VoxEU Column Financial Markets

Property debt overhang: The case of Irish SMEs

The role of credit-fuelled property booms in the Global Crisis has received much high-profile attention in recent years. Using data on Irish small and medium enterprises, this column highlights an additional channel through which such booms can impact post-crisis growth. Firms having difficulty repaying their property-related debts divert resources away from hiring and investment. Property booms thereby induce misallocation of resources in both the boom and the bust.

The detrimental impact of credit and property boom-bust cycles on consumption and growth has received much high-profile attention in the aftermath of the Global Crisis (Mian and Sufi 2013, 2014, Dynan et al. 2012). Separately, an empirical literature on non-financial corporates has shown that debt overhang can negatively impact firm investment (Aivazian et al. 2005, Cai and Zhang 2011, Coricelli et al. 2012).

Using a unique data set on the property and enterprise debts of Irish small and medium enterprises (SMEs) at December 2013, we highlight the extent to which Irish non-real-estate SMEs have borrowed for property investment purposes, before showing the detrimental impact of these property-related borrowings on firms’ ability to repay their enterprise debts (McCann and McIndoe-Calder 2014).1 Firms with property debts have a 5% higher probability of default on their enterprise debts, while a one percentage-point increase in the share of property debt in total firm debt leads to a 0.1% increase in enterprise probability of default. These property-related borrowings represent an inefficient allocation of capital to projects outside a firm’s set of core competencies, and highlight an additional channel through which credit-driven property booms can have long-lasting harmful effects on economic growth prospects. To the best of our knowledge, this is the first case in which firms’ property debt overhang can be identified and analysed.

Our data set covers the population of SME loans at three Irish banks, covering roughly 70% of the total SME credit market. An SME is defined as having a property-related exposure if it has borrowing in a non-property-related sector (referred to as its ‘core activity’), while also having exposures located in the buy-to-let mortgage, personal investment, or commercial real estate portfolios of the same bank. Figure 1 shows that in some sectors the percentage of enterprise SME loans held by firms that also have property-related borrowing can reach close to 20%, while when weighted by loan balance, the figure can reach 30–40%. The largest property exposures are in the hotels and restaurants, wholesale and retail, and business and administrative services sectors.

Figure 1. Percentage of SME loans, by count and balance, with property-related borrowing

Figure 2 exposes the pernicious effects of SME property-market speculation – these firms have higher default rates on their enterprise borrowing than firms with no property exposures. The pattern holds in all sectors of economic activity, with default rates among firms with property debts reaching 60% in some cases.

Figure 2. SME default rates for firms with and without property debts

We formalise the relationship between property borrowing and SME loan default by entering measures of both the existence and the intensity of firms’ property-related borrowing into a cross-sectional SME default model. In all cases, it is the default status of loans related to the core business activity of the firm, rather than the property-related loans of these firms, that are being modelled.

Our results show that, controlling for a wide range of firm-level explanatory factors, firms with property debts have a 5.27% higher probability of default than those with debts relating only to their core enterprise activity. We also introduce a set of dummy variables for the number of property-related loans held by the firm, which suggest that the intensity of property exposures also matters. Firms with five or more property loans have a 14% higher probability of default than those with no property debts. A one percentage-point increase in the share of property-related debts in a firm’s total debts leads to a 0.1% increase in the probability of default. Our results also suggest that there is a strong correlation across asset classes, whereby a 1% increase in the default rate on a firm’s property-related debts leads to a 0.6% increase in the probability of default on a firm’s core business loans.

Finally, we construct a counter-factual observation for each SME loan with a property exposure to identify the effect of property borrowing on default using a propensity score matching (Leuven and Sianesi 2003) model. The counter-factual observation for each SME loan with a property exposure is an SME loan in the data with similar observable characteristics, but without property debt.

Our propensity score matching results show that post-matching, SME loans with property exposure are 6.71% more likely to default than those without property exposure. This is down from a 19.2% higher probability of default pre-matching. Thus, even when controlling for observable firm characteristics, holding property debt increases the likelihood of SME default.

Concluding remarks

The conclusions of the analysis should serve as a stark warning to policymakers in countries experiencing credit-fuelled property price increases. The data presented here suggest that firms and banks will act to allocate capital inefficiently from the productive to the property-related sector of the economy during a property boom. Complementing the literature on household debt overhang, we highlight an additional channel through which credit-fuelled property booms can impact post-crisis growth – firms may also subsequently experience difficulty in repaying their debts as a result of their over-extended property-related borrowings. Such a pattern can have long-run impacts by allocating firm resources away from hiring and investment and towards debt repayments, while allocating lenders’ resources away from new lending and towards the restructuring and resolution of the debt overhang.


Dynan, K, A Mian, and K M Pence (2012), “Is a Household Debt Overhang Holding Back Consumption?”, Brookings Papers on Economic Activity: 299–362. http://www.brookings.edu/research/papers/2012/09/debt-overhang-dynan

Leuven, E and B Sianesi (2003), “PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing”, Statistical Software Components, Boston College Department of Economics, April. http://ideas.repec.org/c/boc/bocode/s432001.html

McCann, F and T McIndoe-Calder (2014), “Property Debt Overhang: The Case of Irish SMEs”, Research Technical Paper, Central Bank of Ireland.

Mian, A, K Rao, and A Sufi (2013), “Household Balance Sheets, Consumption, and the Economic Slump”, Quarterly Journal of Economics, 128(4): 1687–1726.

Mian, A and A Sufi (2014), House of Debt: How They (and You) Caused the Great Recession, and How We Can Prevent It from Happening Again, University of Chicago Press.


1 When referring to ‘enterprise’ debts, we refer to debts categorised as relating to the principal industrial activity of the enterprise.

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