COLLATERAL CHANNEL AND SMALL BUSINESS LENDING

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1 COLLATERAL CHANNEL AND SMALL BUSINESS LENDING Ari Hyytinen Jyväskylä University School of Business and Economics * Ilkka Ylhäinen Jyväskylä University School of Business and Economics Preliminary draft This draft: August 26, 2014 Abstract We use a large panel of privately held small businesses, matched to detailed regional and zoning data, to study the relation between regional house prices and the use of bank loans by the small businesses. Our econometric identification strategy exploits regional differences in zoning between the home municipalities of the small businesses. We find that controlling for firm-specific unobserved heterogeneity and the endogeneity of regional housing prices, housing price increases facilitate the use of bank loans. The effect is most pronounced among the smallest micro firms and for firms with more tangible assets. Our findings also suggest that the effect may have become weaker due to the financial crisis. These findings support the existence of a collateral channel. Keywords: small business finance, house prices, collateral channel JEL codes: G21, G30 * s: [email protected] and [email protected]. This study uses firm-level data from Suomen Asiakastieto Oy. We would like to thank Etlatieto ltd for making this research possible by providing access to the data and Mika Pajarinen for useful comments and help with the construction of the data. Ylhäinen thanks the OP-Pohjola Group Research Foundation and the Foundation for Economic Education for financial support.

2 2 1 INTRODUCTION Is the collateral channel important for small businesses? Many policymakers and researchers alike think so, because firms debt capacity is often found to be directly linked to their ability to pledge collateral. 1 Especially young and smaller firms with nearly non-existent prior credit history seem to suffer from a limited access to bank credit, if they do not own enough collateralizable assets (see, e.g., Kashyap, Stein, and Wilcox, 1993, Gertler and Gilchrist, 1994; and Berger and Udell 1998). 2 It is therefore not surprising that for startups and small firms, housing and real estate owned by the founder-entrepreneurs appears to be an important source of pledgeable collateral (Black et al. 1996, Robb and Robinson 2012). And yet, what is less well understood is how important variation in the value of such housing assets is for the ability of small businesses to borrow from banks. We address this question by using a large panel of privately held Finnish small businesses, matched to detailed regional and zoning data, to study the relation between regional housing prices and the use of bank loans by the small businesses. Our analysis contributes to the emerging literature which suggests that shocks to the prices of real estate owned by firms and their owners may change firms access to credit and thereby their opportunities to enter a market and/or expand. In one strand of this literature, the focus has been on how changes in the values of collateral and collateralizable assets affect established, publicly listed firms. Gan (2007) uses a difference-in-differences approach and finds that publicly traded firms that suffer from greater collateral losses due to the burst of the Japanese land market bubble in the 1990s reduce investment and are less likely to sustain banking relationships. Furthermore, conditional on not discontinuing their banking relationships, these firms are able to raise less bank credit. Chaney et al. (2012) find that improvements in the collateral values of real estate increase the investments of publicly listed corporations in the US. The result also holds when the possibility that the prices of real estates are associated with local demand shocks are controlled for by using local housing elasticities as the instrument. Cvijanovic (2014) uses data on the actual real estate holdings of US listed firms measured in 1993 and land supply elasticities at the Metropolitan Statistical Areas (interacted with a measure of aggregate real estate prices) as an instrument for the potentially endogenous local real estate prices. 3 She 1 The distribution of collateralizable assets across the corporate and household sectors and the valuation of such assets over time can therefore have important implications to the real economy. For example, shocks to collateral values, and their interaction with credit limits, provide the micro foundation for the theories of credit-driven business cycles (Bernanke and Gertler 1989; Kiyotaki and Moore 1997). 2 Economic theory suggests that the reason for this tight link between collateral and use of debt is that collateral pledging is a means to alleviate both ex ante informational asymmetries and ex post contracting frictions between the lenders and borrowers (see, e.g., Stiglitz and Weiss 1981, Bester 1985, Boot, Thakor, and Udell 1991, Townsend 1979 as examples of for the early theoretical contributions). More recently, it has been suggested that the link may also reflect risk-management considerations: Firms can sustain higher leverage when collateral values are high (e.g., Rampini and Viswanathan 2010). 3 Lin (2014) provides closely related evidence, using partly overlapping sample of U.S. listed firms from 2002 to 2011.

3 finds that an increase in the value of real estate increases firms leverage, reduces their costs of borrowing and lessens covenants. 4 In another strand of the literature, researchers have used either aggregated or regional data to study the link between the value of the real estate assets and firm behavior and entrepreneurial activity. In a seminal UK study, Black et al. (1996) provided time-series evidence that improvements in the value of housing equity are positively associated with the formation of new businesses. Balasubramanyan and Coulson (2013) use business starts in the Metropolitan Statistical Areas to document that the local US housing prices are robustly associated with small (but not with larger) business starts, as measured by employment. Recently, Adelino et al. (2014) have used county-level data from the U.S. and classifications by establishment size and industry to explore the effects of shocks to home values on employment in small firms. They find that in areas where the housing prices have increased, the employment of small businesses has grown and that there is no corresponding effect on employment at the larger firms. Analyses of the effects of shocks to the value of the real estate and housing assets owned by small businesses, startups and their owner-managers are more scant. Robb and Robinson (2012) use Kauffman Firm Survey to document that the (surprisingly extensive) reliance of US startups on formal bank lending as a source of liquidity is closely linked to the ability (and willingness) of the founder-entrepreneurs to pledge their personal assets as collateral for the loans. 5 Robb and Robinson find, in particular, that startups located in the areas that have greater elasticity of housing supply carry more bank debt. They argue that this finding is consistent with the collateral channel, because the greater elasticity makes housing prices less volatile and thus housing assets more pledgeable. Schmalz, Sraer and Thesmar (2013) study firm- and individual level data from France and compare entrepreneurial entry and subsequent expansion of homeowners with those of renters within the same geographical region. Using this identification strategy, they find that owning a house in an area where house prices have raised increases the probability of a homeowner becoming an entrepreneur relative to renters. Their findings also suggest that entrepreneurs are 3 4 There is also a strand of the literature which investigates the importance of liquidation values and redeployability of assets for firms financing choices. For example, Benmelech, Garmaise and Moskowitz (2005) show that better asset redeployability correlates not only with use of larger loans and less expensive borrowing, but also with longer maturities and fewer creditors. Benmelech and Bergman (2009) focus on the case of the U.S. airline industry. They find that assets that are easier to redeploy and liquidate are associated with better credit ratings and narrower credit spreads. More recently, Norden and van Kampen (2013) have studied the association between firms leverage and the structure of their assets using a balanced panel of publicly listed U.S. firms. They document that easily redeployable assets, such as property, plant and equipment, are a key part of the mechanism through the collateral channel manifests itself. They also find that the association between the leverage and the ratio of property, plant and equipment to total assets is stronger for bankdependent firms. 5 More generally, the availability of debt financing appears to have a causal impact on the survival and subsequent growth of entrepreneurial firms; see e.g. Fracassi, Garmaise, Kogan, Natividad (2014) for a regression discontinuity analysis of the post-entry performance of startups, financed by a U.S. lender.

4 able to grow their firms faster when the increase in the value of their houses is greater. This study extends and complements the previous literature in three ways: First, the prior literature is surprisingly silent about the effect of regional housing prices on the use of bank loans by small (non-listed) businesses. 6 And yet, the (housing-asset driven) collateral channel ought to be especially important for small businesses borrowing from banks, as bank debt is often secured and as the smaller firms typically cannot easily tap other sources of external finance, such as public debt or bond markets. Unlike the prior work, we focus on the link between local housing prices and the use of bank debt by privately held small businesses in a bank-based financial system. This empirical setup is novel and provides us with new and complementary insights on an important mechanism through which the collateral channel may be at work. Second, a key econometric challenge that also the prior work has faced is that regional (demand) shocks may simultaneously affect the use of bank loans and housing prices. This source of endogeneity makes the identification of the collateral channel problematic. It is also possible that better regional availability of bank loans inflates housing prices. To address these concerns, we have matched the small business and local housing price data with municipalitylevel zoning data. 7 Inspired by the recent literature that instruments local housing prices with housing elasticities (see, e.g., Chaney et al. 2012; Cvijanovic 2014; see also Saiz 2010), we use a novel proxy for the availability of developable land for construction and use it to instrument the housing price changes. Our primary instrument is the bureaucratically and politically determined share of planned area in the municipalities. The validity of this instrument rests on the assumption that after controlling for fixed regional and firm-specific effects, the differences between the municipalities in this share should be unrelated to the use of bank loans by the small businesses. To err on the conservative side, our empirical analysis also exploits municipality mergers as an additional source of exogenous variation in the supply of land available for construction. Third, a unique feature of the firm-level data available to us is that, besides allowing us to control for unobserved firm-specific heterogeneity, it includes a time-varying credit score ( rating ) for each firm in the data. We use this credit score as a further control, since it allows us isolate the effects of the housing price shocks on the use of bank debt from the shocks to the overall creditworthiness of the small businesses. As far as we are aware, the prior work 4 6 To be more precise, Robb and Robinson (2012) do not link the startups use of bank debt to housing prices directly. The findings of Schmalz, Sraer and Thesmar (2013) refer mostly to non-financial outcomes, as they document only that housing capital gains are associated with the larger debt levels at the startup phase. The interpretation of this link is not clear cut, since it is not entirely clear to what extent their debt measure refers to noncollateralizable and/or non-bank borrowing. If the measure also contains noncollateralizable and/or non-bank debt items, the association with housing capital gains could naturally be weaker. Consistent with this, the findings of Schmalz et al. suggest that the association may get weaker after the startup phase. 7 The micro-level panel data is originally compiled by Asiakastieto ltd from various official registers. Asiakastieto is a commercial provider of credit information and firm-level financial statements data.

5 has not considered the possibility that regional shocks change both the average creditworthiness of firms located a region and the region s housing prices. The main result of this paper is that if housing prices increase in a region, it facilitates the use of bank loans by the privately held small businesses located in the region. Four further findings of ours allow us to argue that this effect is due to the collateral channel. First, the effect is most pronounced among the smallest micro firms. These firms are indeed the ones for whom collateralizable housing assets should be most relevant. Second, the effect of the housing prices is larger for firms with more tangible assets. This suggests that at least a part of the effect is related to the real estate and housing assets owned by the firm. 8 Third, we find that the effect of housing prices on the use of bank debt, but not on the total use of debt. This finding provides additional support for the existence of a collateral channel, because total debt contains non-collateralizable and/or non-bank debt items, such as trade credit. The effect on the total debt should therefore be weaker. Finally, our estimates suggest that the effect of housing prices on the use of bank loans is somewhat weaker after the onset of the financial crisis. This, too, is consistent with the collateral channel being at work: Because the crisis originated from the US housing markets and highlighted the uncertainty in how well housing assets protect lenders from the credit losses, it is possible that Northern-European banks have, as a result, become less willing to accept housing or real estate assets as collateral for their small business lending. The rest of the paper is organized as follows: Section 2 describes the data. Section 3 introduces the empirical methods. Section 4 presents the results. Section 5 concludes. 5 8 Our empirical setup is purposefully agnostic about the actual ownership of the real estate and/or housing assets. We have two reasons for this modelling choice. First, a priori, it is not clear whether it is the real estate owned by a small business (the corporation) or by its owner-entrepreneurs that matters for the debt capacity of the firm. The prior literature suggests that both may matter. Second, in small firms, even if they are incorporated, the line between the corporate and personal real estate assets easily gets blurred. For example, it is not uncommon that an entrepreneur lives in a house that her firm owns, or that the firm s office is located at the home of its owner(s).

6 6 2 DATA 2.1 Data sources The data used in this study is compiled of three components: firm-level panel data, regional house price data and municipality-level zoning data. The firm-level data is an unbalanced panel of Finnish firms over years and was compiled by Asiakastieto ltd, a provider of firm and credit score data in Finland. The data include financial statements and associated supplementary data, such as the address (location) of the firms. The financial statement data originate from the Finnish Trade Register, an official register of Finnish firms. The dataset also contains a commercial credit score (and the associated credit ratings ) for each firm, as produced by Asiakastieto. The regional house price data is measured at the zip code -level and are obtained from the Statfin database of Statistics Finland. These data refer to prices (in euros) per square meter of old privately financed condominiums. These annual statistics are compiled from the asset transfer tax data of the Finnish Tax Administration. The house prices data cover the average five-digit zip code level prices of the completed transactions and the number of transactions in each zip code area. We mainly use price data at the two-digit zip code level, which we calculate as a weighted average using the five-digit zip code data. We use the number of transactions in the five-digit zip code areas as the weight. The municipality-level zoning data are obtained from the OIVA databases of the government s environmental management services, operated by the Ministry of the Environment, the Finnish Environmental Institute, the Centres for Economic Development, Transport and the Environment (ELY Centres), and the Regional State Administrative Agencies. The zoning data used in the econometric analysis refer to the percentage of the planned area of the total area of municipalities. The decision to change the planned area is an outcome of a relatively complex bureaucratic and political zoning process that is subject to a number of laws and regulations. The ultimate decision-maker is the municipal council, the members of which are elected in every fourth year. The Finnish municipalities use a proportional election system and multiple parties participate in the elections (and then have seats in the councils). 9 The OIVA database also allows us to also identify the mergers of the municipalities that have taken place during our sample period. 2.2 Construction of the sample The original firm-level data includes all firms for which Asiakastieto has been able to collect financial statements data from the official registers. Using this as the starting point, the estimation sample constructed as follows: First, we only include non-farm and non-financial corporations (limited liability firms). 10 Se- 9 The zoning data are based on the information available in the OIVA databases as of September The following industries were dropped from the sample based on the Standard Industry Classification (SIC) 2008, and if not available, SIC 2002: Agriculture, forestry and fishing;

7 cond, firm-year observations for which total assets are negative are dropped from the sample. Third, the housing prices are matched to the firm-level data using the zip codes of firms addresses and year. 11 Fourth, the zoning data are matched to the financial statement data based on the municipality codes and year. 12 This matching means that if a firm does not have matching information on the zip code or on the municipality, it gets dropped from the sample. Our baseline analysis focuses on years and thus refers to a period before the financial crisis. To focus on the smaller end of the firm size distribution, the estimation sample is further restricted to firms having fewer than 50 employees. Furthermore, the panel is restricted in such a way that firms having less than three time series observations or gaps in the firm-specific time series are removed from the sample. This sample restriction is imposed, because the econometric models used in this study require using lagged variables. 13 A data limitation that we have to deal with is that we do not have data on the real estate or housing assets owned by the small businesses or by their owner-entrepreneurs. It is not uncommon that an entrepreneur lives in a house that her firm owns, or that the firm s office is located at the home of its owner(s). 14 Both kinds of assets can matter for the debt capacity of the small businesses. We take this into account in three ways: First, we try to err on the conservative side and measure housing prices mainly at the more aggregated two-digit zip code level. This is appropriate, if entrepreneurs homes are located a bit farther from where their firms are. We also use the five-digit level price data in a robustness test. The use of this more disaggregated data would be appropriate, if the relevant real estate assets are located at the firms home address, or very nearby. Second, we explore whether our findings change when we focus on the small business with more tangible assets. If they become stronger, it suggests that the real estate assets owned by the firm may matter more. Third, our main results rely on instrumental variable methods, which reduce the concern that measurement error biases our results. 7 Financial and insurance activities; Activities of membership organizations; Public administration and defense; Compulsory social security; Activities of extraterritorial organizations and bodies; Industry unknown. 11 Because the matching of the house prices data is made at the zip code level, firms having their addresses in a box number location (i.e., zip codes ending with one or in nonstandard way) are removed from the estimation sample. 12 The municipality divisions are based on the situation that prevailed in Two additional observations about the construction of the estimation sample are in order: First, as we will explain below, our empirical analysis divides the firms into three size classes. Missing observations for a firm are allowed, if its size classification changes during the sample period and if there remains a sufficient number of observations for a firm to be included in the estimation. Second, we also use a longer sample period of in the analysis. Since the employment figures used for the size classification do not cover all firms in year 2011, the missing values are replaced with the values from the previous period. 14 A manual inspection of the data indeed suggests that a non-negligible number of firms in the sample are located in residential areas.

8 2.3 Variable definitions Our primary dependent variable is Bank debt. It refers to the ratio of outstanding debt from financial institutions to total assets at period t. To avoid outliers, Bank debt values above one are set to one. Most of these instances occur when a firm has negative equity. We use as an alternative outcome variable Total debt. It is equal to the ratio of total debt to total assets. As we explain in greater detail below, this alternative outcome variable is used to demonstrate that the effects we uncover are related to the collateral channel. Our key explanatory variable is House prices. It refers to the two- or fivedigit average prices of old (i.e., previously used and owned) condominiums in each region and is measured in thousands of euros per square meter. Our instruments are defined as follows: Planned area measures the ratio of the planned area to the total area in a given municipality. We allow the instrument have a nonlinear impact on the house prices by also using Planned area squared (= 100 (Planned area)^2). Our empirical analysis also exploits municipality mergers as an additional source of exogenous variation in the supply of land available for construction. Municipality merger is a dummy taking a value of one in the year when the municipality merges and during all periods thereafter, and is zero before the merger. We discuss the relevance and validity of these instruments in greater detail below. We use the following control variables: Firm age is measured by Ln(1+Age) where age is calculated by subtracting the year of birth from the current year. 15 Firm size is measured by Ln(Total assets), where the total assets are measured at t-1. Credit score refers to the commercial credit score of each firm at t-1, as computed by Asiakastieto. It obtains values from 3 to 100, with lower values indicating better creditworthiness. We scale the credit score for the estimations by dividing it by We keep the list of firm-level control variables short for two reasons. On the one hand, we try to avoid the problem of bad controls by not adding variables to which, e.g., anticipated use of bank loans might affect. Second, the credit score is a summary measure which also captures many typically used controls, such as the lagged equity ratio Descriptive statistics The descriptive statistics for firms with fewer than 50 employees are provided in Table 1. The table covers the sample period and includes in total firms. 17 The table shows that the mean percentage of bank debt in the balance sheet is about 14%. The numbers imply that the mean age of the firms in the sample is 10.5 years and the average size of the balance sheet is about The year of birth is defined as the year the firm was registered in the Finnish Trade Register. In the rare instances when the firm age turned out to be negative, the observation was dropped. 16 The credit scores for 2006 are not available in the data because of a change in the dataset. Therefore, values from the previous period are used for that year. If such a value was missing, the following year s credit score was used instead. 17 Table A1 in the Appendix reports the descriptive statistics for the longer sample period of This larger sample includes firms.

9 000 euros. The average credit score is 26, which indicates a credit rating A+ (i.e., satisfactory + ). The average zip code level house prices are a bit over 1800 euros per square meter. The percentage of the planned area of the total area of the municipality is on average 15.6% Table 1: Descriptive statistics for variable mean sd min p50 max NT Bank debt Ln(1+Age) Ln(Total assets) Credit score House prices (5-digit) House prices (2-digit) Planned area Planned area squared Municipality merger Notes: The table reports descriptive statistics for the sample of firms smaller than 50 employees for The statistics include the mean, standard deviation, minimum, median, and maximum, respectively. Bank debt is a ratio of loans from financial institutions to total assets. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years. Ln(Total assets) is a natural logarithm of the total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (scaled by dividing by 100), where lower values imply higher creditworthiness. House prices measure the average regional prices of previously owned condominiums at the five- and two-digit zip code level in thousand euros per square meter, respectively. Planned area measures the share of planned area of the total area in the municipality. Planned area squared is defined as (Planned area*100)^2/100. Municipality merger is a dummy taking a value equal to one in and all the periods after the municipality merger. NT is the number of firm-year observations. Table 2 reports the correlation matrix for the variables used in the estimations. It shows, firstly, that the house prices are negatively correlated with the bank debt. Secondly, the share of planned area in the municipality is positively correlated with regional house prices. A potential explanation for these correlations is that in regions that are doing well in economic terms (e.g., in larger cities and urban areas, with more economic activity), house prices are higher. It is plausible that in such regions, small businesses can rely more on internal financing and have better access to external financing other than bank debt and that the share of (already) planned and thus developed area is greater. These characteristics of the raw data suggest that it is important to carefully control for permanent firm- and region specific heterogeneity and to allow for 18 Graph A1 the appendix shows the regional house prices development in the two-digit zip code areas over the period. The Helsinki region (zip code: 00) and the Uusimaa region (00-10) in general show higher house prices than the rest of the country. The financial crisis year 2009 shows a dip in many of the series. Several areas located in the Eastern and Northern Finland among other show negative nominal prices development during more recent periods.

10 potential nonlinearities in the relation between the share of planned area and regional house prices in the econometric analysis. 10 Table 2: Correlation matrix for Bank debt ln(1+age) ln(total assets) Credit score House prices (5-digit) House Prices (2-digit) Planned area Municipality merger Bank debt ln(1+age) * ln(total assets) * * Credit score * * * House prices (5-digit) * * * House prices (2-digit) * * * * * Planned area * * * * * Municipality merger * * * * * * * Notes: The table reports pairwise correlations between the variables for Bank debt is a ratio of loans from financial institutions to total assets. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years. Ln(Total assets) is a natural logarithm of the total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (scaled by dividing by 100), where lower values imply higher creditworthiness. House prices measure the average regional prices of previously owned condominiums at the five- and two-digit zip code level in thousand euros per square meter, respectively. Planned area measures the share of planned area of the total area in the municipality. Municipality merger is a dummy taking a value equal to one in and all the periods after the municipality merger. The correlations significant at the 5 percent level or better are marked with an asterisk (*).

11 11 3 EMPIRICAL METHODOLOGY 3.1 Econometric model and methods of estimation We build our analysis on the following econometric model: y!" = δy!,!!! + x!!" β + γz!"#$%&',! + α! + μ! + ε!", (1) where is the firms use of debt, is a vector of firm-level control variables, z zipcode,t y it is the measure of zip code level house prices, is a firm-specific fixed effect absorbing also the fixed regional characteristics, μ! refers to year dummies that control for the overall macroeconomic conditions, and ε it is an error term. We estimate this model in three ways: First, we impose restriction δ = 0 (no dynamics) and estimate (1) using the standard fixed effects (FE) within estimator. Second, we continue to impose δ = 0 and use the first-differenced instrumental variables (FD-IV) estimator. This estimator assumes a weak exogeneity of the regressors and allows us to account for the endogeneity of the house prices. 19 Third, we allow for dynamics (δ 0) by using the Arellano-Bond GMM estimator Endogeneity of house prices and instruments x it If there are unobservable time-varying regional factors that affect both the firms use of bank loans and regional house prices, house prices in (1) can be endogenous. Examples of such factors are demand shocks and increased regional availability of bank loans. They may both inflate house prices and increase the use of bank debt by the small businesses. This endogeneity problem can be solved with an instrumental variable that is correlated with the house prices but uncorrelated with the dependent variable, besides its effect via the house prices. The. Based on this logic, the earlier studies (Chaney et al and Adelino et al. 2014) on the house-price driven collateral channel have used a measure for the inelasticity of land supply, perhaps interacted with another variable, as an instrument for house prices. In areas where land supply is relatively inelastic, shocks to the housing demand translate into higher house prices. The main instrument used in this paper, Planned area, refers, as we explained above, to the percentage of the planned area of the total area in the municipality of a firm s location. This is a bureaucratically and politically determined quantity and it exploits regional differences in zoning between different municipalities. The validity of the instrument rests on the assumption that after controlling for fixed regional and firm-specific effects, the differences between α i 19 Weak exogeneity allows feedback effects between the dependent and independent variables. 20 The Arellano-Bond estimator used in this paper refers to the two-step GMM estimator with robust Windmeijer-corrected standard errors. The estimator requires that the firstdifferenced residuals do not show second-order serial correlation. This assumption can be tested by the Arellano-Bond serial correlation test for the first-differenced residuals. The Sargan test for overidentification restrictions can be used to test the validity of the moment restrictions assuming that errors are independent and identically distributed.

12 the municipalities in this share should be unrelated to the use of bank loans by the small businesses. The previous literature suggests that this instrument ought to be relevant (see, e.g., Saiz 2010, Chaney et al and Adelino et al. 2014): In the areas where the share of (already) planned area is low to begin with, an increase in the share of planned area increases land supply on the market and therefore lowers house prices. At the very least, when the local potential supply of land is elastic, an increase in the housing demand ought to translate into more construction rather than in higher land and therefore housing prices. There is, however, a potential nonlinearity between regional house prices and planned area, as there are a couple of rationales which suggests that the relation between house prices and planned area might be positive after a threshold: First, in the areas where the share of (already) planned area is high, new developable land available in prime or easily usable locations may be scant. This suggests that in such areas land supply is relatively inelastic. This means that shocks to the housing demand translate into higher house prices. Second, in the areas where the share of (already) planned area is high to begin with, additional zoning may disproportionately affect the desirability of the areas that have planned and developed earlier. If additional zoning improves the overall infrastructure of an area, this may increase the value of existing, already developed land. To allow for this type of nonlinearities in the simplest possible way, we add the second-order polynomial of the planned area as the instrument. We also exploit municipality mergers, Municipality merger, as an additional instrument. Municipality mergers increase the amount of land available for construction, as they allow zoning and planning across the former municipal borders. They should therefore increase supply elasticity. Figure 1 displays the model fit from a first-stage type of specification where the house prices are regressed on the linear and squared Planned area, Municipality merger and time dummies and (regional) fixed effects. The fixed effects control, for instance, for permanent differences between urban and rural regions. In line with the above discussion, the figure shows a u-shaped pattern in the relation between house prices and planned area: After a cut-off point, the relation switches sign and becomes positive. 21 We stress that having this kind of nonlinearity is not required for the identification of the effect of house prices on small business borrowing Two additional observations are in order: First, the municipalities located on the positively sloped part of the curve consist of municipalities located in or close to more urban regional hubs. Half of these municipalities are located in the Uusimaa region near Helsinki, which is Finland s capital. Second, there are eight municipalities in which the share of the planned area is greater than 30% (Imatra, Järvenpää, Kauniainen, Kerava, Lahti, Raisio, Turku, and Vantaa). One of these municipalities (Kauniainen) is a clear outlier: Its share of planned area is 99.3%. 22 Indeed, Figure A2 in the appendix shows that in the longer sample that also includes the financial crisis, there is a negative relation between regional house prices and planned area once the permanent regional heterogeneity is controlled for.

13 13 House prices, euros per sq meter House prices and zoning Share of planned area, % Figure 1: House prices and planned area Notes: The figure shows the predicted relationship between regional house prices and the share of planned area in municipalities over the period. The model regresses 2- digit regional House prices on Planned area and its square, a municipality merger dummy, and firm and time dummies. The standard errors are adjusted for firm-level clustering.

14 14 4 RESULTS 4.1 Baseline results Our baseline results refer to period and use regional house prices that are measured at the (more aggregated) two-digit zip code level. We report the baseline results for the FE, FD-IV and the Arellano-Bond methods. We start with the FE (within) method, reported in Table 3. Columns 1-3 use three different subsamples, determined on the basis the firm size. The three subsamples overlap and consist of firms having less than five, ten, and fifty employees, respectively. The results show that the coefficient of house prices is positive and significant at better than the 5% level in each column. Its size is the largest for the smallest firms (column (1)). These findings are consistent with the view that higher housing values are associated with greater use of bank debt by small businesses. 23 Table 3: FE (within) estimates for (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt House prices (2-digit) ** ** *** (0.009) (0.007) (0.006) Ln(1+Age) *** *** *** (0.009) (0.007) (0.006) Ln(Total assets) *** *** *** (0.002) (0.002) (0.002) Credit score (0.007) (0.006) (0.005) NT rho r Notes: The table reports the fixed effects (within) estimates of the effects of regional house prices on small firms use of bank loans over the period. The results are reported separately for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is defined as a ratio of bank loans to total assets at t. The independent variables are defined as follows: House prices measure the average prices of previously owned condominiums at the two-digit zip code level measured in thousand euros at t. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in 23 We want to make two further observations about these baseline results. First, we would not get a positive coefficient for the house prices if we did not control for the unobserved permanent firm-specific heterogeneity: Unreported OLS regressions reveal that the coefficient is negative and significant, in line with the raw correlations, when no controls are used. The coefficient remains negative but turns insignificant when municipality-level regional fixed effects and two-digit industry fixed effects are included in the regression. Second, we acknowledge that the distribution of the dependent variable has a probability mass at zero, as many small businesses do not use bank debt. This suggests that we might want to consider using a Tobit model. Using an IV Tobit model with pooled data and the same instruments as in the baseline analysis gives a negative and significant coefficient for the house prices. This finding is consistent with the OLS results, as this IV-Tobit analysis does not control for the unobserved permanent firm-specific heterogeneity.

15 years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firm-year observations. Rho measures the intra-class error correlation. R2 stands for r-squared. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < The standard errors are clustered at the firm level. 15 The baseline results for the FD-IV model are shown in Table 4. These estimates allow for endogenous house prices and require weak (rather than strong) exogeneity of the other explanatory variables. The first stage regressions, reported at the bottom of the table, indicate that the zoning instrument and its square are correlated with regional house prices and have statistically highly significant first-stage coefficients. The municipality merger instrument is highly significant at the 1 % level, too: Its negative coefficient suggests that municipality mergers result enhance housing supply and result in lower house prices. The Kleibergen-Paap Wald F statistics for weak identification of the first-stage regression vary between and These statistics greatly exceed the Stock- Yogo critical values and thus suggest that the instruments are not weak. Hansen s J statistics do not reject the null hypothesis that the overidentification restrictions are valid. The results in Table 4 indicate that higher regional house prices increase the use of bank loans by small businesses. The coefficient is a bit larger for the smaller firms (columns (1) and (2)). However, the relation between the regional house prices and the use of bank loans is somewhat imprecisely measured when the FD-IV method is used: The coefficient of the house prices is significant at the 10% level in columns (2) and (3), but insignificant in column (1). Table 4: FD-IV estimates for (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt House prices (2-digit) * * (0.071) (0.059) (0.049) Ln(1+Age) ** *** *** (0.009) (0.008) (0.007) Ln(Total assets) *** *** *** (0.002) (0.002) (0.002) Credit score * ** *** (0.006) (0.005) (0.005) NT Hansen s J statistics Kleibergen-Paap Wald statistics [0.1481] [0.1328] [0.2078] First-stage House prices House prices House prices Planned area *** *** ***

16 16 (instrument) (0.052) (0.045) (0.040) Planned area squared *** *** *** (instrument) (0.001) (0.001) (0.001) Municipality merger *** *** *** (instrument) (0.002) (0.001) (0.001) Notes: The table reports the first-differenced IV model estimates on the effects of regional house prices on small firms use of bank loans over the period. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is defined as a ratio of bank loans to total assets at t. The independent variables are defined as follows: House prices measure the average prices of previously owned condominiums at the two-digit zip code level measured in thousand euros per square meter at t. The instruments include Planned area, its square, and Municipality merger dummy taking a value of one in and after the period of merger. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firmyear observations. The first-stage results for the instruments are reported omitting the rest of the output from the table. Hansen s J statistics for overidentifying restrictions [p-values in brackets] and the Kleibergen-Paap Wald F statistics for weak identification of the firststage regression are provided. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Our baseline results for the dynamic version of model (1), estimated by the Arellano-Bond method, are provided in Table 5. For the implementation of this method, we assume that firm size is pre-determined and that credit rating is endogenous. These assumptions are conservative, as they allow for feedback effects with the dependent variable and take into account the fact that equity ratio has been used in the computation of the credit rating. The conventional Arellano-Bond instruments consist of the lagged levels of the endogenous and pre-determined variables and the first-differenced exogenous variables. The share of planned area, its square, and municipality merger dummy are included (in differenced form) as additional instruments among the conventional Arellano-Bond instruments. Table 5 provides us with four main findings. First, the lagged dependent variable is positive and highly significant at the 1 % level. This finding indicates that there is persistence in the use of bank loans. 24 Second, the house prices variable is positive and significant at the 1%, 10%, and 5% level in Columns 1-3, respectively. Third, the coefficient estimate of the house prices variable is largest and most significant among the smallest micro firms that have less than five employees. For these firms, the long-run effect of the house prices on Bank debt is This means that in the long-term, a hundred euro increase in regional house prices increases the use of bank debt of the smallest micro firms by about 1.69 percentage points. This is not a small effect, given that the mean of bank debt for this size class is about 13.6%. Finally, it is of interest to note that the credit score, which is now instrumented, switches sign relative to the FD-IV estimates. 24 The Arellano-Bond test for zero second-order autocorrelation in the first differenced residuals and the Sargan test for the over-identification restrictions do not reject the null at the 5% level in any of the specifications.

17 17 Table 5: Arellano-Bond estimates for (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt Bank debt (t-1) *** *** *** (0.023) (0.020) (0.020) House prices (2-digit) *** * ** (0.022) (0.018) (0.016) ln(1+age) * (0.012) (0.011) (0.012) ln(total assets) * (0.013) (0.013) (0.016) Credit score ** * (0.017) (0.015) (0.013) NT Long-run effects *** * ** (0.061) (0.052) (0.044) Arellano-Bond test [0.1973] [0.1526] [0.0576] Sargan test [0.2380] [0.1599] [0.1395] Notes: The table reports the dynamic Arellano-Bond GMM estimates on the effects of regional house prices on small firms use of bank loans over the period. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The independent variables are defined as follows: Bank debt (t-1) is the lagged dependent variable measured at t-1. House prices measure the average regional prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. Planned area, its square, and Municipality merger dummy taking a value of one in and after the period of merger, are included as additional instruments. All the models include year dummies. House prices and Credit score are defined as endogenous and ln(total assets) is defined as predetermined. NT is the number of firmyear observations. The long-run effects of house prices are computed as follows: _b[house prices(t)]/(1-_b[bank debt (t-1)]). The Arellano-Bond test for the second-order autocorrelation in the first differences residuals and the Sargan test for the overidentification restrictions are reported [p-values in brackets]. The estimations use a two-step estimator with robust Windmeijer-corrected standard errors. The Sargan test is based on conventional standard errors. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Is there a collateral channel at work? Our baseline results suggest that if housing values increase, small businesses rely more on bank debt. Moreover, this effect appears to be more pronounced among the smallest firms. In this subsection, we take a look at the possible reasons for these findings. To this end, we ask three questions: First, is the effect greater for firms with more tangible assets? Second, is the effect smaller after the onset of the financial

18 crisis? Third, is there an effect on total debt? As we explain below, it is more likely that there is a collateral channel at work if we answer affirmatively to the first and second question, and negatively to the third question Is the effect greater for firms with more tangible assets? So far, we have been agnostic about whether the real estate or housing assets are owned by the small businesses or personally by their owner-entrepreneurs. To explore this, we drop the firms who are the lowest quartile in the distribution of tangible assets from the sample. This restriction removes firms for whom the ratio of tangible assets to total assets is less than 3%. The FD-IV results are shown in Table 6. The coefficient of the house prices is positive and highly significant. Compared to our baseline FD-IV estimates, the size of the coefficient increases quite a bit. This suggests that the real estate (and other tangible assets, whose value is correlated with housing prices) owned by the small firms facilitates the use of bank loans. Table 6: FD-IV estimates for , firms with tangibles assets (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt House prices (2-digit) ** *** ** (0.090) (0.073) (0.061) Ln(1+Age) ** *** *** (0.011) (0.009) (0.008) Ln(Total assets) ** (0.003) (0.003) (0.002) Credit score ** *** (0.007) (0.006) (0.005) NT Hansen s J statistics Kleibergen-Paap Wald statistics [0.2261] [0.1757] [0.2811] First-stage Planned area *** *** *** (instrument) (0.060) (0.052) (0.046) Planned area squared *** *** *** (instrument) (0.001) (0.001) (0.001) Municipality merger *** *** *** (instrument) (0.002) (0.001) (0.001) Notes: The table reports the first-differenced IV model estimates on the effects of regional house prices on small firms use of bank loans over the period. The subsample focuses on tangible firms defined as those above the lowest quartile in tangibility. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is defined as a ratio of bank loans to total assets at t. The independent variables are defined as follows: House prices measure the aver- 18

19 age prices of previously owned condominiums at the two-digit zip code level measured in thousand euros per square meter at t. The instruments include Planned area, its square, and Municipality merger dummy taking a value of one in and after the period of merger. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firm-year observations. The first-stage results for the instruments are reported omitting the rest of the output from the table. Hansen s J statistics for overidentifying restrictions [p-values in brackets] and the Kleibergen-Paap Wald F statistics for weak identification of the first-stage regression are provided. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < We report the Arellano-Bond estimates in Table 7. We again find that find a positive and statistically significant for the house prices. The long-run coefficients are 0.189, 0.120, and in columns 1-3, respectively. They are all significant at better than the 10% significance level. Table 7: Arellano-Bond estimates for , firms with tangibles assets (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt Bank debt (t-1) *** *** *** (0.030) (0.025) (0.031) House prices (2-digit) ** ** * (0.029) (0.024) (0.020) Ln(1+Age) * *** ** (0.017) (0.015) (0.019) Ln(Total assets) ** * (0.025) (0.021) (0.034) Credit score (0.021) (0.018) (0.016) NT Long-run effects ** ** * (0.076) (0.061) (0.052) Arellano-Bond test [0.2121] [0.2593] [0.1114] Sargan test [0.0051] [0.0302] [0.0559] Notes: The table reports the dynamic Arellano-Bond GMM estimates on the effects of regional house prices on small firms use of bank loans over the period. The subsample focuses on tangible firms defined as those above the lowest quartile in tangibility. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The independent variables are defined as follows: Bank debt (t-1) is the lagged dependent variable measured at t-1. House prices measure the average regional prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. Planned area in the municipality at t, its square, and Municipality merger dummy taking a value of one in and after the period of merger, are 19

20 included as additional instruments. All the models include year dummies. House prices and Credit score are defined as endogenous and ln(total assets) is defined as predetermined. NT is the number of firm-year observations. The long-run effects of house prices are computed as follows: _b[house prices(t)]/(1-_b[bank debt (t-1)]). The Arellano-Bond test for the secondorder autocorrelation in the first differences residuals and the Sargan test for the overidentification restrictions are reported [p-values in brackets]. The estimations use a two-step estimator with robust Windmeijer-corrected standard errors. The Sargan test is based on conventional standard errors. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Is the effect smaller after the onset of the financial crisis? The financial crisis originated from the US housing markets and highlighted the risks in using real estate and housing assets as collateral. It is possible that Northern-European banks have become less willing to accept housing or real estate assets as collateral for their small business lending. If they have, there may be a structural break in the relation between house prices and the use of bank loans by small firms. Such a break would be consistent with the view that our results are related to a collateral channel that works through housing and real estate assets. To explore whether the financial crisis has changed how the house prices affect small business lending, we extend the sample to cover To allow for a break, we add to model (1) an interaction term between the house prices and a dummy for the post-2009 period. We include as additional instruments the interactions of the instruments (Planned area, and its square) with the post-crisis dummy; see Figure A2 in the appendix on how the first-stage works. The FD-IV estimates based on the longer sample are provided in Table The results show that House price still obtains a positive and significant coefficient in each column. We also find that the interaction term between house prices and post-2009 dummy is negative and significant. This shows the link between regional house prices and small firms use of bank loans is weaker in the post-crisis period. This finding suggests that there has been a structural change in the relation between house prices and small business borrowing in the aftermath of the financial crisis. The null hypothesis that the coefficients of House price and the interaction term are jointly zero is rejected at better than the 5% level in each column. Table 8: FD-IV estimates for (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt House prices (2-digit) ** ** *** 25 Table A1 in the appendix gives the descriptive statistics and the correlation matrix for this longer sample. 26 The first-stage regressions indicate that the instruments are highly significant at the 1% level. The Kleibergen-Paap Wald F statistics vary between and These findings indicate that weak identification ought not be a concern for us.

21 21 (0.041) (0.034) (0.029) Post-crisis*house prices ** ** *** (0.002) (0.002) (0.001) Ln(1+Age) *** *** *** (0.006) (0.005) (0.004) Ln(Total assets) *** *** *** (0.001) (0.001) (0.001) Credit score *** *** *** (0.004) (0.004) (0.003) NT Hansen s J statistics Kleibergen-Paap Wald statistics [0.4635] [0.2194] [0.1403] First-stage House prices House prices House prices Planned area *** *** *** (instrument) (0.072) (0.061) (0.054) Planned area squared *** *** *** (instrument) (0.001) (0.001) (0.001) Municipality merger *** *** *** (instrument) (0.001) (0.001) (0.001) Post-crisis planned area *** *** (instrument) (0.008) (0.008) (0.008) Post-crisis planned area squared *** *** (instrument) (0.0002) (0.0002) (0.0002) First-stage Post-crisis house prices Post-crisis house prices Post-crisis house prices Planned area *** *** *** (instrument) (0.351) (0.307) (0.283) Planned area squared *** *** *** (instrument) (0.006) (0.005) (0.005) Municipality merger *** *** *** (instrument) (0.004) (0.003) (0.003) Post-crisis planned area *** *** (instrument) (0.219) (0.222) (0.223) Post-crisis planned area squared *** *** (instrument) (0.005) (0.005) (0.005) Notes: The table reports the first-differenced IV model estimates on the effects of regional house prices on the use of bank loans over the period. The results are reported separately for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The endogenous variables are defined as follows: House prices measure the average prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between a dummy for the period

22 and house prices. The instruments include Planned area, Planned area squared, Municipality merger dummy, Post-crisis planned area, and Post-crisis planned area squared. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firmyear observations. The first-stage results for the instruments are reported omitting the rest of the output from the table. Hansen s J statistics for overidentifying restrictions [p-values in brackets] and the Kleibergen-Paap Wald F statistics for weak identification of the firststage regressions are provided. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Table 9 reports the dynamic Arellano-Bond GMM estimates for the longer sample. These analyses include a second lag of the dependent variable, because it turned out to be necessary for us to be able eliminate serial correlation in the first-differenced residuals. The instruments are the same as before, except that the interactions of Planned area and its square with the post-crisis dummy are also used. As the table shows, the house prices continue to have a positive effect on the bank loans. 27 An exception to this is column 2, where the term is positive but insignificant. The interaction term is negative throughout. The table also shows that the long-run effects are smaller when the longer sample covering the financial crisis is used. 22 Table 9: Arellano-Bond GMM estimates for (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt Bank debt (t-1) *** *** *** (0.019) (0.017) (0.016) Bank debt (t-2) *** *** *** (0.008) (0.007) (0.006) House prices (2-digit) *** *** (0.008) (0.007) (0.006) Post-crisis*house prices *** * *** (0.001) (0.001) (0.001) Ln(1+Age) (0.010) (0.009) (0.009) Ln(Total assets) (0.011) (0.011) (0.014) Credit score (0.015) (0.012) (0.011) NT Long-run effects pre *** *** 27 The Arellano-Bond test for the second-order autocorrelation in the first-differenced residuals does not reject the null of zero autocorrelation. The Sargan test for overidentification restrictions, on the other hand, rejects the null hypothesis for this model and sample. This rejection may be driven by the assumption that the errors ought to be i.i.d.

23 23 crisis (0.030) (0.025) (0.021) Long-run effects post *** *** crisis (0.026) (0.022) (0.018) Arellano-Bond test [0.2320] [0.7087] [0.8321] Sargan test [0.0003] [0.0018] [0.0000] Notes: The table reports the dynamic Arellano-Bond GMM estimates on the effects of regional house prices on small firms use of bank loans over the period. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The independent variables are defined as follows: Bank debt (t-1) and Bank debt (t-2) are the lagged dependent variables measured at t-1 and t-2, respectively. House prices measure the average regional prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between house prices and a dummy for the period. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. Planned area, Planned area squared, Municipality merger, Post-crisis planned area, and Postcrisis planned area squared are included as additional instruments. House prices, Post-crisis house prices and Credit score are defined as endogenous and ln(total assets) is defined as predetermined. All the models include year dummies. NT is the number of firm-year observations. The long-run effects of house prices for the pre- and post-crisis periods are computed as follows: _b[house prices(t)]/( 1-_b[Bank debt (t-1) -_b[bank debt (t-2)]) and (_b[house prices(t)]+_b[post-crisis house prices])/(1-_b[bank debt (t-1) -_b[bank debt (t-2)]), respectively. The Arellano-Bond test for the second-order autocorrelation in the first differences residuals and the Sargan test for the overidentification restrictions are reported [pvalues in brackets]. All the estimations use a two-step estimator with robust Windmeijercorrected standard errors. The Sargan test is based on conventional standard errors. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Is there an effect on total debt? If our main findings are driven by a collateral channel, the results should be weaker for debt items that include non-collateralizable debt. To explore whether this is the case, we use Total debt as an alternative outcome variable. It is defined as the ratio of total debt to total assets. The mean of this variable is Tables A2 and A3 in the Appendix report the results when model (1) is estimated using the FD-IV and Arellano-Bond methods, using the longer sample and house prices at the two-digit zip code level. The results show that Total debt is not positively associated with house prices. 28 These findings indicate that it is bank loans, which are typically secured, that are most affected by regional house prices Summary This subsection has addressed three questions: First, is the effect greater for firms with more tangible assets? Second, is the effect smaller after the onset of 28 The values below zero and above one are set to zero and one, respectively. The estimation results are similar if the variable is trimmed at these values instead.

24 the financial crisis? Third, is there an effect on total debt? We answer affirmatively to the first and second question, and negatively to the third question. We argue that taken together, these findings suggest there is a collateral channel at work Additional robustness test: Does measurement level matter? As a further robustness test, we measure the house prices at the five-digit zip code level. The use of this more disaggregated measure assumes that the relevant real estate and housing assets are located in the same five-digit zip code area where the firm is registered. This would be a reasonable assumption e.g. if the firms operate at the entrepreneurs home addresses and if those assets matter for the small business lending. The FD-IV results and the Arellano-Bond GMM estimates are reported in Table A4 and A5 in the appendix, respectively. The results refer to the longer sample that cover , so the models include the post crisis dummy, interacted with the house prices. The FD-IV estimates suggest that the coefficient of the house prices is positive and highly significant in each specification. The Arellano-Bond GMM estimates largely echo these findings, but are somewhat weaker statistically. A series of unreported regressions show that when the baseline sample and the five-digit zip code level is used, the coefficient of the house prices remain positive. However, the effects are statistically less precisely measured (i.e., the coefficients are weakly significant or insignificant).

25 25 5 CONCLUSIONS This paper has analyzed the importance of collateral channel for the borrowing of privately held small businesses using a large firm-level panel dataset from Finland over We have focused, in particular, on estimating the effect of regional house prices on the use of bank loans by the small businesses. We identify these effects by exploiting differences in municipal zoning. The main result of this paper is that if housing prices increase, it facilitates the use of bank loans by the privately held small businesses located in the region. This positive effect is consistent with the economic theories which postulate that collateral values are import for the borrowing capacity of opaque small businesses. Four further findings of ours allow us to argue that the effect that we document is due to a collateral channel: i) The effect is most pronounced among the smallest micro firms; ii) The effect of the housing prices is larger for firms with more tangible assets; iii) There is an effect of housing prices on the use of bank debt, but not on the total use of debt; and iv) The effect of housing prices on the use of bank loans is somewhat weaker after the onset of the financial crisis. These findings support the existence of a collateral channel, because the smallest firms and/or the firms with tangible assets are indeed the ones for whom housing assets and housing price increases should be most relevant. Moreover, if there is a collateral channel at work, the effect on the total debt should be weaker, because total debt contains non-collateralizable and/or nonbank debt items. Finally, if there is a collateral channel at work, it may well have become weaker after the onset of the financial crisis. The prior work supports the view that the financial crisis has changed the lending behavior of banks (see, e.g., Ivashina and Scharfstein 2010) and that it has had an effect on the collateral channel (see Norden and van Kampen 2013).

26 26 REFERENCES Adelino, M., Schoar, A. and Severino, F House prices, collateral and selfemployment. Journal of Financial Economics, forthcoming. Balasubramanyan, L. and Coulson, E Do house prices impact business starts? Journal of Housing Economics 22, Benmelech, E., Garmaise, M.J. and Moskowitz, T.J Do liquidation values affect financial contracts? Evidence from commercial loan contracts. The Quarterly Journal of Economics 120 (3), Benmelech, E. and Bergman, N.K Collateral pricing. Journal of Financial Economics 91, Berger, A.N. and Udell, G.F The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle. Journal of Banking & Finance 22, Bernanke, B. and Gertler, M Agency costs, net worth, and business fluctuations. The American Economic Review 79 (1), Bester, H Screening vs. rationing in credit markets with imperfect information. The American Economic Review 75 (4), Black, J., de Meza, D. and Jeffreys, D House prices, the supply of collateral and the enterprise economy. The Economic Journal 106 (434), Boot, A.W., Thakor, A.V. and Udell, G.F Secured lending and default risk: Equilibrium analysis, policy implications and empirical results. The Economic Journal 101 (406) Chaney, T., Sraer, D. and Thesmar, D The collateral channel: How real estate shocks affect corporate investment. The American Economic Review 102 (6), Cvijanovic, D Real estate prices and firm capital structure. The Review of Financial studies, forthcoming. Fracassi, C., Garmaise, M.J., Kogan, S., and Natividad, G Business microloans for U.S. Subprime borrowers. Journal of Financial and Quantitative Analysis, forthcoming. Gan, J Collateral, debt capacity, and corporate investment: Evidence from a natural experiment. Journal of Financial Economics 85, Gertler, M. and Gilchrist, S Monetary policy, business cycles, and the behavior of small manufacturing firms. Quarterly Journal of Economics 109 (2), Ivashina, V. and Scharfstein, D Bank lending during the financial crisis of Journal of Financial Economics 97 (3), Kashyap, A.K., Stein, J.C. and Wilcox, D.W Monetary policy and credit conditions: Evidence from the composition of external finance. The American Economic Review 83 (1), Kiyotaki, N. and Moore, J Credit cycles. Journal of Political Economy 105 (21), Lin, L Collateral and the choice between bank debt and public debt. Working paper.

27 Norden, L. and van Kampen, S Corporate leverage and the collateral channel. Journal of Banking & Finance 37 (12), Rampini, A.A. and Viswanathan, S Collateral, risk management, and the distribution of debt capacity. The Journal of Finance 65 (6), Robb, A.M. and Robinson, D.T The capital structure decisions of new firms. The Review of Financial Studies 1 (1), Saiz, A The geographic determinants of housing supply. The Quarterly Journal of Economics, Schmalz, M.C., Sraer, D.A. and Thesmar, D Housing collateral and entrepreneurship. NBER Working Paper Series, Working Paper Stiglitz, J. and Weiss, A Credit rationing in the markets with imperfect information. The American Economic Review 71, Townsend, R.M Optimal contracts and competitive markets with costly state verification. Journal of Economic Theory 21,

28 28 APPENDIX Average house prices (thousand euros per sq meter) Average house prices (thousand euros per sq meter) Year Graphs by zipcode Year Graphs by zipcode Average house prices (thousand euros per sq meter) Average house prices (thousand euros per sq meter) Graphs by zipcode Year Graphs by zipcode Year Figure A1: Two-digit zip code level house prices development over the period Note: The graph shows the average two-digit zip code level regional house prices development during the period. The two-digit zip code level prices are computed as a weighted average from the five-digit -level zip code data and weighted using the number of transactions. Helsinki-Uusimaa area codes are Source of data: Statistics Finland.

29 29 House prices, euros per sq meter House prices and zoning Share of planned area, % Figure A2: House prices and planned area over the period Note: The figure shows the predicted relationship between regional house prices and the share of planned area in municipalities over the period based on the estimation sample. The plot is based on the fixed effects (within) regression of 2-digit regional house prices on planned area, planned area squared, a municipality merger dummy, post-crisis planned area, post-crisis planned area squared (where post-crisis is a dummy for the post-2009 period), and time dummies. The standard errors are adjusted for firm-level clustering.

30 30 Table A1: Descriptive statistics and correlation matrix for Panel A: Descriptive statistics variable mean sd min p50 max NT Bank debt ln(1+age) ln(total assets) Credit score House prices (5-digit) House prices (2-digit) Planned area Planned area squared Municipality merger Notes: The panel reports descriptive statistics for the sample of firms smaller than 50 employees over The statistics include the mean, standard deviation, minimum, median, and maximum, respectively. Bank debt is a ratio of loans from financial institutions to total assets. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years. Ln(Total assets) is a natural logarithm of the total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (scaled by dividing by 100), where lower values imply higher creditworthiness. House prices measure the average regional prices of previously owned condominiums at the five- and two-digit zip code level in thousand euros per square meter, respectively. Planned area measures the share of planned area of the total area in the municipality. Planned area squared is defined as (Planned area*100)^2/100. Municipality merger is a dummy taking a value equal to one in and all the periods after the municipality merger. NT is the number of firm-year observations. Panel B: Correlation matrix for Bank debt Bank debt ln(1+age) ln(total assets) Credit score House prices (5-digit) House prices (2-digit) Planned area Municipality merger ln(1+age) * ln(total assets) * * Credit score * * * House prices (5-digit) * * * * House prices (2-digit) * * * * Planned area * * * * * Municipality merger * * * * * * * Notes: The table reports pairwise correlations for Bank debt is a ratio of loans from financial institutions to total assets. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years. Ln(Total assets) is a natural logarithm of the total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (scaled by dividing by 100), where lower values imply higher creditworthiness. House

31 prices measure the average regional prices of previously owned condominiums at the fiveand two-digit zip code level in thousand euros per square meter, respectively. Planned area measures the share of planned area of the total area in the municipality. Municipality merger is a dummy taking a value equal to one in and all the periods after the municipality merger. The correlations significant at the 5 percent level or better are marked with an asterisk (*). 31 Table A2: FD-IV estimates Total debt as the dependent variable (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Total debt Total debt Total debt House prices (2-digit) (0.049) (0.041) (0.035) Post-crisis*house prices (0.002) (0.002) (0.002) ln(1+age) *** *** *** (0.006) (0.006) (0.005) ln(total assets) *** *** *** (0.002) (0.002) (0.001) Credit score *** *** *** (0.005) (0.004) (0.003) _cons (0.005) (0.004) (0.003) NT Hansen s J statistics Kleibergen-Paap Wald statistics [0.3975] [0.1654] [0.1792] First-stage Planned area *** *** *** (instrument) (0.072) (0.061) (0.054) Planned area squared *** *** *** (instrument) (0.001) (0.001) (0.001) Municipality merger *** *** *** (instrument) (0.001) (0.001) (0.001) Post-crisis planned area *** *** (instrument) (0.008) (0.008) (0.008) Post-crisis planned area squared *** *** (instrument) (0.0002) (0.0001) (0.0002) First-stage Planned area *** *** *** (instrument) (0.351) (0.307) (0.283) Planned area squared *** *** ***

32 32 (instrument) (0.006) (0.005) (0.005) Municipality merger *** *** *** (instrument) (0.004) (0.003) (0.003) Post-crisis planned area *** *** (instrument) (0.219) (0.222) (0.2234) Post-crisis planned area squared *** *** (instrument) (0.005) (0.005) (0.005) Notes: The table reports the first-differenced IV model estimates on the effects of regional house prices on the use of total debt over the period. The results are reported separately for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The endogenous variables are defined as follows: House prices measure the average prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between a dummy for the period and house prices. The instruments include Planned area, Planned area squared, Municipality merger dummy, Post-crisis planned area, and Post-crisis planned area squared. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firmyear observations. The first-stage results for the instruments are reported omitting the rest of the output from the table. Hansen s J statistics for overidentifying restrictions [p-values in brackets] and the Kleibergen-Paap Wald F statistics for weak identification of the firststage regressions are provided. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Table A3: Arellano-Bond GMM estimates for , Total debt as the dependent variable (2-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Total debt Total debt Total debt Total debt (t-1) *** *** *** (0.029) (0.028) (0.021) Total debt (t-2) *** *** *** (0.011) (0.009) (0.006) House prices (2-digit) (0.012) (0.010) (0.008) Post-crisis*house prices (0.002) (0.002) (0.001) Ln(1+Age) (0.015) (0.013) (0.012) Ln(Total assets) (0.019) (0.017) (0.018) Credit score (0.017) (0.014) (0.011) NT Long-run effects pre

33 33 crisis (0.053) (0.042) (0.028) Long-run effects post crisis (0.047) (0.037) (0.025) Arellano-Bond test [0.3229] [0.3767] [0.2599] Sargan test [0.0000] [0.0000] [0.0000] Notes: The table reports the dynamic Arellano-Bond GMM estimates on the effects of regional house prices on small firms use of total debt over the period. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The independent variables are defined as follows: Bank debt (t-1) and Bank debt (t-2) are the lagged dependent variables measured at t-1 and t-2, respectively. House prices measure the average regional prices of previously owned condominiums at the two-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between house prices and a dummy for the period. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. Planned area, Planned area squared, Municipality merger, Post-crisis planned area, and Postcrisis planned area squared are included as additional instruments. House prices, Post-crisis house prices and Credit score are defined as endogenous and ln(total assets) is defined as predetermined. All the models include year dummies. NT is the number of firm-year observations. The long-run effects of house prices for the pre- and post-crisis periods are computed as follows: _b[house prices(t)]/( 1-_b[Bank debt (t-1) -_b[bank debt (t-2)]) and (_b[house prices(t)]+_b[post-crisis house prices])/(1-_b[bank debt (t-1) -_b[bank debt (t-2)]), respectively. The Arellano-Bond test for the second-order autocorrelation in the first differences residuals and the Sargan test for the overidentification restrictions are reported [pvalues in brackets]. All the estimations use a two-step estimator with robust Windmeijercorrected standard errors. The Sargan test is based on conventional standard errors. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < Table A4: FD-IV estimates for (5-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt House prices (5-digit) ** ** *** (0.034) (0.027) (0.022) Post-crisis*house prices ** ** *** (0.002) (0.001) (0.001) Ln(1+Age) *** *** *** (0.006) (0.005) (0.004) Ln(Total assets) *** *** *** (0.001) (0.001) (0.001) Credit score *** *** *** (0.004) (0.004) (0.003) NT Hansen s J statistics Kleibergen-Paap Wald statistics [0.5403] [0.2579] [0.1554]

34 34 First-stage House prices House prices House prices Planned area *** *** *** (instrument) (0.095) (0.082) (0.075) Planned area squared *** *** *** (instrument) (0.001) (0.001) (0.001) Municipality merger *** *** *** (instrument) (0.002) (0.001) (0.001) Post-crisis planned area *** *** (instrument) (0.010) (0.009) (0.008) Post-crisis planned area squared *** *** (instrument) First-stage Post-crisis house prices Post-crisis house prices Post-crisis house prices Planned area *** *** *** (instrument) (0.395) (0.348) (0.323) Planned area squared *** *** *** (instrument) (0.006) (0.006) (0.006) Municipality merger *** *** *** (instrument) (0.005) (0.004) (0.004) Post-crisis planned area *** *** (instrument) (0.253) (0.254) (0.253) Post-crisis planned area squared *** *** (instrument) (0.006) (0.006) (0.006) Notes: The table reports the first-differenced IV model estimates on the effects of regional house prices on the use of bank loans over the period. The results are reported separately for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The endogenous variables are defined as follows: House prices measure the average prices of previously owned condominiums at the five-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between a dummy for the period and house prices. The instruments include Planned area, Planned area squared, Municipality merger dummy, Post-crisis planned area, and Post-crisis planned area squared. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. All the models include year dummies. NT is the number of firmyear observations. The first-stage results for the instruments are reported omitting the rest of the output from the table. Hansen s J statistics for overidentifying restrictions [p-values in brackets] and the Kleibergen-Paap Wald F statistics for weak identification of the firststage regressions are provided. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p < 0.01.

35 Table A5: Arellano-Bond GMM estimates for (5-digit zip code level house prices) (1) (2) (3) Size class <5 employees <10 employees <50 employees Dependent variable Bank debt Bank debt Bank debt Bank debt (t-1) *** *** *** (0.020) (0.016) (0.016) Bank debt (t-2) *** *** *** (0.008) (0.007) (0.006) House prices (5-digit) * ** (0.006) (0.005) (0.004) Post-crisis*house prices * * *** (0.001) (0.001) (0.001) Ln(1+Age) (0.010) (0.009) (0.009) Ln(Total assets) (0.013) (0.011) (0.014) Credit score (0.015) (0.013) (0.011) NT Long-run effects pre * ** crisis (0.023) (0.018) (0.015) Long-run effects post ** crisis (0.020) (0.016) (0.013) Arellano-Bond test [0.2108] [0.6987] [0.8224] Sargan test [0.0004] [0.0022] [0.0000] Notes: The table reports the dynamic Arellano-Bond GMM estimates on the effects of regional house prices on small firms use of bank loans over the period. The results are reported for the size classes of firms having less than five, ten, and 50 workers, respectively. The dependent variable Bank debt is a ratio of bank loans to total assets at t. The independent variables are defined as follows: Bank debt (t-1) and Bank debt (t-2) are the lagged dependent variables measured at t-1 and t-2, respectively. House prices measure the average regional prices of previously owned condominiums at the five-digit zip code level in thousand euros per square meter at t. Post-crisis house prices is an interaction between house prices and a dummy for the period. Ln(1+Age) is a natural logarithm of firm age since incorporation (plus one) in years at t. Ln(Total assets) is a natural logarithm of total assets in euros at t-1. Credit score measures the observed creditworthiness of firms at the interval (divided by 100) at t-1, where lower values imply higher creditworthiness. Planned area, Planned area squared, Municipality merger, Post-crisis planned area, and Postcrisis planned area squared are included as additional instruments. House prices, Post-crisis house prices and Credit score are defined as endogenous and ln(total assets) is defined as predetermined. All the models include year dummies. NT is the number of firm-year observations. The long-run effects of house prices for the pre- and post-crisis periods are computed as follows: _b[house prices(t)]/( 1-_b[Bank debt (t-1) -_b[bank debt (t-2)]) and (_b[house prices(t)]+_b[post-crisis house prices])/(1-_b[bank debt (t-1) -_b[bank debt (t-2)]), respectively. The Arellano-Bond test for the second-order autocorrelation in the first differences residuals and the Sargan test for the overidentification restrictions are reported [pvalues in brackets]. All the estimations use a two-step estimator with robust Windmeijercorrected standard errors. The Sargan test is based on conventional standard errors. Standard errors in parentheses: * p < 0.10, ** p < 0.05, *** p <