Abstract
This paper investigates the effect of fair value reporting and its attributes on audit fees. We use as our primary sample the European real estate industry around mandatory IFRS adoption (under which reporting of property fair values becomes compulsory), due to its unique operating and reporting characteristics. We document lower audit fees for firms reporting property assets at fair value relative to those employing depreciated cost—a difference that appears driven, in part, by impairment tests that occur only under depreciated cost. We further find that audit fees are decreasing in firms’ exposure to fair value and increasing both in the complexity of the fair value estimation and for recognition (versus only disclosure) of fair values. We corroborate our findings in two alternative settings: contrasting UK and US real estate firms and using UK investment trusts. Overall, the results suggest that fair values can lead to lower monitoring costs; however, any reductions in audit fees will vary with salient characteristics of the fair value reporting, including the difficulty to measure and the treatment within the financial statements.
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Notes
Component depreciation can involve: depreciation of separable components of an investment property asset individually over their respective useful lives (e.g., Italy and Spain), separate capitalization and depreciation of significant replacements or enhancements (e.g., Germany), or provisioning for future maintenance and overhauls, which creates depreciation-like expense patterns (e.g., Germany).
Several exceptions from the above classification are noteworthy. In Switzerland, firms may use the cost or fair value models, where the latter requires that fair value changes be reported in net income. In Finland, France and Sweden, while the cost model is the industry practice, ad hoc application of the revaluation model is permitted under certain circumstances.
As in prior research, LogFees is based on the Thomson Reuters Worldscope data item 01801, Auditor Fees, which comprises fees paid to the auditor for both the statutory audit of the financial statements as well as fees for other services. However, our hypotheses pertain to statutory audit fees only. Accordingly, we explore use of audit fee data from Bureau van Dijk’s Financial Analysis Made Easy (FAME) database: this provides more precise fees data that can be disaggregated into statutory and non-audit fees; however, it is available for a very limited set of firms. We note that the Pearson correlation between our LogFees and the FAME statutory fees is 0.94, suggesting very similar constructs. We further find inferences are unchanged when we use the FAME data to replicate the analyses on UK investment trusts of Sect. 4.2, which have the greatest representation of FAME data among our samples of firms.
Several other control variables for client characteristics warrant discussion. First, while prior literature suggests audit fees increase around IFRS adoption, we do not include a related control variable as we exclude firm-years of first-time IFRS adoption (as well as the immediately preceding firm-years) to avoid capturing the increased audit effort due to the implementation of a new accounting framework. Nonetheless, inferences are unchanged to including these transition years and an IFRS adoption control variable in the analysis. Second, we do not include a control variable for cross-listing, as only one sample firm cross-lists in the US. Third, we do not include inventory as another risk control variable, as real estate firms do not typically hold material amounts of inventory; however, including this variable leaves inferences unchanged. Finally, we note the results are unchanged to including two additional controls to capture fundamental performance: the market-to-book ratio and an indicator variable for negative stock returns.
Results also are robust to random-effects estimation, which is similar to a changes analysis by capturing deviations of the firm from its own time-series mean as well as from the sample mean.
Note that we do not predict the sign on the coefficient for HC, which could be positive (if depreciated cost leads to additional audit efforts) or negative (if institutional differences not captured by our control variables lead to higher audit fees in countries requiring fair value). However, untabulated univariate analyses are consistent with the former, revealing higher average audit fees for firms domiciled in countries requiring depreciated cost (t stat = 2.44).
This is unlikely to reflect a decrease in audit fees due to lower enforcement of IFRS in code law countries, as untabulated results are unchanged to adding the interaction of HC with a code law indicator variable.
We note that introduction of Impair_D has a minimal effect on the coefficient of HC × IFRS, which becomes only slightly less negative (from –0.759 to –0.756, with the 0.003 difference insignificant). This is consistent with Impair_D capturing reported (i.e., ex post) impairments, which likely understates the additional audit procedures associated with potential (i.e., ex ante) impairments, and thus biases against H1B.
The variance inflation factors (VIF) across all of our specifications do not exceed four, suggesting multicollinearity is not an issue (Neter et al. 1985).
However, we note that audit firms may assess a litigation premium across all clients within the US, due to the more litigious environment (e.g., Seetharaman et al. 2002). Thus US litigation premiums may occur, even though the particular industry does not, in itself, reflect high litigation risk.
To mitigate possible alternative explanations, we note the following. First, the (generally) more litigious nature of the US could bias in favor of higher audit fees. However, prior research documents litigation premiums in the range of 18–30 % (see Seetharaman et al. 2002); these appear substantially lower than the effects documented in Table 5. Second, the lower fees for UK firms could reflect institutional changes coinciding with mandatory IFRS adoption, rather than benefits from fair value reporting. However, annual regressions reveal that the coefficient on FV_UK remains significant in all sample years, including those preceding IFRS adoption. Similar results obtain when we pool the pre- and post-IFRS observations. These additional findings suggest that the Table 5 results are unlikely driven by differences in litigation risk or by changes in the institutional setting coinciding with mandatory IFRS adoption that are unrelated to fair value reporting.
We cannot examine disclosure versus recognition (i.e., H2C) or use of alternative external monitor (i.e., H2D) in this setting due to a lack of available data.
In the US, SFAS 157: Fair Value Measurement (FASB 2006) distinguishes between fair values reported under three designations: level 1, which reflect observable market values; level 2, which reflect similar, but not identical, market values used as inputs into the fair value estimation; and level 3, which reflect unobservable (i.e., model-based) inputs to derive fair value. Similar guidance has been developed under IFRS (see IASB 2011).
To capture any potential mean differences relative to firms filing consolidated reports, we additionally estimate the Table 6 analysis including an indicator variable equal to one for the 14 firms filing only parent-level reports. Results are unchanged.
We include in Model (4) control variables to capture, among other firm characteristics, complexity. However, the variables FV_TA_IT, and FV_INV could capture other differences between firms (such as complexity or risk of investments) versus the intended exposure to fair value reporting. To mitigate this possibility, we include two additional firm-level variables: HC_Volatil (the standard deviation of changes in value of depreciated cost-based financial instruments) and FV_Volatil (the standard deviation of changes in value of fair value-based financial instruments). Inferences are unchanged.
Note that we cannot directly assess the relative importance of exposure versus complexity within Table 3, as data for several constructs (e.g., complexity) is unavailable. However, as a preliminary analysis of this issue, we conduct the following. (1) We take all Table 3 firm-year observations relating to firms that report under historical cost pre-IFRS (N = 78). (2) We partition these observations into NSegm_High versus NSegm_Low using mean values of NSegm, the number of operating segments, to capture differences in complexity. We use this variable, as it has both a high correlation with our other measure of complexity and high availability among our pre-IFRS historical cost observations. (3) We then estimate LogFees = β0 + β1 IFRS + β2 NSegm_High + β3 IFRS × NSegm_High + ε, finding IFRS (−1.580, t stat = −4.57); NSegm_High (0.807, t stat = 1.28); and IFRS × NSegm_High (1.899, t stat = 2.27). These results suggest that, consistent with H1A, audit fees are reduced following IFRS adoption for low-complexity firms (evidenced in the significantly negative coefficient for IFRS, which captures the effect of moving to fair value reporting on IFRS adoption for the low-complexity firms). However, the results also suggest that any reduction in audit fee is largely eliminated for more complex firms (evidenced through the significantly positive IFRS × NSegm_High, as well as the insignificance of β1 + β3).
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Acknowledgments
We appreciate helpful comments from Jannis Bischof, Jere Francis, Martin Glaum, Brian Mayhew, Paul Michas, Frank Moers, Maximilian A. Müller, George Serafeim, Lakshmanan Shivakumar (editor), Suraj Srinivasan, Ann Vanstraelen, two anonymous reviewers, and workshop participants at HEC Paris, University of Essen, University of Frankfurt, University of Giessen, University of Innsbruck, University of Maastricht, University of Mannheim, University of Missouri, WHU – Otto Beisheim School of Management, the 2011 EAA Annual Congress, and the 2011 AAA Annual Meeting. We also appreciate the insights from audit partners participating in interviews.
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Appendices
Appendix 1
Appendix 2
Variable definitions
Dependent variable | |
LogFees it | The log of total auditor fees paid by firm i for year t |
Control variables | |
LogTA it | The log of firm i’s total assets at the end of year t |
Foreign it | International assets divided by total assets for firm i for year t |
NSegm it | Number of firm i’s operating segments for year t |
ROA it | Firm i’s net income, net of impairment losses, divided by total assets, both measured for year t |
Loss it | An indicator variable equal to 1 if firm i reports negative net income for year t and 0 otherwise |
Receiv it | Firm i’s receivables divided by total assets, both measured for year t |
Lev it | Firm i’s total debt divided by market value of equity for year t |
Distress it | An indicator variable equal to 1 if firm i reports negative book value of equity for year t and 0 otherwise |
Qualified it | An indicator variable equal to 1 if firm i receives a qualified audit opinion for year t or t − 1 and 0 otherwise |
Volatility it | The standard deviation of monthly stock returns for firm i over year t |
BigN it | An indicator variable equal to 1 if firm i uses a large auditor (i.e., Big 4 or Big 6) during year t and 0 otherwise |
Yearend it | An indicator variable equal to 1 if firm i has a fiscal year-end between December and March (corresponding with the audit busy season) for year t and 0 otherwise |
IFRS_Adopt it | An indicator variable equal to 1 for the first fiscal year t, and immediately preceding fiscal year t − 1, of first-time IFRS adoption for firm i and 0 otherwise |
MCap it | The ratio of aggregate stock market capitalization to GDP of country where firm i is domiciled during year t; obtained from the World Bank |
Bdn_Proof it | The burden-of-proof index of country where firm i is domiciled; obtained from La Porta et al. (2006) and captures (1) liability standard for the issuer and its directors; (2) liability standard for underwriters; and (3) liability standard for accountants |
Experimental variables | |
Table 3 | |
HC it | An indicator variable equal to 1 if firm i is domiciled in a country that required property assets to be reported at depreciated cost under pre-IFRS domestic standards and 0 otherwise (i.e., is domiciled in a country that required or permitted property assets to be reported at fair value under pre-IFRS domestic standards or under early IFRS adoption). Countries requiring depreciated cost include Austria, Finland, France, Germany, Italy, Norway, and Spain |
IFRS it | An indicator variable equal to 1 for the years after mandatory IFRS adoption (that is, years 2005–2008) and 0 otherwise (that is, years 2001–2004) |
Impair_D it | An indicator variable equal to 1 if firm i reports impairment charges during year t and 0 otherwise |
Table 4 (all calculated using hand-collected data) | |
FV_Exposure it | The firm’s exposure to assets measured at fair value, calculated in two steps. First, we calculate the proportion of firm i’s total assets measured at fair value. For firms reporting property assets on the balance sheet at fair value, it is the ratio of property fair values to total assets; for firms reporting property on the balance sheet under depreciated cost, it is the ratio of disclosed property fair values to the sum of total assets less recognized property at depreciated cost plus disclosed fair value of property. Second, FV_Exposure equals 1 if this proportion is higher than the sample mean (indicating higher exposure to assets reported at fair value) and 0 otherwise (indicating lower exposure to assets reported at fair value) |
Complex it | The complexity of firm i’s property portfolio in year t, calculated in two steps. First, we sum the square roots of the percentages of property for firm i within each of 11 sectors: land, residential, office, retail, parking, industrial, gastronomy, healthcare, education, leisure, and other. Thus higher values reflect more complex portfolios by reflecting diversity across these sectors. Second, Complex equals 1 if this measure is above the sample mean for firm i in year t (indicating higher portfolio complexity) and 0 otherwise (indicating lower portfolio complexity) |
Recog it | An indicator variable equal to 1 if firm i recognizes property fair values on the balance sheet in year t and 0 otherwise (that is, only discloses property fair values in the footnotes) |
External it | An indicator variable equal to 1 if firm i uses an external appraiser to provide investment property fair values in year t and 0 otherwise |
Table 5 | |
FV_UK it | The firm’s exposure to assets measured at fair value. FV_UK equals 1 if firm i reports its assets on the balance sheet principally at fair value in year t (i.e., is domiciled in the UK, where property assets are reported at fair value) and 0 otherwise (i.e., domiciled in the US, where property assets are reported at historical cost) |
Impair_D it | An indicator variable equal to 1 if a US firm i reports impairment charges in year t and 0 otherwise |
Table 6 (all calculated using hand-collected data) | |
FV_TA_IT it | The proportion of firm i’s total assets measured at fair value. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean |
FV_INV it | The proportion of firm i’s investment portfolio measured at fair value. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean |
FV2/3 t | The proportion of firm i’s fair-valued investments measured using level 2 and 3 inputs. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean |
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Goncharov, I., Riedl, E.J. & Sellhorn, T. Fair value and audit fees. Rev Account Stud 19, 210–241 (2014). https://doi.org/10.1007/s11142-013-9248-5
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DOI: https://doi.org/10.1007/s11142-013-9248-5