WORKING PAPER SERIES

Bank Monitoring with On-Site Inspections

Amanda Rae Heitz

Federal Deposit Insurance Corporation and Tulane University

Christopher Martin

Federal Deposit Insurance Corporation

Alexander Ufier

Federal Deposit Insurance Corporation

August 2022

Last Updated: July 2023

Previously circulated as “Bank Monitoring in Construction Lending”

FDIC CFR WP 2022-09

fdic.gov/cfr

The Center for Financial Research (CFR) Working Paper Series allows CFR staff and their coauthors to circulate 

preliminary research findings to stimulate discussion and critical comment. Views and opinions expressed in CFR Working 

Papers reflect those of the authors and do not necessarily reflect those of the FDIC or the United States. Comments and 

suggestions are welcome and should be directed to the authors. References should cite this research as a “FDIC CFR 

Working Paper” and should note that findings and conclusions in working papers may be preliminary and subject to 

revision.
1 

Bank Monitoring with On-Site Inspections

July 20, 2023 

Amanda Rae Heitz, FDIC and Tulane University, aheitz@tulane.edu

Christopher Martin, FDIC, chrmartin@fdic.gov

Alexander Ufier, FDIC, aufier@fdic.gov

While theoretical papers have emphasized the importance of bank loan monitoring, empirical papers have 

struggled to validate these theories, largely due to limited data availability. Using a proprietary 

transaction-level database of nearly 30,000 multiple-draw construction loans, we empirically examine the 

determinants and effects of bank monitoring through on-site inspections. Consistent with theoretical 

predictions, banks trade off monitoring with favorable origination terms. Monitoring is less frequent 

when the bank has a prior relationship with the borrower, suggesting information transfers across projects. 

Monitoring intensity increases when local economic conditions or bank health deteriorate. Textual 

analysis shows that negative inspection reports are associated with a greater likelihood of banks denying 

draw requests, indicating that the information collected through monitoring is important for decisionmaking. Finally, we provide a comprehensive analysis of construction loan default and show that 

monitoring decreases default. Overall, our results validate the predictions of a large theoretical literature 

emphasizing the important role of bank monitoring. 

Keywords: bank monitoring, financial crisis, construction loan, failed bank 

JEL Codes: G21, G51, G01, G33 

A portion of this paper was completed when Amanda Rae Heitz was a Visiting Scholar with the Federal 

Deposit Insurance Corporation. Amanda Rae Heitz (aheitz@tulane.edu, 504-314-7575), Federal Deposit 

Insurance Corporation and A. B. Freeman School of Business Department of Finance, Tulane University, 

7 McAlister Drive, New Orleans, LA, USA 70118; Christopher Martin (chrmartin@fdic.gov, 202-898-

3934), Federal Deposit Insurance Corporation, 550 17th St NW, Washington, DC, USA 20429; Alexander 

Ufier (aufier@fdic.gov, 202-898-6757), Federal Deposit Insurance Corporation, 550 17th St NW, 

Washington, DC, USA 20429. We are thankful for comments from Rosalind Bennett, Jin Cao 

(discussant), Lucy Chernykh (discussant), Karyen Chu, Mark Flannery (discussant), Nick Frazier, Ivan 

Ivanov (discussant), David Javakhadze (discussant), Stephen Karolyi (discussant), Roni Kisin, Yiming 

Ma (discussant), Jordan Pandolfo, Jon Pogach, Liu Wang (discussant), Smith Williams, Andrew Winton, 

and seminar participants at the American Economic Association Annual Meeting, Arizona State 

University Sonoran Winter Finance Conference, South Carolina Fixed Income and Financial Institutions 

Conference, Community Banking Research Conference, Federal Deposit Insurance Corporation Bank 

Research Conference, IBEFA Summer Conference, Eastern Finance Association, Financial Management 

Association Annual Meeting, Federal Reserve Board, Federal Deposit Insurance Corporation, and Tulane 

University. We also thank Derek Johnson, Janet Garufis, and Jeff Johnson for discussions surrounding 

the details of construction lending and Michael Carabello, Michael Pessin, Noam Weintraub and Kevin 

Wong for excellent research assistance. Views and opinions expressed in this paper reflect those of the 

authors and do not necessarily reflect those of the FDIC or the United States. We are thankful for the best 

paper awards at the 2022 Financial Management Association and Eastern Finance Association Annual 

Meetings. 
2 

1. Introduction

As increasing amounts of lending activity migrate away from banks and into non-bank finance 

companies,1 only a few categories of loans remain largely in the hands of modern banks, including 

commercial real estate (CRE) lending. Using Call Report data, Figure 1 shows that lending in CRE loans 

has increased as a share of bank balance sheets over time, with the largest proportional increases 

occurring in small banks. It is well established that CRE lending is inherently risky, and construction and 

land development (CLD) loans are widely considered the riskiest sub-category of such bank lending 

(Balla, Mazur, Prescott, and Walter, 2019). Banks may remain dominant in these loan categories because 

of their comparative advantage in managing complex lending relationships, such as through monitoring 

loans. 

Even after performing due diligence through underwriting to screen out unacceptable borrowers 

(adverse selection), lenders must expend resources and monitor the loans to temper borrower incentives to 

invest sub-optimally (moral hazard). A large theoretical body of literature examining the unique features 

of banks suggests that a key advantage that banks have over non-banks is the mitigation of this moral 

hazard problem through their superior monitoring capabilities (Diamond, 1984; Fama, 1985; James, 1987; 

Diamond and Rajan, 2001; Kashyap, Rajan, and Stein, 2002). Some models suggest that by specializing 

in certain asset classes, banks have enhanced incentives to monitor (Winton, 1999), and greater levels of 

monitoring can ultimately lead to improvements in the quality of their loan portfolios (Boot and Thakor, 

1997; Winton, 1999). However, because direct evidence of monitoring is rarely observed, empirical 

papers struggle to test many theoretical models of monitoring. 

 

1 For example, fintechs and non-bank lenders increasingly originate many types of loans, securitize them, and then 

sell them to private investors or government entities. Existing studies have established these patterns in mortgage 

lending (Buchak, Matvos, Piskorski, and Seru, 2018), student loans (Berman and Stivers, 2016), and consumer 

finance assets, such as automobile loans, credit card receivables, and student loans (Cetorelli, Mandell and 

Mollineaux, 2012). The two remaining asset classes are commercial and industrial loans and commercial real estate

loans, including construction loans (Cetorelli, Mandell and Mollineaux, 2012). See also Fessenden and Muething 

(2017) for the rising importance of commercial real estate lending for banks.
3

Using a proprietary transaction-level dataset consisting of nearly 30,000 construction loans, primarily 

for residential properties, we observe bank loan monitoring via third-party inspections of the construction 

project. We use this detailed monitoring data to fill the gap in the literature by empirically answering 

three distinct questions. First, what determines bank monitoring decisions? Second, do banks use the 

information they collect through these monitoring efforts? Third, does bank monitoring affect loan 

outcomes?

This unique and powerful dataset from a large bank allows us to directly document the on-site 

monitoring process for a bank’s portfolio of non-syndicated loans to small individual borrowers over the 

entire duration of the loans’ lifetimes. We observe contract terms, origination characteristics, and 

borrower actions throughout the life of the loan, including line drawdowns, payments, and defaults. Most 

importantly, this dataset allows us to directly examine bank monitoring via bank-contracted, third-party 

inspectors (“on-site inspections”). For each construction loan in our sample, we observe the timing and 

frequency of all such inspections. We also observe the text contained in the inspection reports, deepening 

our understanding of how banks use the information they collect from these reports in their decisionmaking. Within the theoretical literature, the concept of bank monitoring is often vague, and it can refer 

to a bank directly taking actions (e.g., making a phone call to a borrower) or requesting information from 

a third party (e.g., appraisal). To our knowledge, we are the first paper in the literature to document the 

process by which banks monitor loans by acquiring information through third parties over the entire 

lifetime of the loans. We are also the first paper to document any type of bank monitoring of consumer 

borrowers.

Construction loans are well-suited for this type of analysis because their unique structure gives rise to 

repeated interactions between the bank and borrower, and these interactions allow the bank to affect 

borrower actions and ultimately loan outcomes. A construction loan’s principal funds the bulk of the 

collateral’s creation. Once the project is completed, the underlying collateral is either sold or rented, and 

the cash flows generated from that transaction are typically used to repay the loan. Thus, moral hazard 

generally involves the borrower drawing the loan funds and failing to produce saleable (or sufficiently 
4

valuable) collateral.2 To alleviate this moral hazard, construction loans are structured with predetermined draw schedules tied to construction-related project milestones such as completing the 

foundation, framing the walls, or installing plumbing. The schedules mandate that the borrower must 

progress sufficiently through the milestones and allow the bank to assess this progress before the bank 

will disburse additional funds to continue work on the project. It is common for banks to hire 

independent, third-party inspectors who specialize in evaluating the progression of construction projects 

to conduct on-site inspections at the borrower’s expense before approving a draw request. However, not 

all draw requests are accompanied by on-site inspections. The bank’s informed decisions about draw 

requests incentivize borrowers to reach milestones, and they also allow the bank to limit its exposure to 

failing projects.

Furthermore, construction loans possess many characteristics which theory predicts would heighten 

bank incentives to monitor. Construction loans are inherently risky, have an illiquid secondary market,

only generate collateral as the project progresses, usually do not generate cash flow, and often lack 

traditional covenants that can temper monitoring incentives (Winton, 1999; Boot and Thakor, 1997; Rajan 

and Winton, 1995; Garleanu and Zwiebel, 2009). Thus, this bank’s construction lending portfolio 

provides us with an ideal laboratory to examine the factors contributing to the bank’s monitoring efforts

by collecting information through on-site inspections, how the bank uses this information in its decisionmaking post-origination, and how this monitoring ultimately influences loan outcomes.

Relying on the predictions of several seminal theoretical models, we first examine whether banks 

substitute monitoring intensity (on-site inspections) with loan origination characteristics, such as price, 

quantity, and term to maturity. Consistent with the predictions in Diamond (1989, 1991), we find 

evidence of a negative relationship between on-site inspection intensity and loan spreads and fees at 

 2 This problem may arise because the borrower or the building contractor simply steal the funds. Alternatively, the 

problem may manifest if the contractor substitutes lower quality materials or if the borrower misdirects the funds to

other projects, which are common issues in construction. For a more detailed discussion of the risks in construction 

lending, see Johnston Ross, Nichols, and Shibut (2021).
5

origination,3 yet a positive relationship between on-site inspection intensity and loan amount. We also 

find that shorter maturity loans have more frequent on-site inspections. This finding is consistent with 

Rajan and Winton (1995), Barclay and Smith (1995), and Park (2000), who argue that banks receive more 

value from monitoring shorter term loans.

Pennacchi (1988) asserts that the heart of the bank’s monitoring function is to limit risk in borrowers’ 

projects and reduce the probability of default, suggesting that banks should be more likely to monitor 

projects with a greater ex ante probability of default. During the housing boom prior to the financial 

crisis of 2008, banks implemented lax underwriting standards and granted high risk construction loans to 

borrowers and projects that were less credit worthy than had historically been the case.4

 The granular 

nature of our loan documentation allows us to examine the relationship between banks collecting 

information through on-site inspections (monitoring) with borrower- and project-risk using proxies based 

on hard underwriting criteria as well as classifications of loans as made by loan officers. We find that 

loans to borrowers with lower credit scores and higher combined loan to value ratios (CLTV) are 

monitored more intensely over the course of the loan, suggesting that loans with higher risk borrowers 

were monitored more. Furthermore, this bank also had a special program marketed to individuals who 

did not necessarily have any construction experience to build their dream homes. Loans made through 

this program were also monitored more. Relatedly, loans flagged as “speculative,” meaning they were 

built to be sold later in the general market, rather than to pre-committed buyers, are also monitored more. 

In sum, these results suggest that while there was a nationwide trend for banks to make higher risk loans 

during the housing boom, banks would still attempt to manage the loan-level risks associated with these 

construction projects.

 3 This result contrasts with the model of Williamson (1987), which assumes an ex post monitoring decision by the 

lender and suggests that monitoring intensity is positively related to the interest rate. Diamond (1989, 1991) instead 

assume that monitoring choices are made ex ante.

4 Ben S. Bernanke, the former Chairman of the Federal Reserve, said, “Stronger regulation and supervision aimed at 

problems with underwriting practices and lenders’ risk management would have been a more effective and surgical 

approach to constraining the housing bubble than a general increase in interest rates.” (Rampell, Catherine (2010), 

“Lax Oversight Caused Crisis, Bernanke Says” The New York Times, January 4).
6 

Extant theoretical models assert that banks are special because they are capable of gaining 

information through lending, which enhances their monitoring ability (Diamond, 1984; James, 1987; 

Besanko and Kanatas, 1993). If repeated interactions through relationships with borrowers provide the 

bank with additional information that can be reusable across projects (Boot, 2000), this could lead to a 

decrease in monitoring. However, to the extent that the bank has additional exposure to the borrower, it 

may monitor more to manage this concentrated exposure. To the best of our knowledge, we are the first 

paper to empirically examine the relationship between any type of bank monitoring and relationships. We 

create a variable indicating whether the borrower has a previous construction loan with the bank and show 

that bank-borrower relationships are associated with a reduction in on-site inspections. One potential 

interpretation for this finding is that these relationships with borrowers provide value to banks by 

reducing monitoring costs because the private information that the banks receive from monitoring is 

reusable and can be transferred between projects, consistent with Boot (2000). 

While time-varying loan-level collateral values are difficult to observe and even difficult to define for 

projects under construction, we examine whether deteriorations in local economic conditions affect bank 

monitoring through on-site inspections and draw request outcomes. We find that construction loans 

located in zip codes with growth in housing prices are less likely to be inspected and less likely to have 

draw requests denied, and those located in areas with higher foreclosure rates are more likely to have 

draw requests denied. To the extent that improvements in local economic conditions negatively correlate 

with the risk of borrower nonpayment, this finding is consistent with the theoretical findings in Rajan and 

Winton (1995).

Further exploiting the long time period covered by our data, we assess how bank information 

acquisition changed as the bank suffered distress and approached failure. On the one hand, theory 

suggests banks nearing failure have incentives to gamble for resurrection (i.e., shift risks to creditors and 

resolution authorities; Jensen and Meckling, 1976). On the other hand, banks in distress might take 

actions to limit their risk-taking and avoid failure. To manage credit risk in existing credit lines (such as 

construction loans already underwritten), banks might increase inspection intensity and be more selective 
7 

about additional extensions of credit. Our results support the latter; as the bank approaches failure, it 

increases its inspection activity and denies more draw requests. 

Next, we examine whether banks use the information they acquire through on-site inspection reports

in their decision-making by linking the content of the on-site inspection reports to bank drawdown 

decisions. Within a panel setting, we map each loan inspection report to the draw request associated with 

it. Using the textual sentiments defined by Loughran and McDonald (2011), we examine the length of the 

inspection reports as well as the ratio of words contained in the reports that are positive or negative. We 

find that on-site inspection reports that are more negative are associated with more draw denials, and 

positive words have the opposite effect. Since each loan in our sample has multiple draw requests, we are 

able to saturate the model with loan-level fixed effects, accounting for time-invariant loan-level 

characteristics, as well as day fixed effects that account for granular time trends.

After establishing that there is a strong relationship between bank incentives to monitor, their realized 

monitoring actions by requesting third-party inspections, and drawdown decisions, we next demonstrate 

that these monitoring actions affect loan default and highlight their relative importance in relation to 

underwriting. Although our initial OLS results indicate that loans that experience more on-site 

inspections default less frequently, this result may be subject to endogeneity bias. Monitoring a loan 

might reduce the risk of the loan defaulting, but the bank may also monitor loans with the highest 

probability of default more intensely, since the marginal benefit of each additional inspection may be 

greatest on the riskiest loans. This endogeneity would bias our results in the positive direction (or, toward 

zero). To alleviate the endogeneity problem, we implement an instrumental variable (IV) framework to 

estimate the causal effects of monitoring with on-site inspections. Within the IV framework, we define an 

instrument that influences monitoring intensity but should not directly affect loan default, controlling for 

the range of construction project, borrower, and loan characteristics: the draw schedule. For the 

exogeneity assumption, we exploit the fact that draw schedules are set at the time of loan origination and 

based upon specific construction tasks; the value of each draw corresponds to the anticipated cost of the 

subsequent project milestone. To the extent that the perceived risk of the loan might have a marginal 
8 

impact on the draw schedule, we control for the risk measures on which the bank conditioned their 

decisions. Moreover, we show that loans with more draws default less frequently. The validity of the 

instrument is straightforward: borrower draw requests give the bank the option to inspect, and the bank 

has incentives to inspect prior to approving the next draw that would increase their exposure to the 

borrower. We also empirically verify that loans with more draws are inspected more frequently. 

Therefore, the number of draws is an exogenous shifter for the number of inspections. Within the IV 

framework, we establish that the negative relationship between monitoring with on-site inspections and 

loan default becomes even stronger. Consistent with theoretical models, this result indicates that 

enhanced monitoring ultimately reduces the probability of default and improves loan performance (Boot 

and Thakor, 1997; Winton, 1999; Acharya, Hasan, and Saunders, 2006). 

This study provides at least three distinct contributions to extant literature. First, leaning on an 

established body of theoretical models, we empirically test the determinants of bank monitoring with onsite inspections. To the best of our knowledge, this is the first paper that directly quantifies and examines 

these theoretical monitoring determinants within a set of loans that are non-syndicated and made to 

individual borrowers. We are also the first paper to directly quantify the frequency and timing of 

inspections within these loans. Since this bank served a broad customer base in many markets, we 

believe that many of the results based on this data set are likely to be broadly representative of how banks 

choose their monitoring actions for construction loans and potentially other types of consumer nonsyndicated loans. Second, we demonstrate that banks use the information gleaned from monitoring 

activities for real-time decision-making, which provides evidence for the foundational assumption that the 

information collected through monitoring activities is useful to the bank. Our analysis indicates that the 

content of inspection reports affects bank credit decisions. Third, using an instrumental variables 

framework and controlling for adverse selection through hard underwriting measures, we establish 

causally that monitoring leads to improvements in loan performance, corroborating the predictions of 

numerous theoretical models that suggest that monitoring can reduce borrower moral hazard post loan 

origination. 
9

Finally, this study is the first to examine the determinants of construction loan default within a 

regression framework. As the result of non-bank lending and asset securitization, bank balance sheets 

have become increasingly concentrated in only a few loan categories, including construction lending. 

Construction lending on bank Call Reports totaled $403 billion as of the fourth quarter of 2021 and was 

one of the primary contributors to bank failures during the financial crisis (Office of Inspector General, 

FDIC, 2012). Thus, it is important for regulators, academics, and banks to understand what contributes to 

default within this asset class.

2. Literature and Background

a. Related Literature

An extensive body of theoretical models suggests that there are three primary advantages banks have 

over non-banks: superior information producing (monitoring) abilities, better lending technologies, and 

the ability to match up liquidity needs between deposit-taking and loan-making (Diamond and Rajan, 

2001; Kashyap, Rajan and Stein, 2002). By assuming that a financial intermediary possesses superior 

monitoring technology (e.g., Diamond, 1991), several studies show that monitoring can positively affect 

loan performance (Leland and Pyle 1977; Diamond 1984; Diamond 1991; Kashyap, Tsomocos, and 

Vardoulakis, 2020). However, empirical researchers have struggled to test directly the three primary 

assumptions underlying this body of theoretical models: 1) banks actively monitor loans, 2) banks use the 

information they collect while monitoring to affect business decisions, and 3) monitoring influences loan 

outcomes. Although banks may monitor borrowers by requesting documents, project updates, or 

collateral appraisals, or by conducting phone calls or on-site inspections over the course of the loan, these 

direct monitoring measures are not often observable to empirical researchers using commercially 

available databases.

Reflecting the difficulty of directly quantifying the frequency or intensity of borrower-lender

interactions, most of the empirically oriented literature relies on indirect measures. For example, several 

studies have proxied for monitoring incentives based on syndicate structure (Lee and Mullineaux, 2004; 
10 

Sufi, 2007; Beatty, Liao, and Zhang, 2019) or distance between the bank and the borrower (Peterson and 

Rajan, 1995; Petersen and Rajan, 2002; Degryse and Ongena, 2005).5

 Other studies attempt to quantify 

the benefits of unobserved monitoring empirically by examining stock returns (James, 1987; Focarelli, 

Pozzolo, and Casolaro, 2008; Addoum and Murfin, 2020), loan contract terms (Bharath, Dahiya, 

Saunders, and Srinivasan, 2009), or debt yields (Datta, Iskandar-Datta, and Patel, 1999). Relatedly, 

elements of the contracting literature analyze the theoretical and empirical determinants of bank 

monitoring incentives through covenant design (Aghion and Bolton, 1992; Dewatripont and Tirole, 1994; 

Rajan and Winton, 1995; Wang and Xia, 2014; Prilmeier, 2017). 

Numerous studies examine the causes and effects of covenant enforcement by lenders to public firms. 

This literature relies on the fact that lenders monitor borrowers to identify cases in which borrowers 

violate covenants, but it does not directly observe or study the underlying bank monitoring actions over 

the lifetime of the loan. Numerous papers study the effect of covenant violations on borrower behavior 

and the terms of credit (Chava and Roberts, 2008; Roberts and Sufi, 2009; Demiroglu and James, 2010; 

Nini, Smith, and Sufi, 2012). Other recent papers study incentives for lenders to use the information 

gleaned from unobserved monitoring by analyzing drivers of lender discretion in enforcement conditional 

on borrowers violating covenants (Acharya, Almeida, Ippolito, and Perez Orive, 2020; Bird, Ertan, 

Karolyi, and Ruchti, 2022a,b; Chodorow-Reich and Falato, 2022). 

Our study most closely relates to a small and incomplete literature that has overcome these data 

challenges to obtain direct evidence of monitoring. Using a direct measure, Cerqueiro, Ongena, and 

Roszbach (2016) show the value of collateral is an important determinant of monitoring, as demonstrated 

by the number of months between instances where the bank reviews borrower documentation, though 

they do not examine its effect on loan outcomes, such as default. Gustafason, Ivanov, and Meisenzahl

(2021) use textual analysis to recover various instances of active monitoring in a series of (partially 

overlapping) cross sectional samples of syndicated loans to large corporate borrowers that are primarily 

 

5 See also Giroud (2013) for evidence on the relationship between distance and monitoring activity within firms.
11

non-investment grade. They highlight the role that covenants and syndicate structures, two determinants 

of loan monitoring in these large corporate loans not generally applicable to our study of smaller retail 

projects, play in their bank monitoring measure. Furthermore, theoretical models suggest that the 

interaction between multiple lenders, such as in syndicates, can have complicated effects on individual 

lender incentives to mitigate adverse selection and moral hazard problems (Diamond 1984; Ramakrishnan

and Thakor 1984; Fama 1985). We document that the monitoring of our loans is dramatically different 

from the group of syndicated loans that they study. Whereas nearly all (96 percent) loans in our sample 

are monitored regularly (an average of 2.31 times per hundred days), they show that only 20 percent of 

syndicated loans are monitored at any point during their lifetime. The longitudinal nature of our panel 

data as well as higher intensity of monitoring in our data better allow us to study the intensive margin of 

monitoring, rather than the extensive margin. In addition to observing monitoring actions over the life of 

loans, we also, uniquely, see the information collected through monitoring and can link this to concrete 

bank decisions.

To the best of our knowledge, our paper is the first study to directly document the on-site monitoring 

process for any sample of non-syndicated loans to individual borrowers over the entire duration of the 

loans’ lifetimes, even in the absence of covenant violations. By focusing on the frequency and timing of 

bank monitoring, using the contents of the on-site inspection reports, and implementing an instrumental 

variables framework, we meaningfully contribute to the literature by directly speaking to the determinants 

of monitoring on the intensive margin and showing that banks use the information that they collect 

monitoring. Furthermore, to the best of our knowledge, we are the first paper to empirically show that 

bank monitoring has a causal effect on loan performance. Loans within our sample also do not have the 

type of financial covenants most commonly examined within the literature, since the underlying collateral 

does not generate income over the life of the construction loans. This allows us to look at the incentives 

of a single lender with limited recourse to monitor individual borrowers.

Our setting also has several parallels with the literature on staged financing by venture capital firms 

(VCs), and in particular allows us to shed light on a key question regarding the impact of VCs on firms. 
12 

Much like investing in a construction project, investing in a start-up business involves limited and 

asymmetric information, and this gives rise to similarities in the economic and contractual settings. Just 

as construction loans are disbursed in discrete stages corresponding to pre-agreed project milestones, VC 

funding is often disbursed in stages. VC funds also engage in similar forms of intensive monitoring 

including visiting the business site and evaluating the achievements of the ex-ante identified milestones. 

In both cases, these features serve to mitigate principal-agent problems such as moral hazard (Gompers, 

1995; Bergemann and Hege, 1998; Neher, 1999). A key question in the VC literature is what impact this 

VC involvement with a firm has on the firm’s success, and the consensus is that it improves outcomes 

(Sørensen, 2007; Puri and Zarutskie, 2012; Bernstein, Giroud, and Townsend; 2016; Ewens, Gorbenko, 

and Korteweg, 2022). Our results on the benefits of monitoring in construction lending are consistent 

with the positive impact of VC “monitoring,” even though our measure of monitoring is more narrow (or 

“pure”) than in the VC space, owing to banks’ (as debt-holders) comparatively passive involvement in 

loans (i.e., monitoring in our case does not include consulting for the builder or providing hands-on 

project management). Thus, our results further suggest that simply employing fixed-schedule staged 

financing with monitoring might improve firm outcomes by mitigating asymmetric information, even 

without the other consultative and managerial services provided by VC firms. 

Finally, we contribute to the sparse literature on construction lending. Some early industry-based 

case studies that predate the savings and loan crisis highlight the risks associated with residential and 

CRE lending and establish their importance to economic growth (Rogers, 1975; Tockarshewsky, 1977; 

Lusht and Leidenberger, 1979). Other studies document that lending in construction and real estate has 

been an underlying cause both of many historical financial crises within the U.S. and globally (Reinhart 

and Rogoff, 2011; Friend, Glenos, and Nichols, 2013; Fenn and Cole, 2008) and of the bank failures 

associated with these crises (Balla, Mazur, Prescott, and Walter, 2019). Using a proprietary database 

consisting of loan-level loss given default (LGD) for a sample of construction loans, Johnston Ross, 

Nichols, and Shibut (2021) examine the impact of certain loan-level, bank-level, or market-level factors, 

though they are unable to observe bank monitoring actions or speak to the probability of these loans 
13

defaulting. Furthermore, to the best of our knowledge, we are the first study that speaks to construction 

loan probability of default (PD).

b. Institutional Background and Motivation

Construction loans fund the creation of a building, and this building serves simultaneously as the 

loan’s collateral and the primary source of funds to repay the loan. Although it is possible for a builder to 

take on the role of an investor/borrower, the borrower typically hires a third-party builder to complete any 

necessary regulatory approvals and then perform the construction tasks.6

 Since collateral behind the loan 

is created over the course of the loan, it is common for lenders to require borrowers to provide personal 

guarantees for construction loans. These guarantees provide the lender recourse in the event of default if 

the value of the land or incomplete project pledged as collateral is insufficient for loan repayment. 

Analogous to a single-family mortgage loan, construction loans typically also require borrowers to 

contribute equity, such as a parcel of land or cash, at the time of the loan’s origination.

Construction loans have relatively short maturities that correspond to the phases of development. In 

the case of an office complex, the borrower may obtain a land development loan to build a parking lot and 

prepare the land for utility hookups. Subsequently, the borrower may obtain a construction loan to build 

the standing components of the complex. These loans are typically structured in tranches, where the next 

segment of the committed balance will only be disbursed to the borrower when certain completion 

thresholds are met. The construction loan documents provide the pre-determined disbursement schedule 

corresponding to the various construction phases. Since construction projects rarely generate cash flows 

to the borrower until construction is complete, the loans are structured such that the borrower does not 

make payments until maturity. At maturity, the borrower can repay the loan, typically with funds from 

selling the newly built property, or they can roll the loan into a permanent mortgage, often funded by 

income from leasing the property. The permanent loan might be issued by the same bank that funded the 

 6 When obtaining a construction loan, investors frequently form a project-specific Limited Liability Corporation 

(LLC) that acts as the official borrower and place the property into the LLC, though less-sophisticated individual 

homeowners may choose not to do so.
14

construction loan, in which case the terms of this permanent loan may have been arranged at the 

beginning of the project.

There are two categories of default associated with construction loans: maturity defaults and term 

defaults. Maturity defaults occur when the borrower is unable to pay off the construction loan in full at 

the end of the term; this typically occurs when borrowers are unable to sell collateral at an adequate price 

or obtain permanent financing. Lenders typically initiate term defaults during the middle of the term of 

the loan when they do not believe that the borrower will be able to make payments, possibly due to 

deteriorating market conditions, lack of project progression, or insufficient anticipated demand. In these 

scenarios, the lender may call the loan or refuse to allow future disbursements.

In comparison to other types of mortgages, monitoring for construction loans is considerably more 

labor intensive and costly. Acquiring loan-specific information requires specialized training and regular, 

on-site visits. Specialized third-party real estate experts conduct the on-site inspections contained within 

our sample, and bank management must interpret their complex reports. This monitoring can also be 

costly for borrowers. In addition to the time it takes to interact with inspectors, borrowers typically incur 

the monetary costs charged by the on-site inspectors.

Monitoring of construction loans is also very important. First, monitoring tempers moral hazard in a 

complex lending relationship with substantial scope for borrower and builder judgment (and incentive 

misalignment). For example, borrowers might steal funds or misallocate them to un-approved tasks or 

projects, or builders might substitute lower quality, cheaper materials to reduce their expenses. The scope 

for mismanagement is significant, especially as unforeseen construction issues, such as changes to timing, 

input prices, or market changes, arise. Banks must acquire detailed information about the regulatory 

approval process, the details of the construction itself, and local market conditions. The lender also 

determines any necessary adjustments to the loan. The job of a third-party on-site inspector is to assess 

the project to determine whether the project is running on time and within budget. The inspector’s 

assessment is a critical input to the bank’s decision whether to make the next disbursement of funds 

according to the pre-arranged draw schedule. The bank’s informed decisions regarding further loan 
15

disbursements incentivize borrowers to complete projects as promised lest they lose access to the rest of a 

credit line or future credit (see Stiglitz and Weiss, 1983, for further theory on this topic).

Second, monitoring limits the bank’s exposure to unprofitable projects. The collateral for the loan is 

created over the course of the loan and is often also the primary source of funds to repay the loan. As a 

result, the borrower’s failure to complete the project can be very costly to the bank. When the project is 

unlikely to reach satisfactory completion, learning this information through monitoring and initiating a 

term default promptly allows the bank to limit its losses. For a variety of reasons, incomplete 

construction projects are highly illiquid and typically sell at a substantial discount; advancing additional 

funding for such a project will increase losses to the lender. For example, transferring a complex 

construction project, including plans and permits, to a new owner and contractor is difficult, particularly 

when some elements of the project (e.g., concrete footers) are no longer visible and defaulting borrowers’ 

incentives are misaligned. Additionally, failing projects are subject to increased risks from moral hazard 

because borrowers may forgo investments that will primarily benefit debtholders (Myers, 1977). As a 

result, such projects likely suffer from additional “cut corners” like substandard workmanship or 

substitutions of lower quality materials. Finally, lender repossession and transfer of a failed project 

involves a period of time in which a construction site sits vacant. Even completed vacant structures 

degrade relatively quickly due to such forces as water infiltration, fire, theft, and vandalism.7 Incomplete 

structures degrade even faster, as they often lack elements of the outer envelope (e.g., windows, doors, or 

gutters) that protect against water and theft, finished fire protection systems, etc.

3. Data

This paper uses construction loan data from the servicing system of a large bank that failed during the 

financial crisis primarily due to the performance of its primary source of business: single-family home 

residential lending. Like many banks during the lead up to the great financial crisis, our sample bank 

 7 For example, see Knight, Meribah, and Bridget O’Shea (2011), “Foreclosures Leave Pockets of Neglect and 

Decay,” The New York Times, October 27.
16

made construction loans all over the country. Approximately 20 percent of sample loans are within 150 

miles of the nearest bank branch. Although lending across all business lines slowed close to failure, the 

bank continued to manage this construction portfolio until the end of its life. After the bank failed, it was 

placed into receivership, and the FDIC collected its data. The banking data were made available to the 

authors under certain provisions, such as keeping the identity of the bank and customers confidential. The 

data consist of approximately ten years of transaction-level data for all construction loans in one of the 

bank’s product lines that targeted mostly smaller builders and homeowners.

We build a daily loan-level dataset using events in the lending system, with multiple updates on some 

days and no updates on others. At origination, the bank collected the identity of the borrower and 

contractor, the loan terms, and the loan origination characteristics. Each day, we observe the state of each

loan as the bank would have recorded it. For variables that are not updated on a daily basis, we can 

calculate the loan’s balance and carry forward previous states and update them as information changes. 

For example, although a borrower’s credit score may vary over time, the bank only records the borrower’s 

credit score at the time of origination, since this is what the bank analyzed to originate the loan. A unique 

strength of analyzing a large construction portfolio over the course of ten years is that we can observe 

repeat interactions between the bank and borrowers. Furthermore, we can control for granular time

trends.

We define our variables of interest in Table 1 and present summary statistics in Table 2. The full 

dataset contains approximately 11.59 million loan-day observations for 28,939 loans for projects located 

all over the continental United Sates. At origination, the average loan has a principal balance 

(LOANAMT) of nearly $450,000 and a term to maturity (TERM) of 13.68 months, an interest rate that is 

3.69 percentage points over the effective federal funds rate at origination (ORIGSPREAD), and 

origination fees (ORIGFEES) of 0.2 percent of the original loan amount. The value of the loan(s) on the 

property to the value of the completed project (CLTV) averages 75.36 percent. Figures 2a, 2b, and 2c 

demonstrate the distribution of the project’s CLTV (in percent), original loan amount (in thousands), and 
17

loan term (in months), respectively. Over a quarter of our sample loans have loan terms that are exactly 

12 months.

The bank also collected information on borrowers. The average loan was given to a borrower with a 

712.5 credit score (FICO). We also define several indicator variables that describe the bank’s 

documentation for the borrower or project. This bank had a special program specifically marketed to 

borrowers who wanted to build their own homes but did not necessarily have construction experience, and 

46 percent of loans fell into this category (OWNERBUILDER =1). Some loans in the sample were given 

to repeat borrowers. Approximately 4 percent of loans in our sample are given to borrowers who had 

previously been granted a construction loan with the bank (REPEATBORROWER = 1).

Finally, the bank collected several items that are unique to construction projects, as opposed to 

completed commercial buildings or single-family homes. Residential construction loans can be made 

with prearranged permanent funding or on a speculative basis, where homes are built to be sold later in 

the general market. Due to their risky nature, banks typically set a predetermined limit on the number of 

unsold units to be financed at any time. This policy alleviates moral hazard problems of contractors 

potentially overextending their capacity. Speculative loans make up 9 percent of our sample 

(SPECULATING). The bank also required borrowers to record the number of budgeted items of a given 

project on their application, where more items represented more complex projects. The average project 

has 58.9 line items (BUDGETITEMS), and the sample standard deviation is 15.1 items.

a. Monitoring Measures

For our sample of construction loans, we observe the timing and frequency at which the bank 

conducts on-site inspections. Before the bank approves a draw request, the site is often inspected. A 

third party typically conducts these on the construction site, and the primary purpose of the inspection is 

to document the project’s progress. Inspectors check for accuracy of the draw request, assess the 

condition of the job site, and evaluate the project’s stage of completion. The inspector delivers a 

comprehensive report to the lender, sometimes including photographs of the property. While they assess 

the project, they do not make recommendations whether the bank should approve or deny the draw 
18

request. Nearly all sample loans have regular on-site inspections, and we construct several variables to 

measure their frequency. For each loan, we construct an indicator variable INSPECTIONDATE that 

takes a value of one on the days that on-site inspections occur. As shown by Table 2 Panel A, inspections 

occurred on approximately 2 percent of sample loan-days.

For each loan in the sample, we count the total number of inspections over the lifetime of the loan, 

ALLINSPECTIONS, and account for skewness by using the log transformation, 

LOG(ALLINSPECTIONS), for our primary analysis.8

 We also include a variable equal to the percentage 

of all days that the loan was active on which an inspection occurred, ALLTOTERMINAL, to capture the 

frequency of inspections. Finally, we calculate the time from origination until the bank’s first inspection 

on the loan (TIMETOFIRST). Since we calculate each of these variables once per loan, their summary 

statistics are shown in Table 2 Panel B. The average loan in our sample has 8.15 inspections, and the first 

inspection occurs 3.01 months after the origination date; on average, inspections occur on 2.31 percent of 

days on which a loan is active. In Figures 2d and 2e, we show the distributions of inspections and draws 

over the loans in our sample. Although the shape of the draw and inspection densities appear similar, as 

previously stated, not all draws are associated with on-site inspections, and there are fewer inspections 

than draws. In Figure 2f, we plot the density of the time until the first on-site inspection (in months). In 

Figures 3a and 3b, we show the average number of days between draws and inspections for the full 

sample of loans. Since these loans can vary significantly by term to maturity and approximately 25 

percent of sample loans have a term to maturity of 12 months, we show analogous figures for the subset 

of loans with 12-month terms to maturity in Figures 3c and 3d.

 8 A total of 1,109 sample loans have zero inspections, so LOG(ALLINSPECTIONS) is undefined for these loans. 

An additional 231 sample loans have dates that have unreliable inspection dates. All results are robust to dropping 

this subset of loans.
19

4. Empirical Determinants of Monitoring Actions

In our initial set of analyses, we examine what loan characteristics are associated with bank 

monitoring activity. First, we examine whether banks trade off monitoring intensity through on-site 

inspections with loan characteristics observable at origination within the following cross-sectional, loanlevel regression framework shown in Equation 1:

MMMMMMMMMMMMMMMMMMggll = γγγγ γγ ll + ββXXll + εεll (1)

The dependent variable represents the cross-sectional monitoring measures of interest, which include the 

logarithm of the total number of on-site inspections (LOG(ALLINSPECTIONS)), the percentage of days 

on which inspections occur (ALLTOTERMINAL; a measure of monitoring frequency), and the time 

between loan origination and the first inspection (TIMETOFIRST). If the private information collected 

through monitoring is more valuable to banks, we would expect both a higher number of inspections in 

total (LOG(INSPECTIONS)) and a higher frequency of inspections (ALLTOTERMINAL). Under the 

same motives, banks may also choose to collect this valuable information sooner, as indicated by lower 

values of TIMETOFIRST. To isolate the conditional correlation between monitoring with on-site 

inspections and origination characteristics, we include a variety of fixed effects, XXll, to control for 

unobservable time and macroeconomic conditions at the time of origination. We control for 

intertemporal variation in monitoring incentives by including fixed effects for each loan origination day, 

along with the three-digit zip code9 associated with the borrower and the three-digit zip code associated 

with the property addresses to control for localized economic conditions.10 We report the results of these 

regressions in Tables 3 and 4.

Second, we exploit the time series dimension of our data to assess the effect of time-varying 

covariates on two types of monitoring-related actions. For each draw request, we create an indicator for

 9 Three-digit zip codes are geographic areas defined by the first three digits of postal codes (i.e., the union of all zip 

codes sharing the same first three digits). As such, they are larger areas than those defined by five-digit zip codes. 10 We are unable to include loan fixed effects in this cross-sectional analysis because loan origination characteristics 

are fixed at the loan-level and would be subsumed by loan fixed effects. In unreported robustness, we replace the 

property zip code, borrower zip code, and day fixed effects with a quarter-property zip code fixed effect, and the 

results are generally unchanged.
20

whether the bank denied the request (DRAWDENIED) rather than approved it; for each loan-day, we 

create a dummy equal to one if the bank inspected the property that day (INSPECTIONDATE). 

Increased draw denial rates are indicative of heightened bank concern with project risks, likely driven by 

information obtained through monitoring with on-site inspections. Increases in inspection intensity are 

also indicative of heightened bank concern and show that the bank is undertaking costly actions to obtain 

more project-specific information.

We conduct a set of two panel regressions, one for outcome each variable, to examine the effect of 

time-varying covariates including measures of local economic conditions and the health of the bank. The 

first panel regression, with observations corresponding to borrower draw requests, is shown in Equation 

2:

DD DDDDDD DDDDdddd = γγCC CC dddd + ζ ZZll + εεdddd (2)

Within Equation 2, DD DDDDDD DDDDdddd is an indicator variable that takes a value of one if a draw request d 

made at time t for loan l is denied, and CC CC dddd represents the time-varying covariates of interest at 

the time of the draw request. Across specifications, the covariates consist of measures of house prices 

and foreclosure rates in the area around the construction project as well as measures of the bank’s time to 

failure. Because each loan has multiple draw requests, we are also able to include loan-level fixed effects 

(ZZll) that absorb all time-invariant loan-level unobservable and observable characteristics, including those 

analyzed in Tables 3 and 4. The presence of the loan fixed effects allows us essentially to compare the 

loan to itself during periods of relative economic growth or contraction.

The second panel regression corresponds to the second outcome variable (INSPECTIONDATE) and 

takes observations at the level of loan-days, as shown in Equation 3:

II IIII II ll = γγ γγ γγ ll + ζ ZZll + εεll (3)

Otherwise, Equation 3 is analogous to the Equation 2, above. We report the results of the panel 

regressions in Tables 5 and 6.
21

a. Loan Characteristics at Origination

The first four rows of Table 3 indicate significant associations between cross-sectional measures of 

monitoring intensity and the four loan characteristics at loan origination: loan amount, term, spread, and 

fees. As seen in Table 3, Columns 1 and 2, there is a positive coefficient on LOG(LOANAMT), 

indicating that larger loans are monitored more overall and with higher frequency. The first two columns 

also indicate that lower interest rate spreads and lower fees at origination are significantly associated with 

more monitoring.

We also find that longer term loans are subject to less intense monitoring. The coefficient on TERM 

in Column 1 of Table 3 is positive, but this likely reflects a mechanical result—loans with longer terms 

mechanically have a longer time period over which inspections can be conducted. In Column 2 of Table 

3, however, the dependent variable is the number of inspections normalized by the number of days the 

loan is open. The coefficient on TERM is negative, which indicates that longer term loans are subject to 

less frequent inspections. These results are consistent with theoretical models showing that banks derive 

more value from monitoring shorter term loans (Rajan and Winton, 1995; Barclay and Smith, 1995; and 

Park, 2000).

In Table 3, Column 3, we examine the amount of time until the first inspection. If banks find the 

information gained from monitoring more valuable, they may be more likely to initiate the first on-site 

inspection sooner. For example, in addition to monitoring a risky project more frequently, the bank may 

initiate site inspections sooner. If this conjecture is true, we may expect the signs on the loan origination 

coefficients in Column 3 to be opposite those in Columns 1 and 2. Although the average loan term in our 

sample is 13.68 months, the first site inspection occurs 3.01 months after origination on average. 

Consistent with this conjecture, we find that most types of loan origination characteristics that are 

associated with more (less) frequent inspections are associated with faster (slower) initial inspections, 

including loans with longer terms to maturity. The results in Table 3 suggest that banks trade off many 

types of loan terms with monitoring intensity, including the speed of on-site inspection initiations.
22

b. Borrower and Project Risk

When underwriting a loan, banks use all of the information available to them to estimate the risk 

associated with the borrower and project. Although hard measures of borrower quality such as FICO 

score may inform the bank about the credit risk of the borrower at the underwriting stage, the bank also 

documented a number of soft measures of borrower and project risk. If a bank suspects that a borrower 

will be unable to pay its debt, it may monitor more intensely in attempt to prevent the loan from 

defaulting, consistent with Pennacchi (1988). In Table 3, we examine the two primary hard underwriting 

criteria designed to capture borrower and project risk, borrower FICO scores and loan CLTV.

Within the panel setting displayed in Columns 1 and 2, we first show that borrowers and projects with 

higher risk characteristics (lower FICO scores and higher CLTV ratings) experience more on-site 

inspections. For FICO scores, the results of Column 3 are also consistent with our expectation: riskier 

loans receive their first inspection sooner.

The bank categorized a subset of loans (9 percent of sample loans) as “speculative,” indicating that 

the homes were built to be sold later in the general market, as opposed to being pre-sold to a specific 

buyer. As discussed in Section 3, banks often cap the number of speculative construction loans that they 

originate because of their risky nature. Furthermore, the bank gave 46 percent of loans to borrowers 

taking part in the special program marketed to owner-builders who may not have much construction 

experience, which may be another indication of a high-risk loan. We also examine whether banks 

monitored more complicated projects, as proxied by the number of budget items, more intensely. In 

Table 3, we find that all three measures of potential risk, as indicated by these soft information measures, 

correlate with more intense monitoring under all three intensity measures. Speculative loans 

(SPECULATING) are first inspected sooner than other loans, by an amount equal to 28.10 percent 

(=0.846/3.01) of the full sample average time to first inspection, and borrowers who participated in the 

special program to build their own home (OWNERBUILDER) received their first on-site inspection 9.5 

percent (=0.286/3.01) sooner than the full sample average. Furthermore, more complicated projects, as 

indicated by more budget items, are inspected more frequently. These results show that the bank 
23 

monitored riskier loans more, potentially because the information it was extracting on these loans was 

valuable and the bank was attempting to prevent loan default.

c. Borrower Relationships 

In our sample, we see a significant number of borrowers conducting repeat business with the bank. 

We mark a repeat borrower at the time of the origination of their second loan. Existing theoretical models 

suggest that the information frictions caused by adverse selection and moral hazard can be mitigated in 

the presence of a single private lender (Diamond 1984; Ramakrishnan and Thakor 1984; Fama 1985). 

Boot (2000) shows that these mitigation benefits can be magnified if the information garnered by banks 

over multiple interactions is costly to produce, proprietary to the lender, and reusable. If repeated 

interactions through relationships provide the bank with additional reusable information across loans, 

such as borrower payment history, this could lead to a decrease in monitoring. However, to the extent 

that the bank has additional exposure to the borrower, the bank may choose to monitor these loans more 

frequently to manage this concentration risk. We examine the association between borrower relationships 

and bank monitoring with on-site inspections in Table 4, where we continue to control for all loan 

characteristics from Table 3.

Table 4, Columns 1 and 2 indicate that loans with repeat borrowers have fewer and less frequent 

inspections. Column 3 shows that banks initiate inspections approximately one week later for repeat 

borrowers (0.294 months). Together, results suggest a novel dimension surrounding the benefits of 

borrower relationships: reduced monitoring.

d. Local Economic Conditions and Collateral Value 

Now, we turn to time-varying covariates of bank monitoring actions. In this section, we examine 

whether local economic conditions affect bank monitoring decisions, and we examine the effects of bank 

health in the next section. 

Over the course of the project, local economic conditions may change, affecting both the value of the 

underlying collateral as well as the probability the borrower will default. For example, borrowers may be 

less likely to walk away from a construction project in an area that is experiencing high levels of price 
24 

appreciation since the completed project may be more valuable. However, decreasing housing prices and 

high foreclosure rates may depress the value of the underlying collateral or may make it more likely for 

borrowers to strategically default on the loan.

We construct two measures of local real estate conditions using data from the Federal Housing 

Finance Agency (FHFA) and Corelogic. Our first variable, HOUSING PRICE INDEX from FHFA, 

calculates the annualized growth rate in prices within the five-digit zip code corresponding to the project 

address. The variable FORECLOSURE RATE from Corelogic measures the foreclosure rate of singlefamily residences in the zip code, updated monthly. The average loan is in a zip code experiencing an 

annualized increase in housing prices of 8.98 percent and a foreclosure rate of 0.60 percent. We then run 

panel regressions corresponding to Equations 2 and 3, above, to investigate the impact of these timevarying real estate conditions on draw denials and inspections (see Table 5).

11

In Table 5, we show that increases in housing prices are associated with a decreased likelihood both 

of a draw request being denied and of the bank conducting an on-site inspection. When foreclosure rates 

increase, draw requests are more likely to be denied, though there is no statistically significant effect on 

the probability of an on-site inspection. Rajan and Winton (1995) show that collateral can be an 

important determinant of monitoring. To the extent that improvements in local economic conditions 

negatively correlate with the risk of the underlying collateral, this finding is consistent with the theoretical 

findings in Rajan and Winton (1995).

e. Bank Condition and Distress 

Since we have daily loan-level data spanning approximately ten years (i.e., well before bank distress 

and failure), we can examine whether the bank changed its monitoring efforts as it approached failure.12 

We define two indicator variables that take a value of one for time periods when the bank is approaching 

 

12 According to the FDIC Improvement Act (FDICIA), generally, a receiver or conservator must be appointed or 

other appropriate action taken within 90 days of a bank becoming critically undercapitalized. For more information 

on the resolution process, see the FDIC's Resolution Handbook 

(https://ypfsresourcelibrary.blob.core.windows.net/fcic/YPFS/resolutions-handbook.pdf).
25

failure. The first indicator variable, YEARBEFOREFAILURE, is an indicator variable that takes a value 

of one on loan-days within 365 days of the bank’s failure, and STARTOFYEARBEFOREFAILURE is an 

indicator variable equal to one if the loan-day is within the same calendar year of the bank’s failure. We 

test whether the bank’s monitoring efforts changed just prior to failure compared to earlier periods using 

the panel regression frameworks in Equations 2 and 3 and present the results in Table 6. As previously 

noted, the presence of the loan fixed effects allows us essentially to compare the loan to itself during 

periods of relative bank health and bank distress. The results in Table 6 indicate that just prior to failure, 

the bank is more likely to conduct inspections and more likely to deny draw requests, indicating 

heightened bank caution close to failure. This result is consistent with the bank actively attempting to 

avoid failure and is inconsistent with gambling for resurrection.

5. Bank Monitoring and Decision-Making

Thus far, our analysis has largely focused on determinants of on-site inspection activity. We now 

focus on how banks use the information contained within these on-site inspection reports. As discussed 

in Section 2b, draw schedules are determined at the time of the loan origination. After assessing whether 

borrowers have made adequate project progress, banks can approve or deny draw requests. Although 

draw requests are often associated with on-site inspections, the inspections do not occur for each draw 

request.

Using the text from the on-site inspection reports, we follow the textual analysis procedure outlined in 

Loughran and McDonald (2011) to calculate the number of words in these reports that are positive 

(POSITIVEWORDS) and negative (NEGATIVEWORDS), divided by total comment length in 

characters. We also include the comment length as measured by the number of characters contained in 

each report. We then match these on-site inspection reports to their associated draw requests and 

investigate the impact of the text sentiment in inspection reports on draw denial rates using a panel 

framework analogous to Equation 2, above. We report the results of this analysis in Table 7. We can 
26

match 143,074 inspection reports to draw requests, and 13 percent of the matched draws are denied.13 

Across columns, we vary the use of fixed effects and other loan level controls, variously including time 

and loan fixed effects and time-invariant loan characteristics from Tables 3 and 4. Notably, we also 

implement inspector-level fixed effects in place of loan-level fixed effects in Column 3 and combine 

inspector fixed effects with the Table 3 and 4 characteristics in Column 5.14 To the extent that inspectors 

use a time-invariant standard inspection template, the inspector fixed effect will difference out any 

standard language.

Regardless of the fixed effects specification shown in Table 7, the results tell a consistent story. 

Reports with larger proportions of negative words are associated with a greater likelihood of draw denials, 

and reports with a larger proportion of positive words are less likely to have draw requests denied. The 

total length of comments generally has less effect, though some specifications indicate longer comments 

are associated with more denials. These results suggest that banks are actively using the information 

contained within these reports to determine whether to approve draw requests.

6. Monitoring and Loan Outcomes

a. Determinants of Loan Default

In this section, we explore the determinants of construction loan default before we attempt to quantify 

any additional effect monitoring with on-site inspections has on loan outcomes. We are the first paper in 

the literature to conduct such an analysis, primarily due to the lack of data available to other researchers. 

Using a cross-sectional regression framework, we explore how the loan origination characteristics, 

borrower and project risk, borrower relationships, and borrower actions affect loan default using the 

regression framework shown in Equation 4:

EEEEEEEEEEEEEEEE EEEEEEEEEEEEll = γγ1 VVVV ll + ββXXll + εεll (4)

 13 The analysis in this section links on-site inspection reports to the corresponding subsample of 143,073 draw 

requests. The analysis presented earlier in Sections 4d and 4e utilizes the full sample of 355,890 draw requests, not 

all of which are associated with on-site inspections or can be directly linked to inspector comments. 14 Inspectors are fixed at the loan level.
27

We present regression results in Table 8, where the outcome variable of interest, 

EVENTUALDEFAULT, is an indicator variable taking a value of one for loans that end in default. The 

results presented in Table 8 indicate that loans with longer maturities and greater origination fees are 

more likely to default, though larger loans are less likely to default. Loans to lower quality borrowers, 

such as borrowers with lower credit scores, are more likely to default. Riskier projects, including those 

with higher CLTV ratios and those categorized as speculative are more likely to default, though the 

coefficient on SPECULATING is not statistically significant. Projects with more budget items are more 

likely to default. Loans made to owners building their own homes are less likely to default, potentially 

because owners have the intent to occupy their dream home.

b. Marginal Effect of Monitoring

Since monitoring can be costly for the bank, it will only conduct an on-site inspection when it 

believes that the benefits of the information acquired through this inspection are greater than the incurred 

costs. One potential reason monitoring may benefit banks is improving loan outcomes, such as lowering 

the probability of default (Pennacchi, 1988). Therefore, the marginal effect of additional inspections on 

default risk should be negative. However, the bank may choose to monitor loans where the marginal 

benefit of an inspection reducing the default risk is greatest, which is likely for the loans with the highest 

risk; this would tend to induce a positive correlation between inspections and default risk. This is similar 

to the problem of identifying the effect of police on crime rates in Levitt (1997), where it is common to 

have high crime cities with large police forces. Further, more inspections may result in detection of more 

problems that would lead to default, much like increased policing may observe more crime that would 

otherwise go unreported. Therefore, our analysis suffers from a classic endogeneity problem, where 

banks may conduct property inspections of the loans that are most likely to default, and directly 

examining the relationship between inspections and default probability will produce biased estimates. If 

this simultaneity problem is severe enough, it may even result in a positive relationship between 

inspections and default.
28

To resolve this problem in an instrumental variable setting, we require an instrument to be correlated 

with the number of inspections performed, yet it cannot be otherwise correlated with the default 

probability conditional on our set of controls. Because borrower draw requests prompt the bank to initiate 

inspections before disbursing funds, draw attempts will be correlated with inspection activity. We 

observe the individual line drawdown requests that prompt inspections from the bank records.

At the same time, the number of draws themselves should not be directly correlated with loan default 

conditional on all other observables because they are set ahead of time, corresponding to constructionrelated project milestones that are costly to divide ex-ante,

15 and they cannot be ex post manipulated 

easily by the bank or borrower.

16 To prevent borrowers from drawing down all available loan funds at 

once, potentially failing to produce the collateral that the project intended to create, draw schedules for 

the loans are set at the time of the loan origination. Banks collect information during the underwriting 

process to determine the loan’s draw schedule conditional on the construction project and loan 

characteristics. Since the draw schedule is determined at the loan’s origination, the best measures that the 

bank has for the project’s risk is based on the information collected during the underwriting process. 

Within our regression framework, we control for available information garnered during the initial 

underwriting process, accounting for measures of initial credit quality that might affect the scheduled 

number of draws. Because individual draw requests require time-consuming recordkeeping and oversight 

(such as phone calls), even if they do not involve on-site inspections, they are costly to the bank and to the 

borrower, and the bank cannot simply set an infinite or arbitrarily large number of draws, or change them 

without costs.

We use a measure of draw attempts, DRAWTOTERMINAL, defined as the percentage of all days 

that the loan is active on which a draw attempt occurs. This will operate as an exogenous shifter on 

 15 Dividing individual construction tasks across multiple loan draws to allow more bank inspections introduces 

several frictions, such as the need to call specialists like plumbers and electricians to the build site multiple times, 

raising the cost of the job. Additionally, some construction tasks may be difficult to meaningfully inspect on a 

partial basis, such as pouring a foundation. 16 Changing the terms of a loan, such as the draw schedule, may alienate bank clients or come with other 

reputational costs which banks seek to avoid (see Chemmanur and Fulghieri, 1994).
29

inspection intensity, with loans with more draws tending to have more inspections. The exclusion 

restriction is satisfied if the draw attempt frequency does not affect the default rate other than through this 

inspection channel, conditional on the other controls. Since we control for the broad range of project, 

borrower, and loan characteristics available to the bank in making the loan and setting its terms, including 

the draw schedule, the instrument DRAWTOTERMINAL is conditionally exogenous to the risk profile or 

default rate of the loan. Table 2 shows that when measured on the last day of the loan, on average, 2.31 

percent of the days the loan was open had an inspection (ALLTOTERMINAL) and 3.64 percent of the 

days had a draw attempt (DRAWTOTERMINAL).

We present our analysis in Table 9. Table 9, Column 1 shows the marginal effect of more inspections 

(ALLTOTERMINAL) on default (EVENTUALDEFAULT) under the IV framework. A one percentage 

point increase in ALLTOTERMINAL, which can be interpreted as an increase from, say, two to three 

inspections in a 100-day period, would lower the probability of default by 3.64 percentage points. As the 

default probability is approximately 5 percent for these loans, this is a meaningful improvement in default 

probability.

Table 9, Column 2 shows the result of the first stage regression, regressing the inspection measure on 

the draw measure. The first stage has an F statistic of 11, above the rule of thumb of 10 for a weak 

instrument, and a positive direction as predicted by the relationship that more draws should lead to more 

inspections. Column 3 shows the same specification as Column 1 within an OLS framework without 

correcting for the endogeneity problem. Notably, the economic magnitude of the IV analysis is 

approximately twice the magnitude of the OLS coefficient (Columns 1 and 3), and these differences are 

statistically significant at the 1 percent level. This result is consistent with our expectation that OLS will 

underestimate the magnitude of inspections’ causal effect as a result of the bank focusing monitoring 

activities on high risk loans where the marginal benefit of inspections may be highest. Within the IV 

(OLS) framework, a one standard deviation increase in inspections (ALLTOTERMINAL) is associated 
30

with a decreased probability of default of 5.61 percentage points (2.85 percentage points).17 Analogously, 

in the IV framework, a one standard deviation increase in inspections results in a decrease in default risk 

equal to about one quarter of the standard deviation of the default rate (.22, or 22 percent, being the 

standard deviation of EVENTUALDEFAULT from Table 2).

A potential concern with this framework is that the exclusion restriction would be violated if riskier 

loans are assigned schedules with more draws. While we empirically control for all measures of risk that 

the bank recorded in their lending system, and projects lack infinitely divisible milestones that allow free 

assignment or reassignment of draws, this concern could still be valid if, at the time of setting the draw 

schedule, bank management possessed information about the riskiness of the loan that is not available to 

the econometrician. In this case, management might be expected to schedule more draws for riskier loans 

to give the bank more control. To the extent that managers’ ex-ante information was accurate, and the 

loans did default more ex-post, this might be expected to generate a positive correlation between draws 

and defaults even conditional on our controls. Therefore, it is notable that Table 9, Column 4 indicates

that the reduced form relationship between the number of draws and default is negative, indicating loans 

with more draws are actually less likely to default conditional on our controls, which provides evidence 

against this conjecture. Further, this would mean our estimate of the causal effect of inspections on 

default is a lower bound. 

7. Additional Analysis

a. Expected Collateral Value Upon Completion

In this section, we examine how expected collateral value at completion affects bank monitoring with 

on-site inspections. For single-family homes and other completed structures, a creditor repossesses the 

collateral of the underlying home and sells it to collect payment if the borrower defaults. If value of the 

 17 The impact in the IV framework of a 5.61 percentage point decline is obtained by multiplying the standard 

deviation of ALLTOTERMINAL (1.54, see Table 2) by the corresponding coefficient in Table 7, Column 1 (-

0.0364). Similarly, the OLS effect of 2.85 is obtained by multiplying 1.54 with the coefficient from Table 7,

Column 3 (-0.0185).
31

underlying collateral exceeds the debt that the borrower owes the bank, upon liquidation, the bank is paid 

in full. However, when faced with a default on a construction project, a bank faces significantly more 

frictions when trying to liquidate the associated assets, since the bank may only be capable of selling the 

incompletely assembled hard materials. Conversations with industry professionals specializing in 

construction loans indicate that unfinished projects are typically sold at a substantial discount.

Although it is difficult to quantify the time-varying value of collateral associated with unfinished 

construction projects, the bank recorded the anticipated collateral at the completion of the loan. At the 

time of the loan origination, the borrower and bank work together with an appraiser to produce an 

estimate of the value of the completed project relative to the loan amount. When pursuing a construction 

project, both the bank and borrower hope that the project creates value above the value of the loan. At 

origination, the bank records the anticipated value of this project. This variable is different from the 

CLTV of the project, which is the value of the loan divided by the expected value of the finished project, 

incorporating both expectations of appreciation and borrower contribution. The loans in our sample have 

an average anticipated value (VALUEADDRATIO) of 110.9 and a standard deviation of 18.8, indicating 

that, on average, the bank anticipated that the completed construction project would be worth 

approximately 110.9 percent of the loan plus posted borrower equity.

The empirical predictions associated with this measure are ambiguous. On one hand, if the value of 

the finished product associated with a loan is expected to be relatively high, the bank may be less likely to 

monitor the loan because it is less likely to worry about repayment, which is consistent with the empirical 

results presented in Cerqueiro, Ongena, and Roszbach (2016). On the other hand, a high valuation may 

be indicative of naïve optimism or a hot speculative market.

In Table 10, we show that the coefficient on VALUEADDRATIO is positive and significant in 

Columns 1 and 2, indicating that projects with greater promise experience more and more frequent on-site 

inspections, pointing more towards the hot market and speculation motive. In contrast to our expectations 

discussed with respect to Table 3, a one standard deviation increase in VALUEADDRATIO is also 

associated with a greater time period until first inspection (Column 3). However, the effect is 
32

economically rather small: a one standard deviation increase in VALUEADDRATIO increases 

TIMETOFIRST by about 3 days (0.00563*18.8=0.106 months).

b. Over-Budget Projects and Borrower Capital Injections

The bank also recorded a measure of “hard costs” at the termination date of each loan. In the process 

of completing a project, a borrower deploys some capital to pay contractor wages, which have no 

liquidation value, or hard costs that have some liquidation values, such as construction materials and 

fixtures. Hard costs can easily exceed the loan amount if the project goes over budget. The borrower 

may contribute additional personal funds or attempt to secure outside funding. We define a dummy 

variable that takes a value of one if at the termination date, hard costs exceed the committed amount 

(BORROWERINPUT), which occurs for 7 percent of the sample loans. A second continuous variable 

quantifies the amount of hard costs that exceeds the committed loan amount (BORROWERAMT). To a 

certain extent, both BORROWERINPUT and BORROWERAMT represent borrower “skin in the game,” 

since they are deploying additional personal funds into the project. The borrower associated with the 

average sample loan had hard costs that exceeded the loan amount by 24.21 percent (BORROWERAMT), 

and the standard deviation was 139.2 percent, indicating that a number of projects significantly exceeded 

costs.

The analysis in Table 10 indicates that both measures of borrower “skin in the game” are negatively 

correlated with on-site inspections. One possible interpretation of this is that banks monitor less when 

borrowers use additional personal capital (or outside funding) to fund the project because they are more 

personally invested in the project, potentially reflecting more closely aligned incentives between the bank 

and borrower. Alternatively, because these cross-sectional regressions only measure conditional 

correlations, it is possible that the bank’s lax monitoring caused the project to go over budget.

8. Conclusion

A seminal body of theoretical literature asserts that a key advantage banks have over non-banks is 

their ability to monitor borrowers (Diamond, 1984; Fama, 1985; James, 1987; Diamond and Rajan, 2001; 
33

Kashyap, Rajan, and Stein, 2002). A growing body of theoretical models builds upon these early studies 

by showing the conditions under which banks have incentives to monitor and whether monitoring 

improves loan performance. However, primarily due to lack of data available to researchers, empirical 

scholars have struggled to test these theories.

Our proprietary, granular transaction-level data allows us to fill an important gap in the literature by 

testing well established theories surrounding the determinants of monitoring, understand how banks use 

the information garnered when monitoring, and understand the influence monitoring has on loan 

performance. We use a novel measure of monitoring: on-site inspections. We observe this measure over 

the entire term of the loan. To the best of our knowledge, we are the first study to test these theoretical 

predictions regarding monitoring frequency within a set of loans that are non-syndicated and made to 

individual borrowers.

Using our measure of on-site inspections, we show that lenders are likely to trade off monitoring with 

more favorable loan terms. Riskier borrowers and projects, as indicated by both harder and softer 

information measures, have inspections that are more frequent and initiated sooner. We also show that 

bank lending relationships have a negative association with on-site inspections, potentially due to banks 

transferring information between projects. Using textual analysis, we show that banks use the information 

that they acquire from monitoring with on-site inspections in real time. On-site inspection reports with a 

more negative (or less positive) text sentiment are associated with a greater likelihood of borrowers being 

denied draws.

In subsequent analysis, we provide a comprehensive assessment of the determinants of construction 

default, the first study of its kind, as a preamble to analyzing the incremental effect of monitoring. After 

implementing an instrumental variable framework and controlling for relevant determinants, we find that 

loans with more on-site inspections are less likely to default. This result suggests that, in line with 

theoretical predictions, monitoring ultimately improves loan outcomes and adds value to banks.
34 

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40 

Tables and Figures

Figure 1: Bank Loan Portfolios over Time by Bank Size 

 

a. Banks with Assets less than $10 billion b. Banks with Assets between $10 and $50 billion 

 

c. Banks with Assets between $50 and $100 billion d. Banks with Assets greater than $100 billion 
41 

Figure 2: Loan Characteristics

 

a. Combined Loan to Value of Projects (%) b. Original Loan Amount of Projects (Thousands) c. Loan Term (Months)

 

d. Lifetime Inspections of Projects (Count) e. Lifetime Project Draws (Count) f. Time to First Inspection (Months) 
42

Figure 3: Average Times to Draws and Inspections 

 

a. Days Between Draws (All loans) b. Days between Inspections (All loans) 

 

c. Days Between Draws (12 month loans) d. Days between Inspections (12 month loans) 
43

Table 1: Variable Definitions

Variable Definition Source

ALLDRAWS is the total amount of draws over the lifetime of the loan. FDIC

ALLINSPECTIONS is equal to the number of inspections performed on the loan 

over its lifetime.

FDIC

ALLTOTERMINAL is equal to the percentage of all days that the loan is active on 

which an inspection occurs, capturing the frequency of 

inspections.

FDIC

BORROWERAMT is the proportion of expenses in excess of the disbursed 

amount, with a project 100% over budget having a value of 

100. Projects remaining under budget have a value of zero.

FDIC

BORROWERINPUT is an indicator variable equal to one if the expenses on the 

project exceeded the disbursed loan amount, thus requiring the 

borrower to add money, and zero otherwise.

FDIC

BORROWERZIP is the first three digits of the borrower's zip code. FDIC

BUDGETITEM is equal to the count of line items enumerated in the plan for 

the structure.

FDIC

CLTV is the value of the loan divided by the expected value of the 

project expressed as a percent (e.g., 100% CLTV is 100).

FDIC

COMMENTLENGTH is the length of an inspector’s comment in characters. FDIC

DRAWDENIED is an indicator variable equal to one if the draw that day is 

denied and zero otherwise.

FDIC

DRAWSAPPROVEDTODATE is the total number of draws that are approved over the course 

of the loan.

FDIC

DRAWSDENIEDTODATE is the total number of draws that are denied over the course of 

the loan.

FDIC

DRAWTOTERMINAL is equal to the percentage of all days that the loan is active on 

which a draw request occurs.

FDIC

EVENTUALDEFAULT is an indicator variable that takes a value of one if the loan 

defaults and is 60 or more days past due.

FDIC

FICO is the FICO score of the individual borrower from 300 to 850. FDIC

FORECLOSURE RATE is the monthly foreclosure rate for all housing. Original data is 

at the five-digit zip code level, updated monthly.

Corelogic
44

HARDCOSTS is equal to the total hard costs spent on the project divided by 

the total loan amount disbursed by the end of the bank’s record 

of the loan. Thus, 100 would indicate total hard costs are 

equal to the amount of the loan.

FDIC

HOUSING PRICE INDEX is equal to the change in housing prices at an annualized rate, 

for all housing. Original data is at the five-digit zip code level, 

updated annually.

FHFA

INSPECTIONDATE is an indicator variable that takes a value of one if an 

inspection occurred that day and zero otherwise.

FDIC

LOANAMT is equal to the original loan commitment amount. FDIC

LOG(ALLINSPECTIONS) is equal to the natural log of the number of inspections 

performed on the loan over its lifetime. This variable is not 

calculated for loans with zero inspections, where the logarithm 

would be undefined.

FDIC

LOG(LOANAMT) is equal to the natural log of the original loan commitment 

amount plus one dollar.

FDIC

NEGATIVEWORDS is equal to the number of negative words, as defined by the 

Loughran-McDonald word sentiment dictionary, in a comment 

divided by its length in characters time 100. Thus, a value of

one corresponds to a single negative word in 100 characters.

FDIC

NOTEDATE is the year the loan note was opened or originated. FDIC

ORIGFEES is the total fees paid over the first 30 days of the project 

divided by the original line commitment.

FDIC

ORIGSPREAD is the interest rate spread at time of origination of the loan over 

the federal funds rate.

FDIC

OWNERBUILDER is an indicator variable that takes a value of one if this is a loan 

taken by an individual looking to construct a home for them to 

own and live in, and zero otherwise.

FDIC

POSITIVEWORDS is equal to the number of positive words, as defined by the 

Loughran-McDonald word sentiment dictionary, in a comment 

divided by its length in characters times 100. Thus, a value of 

one corresponds to a single positive word in 100 characters.

FDIC

PROPERTY ZIP is the first three digits of the property's zip code. FDIC

REPEATBORROWER is an indicator variable that takes a value of one if the borrower 

has had a loan with the bank before.

FDIC
45

SPECULATING is an indicator variable that takes a value of one if the bank 

indicated the loan was speculative, meaning that the loan was 

built without a buyer committed to purchase the property upon 

completion.

FDIC

STARTOFYEARBEFOREFAILURE is an indicator variable equal to one if the loan-day is within 

the same calendar year of the bank’s failure.

FDIC

TERM is equal to the contractual term of the loan in months. FDIC

TIMETOFIRST is the number of months passing between the opening of the 

loan and the first inspection. This variable is not calculated for 

loans which were never inspected and loans where the first 

inspection is recorded before the loan’s origination date.

FDIC

VALUEADDRATIO is the expected value of the finished project divided by the sum 

of the borrower equity pledged and loan amount.

FDIC

YEAR is the year of the loan-day being observed. FDIC

YEARBEFOREFAILURE Is an indicator variable equal to one if the loan-day is one year 

or less before the bank’s failure and zero otherwise.

FDIC
46

Table 2: Summary Statistics 

(1)

Variable

(2)

N

(3)

Mean

(4)

SD

Panel A: Loan-Day Variables

INSPECTIONDATE 11,585,108 0.02 0.14

COMMENTLENGTH 143,074 177.3 196.5

POSITIVEWORDS 143,074 1.40 1.47

NEGATIVEWORDS 143,074 2.33 2.11

DRAWDENIED 355,890 0.12 0.32

HOUSING PRICE INDEX 10,805,736 8.98 11.60

FORECLOSURE RATE 11,537,601 0.60 1.04

YEARBEFOREFAILURE 11,585,108 0.18 0.28

STARTOFYEARBEFOREFAILURE 11,585,108 0.08 0.39

Panel B: Loan-Level Variables

ALLINSPECTIONS 28,939 8.15 5.17

Log(ALLINSPECTIONS) 27,803 1.97 0.62

ALLTOTERMINAL 28,939 2.31 1.54

TIMETOFIRST 27,567 3.01 2.93

EVENTUALDEFAULT 28,939 0.05 0.22

LOANAMT 28,939 448,303 416,068

LOG(LOANAMT) 28,939 12.76 0.70

ORIGSPREAD 28,939 3.69 1.12

TERM 28,939 13.68 5.62

ORIGFEES 28,939 0.20 0.66

FICO 28,939 712.50 48.62

CLTV 28,939 75.36 13.09

SPECULATING 28,939 0.09 0.28

OWNERBUILDER 28,939 0.46 0.498

BUDGETITEM 28,939 58.90 15.10

VALUEADDRATIO 28,939 110.90 18.80

BORROWERINPUT 28,939 0.07 0.25

BORROWERAMT 28,939 24.21 139.20

HARDCOSTS 28,939 91.55 146.30

REPEATBORROWER 28,939 0.04 0.20

DRAWSDENIEDTODATE 28,939 1.50 1,97

DRAWSAPPROVEDTODATE 28,939 11.03 7.33

ALLDRAWS 28,939 12.52 8.33

DRAWTOTERMINAL 28,939 3.64 2.41
47

Table 3: Inspections with Loan Origination Characteristics

This table presents the ordinary least squares results where the dependent variables of interest are defined 

as follows: LOG(ALLINSPECTIONS) is the logarithm of the total number of inspections over the course 

of the loan, ALLTOTERMINAL is defined as the ratio of total inspections to the number of days between 

loan origination and the loan’s terminal state, and TIMETOFIRST is the number of months until the 

loan’s first on-site inspection date. Table 1 provides further details on variable construction. T-statistics 

are presented in parentheses and significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3)

LOG(ALLINSPECTIONS) ALLTOTERMINAL TIMETOFIRST

LOG(LOANAMT) 0.132*** 0.242*** -0.508***

(18.68) (14.92) (-15.71)

ORIGSPREAD -0.0126*** -0.0413*** 0.0371*

(-2.96) (-4.02) (1.91)

TERM 0.0182*** -0.0890*** 0.278***

(24.91) (-50.77) (83.40)

ORIGFEES -0.0113* -0.0261* -0.187***

(-1.83) (-1.81) (-6.60)

CLTV 0.00251*** 0.00419*** 0.0162***

(8.62) (5.93) (12.18)

FICO -0.00163*** -0.00327*** 0.00116***

(-22.27) (-18.36) (3.48)

SPECULATING 0.105*** 0.344*** -0.846***

(7.77) (10.37) (-13.62)

OWNERBUILDER 0.126*** 0.249*** -0.286***

(16.52) (13.44) (-8.20)

BUDGETITEM 0.00615*** 0.0158*** -0.000766

(20.70) (24.25) (-0.56)

Property Zip Fixed Effects YES YES YES

Borrower Zip Fixed Effects YES YES YES

Loan Origination Day Fixed Effects YES YES YES

Observations 27,803 28,939 27,567

R-squared 0.352 0.347 0.399
48

Table 4: Inspections with Relationship Characteristics

This table presents the ordinary least squares results where the dependent variables of interest are defined 

as follows: LOG(ALLINSPECTIONS) is the logarithm of the total number of inspections over the course 

of the loan, ALLTOTERMINAL is defined as the ratio of total inspections to the number of days between 

loan origination and the loan’s terminal state, and TIMETOFIRST is the number of months until the 

loan’s first on-site inspection date. Table 1 provides further details on variable construction. T-statistics 

are presented in parentheses and significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3)

Log(ALLINSPECTIONS) ALLTOTERMINAL TIMETOFIRST

REPEATBORROWER -0.0669*** -0.114** 0.294**

(-3.46) (-2.45) (3.32)

Table 3 Controls YES YES YES

Property Zip Fixed Effects YES YES YES

Borrower Zip Fixed Effects YES YES YES

Loan Origination Day Fixed Effects YES YES YES

Observations 27,803 28,939 27,567

R-squared 0.353 0.347 0.399
49

Table 5: Macroeconomic Variables 

This table presents the ordinary least squares results where the dependent variables of interest are DRAWDENIED, an indicator variable 

corresponding to draw requests that is equal to one if the draw request is denied and zero if it is approved, and INSPECTIONDATE, which is

equal to one if there is an inspection and zero otherwise. Table 1 provides further details on variable construction. T-statistics are presented in 

parentheses, robust standard errors are clustered at the loan-level, and significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3) (4) (5) (6)

DRAWDENIED INSPECTIONDATE DRAWDENIED INSPECTIONDATE DRAWDENIED INSPECTIONDATE

HOUSING PRICE INDEX -0.00201*** -0.0000258** -0.00135*** -0.0000302***

(-12.99) (-2.42) (-8.40) (-2.61)

FORECLOSURE RATE 0.0322*** -0.0000384 0.0336*** -0.0000962

(11.56) (-0.44) (10.62) (-1.01)

Loan Fixed Effects YES YES YES YES YES YES

Observations 330,594 10,805,736 354,787 11,537,601 330,594 10,805,736

R-Squared 0.001 0.000 0.001 0.000 0.002 0.000
50

Table 6: Bank Actions over Time

This table presents the ordinary least squares results where the dependent variables of interest are

DRAWDENIED, an indicator variable corresponding to draw requests that is equal to one if the draw 

request is denied and zero if it is approved, and INSPECTIONDATE, which is equal to one if there is an 

inspection and zero otherwise. Table 1 provides further details on variable construction. T-statistics are 

presented in parentheses, robust standard errors are clustered at the loan-level, and significance is denoted 

by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3) (4)

DRAWDENIED INSPECTIONDATE DRAWDENIED INSPECTIONDATE

YEARBEFOREFAILURE 0.0767*** 0.00241***

(20.50) (8.09)

STARTOFYEARBEFOREFAILURE 0.0920*** 0.000803**

(21.38) (2.56)

Loan Fixed Effects YES YES YES YES

Observations 355,890 11,585,108 355,890 11,585,108

R-Squared 0.002 0.000 0.003 0.000
51

Table 7: Draw Decisions Based on Inspector Comments 

This table presents the ordinary least squares results where the dependent variable of interest,

DRAWDENIED, is one if the draw request is denied and zero if it is approved, and independent variables 

include measures of inspector comments. Table 1 provides further details on variable construction. Tstatistics are presented in parentheses, robust standard errors are clustered at the loan-level, and 

significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3) (4) (5)

DRAWDENIED DRAWDENIED DRAWDENIED DRAWDENIED DRAWDENIED

POSITIVEWORDS -0.00181** -0.00206** -0.00243*** -0.000913 -0.000658

(-2.15) (-2.45) (-2.98) (-0.76) (-0.76)

NEGATIVEWORDS 0.00164*** 0.00149*** 0.00234*** 0.00372*** 0.00240***

(2.84) (2.65) (4.06) (5.11) (4.16)

COMMENTLENGTH 0.00000179 0.00000172 0.0000131* 0.0000185 0.0000177**

(0.26) (0.25) (1.76) (1.63) (2.39)

Day Fixed Effects NO YES YES YES YES

Loan Fixed Effects NO NO NO YES NO

Inspector Fixed Effects NO NO YES NO YES

Property Zip Fixed Effects YES YES NO NO YES

Borrower Zip Fixed Effects YES YES NO NO YES

Loan Origination Day Fixed 

ff

YES YES NO NO YES

Table 3 and 4 Controls YES YES NO NO YES

Observations 143,074 143,074 143,074 143,074 143,074

R-Squared 0.048 0.088 0.048 0.044 0.089
52

Table 8: Determinants of Default

This table presents the ordinary least squares results where the dependent variable is 

EVENTUALDEFAULT, which is an indicator variable that takes a value of one for loans that eventually 

defaulted. Table 1 provides further details on variable construction. T-statistics are presented in 

parentheses and significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1)

EVENTUALDEFAULT

LOG(LOANAMT) -0.0396***

(-15.42)

ORIGSPREAD 0.00221

(1.36)

TERM 0.00626***

(22.54)

FEES 0.0681***

(29.93)

CLTV 0.000738***

(6.60)

FICO -0.000276***

(-9.79)

SPECULATING 0.00741

(1.32)

OWNERBUILDER -0.0116***

(-3.94)

BUDGETITEM 0.000423***

(4.09)

REPEATBORROWER -0.00462

(-0.63)

Property Zip Fixed Effects YES

Borrower Zip Fixed Effects YES

Loan Origination Day Fixed Effects YES

Observations 28,939

R-squared 0.251
53

Table 9: Instruments for Inspections to Predict Default

Column (1) shows the results of the IV estimation second stage, Column (2) the first stage, Column (3) 

OLS estimation, and Column (4) the reduced form. The dependent variables of interest are 

EVENTUALDEFAULT, which is an indicator variable that takes a value of one for loans that eventually 

defaulted, and ALLTOTERMINAL, defined as the ratio of total inspections to the number of days

between loan origination and the loan’s terminal state. This specification uses the draw schedule,

DRAWTOTERMINAL, as an instrument. Table 1 provides further details on variable construction. Tstatistics are presented in parentheses and significance is denoted by * p <0.10, ** p < 0.05, and *** 

p<0.01.

Draw Schedule as an Instrument

(1) (2) (3) (4)

IV Second First Reduced

Stage Stage OLS Form

EVENTUALDEFAULT ALLTOTERMINAL EVENTUALDEFAULT EVENTUALDEFAULT

ALLTOTERMINAL -0.0364*** -0.0185***

(-23.36) (-18.81)

DRAWTOTERMINAL 0.363*** -0.0132***

(119.63) (-22.22)

Table 3 and 4 Controls YES YES YES YES

Property Zip Fixed Effects YES YES YES YES

Borrower Zip Fixed Effects YES YES YES YES

Loan Origination Day Fixed 

Effects YES YES YES YES

Observations 28,939 28,939 28,939 28,939

R-squared 0.252 0.581 0.261 0.265
54

Table 10: Inspections with Borrower Characteristics

This table presents the ordinary least squares results where the dependent variables of interest are defined 

as follows: LOG(ALLINSPECTIONS) is the logarithm of the total number of inspections over the course 

of the loan, ALLTOTERMINAL is defined as the ratio of total inspections to the number of days between 

loan origination and the loan’s terminal state, and TIMETOFIRST is the number of months until the 

loan’s first on-site inspection date. Table 1 provides further details on variable construction. T-statistics 

are presented in parentheses and significance is denoted by * p <0.10, ** p < 0.05, and *** p<0.01.

(1) (2) (3)

LOG(ALLINSPECTIONS) ALLTOTERMINAL TIMETOFIRST

VALUEADDRATIO 0.00411*** 0.00760*** 0.00563***

(18.48) (14.29) (5.31)

BORROWERAMT -0.00140*** -0.000881***

(-14.53) (-10.37)

BORROWERINPUT -0.617*** -1.116***

(-33.28) (-25.82)

Table 3 Controls YES YES YES

Property Zip Fixed Effects YES YES YES

Borrower Zip Fixed Effects YES YES YES

Loan Origination Day Fixed Effects YES YES YES

Observations 27,803 28,939 27,567

R-squared 0.412 0.394 0.399