2002. The instrument for Lending Locallycb2002 is a dichotomous variable based on a bank’s
M&A activity. This variable equals 1 for the branch network that the acquiring bank incrementally gains
from acquiring another bank. The standard errors are clustered by county and bank, and t-statistics arepresented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.
(1)

(2)

(3)

Dependent Variable - Lending Locally in 2002 (1 = Yes, 0 = No)
Lending Locally Based on the Incremental Branch Network due to M&A Activity

N
County FE
Bank FESE Clustered by

2

0.687***(24.146)

0.669***(22.918)

0.662***(22.588)

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Table A.2: Instrumental Variables Regression Analysis - M&A Based Branch Network
This table presents baseline results under the Instrumental Variables Approach. In Panel A, the dependent variable, Applications Originated Growthcb2006 , equals growth in the number of loan originations in 2006 relative to 2002. In Panel B, the dependent variable is Loan Volume Growthcb2006 , whichequals growth in the aggregate amount of loans originated in 2006 relative to 2002. Lending Locallycb2002
is a dichotomous variable that equals 1 (0 otherwise) if a bank has a branch in a county in 2002.
Home Value Growthc2006 is the average growth in the zip code level median home value index between
2002 and 2006. The instrument for Lending Locallycb2002 is a dichotomous variable based on a bank’s
M&A activity. This variable equals 1 for the branch network that the acquiring bank incrementally gains
from acquiring another bank. Application Market Share cbt equals a bank’s share of the number of loan
originations in a county in 2002. Applicant Income Growthcbt equals growth in the income of a bank’s representative borrower, for whom a loan originates, between 2002 and 2006, and Loan-to-Income Growthcbt
is defined analogously for loan-to-income ratio. The standard errors are clustered by county and bank,and t-statistics are presented in parentheses. Significance levels: * p < 0.10, ** p < 0.05, *** p < 0.01.

(1)

(2)

(3)

-1.585***(-3.007)-1.180***(-6.508)-1.589***(-3.016)-1.167***(-6.325)-0.474**(-2.310)-1.650***(-3.002)-1.209***(-7.139)-0.467**(-2.213)0.107***(2.595)0.225***(3.833)

0.013

0.013

0.018

-3.012**(-2.428)-2.229***(-3.011)-3.025**(-2.436)-2.182***(-2.887)-1.735***(-2.692)-3.767***(-3.160)-1.607***(-3.063)-0.661(-1.169)2.324***(12.967)2.744***(9.891)

0.012

0.013

0.124

29,129✓✓Bank and County

29,129✓✓Bank and County

27,954✓✓
Bank and County

Panel A: Dependent Variable - Applications Originated Growth
Lending Locally×Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

Panel B: Dependent Variable - Loan Volume Growth
Lending Locally×Home Value Growth
Lending Locally (1 = Yes, 0 = No)
Application Market Share
Applicant Income Growth
Loan to Income Growth
R2

N
County FE
Bank FESE Clustered by
Instrument

Incremental Branch Network due to M&A Activity

3