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Housing Price Booms and Crowding-Out Effects in Bank Lending Abstract Analyzing the period 1988–2006, we document that banks that are active in strong housing markets increase mortgage lending and decrease commercial lending. Firms that borrow from these banks have significantly lower investment. This is especially pronounced for firms that are more capital constrained or borrow from more-constrained banks. Various extensions and robustness analyses are consistent with the interpretation that commercial loans were crowded out by banks responding to profitable opportunities in mortgage lending, rather than with a demand-based interpretation. The results suggest that housing prices appreciations have negative spillovers to the real economy, which were overlooked thus far. Received November 29, 2016; editorial decision January 12, 2018 by Editor David Hirshleifer. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web Site next to the link to the final published paper online. The years leading up to the 2007–08 financial crisis were characterized by a significant boom in real estate prices. A similar pattern has been observed in previous episodes, in which real estate prices increase leading up to a crisis and then crash at the onset of the crisis. Much has been written about the negative real effects of asset price crashes (see, e.g., Gan 2007a,b; and Peek and Rosengren 2000). The logic behind this effect is that firms that own real estate can borrow less and invest less following the decline in the value of their assets (the collateral channel). In addition, banks exposed to real estate prices decrease their lending following the crash, causing further deterioration in firms’ access to capital and investment (the lending channel). Much less is known, however, about the real effects of the boom phase in asset prices. We explore these effects, focusing on the bank lending channel, in this paper. Specifically, we study the effect of housing prices on bank commercial lending and firm investment in the United States in the period between 1988 and 2006. We document a crowding-out effect, whereby the lending opportunities in the real estate market, following the boom in real estate prices, have led banks to reduce commercial lending. This has caused firms that depend on these loans to reduce investment, hence having a negative real effect.1 Our empirical analysis hinges on the differences across banks in their exposure to the real estate market. We use the location of banks’ deposit branches to proxy for the location of mortgage activity, since banks are more likely to do mortgage lending if there is larger price appreciation in the areas where they have branches. We then compare the behavior of banks that are more exposed with that of banks that are less exposed to housing price booms, and explore the implications for firms related to them. The premise underlying this crowding-out behavior is that banks are constrained in raising new capital or selling their loans, and so when highly profitable lending opportunities arise in one sector (mortgage lending), they choose to pursue them by cutting their lending in another sector (commercial lending). Consistent with this argument, we find that across different specifications, our crowding-out results hold much more strongly and significantly for constrained banks; these are the banks that are smaller, more levered, and less active in securitization markets. We also explore a personnel-based constraint and find similar results, which suggests that some of the crowding-out effect can also be attributed to the difficulty banks face in expanding their workforce and increasing the overall volume of their lending activities. Similarly, the results about a decrease in firms’ investment following the substitution in lending made by banks rely on the idea that firms are constrained and cannot easily substitute bank lending for new sources of capital. Indeed, we find that our results hold much more strongly for constrained firms across different measures. An important issue in interpreting our empirical results, as in most papers in empirical corporate finance, is endogeneity. Is the reduction in commercial loans and firm investment a result of a decrease in the supply of loans from banks due to their opportunities in real estate markets, as we argue, or does it stem from a decrease in the demand for loans due to a decrease in firms’ investment opportunities? A demand-based story could emerge if the housing prices that a bank is exposed to, based on its location, are correlated with the demand for loans by firms related to this bank. This could be argued most reasonably in cases where the firms are located near the banks that they borrow from.2 It should be noted, however, that endogeneity here is more likely to work against the crowding-out story and makes it more difficult to find this result. This is because increased housing prices usually coincide with economic growth, and so one would expect a positive relation between housing prices and firm investment opportunities. This implies that, if anything, the basic regressions possibly underestimate the reduction in lending and investment due to a positive real estate price shock that is unrelated to firm demand for capital. To address the endogeneity issue and estimate the direct effect of a shock to real estate prices in the location of the bank on lending and investment, we start by using the instrumental variable that was developed by Saiz (2010) and applied extensively in the literature. The instrument measures the availability of developable land in terms of topographic restrictions. To introduce time variation in the instrument, we also include the national 30-year fixed mortgage interest rate. This average mortgage interest rate is interacted with the land unavailability measure. These instruments are motivated by the idea that for a given decrease in mortgage rates, there will be an increase in housing demand. In areas where land cannot be easily developed into new housing, this increase in housing demand should translate to higher housing prices, compared with areas that can easily accommodate more housing. Further, the assumption is that housing elasticity differences due to the presence of undevelopable land are exogenous to any underlying economic activity. Thus, the instruments provide a component of housing price appreciation in the bank’s region that is not related to firm financing and investment choices except through its effect on housing prices. Our approach is similar to that taken by Adelino, Schoar, and Severino (2015); Chaney, Sraer, and Thesmar (2012); and Loutskina and Strahan (2015), among others. Using these instruments, we find that firm-level lending growth decreases by 42.3% from a given bank for a one-standard-deviation increase in housing prices. At the same time, firms that borrow from banks exposed to these appreciations also face large effects: a one-standard-deviation increase in housing prices decreases firm investment by 20.9% as a fraction of investment.3 As expected, these results are more statistically and economically significant than those obtained without instrumentation: the potential endogeneity between loan supply and firm demand makes it more difficult to find the crowding-out effect, and once it is addressed, the reduction in commercial lending that translates into a reduction in firm investment is clearly observed. Comparing the magnitudes of our results suggests that for a 1% reduction in loan growth, firms reduce investment by about 0.49%. Thus, firms are able to internalize a fraction of the loss in credit supply, but a large piece of the reduction translates into real effects. We also document the effects of bank exposure to housing prices on other real activities of the firms, as well as their payout policies and capital structures. In evaluating the results with instrumentation, it is important to consider the recent critique of Davidoff (2016). He argues that the elasticity of supply is a problematic instrument for housing prices because it is correlated with housing desirability and therefore with unobserved demand factors. His critique seems less pertinent to our setting than to previous ones for two reasons. First, the bias he documents implies that lower supply elasticity is correlated with high demand and economic activity, and so this works against our results. This suggests that, if anything, even our instrumented results may be understating the negative effect of housing prices on real investment via the bank lending channel. Second, a fundamental distinction of our setting versus those that Davidoff (2016) critiques is that we can separate housing prices at the bank’s location from those at the firm’s location, given that firms do not always borrow from nearby banks. Hence, we can address the concerns of Davidoff (2016) by controlling for firms’ local demand shocks in our firm-related regressions. Specifically, in our firm-related regressions we include specifications with a firm’s state-year fixed effect (or a firm’s county-year fixed effect). This control removes the omitted demand factors that Davidoff (2016) is concerned about. Moreover, in further robustness tests, we also run the analysis on a subsample that requires the firm and bank to have geographically separate footprints. Our results remain the same. Finally, for firms’ loan growth, we include a specification in the style of Khwaja and Mian (2008) that uses firm-year fixed effects to compare loan growth changes for a given firm in a given year across different lenders with different exposures to housing price booms. We show that the same firm is borrowing less from banks that have greater exposure to housing price increases than from banks that have lower exposure. These findings provide strong evidence against the firm-demand explanation and provide further support for the supply-based explanation in which loans decrease as a result of crowding-out due to the bank’s more attractive lending opportunities. In further results, we show that an increase in housing prices in the bank’s location leads to an increase in the interest rate for commercial loans provided by the bank, particularly for constrained firms and constrained banks. This effect is again consistent with a decrease-in-supply story and not with a decrease-in-demand story. We also investigate the profitability of different types of loans. Consistent with a supply effect, the commercial and industrial (C&I) loan profitability of banks is sensitive to increases in housing prices. That is, as housing prices increase, banks cut more C&I loans, and so the loans they continue to extend have higher average profitability. Still, we show that while both commercial lending and mortgage lending profitability increase in response to increasing housing prices, mortgage lending profitability increases more, supporting the basic claim that housing price increases make lending opportunities in the housing market more lucrative and trigger the crowding-out of C&I loans. Another possible issue of endogeneity arises with the matching between banks and firms. If firms with poor investment opportunities borrow from more-constrained banks, it could contribute to our results.4 To address this concern, we use firm-bank fixed effects in our firm-level regressions to control for persistent differences across lending relationships. If a firm with consistently poor investment opportunities matches with a financially constrained bank, the average level of the firm’s investment will be controlled for. Therefore, any reduction in investment related to the bank restricting capital due to increasing housing prices would be a deviation from the firm’s average investment levels over the course of their relationship, and not a cross-sectional difference between firms with different investment opportunities. We also conduct additional analysis using bank branching deregulations as shocks to bank-level constraints to confirm that the crowding-out effect is not due to endogenous matching concerns. The channel we explore in this paper is an extension of the bank lending channel, whereby shocks to banks affect their ability to lend and end up affecting the firms that borrow from them. Many empirical papers have indeed provided evidence consistent with this view and demonstrating the bank lending channel. Examples include Kashyap and Stein (1995, 2000), Kishan and Opiela (2000), and Ashcraft (2006).5 At the heart of this channel stands the premise that banks are financially constrained, motivated by a large theoretical literature. Stein (1998), for example, provides a model in which banks have inside information about the quality of their assets, limiting their ability to raise uninsured external funds.6 A novel feature of our empirical analysis is that the shock to the bank is not a typical negative shock to capital, but rather a positive shock to the bank’s other lending opportunities that leads to substitution away from commercial loans. This bears resemblance to the discussion in the internal-capital markets literature in which constrained headquarters have to decide how to allocate resources among competing projects, as in Stein (1997) and Scharfstein and Stein (2000), and so will allocate less to some projects when other projects appear more profitable. Banks may face similar decisions and allocate resources to real estate loans at the expense of commercial loans in the face of real estate price appreciations. An important question in evaluating the role of banks’ constraints is why they cannot be overcome by securitization or loan sales. The key point here is that securitization and loan sales are subject to the same problems of incentives and asymmetric information that create financial constraints to begin with.7 Hence, there are barriers to their widespread use. For example, risk retention is a common feature of securitization by banks, whereby banks keep some of the risk associated with the securitized product on the books to alleviate information frictions, implying that they still need to hold significant capital (see Acharya, Schnabl, and Suarez 2013; and Begley and Purnanandam 2017).8 Indeed, looking at our sample period, securitization is limited. Moreover, it is used mostly by more reputable and larger banks. We explore this dimension in the paper to show that our results come more from banks that are not active in securitization, as one should expect. The real effect that we document in the paper builds on a long line of literature establishing the dependence of firms on banks and the fact that many firms cannot easily substitute bank financing for other sources of financing. Hence, if their banks cut back on commercial lending, they will see real negative consequences in their investment activities. Papers in this line of work include Faulkender and Petersen (2006), Sufi (2009), Leary (2009), Lemmon and Roberts (2010), and Chava and Purnanandam (2011). Our results on the effect of housing price booms bring a very new angle to the empirical literature, which argues that asset prices have a positive relation to lending and real investment. The papers by Gan (2007a,b) and Peek and Rosengren (2000) mentioned above show how decreases in asset prices tighten financial constraints of banks and firms, decreasing lending, borrowing, and investment.9 In a similar vein, a recent paper by Chaney, Sraer, and Thesmar (2012) documents that U.S. firms owning real estate benefited from the increase in real estate prices during the period of our study due to the collateral channel. While we confirm their results in our data, we document an additional effect operating in the opposite direction: Firms that depend on bank loans are harmed by the appreciation in real estate prices if their banks had a large exposure to real estate markets. This empirical result is related to the model of Farhi and Tirole (2012), which produces a similar substitution effect. To the best of our knowledge, our paper is the first to show a negative real effect of housing price appreciation. This result has important implications for models in macroeconomics. Such models (e.g., Bernanke and Gertler 1989; and Kiyotaki and Moore 1997) often emphasize the positive effect of an increase in asset prices on real investments. Hence, they generate amplification of shocks—a positive shock in the economy leads to an increase in asset prices, enabling firms to borrow and invest more and thus magnifying the initial shock.10 However, we show that the opposite also occurs: positive shocks to asset prices sometimes discourage real investment, leading to a dampening of the initial shock. We discuss some basic calculations regarding the size of the macroeconomic effect in Section 5. In particular, we show that the bank lending channel we highlight generates an effect that is similar in magnitude to the collateral channel in Chaney, Sraer, and Thesmar (2012). There are also important implications for policy, as policymakers often attempt to support real estate prices in the hope that this will help boost the real economy. Our results demonstrate that this may not be the case. Our results do not say directly whether the decrease in lending and real investment following real estate price appreciation is bad for welfare and efficiency. Making such a statement would require us to know at least whether the appreciation is the result of a bubble or not. Second, the real estate market boom supported the construction sector, which may have been distortionary, but still created jobs. Further, one could argue that the policies supporting the real estate sector in the United States are driven by social goals of higher homeownership and not purely economic goals. Instead, we just document the negative relation in our setting and argue that macroeconomists and policymakers should not assume that asset price booms translate to a boost in economic activity, as the opposite occurs in some cases. This finding is consistent with the theoretical analysis of Bleck and Liu (2013), who show that in an economy with two sectors, the injection of liquidity by the government may hurt the more constrained sector, due to a crowding-out effect that we capture in our empirical analysis. Finally, our paper is related to the quickly growing literature studying the impact of the U.S. real estate boom on the larger economy. One paper in this literature is Chaney, Sraer, and Thesmar (2012), which we discussed already. In a related paper, Cvijanović (2014) investigates the impact of the collateral channel on the firm’s capital structure decisions and finds results consistent with the firm’s real estate collateral alleviating credit frictions. Adelino, Schoar, and Severino (2015) find increases in small business starts and self-employment in areas with large housing price appreciations. Not finding the same effects for larger firms in the same industries, they conclude that individual homes serve as an important source of collateral. Mian and Sufi (2011) find a housing-credit effect of consumers increasing consumption from rising home equity values. Loutskina and Strahan (2015) consider the role of financial integration among banks in amplifying housing price shocks during this period. They find that banks move mortgage capital out of low-appreciating housing markets and into high-appreciating housing markets within their own branch networks. Taken together, these papers suggest banks had an active role in the housing boom, and serve as a complement to our finding of the movement of bank capital away from commercial lending and into mortgage lending. 1. Data and Identification Strategy This paper traces the crowding-out effects due to housing price booms from lending banks to borrowing firms. Our main analysis is conducted at three levels: at the firm-bank relationship level, at the bank level, and at the firm level. For this analysis, we use loan-level data from DealScan to identify firm-bank relationships. We combine this loan-level data with firm-level data from Compustat and additional bank-level data from the Call Reports. To measure the effect of housing prices on banks, we create a bank-specific housing price index that uses Summary of Deposits data from the Federal Deposit Insurance Corporation (FDIC) and housing price data from the Federal Housing Finance Agency (FHFA). We instrument housing prices with land unavailability data from Saiz (2010) and national 30-year mortgage interest rate data from the St. Louis Federal Reserve Economic Database (FRED). Our sample period is from 1988 through 2006. Since we use lagged data in many specifications, our earliest data goes back to 1987. 1.1 Firm-bank relationships and loan data We rely on DealScan to conduct analysis on firm-bank relationships and bank-level commercial lending at a granular level.11 DealScan provides origination information on syndicated and sole-lender loans. We consider the presence of any loan between the bank and borrowing firm to be evidence of a relationship. In the case of syndicated loans with multiple lenders, we consider the relationship bank to be the one that serves as lead agent on the loan. The length of the relationship is defined as follows: it begins in the first year that we observe an originated loan between the firm and bank and ends when the last loan observed between the firm and bank matures. Firms and banks are considered in an active relationship for each year of this period, including years when a new loan is not originated. Beyond determining firm-bank relationships, we use DealScan for data on firm-bank level loan growth, the total amount of commercial lending from lenders, and loan interest rates and other contract terms. We link DealScan with additional data sources for the firms and banks. Following Chava and Roberts (2008), we link the DealScan borrowers to Compustat for firm-specific information using their link table. To obtain additional information regarding the lending banks, we create our own link table, which matches DealScan lenders to their bank holding companies in the Call Report data. We are able to match 753 DealScan lenders to 120 bank holding companies (BHCs) in the Call Report data.12 These matches are determined by hand using the FDIC’s Summary of Deposits data and other available data on historical BHC structures. Additional details on how we construct relationships are in Online Appendix A.1. We present the statistics on the number of relationships between borrowers, DealScan lenders, and BHCs in panel A of Table 1. Table 1 Summary statistics Panel A: Relationship, loan, and firm variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Length/Frequency of Relationships $$\quad$$ Length of Relationship 5.17 3.65 3 5 7 14,377 $$\quad$$ Number of Loan Packages 2.33 1.87 1 2 3 19,116 $$\quad$$ Loan Facilities per Loan Package 1.40 0.75 1 1 2 19,116 Number of Relationships $$\quad$$ DealScan Lenders per Borrower 2.87 1.80 1 2 4 14,377 $$\quad$$ Bank Holding Companies per Borrower 2.44 1.50 1 2 3 12,880 $$\quad$$ Borrowers per DealScan Lender 319.6 380.8 62 179 463 14,377 $$\quad$$ Borrowers per Bank Holding Company 755.3 689.5 218 465 1811 12,880 $$\quad$$ DealScan Lenders per Bank 21.9 18.2 7 15 33 655 $$\qquad$$ Holding Company Loan Characteristics $$\quad$$ All In Drawn Spread (bps) 181.7 131.5 75 162.5 262.0 21,523 $$\quad$$ Loan Amount 281.1 761.0 26.0 78.8 211.8 19,831 $$\quad$$ Maturity (months) 41.7 27.2 18 36 60 21,523 $$\quad$$ Takeover Loan 0.16 0.36 0 0 0 21,523 $$\quad$$ Revolving Credit Line 0.85 0.36 1 1 1 21,523 Firm Variables $$\quad$$ Loan Growth 0.052 0.22 –0.081 0.037 0.18 5,823 $$\quad$$ Investment 29.7 45.3 10.5 18.5 32.7 60,995 $$\quad$$ Market to Book 1.68 1.45 1.05 1.33 1.83 53,404 $$\quad$$ Cash Flow 38.5 99.4 9.76 24.9 54.7 61,523 $$\quad$$ Firm Size 6.50 2.04 5.10 6.44 7.85 62,947 $$\quad$$ Altman’s Z-Score 1.28 3.19 0.69 1.49 2.35 59,155 $$\quad$$ Acquisitions 33.2 115.8 0 0 8.70 58,650 $$\quad$$ R&D Expense 51.0 482.8 0 6.15 26.0 28,999 $$\quad$$ Dividend Payout 21.1 2948.4 0 0 2.60 66,411 $$\quad$$ Book Leverage 34.6 27.1 17.2 31.4 45.9 62,780 $$\quad$$ Change in Leverage –0.063 16.5 –4.38 –0.39 4.00 61,822 $$\quad$$ Change in Debt 4.94 22.4 –3.68 0.019 7.41 61,829 $$\quad$$ Change in Equity 11.9 27.5 0 0.82 6.11 60,374 $$\quad$$ Industry Land Intensity 6.62 7.60 2.40 3.30 8.80 62,538 $$\quad$$ Market Value of Buildings 1.28 2.27 0.30 0.69 1.31 19,436 Panel B: Bank, housing, and macroeconomic variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Bank Variables $$\quad$$ Log(Dollar Outstanding Loans) 4.95 2.47 3.22 4.87 6.38 6,184 $$\quad$$ Log(Number Outstanding Loans) 2.05 1.74 0.69 1.79 3.04 6,184 $$\quad$$ Real Estate Loans 20.0 10.4 13.1 19.2 25.9 1,498 $$\quad$$ MBS 8.60 7.81 2.38 6.86 12.3 1,498 $$\quad$$ Commercial Mortgages 8.97 5.90 4.82 8.04 11.7 1,498 $$\quad$$ C&I Loans 16.4 7.52 11.3 15.7 20.2 1,498 $$\quad$$ Consumer Loans 9.17 5.90 4.33 8.97 13.1 1,498 $$\quad$$ C&I Loan Profitability 7.21 2.40 5.72 7.35 8.67 1,497 $$\quad$$ Real Estate Loan Profitability 7.14 1.76 5.88 7.33 8.30 1,472 $$\quad$$ Profitability Ratio 1.05 0.40 0.88 1.00 1.15 1,472 $$\quad$$ Bank’s Size 16.3 1.62 15.2 16.2 17.5 1,498 $$\quad$$ Bank’s Equity Ratio 8.19 2.10 6.92 7.88 8.99 1,498 $$\quad$$ Bank’s Net Income 1.08 0.49 0.90 1.12 1.33 1,498 $$\quad$$ Bank’s Cost of Deposits 3.29 1.50 2.35 3.13 4.10 1,498 $$\quad$$ Securitization Activity 0.24 0.43 0 0 0 1,241 $$\quad$$ Employee Growth 8.81 40.5 –5.85 0.78 9.84 1,298 $$\quad$$ Deregulation Measure 0.71 0.36 0.31 0.90 1 6,184 Housing Variables $$\quad$$ Housing Price Index, Bank’s State(s) 298.5 104.0 230.2 272.2 349.1 66,443 $$\quad$$ Return on Housing, Bank’s State(s) 6.25 7.59 2.23 5.48 10.00 65,477 $$\quad$$ Land Unavailability, Bank’s State(s) 24.5 8.46 19.8 23.0 28.9 66,425 $$\quad$$ Office Price Index, Firm’s State 166.3 77.6 120.9 141.2 190.2 70,578 Macroeconomic Variables $$\quad$$ Change in Unemp. Rate, Firm’s State –0.075 0.82 –0.60 –0.30 0.30 63,903 $$\quad$$ Change in Unemp. Rate, Bank’s State(s) –0.062 0.79 –0.58 –0.20 0.20 66,443 $$\quad$$ National 30-Year Mortgage Rate 7.15 1.12 6.14 7.10 7.60 66,443 Panel A: Relationship, loan, and firm variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Length/Frequency of Relationships $$\quad$$ Length of Relationship 5.17 3.65 3 5 7 14,377 $$\quad$$ Number of Loan Packages 2.33 1.87 1 2 3 19,116 $$\quad$$ Loan Facilities per Loan Package 1.40 0.75 1 1 2 19,116 Number of Relationships $$\quad$$ DealScan Lenders per Borrower 2.87 1.80 1 2 4 14,377 $$\quad$$ Bank Holding Companies per Borrower 2.44 1.50 1 2 3 12,880 $$\quad$$ Borrowers per DealScan Lender 319.6 380.8 62 179 463 14,377 $$\quad$$ Borrowers per Bank Holding Company 755.3 689.5 218 465 1811 12,880 $$\quad$$ DealScan Lenders per Bank 21.9 18.2 7 15 33 655 $$\qquad$$ Holding Company Loan Characteristics $$\quad$$ All In Drawn Spread (bps) 181.7 131.5 75 162.5 262.0 21,523 $$\quad$$ Loan Amount 281.1 761.0 26.0 78.8 211.8 19,831 $$\quad$$ Maturity (months) 41.7 27.2 18 36 60 21,523 $$\quad$$ Takeover Loan 0.16 0.36 0 0 0 21,523 $$\quad$$ Revolving Credit Line 0.85 0.36 1 1 1 21,523 Firm Variables $$\quad$$ Loan Growth 0.052 0.22 –0.081 0.037 0.18 5,823 $$\quad$$ Investment 29.7 45.3 10.5 18.5 32.7 60,995 $$\quad$$ Market to Book 1.68 1.45 1.05 1.33 1.83 53,404 $$\quad$$ Cash Flow 38.5 99.4 9.76 24.9 54.7 61,523 $$\quad$$ Firm Size 6.50 2.04 5.10 6.44 7.85 62,947 $$\quad$$ Altman’s Z-Score 1.28 3.19 0.69 1.49 2.35 59,155 $$\quad$$ Acquisitions 33.2 115.8 0 0 8.70 58,650 $$\quad$$ R&D Expense 51.0 482.8 0 6.15 26.0 28,999 $$\quad$$ Dividend Payout 21.1 2948.4 0 0 2.60 66,411 $$\quad$$ Book Leverage 34.6 27.1 17.2 31.4 45.9 62,780 $$\quad$$ Change in Leverage –0.063 16.5 –4.38 –0.39 4.00 61,822 $$\quad$$ Change in Debt 4.94 22.4 –3.68 0.019 7.41 61,829 $$\quad$$ Change in Equity 11.9 27.5 0 0.82 6.11 60,374 $$\quad$$ Industry Land Intensity 6.62 7.60 2.40 3.30 8.80 62,538 $$\quad$$ Market Value of Buildings 1.28 2.27 0.30 0.69 1.31 19,436 Panel B: Bank, housing, and macroeconomic variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Bank Variables $$\quad$$ Log(Dollar Outstanding Loans) 4.95 2.47 3.22 4.87 6.38 6,184 $$\quad$$ Log(Number Outstanding Loans) 2.05 1.74 0.69 1.79 3.04 6,184 $$\quad$$ Real Estate Loans 20.0 10.4 13.1 19.2 25.9 1,498 $$\quad$$ MBS 8.60 7.81 2.38 6.86 12.3 1,498 $$\quad$$ Commercial Mortgages 8.97 5.90 4.82 8.04 11.7 1,498 $$\quad$$ C&I Loans 16.4 7.52 11.3 15.7 20.2 1,498 $$\quad$$ Consumer Loans 9.17 5.90 4.33 8.97 13.1 1,498 $$\quad$$ C&I Loan Profitability 7.21 2.40 5.72 7.35 8.67 1,497 $$\quad$$ Real Estate Loan Profitability 7.14 1.76 5.88 7.33 8.30 1,472 $$\quad$$ Profitability Ratio 1.05 0.40 0.88 1.00 1.15 1,472 $$\quad$$ Bank’s Size 16.3 1.62 15.2 16.2 17.5 1,498 $$\quad$$ Bank’s Equity Ratio 8.19 2.10 6.92 7.88 8.99 1,498 $$\quad$$ Bank’s Net Income 1.08 0.49 0.90 1.12 1.33 1,498 $$\quad$$ Bank’s Cost of Deposits 3.29 1.50 2.35 3.13 4.10 1,498 $$\quad$$ Securitization Activity 0.24 0.43 0 0 0 1,241 $$\quad$$ Employee Growth 8.81 40.5 –5.85 0.78 9.84 1,298 $$\quad$$ Deregulation Measure 0.71 0.36 0.31 0.90 1 6,184 Housing Variables $$\quad$$ Housing Price Index, Bank’s State(s) 298.5 104.0 230.2 272.2 349.1 66,443 $$\quad$$ Return on Housing, Bank’s State(s) 6.25 7.59 2.23 5.48 10.00 65,477 $$\quad$$ Land Unavailability, Bank’s State(s) 24.5 8.46 19.8 23.0 28.9 66,425 $$\quad$$ Office Price Index, Firm’s State 166.3 77.6 120.9 141.2 190.2 70,578 Macroeconomic Variables $$\quad$$ Change in Unemp. Rate, Firm’s State –0.075 0.82 –0.60 –0.30 0.30 63,903 $$\quad$$ Change in Unemp. Rate, Bank’s State(s) –0.062 0.79 –0.58 –0.20 0.20 66,443 $$\quad$$ National 30-Year Mortgage Rate 7.15 1.12 6.14 7.10 7.60 66,443 This table presents summary statistics of the merged sample of banks and borrowing firms as obtained from the Dealscan, Compustat, and Call Report databases. Panel A presents the summary statistics for the borrower-lender relationships, the loan characteristics, and the firm variables. Panel B presents the summary statistics for the bank balance sheet variables, housing price variables, and other macroeconomic variables used in the analysis. All firm, loan, and bank ratio variables are scaled by 100. Table 1 Summary statistics Panel A: Relationship, loan, and firm variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Length/Frequency of Relationships $$\quad$$ Length of Relationship 5.17 3.65 3 5 7 14,377 $$\quad$$ Number of Loan Packages 2.33 1.87 1 2 3 19,116 $$\quad$$ Loan Facilities per Loan Package 1.40 0.75 1 1 2 19,116 Number of Relationships $$\quad$$ DealScan Lenders per Borrower 2.87 1.80 1 2 4 14,377 $$\quad$$ Bank Holding Companies per Borrower 2.44 1.50 1 2 3 12,880 $$\quad$$ Borrowers per DealScan Lender 319.6 380.8 62 179 463 14,377 $$\quad$$ Borrowers per Bank Holding Company 755.3 689.5 218 465 1811 12,880 $$\quad$$ DealScan Lenders per Bank 21.9 18.2 7 15 33 655 $$\qquad$$ Holding Company Loan Characteristics $$\quad$$ All In Drawn Spread (bps) 181.7 131.5 75 162.5 262.0 21,523 $$\quad$$ Loan Amount 281.1 761.0 26.0 78.8 211.8 19,831 $$\quad$$ Maturity (months) 41.7 27.2 18 36 60 21,523 $$\quad$$ Takeover Loan 0.16 0.36 0 0 0 21,523 $$\quad$$ Revolving Credit Line 0.85 0.36 1 1 1 21,523 Firm Variables $$\quad$$ Loan Growth 0.052 0.22 –0.081 0.037 0.18 5,823 $$\quad$$ Investment 29.7 45.3 10.5 18.5 32.7 60,995 $$\quad$$ Market to Book 1.68 1.45 1.05 1.33 1.83 53,404 $$\quad$$ Cash Flow 38.5 99.4 9.76 24.9 54.7 61,523 $$\quad$$ Firm Size 6.50 2.04 5.10 6.44 7.85 62,947 $$\quad$$ Altman’s Z-Score 1.28 3.19 0.69 1.49 2.35 59,155 $$\quad$$ Acquisitions 33.2 115.8 0 0 8.70 58,650 $$\quad$$ R&D Expense 51.0 482.8 0 6.15 26.0 28,999 $$\quad$$ Dividend Payout 21.1 2948.4 0 0 2.60 66,411 $$\quad$$ Book Leverage 34.6 27.1 17.2 31.4 45.9 62,780 $$\quad$$ Change in Leverage –0.063 16.5 –4.38 –0.39 4.00 61,822 $$\quad$$ Change in Debt 4.94 22.4 –3.68 0.019 7.41 61,829 $$\quad$$ Change in Equity 11.9 27.5 0 0.82 6.11 60,374 $$\quad$$ Industry Land Intensity 6.62 7.60 2.40 3.30 8.80 62,538 $$\quad$$ Market Value of Buildings 1.28 2.27 0.30 0.69 1.31 19,436 Panel B: Bank, housing, and macroeconomic variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Bank Variables $$\quad$$ Log(Dollar Outstanding Loans) 4.95 2.47 3.22 4.87 6.38 6,184 $$\quad$$ Log(Number Outstanding Loans) 2.05 1.74 0.69 1.79 3.04 6,184 $$\quad$$ Real Estate Loans 20.0 10.4 13.1 19.2 25.9 1,498 $$\quad$$ MBS 8.60 7.81 2.38 6.86 12.3 1,498 $$\quad$$ Commercial Mortgages 8.97 5.90 4.82 8.04 11.7 1,498 $$\quad$$ C&I Loans 16.4 7.52 11.3 15.7 20.2 1,498 $$\quad$$ Consumer Loans 9.17 5.90 4.33 8.97 13.1 1,498 $$\quad$$ C&I Loan Profitability 7.21 2.40 5.72 7.35 8.67 1,497 $$\quad$$ Real Estate Loan Profitability 7.14 1.76 5.88 7.33 8.30 1,472 $$\quad$$ Profitability Ratio 1.05 0.40 0.88 1.00 1.15 1,472 $$\quad$$ Bank’s Size 16.3 1.62 15.2 16.2 17.5 1,498 $$\quad$$ Bank’s Equity Ratio 8.19 2.10 6.92 7.88 8.99 1,498 $$\quad$$ Bank’s Net Income 1.08 0.49 0.90 1.12 1.33 1,498 $$\quad$$ Bank’s Cost of Deposits 3.29 1.50 2.35 3.13 4.10 1,498 $$\quad$$ Securitization Activity 0.24 0.43 0 0 0 1,241 $$\quad$$ Employee Growth 8.81 40.5 –5.85 0.78 9.84 1,298 $$\quad$$ Deregulation Measure 0.71 0.36 0.31 0.90 1 6,184 Housing Variables $$\quad$$ Housing Price Index, Bank’s State(s) 298.5 104.0 230.2 272.2 349.1 66,443 $$\quad$$ Return on Housing, Bank’s State(s) 6.25 7.59 2.23 5.48 10.00 65,477 $$\quad$$ Land Unavailability, Bank’s State(s) 24.5 8.46 19.8 23.0 28.9 66,425 $$\quad$$ Office Price Index, Firm’s State 166.3 77.6 120.9 141.2 190.2 70,578 Macroeconomic Variables $$\quad$$ Change in Unemp. Rate, Firm’s State –0.075 0.82 –0.60 –0.30 0.30 63,903 $$\quad$$ Change in Unemp. Rate, Bank’s State(s) –0.062 0.79 –0.58 –0.20 0.20 66,443 $$\quad$$ National 30-Year Mortgage Rate 7.15 1.12 6.14 7.10 7.60 66,443 Panel A: Relationship, loan, and firm variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Length/Frequency of Relationships $$\quad$$ Length of Relationship 5.17 3.65 3 5 7 14,377 $$\quad$$ Number of Loan Packages 2.33 1.87 1 2 3 19,116 $$\quad$$ Loan Facilities per Loan Package 1.40 0.75 1 1 2 19,116 Number of Relationships $$\quad$$ DealScan Lenders per Borrower 2.87 1.80 1 2 4 14,377 $$\quad$$ Bank Holding Companies per Borrower 2.44 1.50 1 2 3 12,880 $$\quad$$ Borrowers per DealScan Lender 319.6 380.8 62 179 463 14,377 $$\quad$$ Borrowers per Bank Holding Company 755.3 689.5 218 465 1811 12,880 $$\quad$$ DealScan Lenders per Bank 21.9 18.2 7 15 33 655 $$\qquad$$ Holding Company Loan Characteristics $$\quad$$ All In Drawn Spread (bps) 181.7 131.5 75 162.5 262.0 21,523 $$\quad$$ Loan Amount 281.1 761.0 26.0 78.8 211.8 19,831 $$\quad$$ Maturity (months) 41.7 27.2 18 36 60 21,523 $$\quad$$ Takeover Loan 0.16 0.36 0 0 0 21,523 $$\quad$$ Revolving Credit Line 0.85 0.36 1 1 1 21,523 Firm Variables $$\quad$$ Loan Growth 0.052 0.22 –0.081 0.037 0.18 5,823 $$\quad$$ Investment 29.7 45.3 10.5 18.5 32.7 60,995 $$\quad$$ Market to Book 1.68 1.45 1.05 1.33 1.83 53,404 $$\quad$$ Cash Flow 38.5 99.4 9.76 24.9 54.7 61,523 $$\quad$$ Firm Size 6.50 2.04 5.10 6.44 7.85 62,947 $$\quad$$ Altman’s Z-Score 1.28 3.19 0.69 1.49 2.35 59,155 $$\quad$$ Acquisitions 33.2 115.8 0 0 8.70 58,650 $$\quad$$ R&D Expense 51.0 482.8 0 6.15 26.0 28,999 $$\quad$$ Dividend Payout 21.1 2948.4 0 0 2.60 66,411 $$\quad$$ Book Leverage 34.6 27.1 17.2 31.4 45.9 62,780 $$\quad$$ Change in Leverage –0.063 16.5 –4.38 –0.39 4.00 61,822 $$\quad$$ Change in Debt 4.94 22.4 –3.68 0.019 7.41 61,829 $$\quad$$ Change in Equity 11.9 27.5 0 0.82 6.11 60,374 $$\quad$$ Industry Land Intensity 6.62 7.60 2.40 3.30 8.80 62,538 $$\quad$$ Market Value of Buildings 1.28 2.27 0.30 0.69 1.31 19,436 Panel B: Bank, housing, and macroeconomic variable statistics Mean Std. dev. 25th pctile Median 75th pctile # obs. Bank Variables $$\quad$$ Log(Dollar Outstanding Loans) 4.95 2.47 3.22 4.87 6.38 6,184 $$\quad$$ Log(Number Outstanding Loans) 2.05 1.74 0.69 1.79 3.04 6,184 $$\quad$$ Real Estate Loans 20.0 10.4 13.1 19.2 25.9 1,498 $$\quad$$ MBS 8.60 7.81 2.38 6.86 12.3 1,498 $$\quad$$ Commercial Mortgages 8.97 5.90 4.82 8.04 11.7 1,498 $$\quad$$ C&I Loans 16.4 7.52 11.3 15.7 20.2 1,498 $$\quad$$ Consumer Loans 9.17 5.90 4.33 8.97 13.1 1,498 $$\quad$$ C&I Loan Profitability 7.21 2.40 5.72 7.35 8.67 1,497 $$\quad$$ Real Estate Loan Profitability 7.14 1.76 5.88 7.33 8.30 1,472 $$\quad$$ Profitability Ratio 1.05 0.40 0.88 1.00 1.15 1,472 $$\quad$$ Bank’s Size 16.3 1.62 15.2 16.2 17.5 1,498 $$\quad$$ Bank’s Equity Ratio 8.19 2.10 6.92 7.88 8.99 1,498 $$\quad$$ Bank’s Net Income 1.08 0.49 0.90 1.12 1.33 1,498 $$\quad$$ Bank’s Cost of Deposits 3.29 1.50 2.35 3.13 4.10 1,498 $$\quad$$ Securitization Activity 0.24 0.43 0 0 0 1,241 $$\quad$$ Employee Growth 8.81 40.5 –5.85 0.78 9.84 1,298 $$\quad$$ Deregulation Measure 0.71 0.36 0.31 0.90 1 6,184 Housing Variables $$\quad$$ Housing Price Index, Bank’s State(s) 298.5 104.0 230.2 272.2 349.1 66,443 $$\quad$$ Return on Housing, Bank’s State(s) 6.25 7.59 2.23 5.48 10.00 65,477 $$\quad$$ Land Unavailability, Bank’s State(s) 24.5 8.46 19.8 23.0 28.9 66,425 $$\quad$$ Office Price Index, Firm’s State 166.3 77.6 120.9 141.2 190.2 70,578 Macroeconomic Variables $$\quad$$ Change in Unemp. Rate, Firm’s State –0.075 0.82 –0.60 –0.30 0.30 63,903 $$\quad$$ Change in Unemp. Rate, Bank’s State(s) –0.062 0.79 –0.58 –0.20 0.20 66,443 $$\quad$$ National 30-Year Mortgage Rate 7.15 1.12 6.14 7.10 7.60 66,443 This table presents summary statistics of the merged sample of banks and borrowing firms as obtained from the Dealscan, Compustat, and Call Report databases. Panel A presents the summary statistics for the borrower-lender relationships, the loan characteristics, and the firm variables. Panel B presents the summary statistics for the bank balance sheet variables, housing price variables, and other macroeconomic variables used in the analysis. All firm, loan, and bank ratio variables are scaled by 100. To investigate firm-bank relationships, we follow Khwaja and Mian (2008) to create a loan growth variable. However, in our case, we do not observe credit registry–level data. Hence, similar to Lin and Paravisini (2013), we create a panel that emulates a credit registry by aggregating DealScan lending data at the firm-bank relationship level. Given that loan originations can be infrequent, we compare lending between individual firms and their relationship banks over subsequent five-year windows to get a better picture of the firm-bank relationship. DealScan data also allows us to measure the amount of commercial lending at the bank level. Since there are sufficient originations per year by each bank, we consider the lender’s total loan amount on an annual basis. This creates a balance sheet panel of the bank’s commercial loans. The advantage of this approach compared to using annual C&I data from the Call Reports is that we are able to focus on the lending to the firms that are relevant to our analysis. To fully capture the crowding-out effects for a bank, we create the commercial loan balance sheet of all DealScan loan amounts held by the bank. This includes loans for which the bank is the lead agent and loans for which the bank is a syndicate member. For robustness tests, we create an alternative sample that aggregates only the loan amounts for which the bank is the lead agent. We also include a variable based on the number of loans originated by a lender. To calculate the interest rates and maturities of loan packages (which can contain multiple loan facilities), we average the individual facility values by their respective dollar amounts. In our interest rate analysis, we also include indicators if the loan package is designated for takeover purposes or contains a revolving credit line. The summary statistics for these variables are included in panel A of Table 1, and exact variable definitions are included in Table A.1 in the Online Appendix. 1.2 Firm and bank data As we are focusing on how financial intermediaries affect borrowing firms’ real activity, we exclude any borrowing firms that are financial companies. We consider several dimensions of firm activity using Compustat data in our analysis, including investment, acquisitions, research and development (R&D) expenses, dividend payout, and changes in leverage, debt, and equity. We use market-to-book ratio, cash flow, firm size, book leverage, and Altman’s z-score as control variables in many of our specifications. We also include a measure of the market value of the firm’s buildings (following Chaney, Sraer, and Thesmar 2012) and an industry-level measure of the share of capital income that is attributable to land (Industry Land Intensity) for some of our additional analysis. Panel A of Table 1 includes the summary statistics for these variables. On the bank side, we supplement our loan information from DealScan with Call Report data at the BHC level. In our analysis, we consider the following additional asset classes: unsecuritized noncommercial real estate loans, mortgage-backed securities (MBS), commercial mortgages, and consumer loans. The summary statistics of these bank loan variables, all scaled by the bank’s total assets, are reported in panel B of Table 1. We include measures of C&I and mortgage loan profitability, which are the interest and fee income divided by the total amount of loans for each type. We also include four additional bank control variables: the bank’s size, equity ratio, net income, and cost of deposits. We use measures of securitization activity and employee growth at the bank level in tests of bank-level constraints. As regional economic controls, we include changes in unemployment rates in the firm’s state and in the bank’s states of operation. Beyond the inclusion of various controls, in the cross-section of bank holding companies, it is likely that the largest bank holding companies are still significantly less constrained than the smaller bank holding companies. In much of our analysis, we allow the three largest bank holding companies—Citigroup, Bank of America, and JPMorgan Chase—to have a differential effect when it comes to the bank lending channel.13 1.3 Housing exposure of banks At the core of our analysis is the weighted index of housing prices per bank holding company. We use the state-level House Price Index (HPI) from the FHFA as the basis for this variable. To determine the exposure of each bank holding company to different state-level housing prices, we use the Summary of Deposits data from June of the prior year, aggregated to the BHC level. Using the percentage of deposits in each state as weights, we create a measure of housing prices that is specific to each bank and each year.14 Our bank-specific housing price index is scaled such that an index value of 100 corresponds to $${\}$$50,000 in year 2000 dollars. Additional details of the variable’s construction are provided in Online Appendix A.3. Figure 1 presents both the level of our index and the annual changes in our index for each bank. The figure shows an upward trend in housing prices over our sample period, but also substantial cross-sectional variation across bank holding companies. In Figure 2, we plot the relation between banks’ real estate–related lending, commercial and industrial lending, and housing prices, using a local polynomial regression. We focus on the effect of changes in housing prices on a given bank’s holdings by considering within-bank variation only, using the sample of the 106 BHCs we match to Compustat borrowers. We plot one standard deviation above and below each bank’s average housing price index level. Figure 2 suggests banks are, on average, increasing real estate lending and decreasing commercial lending as housing prices increase in the bank’s deposit area. In the remainder of the paper, we investigate how housing prices affect bank-level, firm-level, and loan-level outcomes more formally in a multivariate setting. Figure 1 View largeDownload slide Housing prices in banks’ deposit areas This figure plots the weighted housing price index (top) and return on the weighted housing price index (bottom) in the locations where the bank has depository branches. Figure 1 View largeDownload slide Housing prices in banks’ deposit areas This figure plots the weighted housing price index (top) and return on the weighted housing price index (bottom) in the locations where the bank has depository branches. Figure 2 View largeDownload slide Relation between the housing price index and either MBS and real estate loans or C&I loans The top figure plots the fraction of the bank’s total assets that are MBS and real estate loans (excluding commercial mortgages) against the prior year’s housing prices where the bank has depository branches, relative to the bank’s average levels. The bottom figure plots the fraction of the bank’s total assets that are C&I loans against the prior year’s housing price index where the bank has depository branches, relative to the bank’s average levels. We demean each variable at the bank level. Both loan variables are scaled by 100. Confidence intervals of 95% are provided for the local polynomial regression estimates. Figure 2 View largeDownload slide Relation between the housing price index and either MBS and real estate loans or C&I loans The top figure plots the fraction of the bank’s total assets that are MBS and real estate loans (excluding commercial mortgages) against the prior year’s housing prices where the bank has depository branches, relative to the bank’s average levels. The bottom figure plots the fraction of the bank’s total assets that are C&I loans against the prior year’s housing price index where the bank has depository branches, relative to the bank’s average levels. We demean each variable at the bank level. Both loan variables are scaled by 100. Confidence intervals of 95% are provided for the local polynomial regression estimates. 1.4 Identification strategy There are a few identification concerns that we address in our empirical approach. The first concern is that housing prices are likely correlated with unobserved economic shocks. The omitted economic shocks, which may affect firm demand for loans as well as housing prices, would bias our estimates. The next issue is whether the instrumental variables approach that we employ fully addresses the concerns regarding unobserved demand-side factors. A final concern is that the mechanism that causes certain firms to match with certain banks could be contributing to our results. We discuss each of these issues in turn. To address the first concern of an omitted variable bias, we use an instrumental variables approach. Our instrument set is a measure of land area that is unavailable for residential or commercial real estate development (Saiz 2010), the national-level $$30$$-year mortgage rate, which measures housing and mortgage demand for consumers, and the interaction of the land unavailability and mortgage rate measures.15 Using the deposit weights for each bank’s exposure to different states, we calculate the percentage of unavailable land in each bank’s region of operation. The instruments are designed to capture variation in housing prices that is not correlated with local economic conditions. For similar housing demand shocks, areas with less available land will experience larger price increases since additional housing construction is more costly. Interacting this unavailability measure with the mortgage rate captures the housing price dynamics further. As mortgage rates decrease (and housing demand increases), areas with less available land will see a relatively higher increase in housing prices than areas with more available land. We provide additional discussion of the instrumental variables and confirm they affect housing prices in the expected manner in Online Appendix B. There are two related concerns about this instrumental variables approach. First, Davidoff (2016) argues that the elasticity of supply is not a valid instrument for housing prices because it is correlated with housing desirability and therefore unobserved demand factors. As this argument implies that lower elasticity is positively correlated with economic activity and firm investment, this bias would go against our results. Second, the possibility that housing prices and real estate costs directly influence firm decisions (e.g., as an input cost for production) is not addressed by our instrumental variables approach. These concerns are not unique to our paper, as they apply to prior papers that use similar instrument sets, whether for firm investment (Chaney, Sraer, and Thesmar 2012) or employment growth (Adelino, Schoar, and Severino 2015; Loutskina and Strahan 2015). We address these concerns in a few ways. First, we stress that our housing price variable is calculated at the bank, rather than firm, location. The majority of bank holding companies in our main sample operate across multiple states.16 Further, the inclusion of a firm’s state-year fixed effects removes any potentially time-varying unobserved demand factors at the firm’s location. These factors would include differences in housing desirability (as raised by Davidoff 2016) and the cost of land as an input in the firm’s production decision. For firms with multiple lenders, we go further and use firm-time fixed effects to determine the effect of housing prices on loan growth. These specifications not only remove local demand concerns, but any demand factors specific to a firm at a given point in time. Besides a firm’s state-year fixed effects, we confirm our investment findings by: directly including the firm’s state HPI as a separate control, using firm county-year fixed effects as a finer local demand control, and considering a subsample in which we require firms and banks to have geographically separate footprints. These tests address the two related concerns regarding the instrumental variables approach. As an additional test for the concern that housing prices directly affect firm investment decisions, we use a subsample in which we exclude firms from the most land-intensive industries. We also incorporate the price of commercial real estate in the firm’s state and the importance of land for different firms to directly consider the economic importance of the cost of real estate affecting firm investment demand. These results are provided in Online Appendix C.2. A different source of potential endogeneity is that the matches between firms and banks are not random: more constrained firms with potentially fewer investment opportunities may tend to borrow from weaker, more constrained banks. If the investment of firms that borrow from constrained banks is more negatively affected by housing price booms, and these firms also have fewer investment opportunities, then their larger investment declines may be driven by fewer investment opportunities and not necessarily a larger credit supply shock. A related but distinct concern is that firms that borrow from constrained banks are more bank-dependent than those that borrow from unconstrained banks. If this is the case, then firms that borrow from constrained banks would be more affected by bank credit supply shocks. However, Schwert (forthcoming) finds evidence that constrained firms borrow from well-capitalized banks. These findings would imply that, if anything, any differences from matching are likely to go against finding our crowding-out result. Nevertheless, to address concerns that nonrandom firm-bank matches are driving investment results, we include firm-bank fixed effects when we consider firm-level outcome variables. Any negative effects in firm outcomes from reductions in bank capital are identified from deviations in the average level of that firm’s investment over its relationship with the given bank, and not from cross-sectional differences between firms with stronger or weaker investment opportunities. As an additional strategy, in Section 3.4 we exploit intrastate branching deregulation as an exogenous shock to the cross-sectional variation in banks’ constraints. Supporting the argument that the results are driven by bank credit supply changes rather than firm demand or endogenous matching, we find that following state-level deregulation, less capital is crowded out from C&I lending than before deregulation when the banking sector was more constrained. 2. Housing Prices, Bank Lending, and Firm Investment 2.1 Relationship lending If the housing boom is crowding out commercial borrowing and investment through the lending channel, we expect a decrease in lending to firms in response to higher housing prices. To test if this is the case, we first consider loan growth at the firm-bank level. We follow Lin and Paravisini (2013) for a modified approach of Khwaja and Mian (2008) that is applicable in our setting. The approach by Khwaja and Mian (2008) relies on credit registry data, where the firm-bank pair’s loan balances are observed continuously. To create a panel that is similar to a credit registry, we aggregate DealScan lending data at the relationship level between each firm and bank. Specifically, we sum the total amount of lending between a firm and bank over subsequent five-year periods and use these aggregated loan amounts to compute the loan growth. Thus, when a new loan is initiated between a firm and bank, we can compare the amount borrowed that year (and the following four years) to the amount borrowed in the five years prior to the new loan. Aggregating the loan data over multiple years is helpful as new loans are not initiated every single year between each bank and firm. In this framework, identification is based on changes in lending for a firm-bank pair as housing prices change in the bank’s geographic footprint. We run specifications for firm $$i$$, bank $$j$$ pairs for which time period $$t$$ represents a five-year window. For our fixed effects, we utilize information about the firm’s industry ($$ind$$), size quintile ($$size$$), and state ($$s$$). The initial specification is as follows: \begin{align} \text{Loan Growth}_{ijt} = \hspace{3pt} &\alpha_{ind,size} + \gamma_{st} + \delta_{j} + \vartheta_1 \text{Housing Prices}_{jt-1} \notag \\ &+ \vartheta_2\text{Bank Vars.}_{jt-1} + \vartheta_3\text{Macro Vars.}_{jt-1} + \varepsilon_{ijt}. \label{eq:loan_amt_split} \end{align} (1) Table 2 reports the results with annualized loan growth at the firm-bank level as the dependent variable. Across all our analysis, we include the following bank-level variables—the bank’s size, equity ratio, net income, and cost of deposits—to control for differences in the condition of banks. We also include changes in the unemployment rate in the bank’s states as a regional macroeconomic control. All control variables are from the final year of the prior five-year window, and continuous control variables are scaled by their sample standard deviations to aid in interpreting their economic importance. As in Lin and Paravisini (2013), Columns 1–4 are not estimated within firm but across SIC-2 level and size quintiles ($$\alpha_{ind,size}$$). We also include bank fixed effects ($$\delta_{j}$$) and firms’ state-time fixed effects ($$\gamma_{st}$$), which capture both persistent and time-varying differences across firm locations. These state-time fixed effects address the concern that housing prices or other economic forces in the firm’s state (and not the bank’s states) are yielding our results. Columns 5–8 include firm-bank fixed effects (instead of $$\alpha_{ind,size}$$ and $$\delta_{j}$$) to control for any persistent differences in a firm’s relation with a particular bank. Columns 9 and 10 include bank and firm-time fixed effects. The latter controls for any firm-specific demand-side factors that might affect loan growth. Table 2 Loan growth regression Loan growth (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Housing Price Index, –0.0396*** –0.143** –0.0491*** –0.219*** –0.134*** –0.423*** –0.263*** –0.694*** –0.350*** –0.888*** Bank’s State(s) (0.00614) (0.0598) (0.0147) (0.0778) (0.0251) (0.152) (0.0450) (0.244) (0.0635) (0.322) Top-3 $$\times$$ HPI, Bank’s State(s) 0.0124 0.132 0.155*** 0.218 0.257*** 0.798** (0.0170) (0.0837) (0.0358) (0.247) (0.0659) (0.320) Bank’s Size –0.0250 0.0725 –0.0269 0.0207 –0.184** 0.127 –0.209** 0.187 –0.472*** –0.398** (0.0207) (0.0558) (0.0210) (0.0447) (0.0760) (0.187) (0.0850) (0.155) (0.0944) (0.170) Bank’s Cost of Deposits –0.00554 –0.0167 –0.00554 –0.0204 0.00721 –0.0243 0.00633 –0.0353 0.00964 0.0215 (0.0127) (0.0192) (0.0131) (0.0216) (0.0248) (0.0328) (0.0246) (0.0369) (0.0376) (0.0433) Bank’s Equity Ratio –0.0281*** –0.0110 –0.0269** –0.0117 –0.0586*** –0.0522*** –0.0531*** –0.0424*** –0.0250 –0.00499 (0.0108) (0.00948) (0.0112) (0.0142) (0.0122) (0.0133) (0.0125) (0.0143) (0.0209) (0.0297) Bank’s Net Income 0.00685 0.00807 0.00625 –0.00130 –0.00516 –0.00925 –0.0107 –0.0182 –0.00359 –0.00634 (0.00532) (0.00610) (0.00560) (0.00972) (0.00838) (0.0114) (0.0101) (0.0187) (0.0178) (0.0215) Change in Unemp. Rate, –0.0100* –0.00887* –0.00953 –0.00657 –0.0101 –0.0182 –0.00761 –0.0173 –0.0234 –0.00291 Bank’s State(s) (0.00603) (0.00504) (0.00604) (0.00503) (0.0144) (0.0151) (0.0138) (0.0163) (0.0203) (0.0270) Industry and Size Quintile Fixed Effects Yes Yes Yes Yes No No No No No No Bank Fixed Effects Yes Yes Yes Yes No No No No Yes Yes Firm-Bank Fixed Effects No No No No Yes Yes Yes Yes No No Firm-Year Fixed Effects No No No No No No No No Yes Yes Firm’s State-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes No No Observations 5,191 5,191 5,191 5,191 3,323 3,323 3,323 3,323 3,163 3,163 Firms 2,449 2,449 2,449 2,449 1,054 1,054 1,054 1,054 1,026 1,026 Banks 155 155 155 155 106 106 106 106 105 105 Adjusted $$R^{2}$$ 0.0687 0.0376 0.0687 0.0318 0.500 0.379 0.512 0.303 0.580 0.499 Loan growth (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Housing Price Index, –0.0396*** –0.143** –0.0491*** –0.219*** –0.134*** –0.423*** –0.263*** –0.694*** –0.350*** –0.888*** Bank’s State(s) (0.00614) (0.0598) (0.0147) (0.0778) (0.0251) (0.152) (0.0450) (0.244) (0.0635) (0.322) Top-3 $$\times$$ HPI, Bank’s State(s) 0.0124 0.132 0.155*** 0.218 0.257*** 0.798** (0.0170) (0.0837) (0.0358) (0.247) (0.0659) (0.320) Bank’s Size –0.0250 0.0725 –0.0269 0.0207 –0.184** 0.127 –0.209** 0.187 –0.472*** –0.398** (0.0207) (0.0558) (0.0210) (0.0447) (0.0760) (0.187) (0.0850) (0.155) (0.0944) (0.170) Bank’s Cost of Deposits –0.00554 –0.0167 –0.00554 –0.0204 0.00721 –0.0243 0.00633 –0.0353 0.00964 0.0215 (0.0127) (0.0192) (0.0131) (0.0216) (0.0248) (0.0328) (0.0246) (0.0369) (0.0376) (0.0433) Bank’s Equity Ratio –0.0281*** –0.0110 –0.0269** –0.0117 –0.0586*** –0.0522*** –0.0531*** –0.0424*** –0.0250 –0.00499 (0.0108) (0.00948) (0.0112) (0.0142) (0.0122) (0.0133) (0.0125) (0.0143) (0.0209) (0.0297) Bank’s Net Income 0.00685 0.00807 0.00625 –0.00130 –0.00516 –0.00925 –0.0107 –0.0182 –0.00359 –0.00634 (0.00532) (0.00610) (0.00560) (0.00972) (0.00838) (0.0114) (0.0101) (0.0187) (0.0178) (0.0215) Change in Unemp. Rate, –0.0100* –0.00887* –0.00953 –0.00657 –0.0101 –0.0182 –0.00761 –0.0173 –0.0234 –0.00291 Bank’s State(s) (0.00603) (0.00504) (0.00604) (0.00503) (0.0144) (0.0151) (0.0138) (0.0163) (0.0203) (0.0270) Industry and Size Quintile Fixed Effects Yes Yes Yes Yes No No No No No No Bank Fixed Effects Yes Yes Yes Yes No No No No Yes Yes Firm-Bank Fixed Effects No No No No Yes Yes Yes Yes No No Firm-Year Fixed Effects No No No No No No No No Yes Yes Firm’s State-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes No No Observations 5,191 5,191 5,191 5,191 3,323 3,323 3,323 3,323 3,163 3,163 Firms 2,449 2,449 2,449 2,449 1,054 1,054 1,054 1,054 1,026 1,026 Banks 155 155 155 155 106 106 106 106 105 105 Adjusted $$R^{2}$$ 0.0687 0.0376 0.0687 0.0318 0.500 0.379 0.512 0.303 0.580 0.499 Standard errors in parentheses. * $$p<.10$$, ** $$p<.05$$, *** $$p<.01$$ Columns (1) through (10) are panel fixed effect regressions. Loan Growth is the loan growth for a firm-bank pair, aggregated over five years and annualized. Housing Price Index, Bank’s State(s) is the bank holding company’s housing price index in a given year. Top-3 is an indicator for the three largest banks in our sample. Columns (2), (4), (6), (8), and (10) use the unavailable land measure and its interaction with the national 30-year mortgage rate as instruments. All continuous independent variables are scaled by their respective standard deviations. Standard errors are clustered by firm, bank, and year. In this and following tables, OLS refers to ordinary least squares and IV refers to instrumental variables specifications. Table 2 Loan growth regression Loan growth (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Housing Price Index, –0.0396*** –0.143** –0.0491*** –0.219*** –0.134*** –0.423*** –0.263*** –0.694*** –0.350*** –0.888*** Bank’s State(s) (0.00614) (0.0598) (0.0147) (0.0778) (0.0251) (0.152) (0.0450) (0.244) (0.0635) (0.322) Top-3 $$\times$$ HPI, Bank’s State(s) 0.0124 0.132 0.155*** 0.218 0.257*** 0.798** (0.0170) (0.0837) (0.0358) (0.247) (0.0659) (0.320) Bank’s Size –0.0250 0.0725 –0.0269 0.0207 –0.184** 0.127 –0.209** 0.187 –0.472*** –0.398** (0.0207) (0.0558) (0.0210) (0.0447) (0.0760) (0.187) (0.0850) (0.155) (0.0944) (0.170) Bank’s Cost of Deposits –0.00554 –0.0167 –0.00554 –0.0204 0.00721 –0.0243 0.00633 –0.0353 0.00964 0.0215 (0.0127) (0.0192) (0.0131) (0.0216) (0.0248) (0.0328) (0.0246) (0.0369) (0.0376) (0.0433) Bank’s Equity Ratio –0.0281*** –0.0110 –0.0269** –0.0117 –0.0586*** –0.0522*** –0.0531*** –0.0424*** –0.0250 –0.00499 (0.0108) (0.00948) (0.0112) (0.0142) (0.0122) (0.0133) (0.0125) (0.0143) (0.0209) (0.0297) Bank’s Net Income 0.00685 0.00807 0.00625 –0.00130 –0.00516 –0.00925 –0.0107 –0.0182 –0.00359 –0.00634 (0.00532) (0.00610) (0.00560) (0.00972) (0.00838) (0.0114) (0.0101) (0.0187) (0.0178) (0.0215) Change in Unemp. Rate, –0.0100* –0.00887* –0.00953 –0.00657 –0.0101 –0.0182 –0.00761 –0.0173 –0.0234 –0.00291 Bank’s State(s) (0.00603) (0.00504) (0.00604) (0.00503) (0.0144) (0.0151) (0.0138) (0.0163) (0.0203) (0.0270) Industry and Size Quintile Fixed Effects Yes Yes Yes Yes No No No No No No Bank Fixed Effects Yes Yes Yes Yes No No No No Yes Yes Firm-Bank Fixed Effects No No No No Yes Yes Yes Yes No No Firm-Year Fixed Effects No No No No No No No No Yes Yes Firm’s State-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes No No Observations 5,191 5,191 5,191 5,191 3,323 3,323 3,323 3,323 3,163 3,163 Firms 2,449 2,449 2,449 2,449 1,054 1,054 1,054 1,054 1,026 1,026 Banks 155 155 155 155 106 106 106 106 105 105 Adjusted $$R^{2}$$ 0.0687 0.0376 0.0687 0.0318 0.500 0.379 0.512 0.303 0.580 0.499 Loan growth (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (OLS) (IV) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Housing Price Index, –0.0396*** –0.143** –0.0491*** –0.219*** –0.134*** –0.423*** –0.263*** –0.694*** –0.350*** –0.888*** Bank’s State(s) (0.00614) (0.0598) (0.0147) (0.0778) (0.0251) (0.152) (0.0450) (0.244) (0.0635) (0.322) Top-3 $$\times$$ HPI, Bank’s State(s) 0.0124 0.132 0.155*** 0.218 0.257*** 0.798** (0.0170) (0.0837) (0.0358) (0.247) (0.0659) (0.320) Bank’s Size –0.0250 0.0725 –0.0269 0.0207 –0.184** 0.127 –0.209** 0.187 –0.472*** –0.398** (0.0207) (0.0558) (0.0210) (0.0447) (0.0760) (0.187) (0.0850) (0.155) (0.0944) (0.170) Bank’s Cost of Deposits –0.00554 –0.0167 –0.00554 –0.0204 0.00721 –0.0243 0.00633 –0.0353 0.00964 0.0215 (0.0127) (0.0192) (0.0131) (0.0216) (0.0248) (0.0328) (0.0246) (0.0369) (0.0376) (0.0433) Bank’s Equity Ratio –0.0281*** –0.0110 –0.0269** –0.0117 –0.0586*** –0.0522*** –0.0531*** –0.0424*** –0.0250 –0.00499 (0.0108) (0.00948) (0.0112) (0.0142) (0.0122) (0.0133) (0.0125) (0.0143) (0.0209) (0.0297) Bank’s Net Income 0.00685 0.00807 0.00625 –0.00130 –0.00516 –0.00925 –0.0107 –0.0182 –0.00359 –0.00634 (0.00532) (0.00610) (0.00560) (0.00972) (0.00838) (0.0114) (0.0101) (0.0187) (0.0178) (0.0215) Change in Unemp. Rate, –0.0100* –0.00887* –0.00953 –0.00657 –0.0101 –0.0182 –0.00761 –0.0173 –0.0234 –0.00291 Bank’s State(s) (0.00603) (0.00504) (0.00604) (0.00503) (0.0144) (0.0151) (0.0138) (0.0163) (0.0203) (0.0270) Industry and Size Quintile Fixed Effects Yes Yes Yes Yes No No No No No No Bank Fixed Effects Yes Yes Yes Yes No No No No Yes Yes Firm-Bank Fixed Effects No No No No Yes Yes Yes Yes No No Firm-Year Fixed Effects No No No No No No No No Yes Yes Firm’s State-Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes No No Observations 5,191 5,191 5,191 5,191 3,323 3,323 3,323 3,323 3,163 3,163 Firms 2,449 2,449 2,449 2,449 1,054 1,054 1,054 1,054 1,026 1,026 Banks 155 155 155 155 106 106 106 106 105 105 Adjusted $$R^{2}$$ 0.0687 0.0376 0.0687 0.0318 0.500 0.379 0.512 0.303 0.580 0.499 Standard errors in parentheses. * $$p<.10$$, ** $$p<.05$$, *** $$p<.01$$ Columns (1) through (10) are panel fixed effect regressions. Loan Growth is the loan growth for a firm-bank pair, aggregated over five years and annualized. Housing Price Index, Bank’s State(s) is the bank holding company’s housing price index in a given year. Top-3 is an indicator for the three largest banks in our sample. Columns (2), (4), (6), (8), and (10) use the unavailable land measure and its interaction with the national 30-year mortgage rate as instruments. All continuous independent variables are scaled by their respective standard deviations. Standard errors are clustered by firm, bank, and year. In this and following tables, OLS refers to ordinary least squares and IV refers to instrumental variables specifications. Column 1 shows that, after controlling for industry, size quintile, bank, and firms’ state-time fixed effects, loan growth decreases when housing prices increase in the bank’s location. As discussed in Section 1.4, it is plausible that housing prices may be endogenous to the firm’s borrowing and investment decisions. Specifically, if the bank’s regional housing prices are correlated with any omitted variables related to the commercial lending of the bank or the investment opportunities of the borrowing firm, the estimate of the effect of the bank’s housing exposure may be biased. We believe the source of the bias is likely positive, as housing prices are generally positively correlated with economic growth. A positive bias works against finding the result that an increase in housing prices crowds out commercial lending. Indeed, Column 2 shows that after instrumenting housing prices, the negative effect remains statistically and economically significant and is stronger.17 For a one-standard-deviation increase in housing prices, loan growth falls by 14.3% per year. Column 3 includes an interaction term (Top-3 $$\times$$ HPI, Bank’s State(s)) to separately capture the effect of increasing housing prices for the three largest banks by deposits in the United States in our sample.18 The decision to separate these three banks (Citigroup, Bank of America, and JPMorgan Chase), which are likely the least constrained, is discussed in more detail in Section 1.2. When we separate the top-three banks, the remaining banks still have a statistically significant negative estimate. Column 4 runs the same specification as Column 3 but uses instrumental variables and finds stronger negative results. Columns 5 through 8 include firm-bank fixed effects to control for differences in firm-bank relationships that may affect loan growth. Such differences could be related to the investment opportunities of specific firms or possible endogenous matches between banks and firms, as discussed in Section 1.4. Columns 5 and 6 show that within a firm-bank pair, the negative effect of housing price increases on loan growth is statistically significant and large in magnitude. Comparing these estimates to Columns 1 and 2, the persistent firm-demand and matching effects controlled for in Columns 5 and 6 appear to have a positive bias on the effect of housing prices on loan growth. In Column 6, a one-standard-deviation increase in housing prices is associated with a 42.3% decrease in loan growth. Columns 7 and 8 include an interaction term for housing prices with the top-three banks. In Column 7, we find that the three largest banks have a smaller but still negative crowding-out effect, which is significant at the 1% level. Column 8 presents the results of the instrumented specification. Alternatively, Columns 9 and 10 include firm-time fixed effects to control for any possible, potentially time-varying, firm demand-side factors. The estimates in these columns are based on comparing the loan growth of different banks lending to the same firm in the same time period. The results remain similar in this case to Columns 7 and 8. The fact that the estimates in Columns 9 and 10 are uniformly more negative than the estimates in Columns 3 and 4 again suggest that omitted loan demand factors of the firm likely bias our estimates in a positive direction. 2.2 Bank-level lending Section 2.1 shows evidence of loan growth being reduced for individual firm-bank relationships. Next, we analyze how commercial lending at the bank level is affected by housing price booms. One approach is to utilize balance sheet data for BHCs available from the Call Reports. This approach does not focus on the loans originated to the relevant firms analyzed in our paper. Since we have access to more granular information regarding commercial lending from DealScan, we can improve on the approach by creating a bank-level panel of commercial loans to firms in the DealScan sample. Bank observations in this panel are grouped at the DealScan-lender level by aggregating all new and outstanding lending to the set of firms in the DealScan data set.19 This creates the balance sheet of a bank’s commercial lending for the relevant sample of firms. In contrast to loan originations at a firm-bank relationship level, there is frequent lending in each year at the bank level. Therefore, we are able to create a panel at the annual level. We create the commercial loan balance sheet for each bank by including all loans extended by the bank as a lead agent and as a syndicate participant. We do this to get a complete picture of the lending by the bank to DealScan borrowers. However, as a robustness test in Online Appendix C.1, we also create a panel by only aggregating loans where the bank is a lead lender. To investigate how housing prices affect a bank’s commercial lending across its borrower firms, we use the following regression specification for bank $$j$$ in year $$t$$: \begin{align} \text{Comm. Lending}_{jt} = \hspace{3pt} & \alpha_{j} + \gamma_{t} + \lambda_1\text{Housing Prices}_{jt-1} + \lambda_2\text{Bank Vars.}_{jt-1} \notag \\ & + \lambda_3\text{Macro Vars.}_{jt-1} + \varepsilon_{ijt}. \end{align} (2) We include bank fixed effects ($$\alpha_{j}$$), year fixed effects ($$\gamma_{t}$$), and the same bank-specific controls as in the prior specifications. Table 3 focuses on commercial loans in terms of dollar amounts and the number of outstanding loans to provide alternative measures of lending at the bank level. Columns 1 and 2 present the effect of housing prices on the amount of loans without and with instrumentation, respectively. Column 1 finds that for a one-standard-deviation increase in housing prices in a bank’s states, the dollar amount of commercial loans decreases by 19% ($$e^{-0.208}-1$$). The instrumented specification suggests an even larger effect. Column 3 includes the interaction of an indicator variable for the three largest banks with housing prices in the banks’ states. The positive and statistically significant coefficient suggests that the top-three banks reduce their commercial lending less in response to higher housing prices.20 This result supports the main argument of our paper that constraints at the bank level are driving the crowding-out result. Table 3 Outstanding loans regression Log(dollar outstanding loans) Log(number outstanding loans) (OLS) (IV) (IV) (OLS) (IV) (IV) (1) (2) (3) (4) (5) (6) Housing Price Index, –0.208* –0.735*** –0.964*** –0.208* –0.583*** –0.729*** Bank’s State(s) (0.125) (0.241) (0.262) (0.123) (0.216) (0.244) Top-3 $$\times$$ HPI, Bank’s State(s) 0.475** 0.391** (0.185) (0.169) Bank Size 0.145 0.223 0.244 0.113 0.167 0.206 (0.205) (0.207) (0.203) (0.187) (0.187) (0.183) Bank Equity to Assets –0.127*** –0.119** –0.0732 –0.0798** –0.0708* –0.0324 (0.0477) (0.0538) (0.0466) (0.0391) (0.0419) (0.0414) Bank Income to Assets –0.0375 –0.0147 –0.0257 0.00147 0.0216 –0.00330 (0.0392) (0.0387) (0.0369) (0.0275) (0.0286) (0.0271) Bank Cost of Deposits –0.234* –0.281** –0.202 –0.174* –0.213** –0.105 (0.142) (0.141) (0.145) (0.103) (0.104) (0.114) Change in Unemp. Rate, 0.0158 0.0426 0.0555 –0.0377 –0.0106 –0.0175 Bank’s State(s) (0.0470) (0.0469) (0.0493) (0.0378) (0.0339) (0.0364) Bank Fixed Effects Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Observations 5,215 5,215 5,215 5,215 5,215 5,215 Banks 617 617 617 617 617 617 Adjusted $$R^{2}$$ 0.748 0.745 0.743 0.727 0.724 0.724 Log(dollar outstanding loans) Log(number outstanding loans) (OLS) (IV) (IV) (OLS) (IV) (IV) (1) (2) (3) (4) (5) (6) Housing Price Index, –0.208* –0.735*** –0.964*** –0.208* –0.583*** –0.729*** Bank’s State(s) (0.125) (0.241) (0.262) (0.123) (0.216) (0.244) Top-3 $$\times$$ HPI, Bank’s State(s) 0.475** 0.391** (0.185) (0.169) Bank Size 0.145 0.223 0.244 0.113 0.167 0.206 (0.205) (0.207) (0.203) (0.187) (0.187) (0.183) Bank Equity to Assets –0.127*** –0.119** –0.0732 –0.0798** –0.0708* –0.0324 (0.0477) (0.0538) (0.0466) (0.0391) (0.0419) (0.0414) Bank Income to Assets –0.0375 –0.0147 –0.0257 0.00147 0.0216 –0.00330 (0.0392) (0.0387) (0.0369) (0.0275) (0.0286) (0.0271) Bank Cost of Deposits –0.234* –0.281** –0.202 –0.174* –0.213** –0.105 (0.142) (0.141) (0.145) (0.103) (0.104) (0.114) Change in Unemp. Rate, 0.0158 0.0426 0.0555 –0.0377 –0.0106 –0.0175 Bank’s State(s) (0.0470) (0.0469) (0.0493) (0.0378) (0.0339) (0.0364) Bank Fixed Effects Yes Yes Yes Yes Yes Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes Observations 5,215 5,215 5,215 5,215 5,215 5,215 Banks 617 617 617 617 617 617 Adjusted $$R^{2}$$ 0.748 0.745 0.743 0.727 0.724 0.724 Standard errors in parentheses. * $$p<.10$$, ** $$p<.05$$, *** $$p<.01$$ Columns (1) through (6) are panel fixed effect regressions. Log(Dollar Outstanding Loans) is the log amount of outstanding DealScan loans with each bank in a given year. Log(Number Outstanding Loans) is calculated by taking the log-transform of the number of firms that have outstanding DealScan loans with each bank. Housing Price Index, Bank’s State(s) is the bank holding company’s housing price index in a given year. Top-3 is an indicator for the three largest banks in our sample. Columns (2), (3), (5), and (6) use the u