Electronic copy available at: http://ssrn.com/abstract=1855628

Fair Value and Audit Fees

Igor Goncharov WHU – Otto Beisheim School of Management

Edward J. Riedl *

Harvard Business School

Thorsten Sellhorn WHU – Otto Beisheim School of Management

This version: May 2011 Abstract: We investigate the effect of fair value reporting and its attributes on audit fees. We use as our primary setting the European real estate industry around the time of IFRS adoption, which provides unique heterogeneity on a number of dimensions to assess fair value. We first contrast audit fees across firms applying fair value versus amortized cost as the reporting model for their property assets. We find that firms reporting under fair value exhibit lower audit fees, and that this is (in part) driven by impairment tests that occur only under amortized cost. We then find that audit fees are increasing in the complexity of the fair value estimation, and are higher for fair values that are recognized (versus only disclosed). Overall, the results complement prior findings that fair values can reduce information asymmetries by suggesting they can also lead to lower contracting costs. However, the results further suggest that any reductions in audit fees vary with salient characteristics of the fair value reporting, including the difficulty to measure and treatment within the financial statements. Key Terms: fair value, audit fees, audit pricing, real estate industry, IFRS * Corresponding author: Harvard Business School Morgan Hall 365 Boston, MA 02163 Phone: 1 617 495 6368 Fax: 1 617 496 7363 Email: eriedl@hbs.edu Acknowledgements: We appreciate helpful comments from Jere Francis, Martin Glaum, Paul Michas, Frank Moers, George Serafeim, Suraj Srinivasan, Ann Vanstraelen, and workshop participants at HEC Paris, University of Essen, University of Giessen, University of Innsbruck, University of Maastricht, University of Missouri, WHU – Otto Beisheim School of Management, and the 2011 EAA Annual Congress. We also appreciate the insights from audit partners participating in interviews.

Electronic copy available at: http://ssrn.com/abstract=1855628

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Fair Value and Audit Fees Abstract: We investigate the effect of fair value reporting and its attributes on audit fees. We use as our primary setting the European real estate industry around the time of IFRS adoption, which provides unique heterogeneity on a number of dimensions to assess fair value. We first contrast audit fees across firms applying fair value versus amortized cost as the reporting model for their property assets. We find that firms reporting under fair value exhibit lower audit fees, and that this is (in part) driven by impairment tests that occur only under amortized cost. We then find that audit fees are increasing in the complexity of the fair value estimation, and are higher for fair values that are recognized (versus only disclosed). Overall, the results complement prior findings that fair values can reduce information asymmetries by suggesting they can also lead to lower contracting costs. However, the results further suggest that any reductions in audit fees vary with salient characteristics of the fair value reporting, including the difficulty to measure and treatment within the financial statements. Key Terms: fair value, audit fees, audit pricing, real estate industry, IFRS

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Fair Value and Audit Fees

1 Introduction

This paper examines the effect of fair value reporting and its attributes upon audit

fees. Prior research documents the magnitude and determinants of audit fees (for a review,

see Hay et al., 2006). Prior research also documents that fair value reporting is both priced

by the equity market (e.g., Easton et al., 1993), and has the capacity to reduce information

asymmetries across investors (e.g., Muller et al., 2011). We combine both literatures to

investigate how fair value reporting, and various salient attributes of how it is implemented,

affects the pricing of audit services―a major contracting cost (Jensen and Meckling, 1976).

We use as our primary setting real estate firms domiciled in Europe during the period

2001–2008, a setting which provides several benefits. First, the within-industry design holds

constant other factors that could drive audit fee differences across industries, allowing us to

structure analyses that better isolate the effect of fair value reporting. Second, European real

estate firms exhibit substantial variation in the reporting of fair values for their property

assets, which we exploit in our analyses. Specifically, prior to adoption of International

Financial Reporting Standards (IFRS) by the European Union effective 2005, domestic

standards varied in their requirements for the reporting of real estate assets on the balance

sheet: either at fair value, or at amortized cost subject to impairment. Subsequent to IFRS

adoption, the applicable standard allowed firms to report property assets either under the fair

value or cost models; however, IFRS also introduced a mandatory footnote disclosure

requirement of property fair values for those firms electing the cost model. Third, this

industry setting allows detailed examination of four specific attributes of fair value reporting:

the proportion of a firm’s assets reported at fair value; the difficulty of estimating the fair

values reliably; whether the fair values are recognized on the face of the financial statements

or disclosed in the footnotes; and the effect of using an alternative external monitor―an

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external appraiser―to derive the fair value estimates. No other institutional setting allows

these features to be concurrently examined.

Empirical results reveal that, controlling for other determinants, audit fees are

significantly lower for firms reporting property assets at fair value relative to those reporting

property assets at amortized cost. To further assess this arguably unexpected result, we

conduct interviews with real estate audit partners: these interviews suggest that (potential and

actual) impairments are likely a significant source of higher audit effort for amortized cost

firms, as are other reporting requirements (such as component depreciation) which arise only

within amortized cost contexts.1 Testing these expectations, we indeed find that impairments

reported by amortized cost firms are a significant driver of observed higher average fees.

Our analyses of particular attributes of fair value implementation reveal that audit fees

are lower for firms with above-average exposure to assets recognized or disclosed at fair

value, consistent with auditors being able (on average) to reduce effort and/or risk when a

greater proportion of the client firm’s assets are reported at fair value. In addition, audit fees

are higher for more complex property portfolios, consistent with multiple-sector portfolios

(e.g., retail, office, industrial, residential) being more difficult to value and audit. Finally,

audit fees are higher for fair values that are recognized on the face of the financial statements

versus only disclosed in the footnotes, consistent with incremental audit effort for reporting

elements recognized within the financial statements (e.g., Libby et al., 2006). We fail to find

evidence that use of an external appraiser attenuates audit fees. As above, the viability of

these results is again confirmed via interviews with real estate audit partners.

We then re-assess these findings by exploiting two alternative settings. First, to

mitigate concerns that results are confined to the European setting, we reassess the relation of

                                                            1 As discussed later, component depreciation is required under the amortized cost model, and requires that

firms allocate portions of buildings to particular functions and depreciation schedules (e.g., piping, structure, office improvements). Fair value reporting does not require such allocations, which are effectively captured in the estimate of the property’s fair value.

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fair value reporting to audit fees by comparing audit pricing for UK versus US real estate

firms; these firms are selected using propensity score matching. Since the UK and US are

among the world’s largest and most developed real estate markets, this analysis benefits from

a larger sample size and isolates the main difference between both countries for this industry:

the financial reporting model. Specifically, UK firms report property assets at fair value,

while US firms report them at amortized cost. Further, while the US generally is considered

to have higher audit litigation risk than other countries, prior research fails to designate the

real estate industry as a high risk setting, suggesting that litigation differences are unlikely to

be the primary source of audit pricing differences across the US and UK for this sector.

Consistent with our previous results, we document that audit fees are significantly lower for

firms reporting property assets at fair value (i.e., the UK firms) relative to amortized cost

(i.e., the US firms). Further, impairments again appear to be a primary driver of higher audit

fees for amortized cost firms.

Second, we re-assess the effects of exposure to fair value and complexity in fair value

measurement using a sample of UK investment trusts. Restricting the analysis within the UK

eliminates cross-country differences in institutional features as a potential source of variation

in audit fees. Further, this industry provides a potentially stronger assessment of the

difficulty to measure fair value by exploiting fair values calculated based on market inputs

(level 1 fair values) versus those based on less reliable valuation inputs (level 2 and 3 fair

values). Consistent with our previous results, we find that audit fees are decreasing in the

firm’s exposure to assets reported at fair value, and increasing in the firm’s exposure to more

difficult-to-measure (i.e., levels 2 and 3) fair values.

The results are also robust to sensitivity analyses including (1) removal of firm-year

observations potentially affected by the global financial crisis, (2) control for country effects,

and (3) alternative measurement of the dependent variable.

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Overall, the results reveal that reporting assets at fair value (on average) reduces audit

fees, where a primary driver of higher audit fees observed under amortized cost appears to be

impairments. Further, we find any reduction in audit fees relative to amortized cost depends

on several salient characteristics of the fair value reporting: the overall exposure to fair value

measurement; the complexity of the fair value measurement; and whether the fair values are

recognized in the primary financial statements versus only disclosed in the footnotes.

Combined with previous findings that fair value reporting can reduce information

asymmetries, these results suggest that fair value reporting can have benefits both for

decision usefulness as well as contracting, fostering both objectives of financial reporting.

These findings are potentially informative to standard-setters in their ongoing deliberations

about the role of fair value measurement in general-purpose financial statements.

Section 2 presents the prior literature, primary setting, and hypothesis development.

Section 3 presents the base audit fee regression. Section 4 presents the primary analyses.

Section 5 presents alternative settings to assess the hypotheses. Section 6 presents sensitivity

analyses, and Section 7 concludes.

2 Prior literature, primary setting, and hypothesis development

2.1 Prior literature

This study builds upon two broad literatures: that examining the determinants of audit

fees, and that examining the effects of fair value reporting. There is a substantial literature on

audit pricing, with Simunic (1980) among the earliest to provide theoretical and empirical

evidence on the determinants of this contracting cost.2 Hay et al. (2006), in a survey of the

literature on the determinants of audit fees, suggests that under a competitive audit market

                                                            2 We follow Jensen and Meckling (1976) in viewing audit fees as one of the agency costs arising from a

contractual arrangement between the owners (principal) and the management (agent) of a firm; that is, audit fees represent monitoring (bonding) costs. See also Watts and Zimmerman (1986). 

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these determinants may be classified into several major categories: client attributes, auditor

attributes, and characteristics specific to the audit engagement. Among these, client attributes

have received the most attention, and commonly reflect the firm characteristics of size, risk,

and complexity. In particular, consistent with theory on audit effort and litigation, audit fees

generally are found to increase in the client’s size (e.g., Simunic, 1980), risk (e.g., Stice,

1991), and complexity (e.g., Hackenbrack and Knechel, 1997).

The literature examining the effects of fair value reporting is also extensive, with

many papers analyzing the relation of fair values and equity prices. Earlier empirical papers,

such as Barth (1994) and Eccher et al. (1996) exploit disclosed fair values relating to

financial instruments and provide evidence that fair values are value relevant: that is,

reflected in stock prices. Similar results are found for alternative non-financial asset classes

(e.g., Easton et al., 1993). Other studies examine the effect of fair values on the information

environment, documenting that asymmetry is reduced when fair values are disclosed (e.g.,

Muller et al., 2011), but that information risk is higher when fair values are based on

unobservable inputs (Riedl and Serafeim, 2011). Among the few papers speaking to

contracting implications of fair value accounting, Barth et al. (1995) suggests that fair value

accounting, if applied to assess banks’ regulatory capital, can address struggling financial

institutions’ problems earlier than does amortized cost. Benston (2006) argues that fair value

accounting is ill-suited as a basis for accounting-based management compensation.

We combine and contribute to these literatures by investigating the effect of fair value

reporting upon audit fees in two ways. First, we contrast observed audit fees across firms

using fair value relative to those using amortized cost as the principal reporting model for

their primary operating assets. Second, we exploit known variation in the salient

characteristics of reported fair values to identify predictable differences in audit fees.

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2.2 Primary setting: the European real estate industry

Although fair value accounting has been adopted by standard setters for numerous

reporting elements spanning a number of industries, it is difficult to identify a single setting

in which multiple attributes of fair value reporting can be observed simultaneously. For this

reason, we use the European real estate industry as the primary setting, which has several

advantageous features. First, firms in this industry share a common primary operating asset:

real estate (i.e., investment property). That is, these firms acquire (through purchase, lease,

or development), manage, and sell real estate to generate profits through rentals and/or capital

appreciation. Typically, firms either acquire legal ownership through a purchase or hold the

property under a finance lease. Accordingly, the primary asset for our sample firms is held

constant: long-lived tangible real estate assets.3

Second, the industry is well-developed. Within Europe, there are over 180 publicly

traded real estate firms, with an aggregate equity market value exceeding €150 billion on

December 31, 2005. The firms are domiciled across most European countries, with the larger

economies (e.g., France, Germany, and UK) having a higher representation.

Third, this industry exhibits substantial variation in how firms report property assets.

Prior to the mandatory transition by many European countries from domestic standards to

IFRS in 2005, domestic standards varied considerably in the required accounting treatment

for real estate assets: some required reporting at amortized cost (e.g., Germany) and some

required reporting at fair value (e.g., the UK). Even upon IFRS adoption, variation in

reporting continued. Specifically, the relevant standard addressing the accounting for real

estate assets, International Accounting Standard (IAS) 40: Investment Property (IASB, 2000),

allows firms to choose between reporting these assets at fair value or at amortized cost on the

                                                            3 Note that real estate assets for our sample firms reflect real estate assets used for investment, as opposed to

real estate assets used in production or operations.

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balance sheet. Thus, the reporting of real estate assets exhibits significant variation, both

prior to and subsequent to IFRS adoption, which we exploit in our analyses.

Finally, firms in this industry exhibit substantial variation in salient fair value

attributes beyond the principal reporting model, allowing for further analysis. Specifically,

real estate firms vary in their exposure to assets reported at fair value, owing to other

operating segments or balance sheet items. The firms also vary in the complexity of the fair

value measurement, which is driven in large part by variation in property portfolios,

particularly across sectors such as retail, office, industrial, and residential. The firms also

vary in the recognition versus disclosure of investment property fair values. Under IAS 40,

firms can elect to either recognize real estate fair values on the balance sheet under the fair

value model, or provide mandatory footnote disclosure of these fair values if electing the cost

model. Finally, these firms vary in the use of a key alternative external monitor to derive fair

value estimates: the external property appraiser.

2.3 Hypothesis development

We first examine the effect of reporting model on observed audit fees, contrasting fair

value versus amortized cost. Our focus on the real estate industry allows us to partition firms

into those reporting their primary operating asset under either of these two models. The

effect of reporting assets at fair value relative to amortized cost upon audit effort, as well as

assessed reputation and litigation risk, is unclear a priori. Critics maintain that fair value

reporting introduces substantial discretion into management estimates (e.g., Watts, 2006;

Ramanna and Watts, 2010). This increased discretion can compound agency costs, leading

auditors to increase their assessment of reputation and/or litigation risk and, consequently,

their efforts to verify fair value estimates. This risk may be greater in contexts, such as real

estate, where market prices for identical assets are generally unavailable. Interviews with

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real estate audit partners confirm that fair values for real estate assets are viewed as either

level 2-type values (with market values available for similar, but not identical, properties) or

as level 3-type values (with simplistic discounted cash flow analysis used in the absence of

property-specific market parameters).

Alternatively, fair values may reduce auditor reliance on management estimates and

litigation risk (e.g., to the extent these values are derived from observable inputs). For

example, firms with real estate assets typically have rent rolls for clients having multi-year

leases within their properties, allowing auditors to more clearly identify and support

management forecasts of future cash flows, and thus to more easily ascertain property value.

In addition, amortized cost based reporting has several aspects that introduce complexity and

uncertainty into the auditing process. Two examples most relevant to the real estate setting

include component depreciation and impairment testing. Under component depreciation, a

property is depreciated based on the lives of individual elements within it. For example,

within a given property, certain electrical/plumbing components can be assigned a 20-year

life, the roof a 15-year life, and the foundation/structure a 50-year life. The assignment

process across multiple components for multiple properties can increase audit costs; such

costs do not directly arise for firms reporting under fair value, where such factors are

incorporated into the fair value of the property.

Second, impairment testing requirements under amortized cost can lead to higher

audit costs. Under fair value reporting, firms must establish a process of valuation; this

process becomes the focal point of the annual audit, regardless of upward or downward

changes in property values. That is, the valuation process provides a basis for repeated

annual discussions with the auditor; to the extent this process does not change, the audit

process is focused primarily on updating assumptions and inputs into the fair value process

for the current year. In contrast, if a property value decline leads to impairment

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considerations for a firm applying amortized cost, the auditor must now assess both the firm’s

valuation and process in a non-routine (and likely contentious) setting. Further, this process

can differ from an assessment of fair value as impairments anchor on the notion of

recoverable amount. Recoverable amount reflects the higher of fair value or firm-specific

value in use, where the latter may substantially deviate from the former and involve higher

discretion by management. Thus, it is probable that impairments lead to substantial frictions,

and thus higher audit fees, for firms reporting under amortized cost.

Taken together, this reasoning leads to our first two hypotheses (in alternative form):

H1A Audit fees differ for firms applying amortized cost versus fair value reporting to their primary operating assets.

H1B Impairments lead to higher audit fees for firms reporting their primary operating assets under amortized cost.

We next examine attributes of fair value reporting likely to lead to heterogeneity in

auditor efforts. To assess these effects, we focus on four attributes of fair value reporting

likely to affect audit fees: the firm’s exposure to fair value reporting; the complexity of the

fair value measurements; whether fair value is recognized on the face of the financial

statements versus disclosed in the footnotes; and the use of alternative non-auditor external

monitors in the fair value measurement process. All four attributes have observable variation

within the real estate industry.

First, we examine the firm’s exposure to fair value reporting. Firms with greater

proportions of their primary operating assets reported at fair value may require additional

audit efforts, owing to incremental procedures necessary to confirm additional fair values.

Alternatively, to the extent the audit focuses on the process, firms with greater proportions of

their operating assets reporting under fair value may require fewer efforts (e.g., due to a lack

of component depreciation or impairment testing, or due to economies of scale arising

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through audit of the fair value process versus audit of particular valuation estimates). This

leads to the following hypothesis:

H2A Audit fees differ for firms reporting a higher proportion versus a lower proportion of their primary operating assets at fair value.

Second, we assess the role of complexity in fair value measurement, which reflects

challenges inherent in the estimation process. These include estimation using a long time

series of cash flows, or the availability of benchmarks to approximate fair value. Both

notions are considered in applicable US accounting principles and IFRS regarding fair value

measurement. Consistent with this framework, we expect that audit effort will be higher for

fair values requiring more complex estimation procedures (e.g., Hackenbrack and Knechel,

1997). That is, property portfolios that are more complex due to spanning multiple sectors, or

due to unavailability of market-derived valuation inputs, likely require additional audit

procedures. This leads to our second hypothesis:

H2B Audit fees are higher for firms with more difficult-to-measure fair values.

Third, we assess the role of recognition versus disclosure. Numerous standards have

increased fair value disclosure requirements; others have required that fair value levels and

changes be incorporated into the primary financial statements.4 Consistent with prior

research (Libby et al., 2006), we predict that audit fees are higher for fair values that are

recognized versus disclosed, consistent with auditors expending more effort to validate

information recognized in the primary financial statements. This test allows us to exploit the

IAS 40 option for real estate firms to elect recognition under the fair value model, or

disclosure of fair values under the cost model. This leads to our third hypothesis:

                                                            4 Examples of standards requiring disclosure of fair values include in the US SFAS 107 Disclosures about

Fair Value of Financial Instruments (FASB, 1991), and in the EU IFRS 7 Financial Instruments: Disclosures (IASB, 2005). Examples of standards requiring recognition of fair values include in the US SFAS 115 Accounting for Certain Investments in Debt and Equity Securities (FASB, 1993) and SFAS 123 Accounting for Stock-Based Compensation (FASB, 1995), and in the EU IFRS 9 Financial Instruments (IASB, 2010b), IAS 39 Financial Instruments: Recognition and Measurement (IASB, 1999), and IAS 41 Agriculture (IASB, 2001).

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H2C Audit fees are higher for firms reporting assets at fair value that are recognized on the balance sheet relative to firms only disclosing them in the footnotes.

To the extent audit effort does not differ across recognized versus disclosed reporting items,

this will bias against H2C.

Finally, other external monitors may provide inputs into the audit process, with real

estate firms frequently employing external appraisers in the fair value measurement process.

Auditing standards recognize the role of experts, such as International Standard on Auditing

500: Audit Evidence (International Federation of Accountants, 2010), which states that

auditors may accept the findings of a specialist hired by management as appropriate audit

evidence. This suggests a substitution role: that is, specialists may provide expertise and

insights, which can potentially reduce necessary efforts by the auditor to achieve a particular

level of audit risk. Prior research provides evidence consistent with this notion: Muller and

Riedl (2002) documents that information asymmetry is lower for property firms employing

external (versus internal) appraisers in the real estate industry, and Cotter and Richardson

(2002) similarly document that revaluations of property, plant and equipment by independent

appraisers are more reliable than those conducted by directors. Accordingly, we predict that

audit fees are lower for firms employing external monitors as part of the fair value reporting

process, reflecting potential substitution of efforts. This leads to our final hypothesis:

H2D Audit fees are lower for firms reporting assets at fair value derived using (non-auditor) external monitors relative to firms that do not use such monitors.

To the extent there is variation in the quality of external appraisals, this can bias against H2D.

3 Research design: base model of audit fee determinants

Prior research has accumulated evidence of a number of audit fee determinants. For

our main research setting, the European real estate industry, we maintain the relevant

determinants, using the following basic audit fee model:

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LogFeesit = α0 + α1LogTAit + α2IFRS_Adoptit + α3Foreignit + α4NSegmit + α5ADRit

+ α6ROAit + α7Lossit + α8Receivit + α9Levit + α10Distressit

+ α11Qualifiedit + α12Volatilityit + α13BigNit + α14Yearendit + ψit (1)

where:

LogFeesit is the log of total auditor fees paid by firm i for year t;

LogTAit is the log of firm i’s total assets at the end of year t;

IFRS_Adoptit is 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;

Foreignit is international assets divided by total assets for firm i for year t;

NSegmit is the number of firm i’s operating segments for year t;

ADRit is an indicator variable equal to 1 if firm i is cross-listed in the United States for year t, and 0 otherwise;

ROAit is firm i’s net income, net of impairment losses, divided by total assets, both measured for year t;

Lossit is an indicator variable equal to 1 if firm i reports negative net income for year t, and 0 otherwise;

Receivit is firm i’s receivables divided by total assets, both measured for year t;

Levit is firm i’s total debt divided by market value of equity for year t;

Distressit is an indicator variable equal to 1 if firm i reports negative book value of equity for year t, and 0 otherwise; and

Qualifiedit is an indicator variable equal to 1 if firm i receives a qualified audit opinion for year t or t-1, and 0 otherwise;

Volatilityit is the standard deviation of monthly stock returns for firm i over year t;

BigNit is 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; and

Yearendit is 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.

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Our dependent variable is LogFees, the log of total auditor fees.5 Consistent with

prior studies examining the determinants of audit fees, we express the dependent variable in

log form to mitigate the effects of non-linear relations (see Hay et al., 2006).

We then include variables assessed in prior research to drive audit fees. We use two

primary groupings: characteristics of the audit client, and those of the audit firm. Regarding

audit client characteristics, we first include LogTA; as audit effort is expected to increase in

the scale of the client, the predicted sign is positive (Simunic, 1980). Given the asset-

intensive nature of the real estate industry, this variable appears particularly relevant.

To capture audit complexity, we include IFRS_Adopt, Foreign, and NSegm. As audit

effort is expected to be higher for real estate firms transitioning to IFRS, having more

international operations, or having more complex operations, the expected sign on the

coefficients for these three variables is positive. We also include ADR to capture additional

audit effort or litigation risk arising from exposure to the US capital market through a cross-

listing; the predicted sign is positive.

Next, we include variables to capture firm risk, which has been documented to have a

positive association with audit fees (e.g., Stice, 1991). We include accounting measures of

firm performance, both assessed as a continuous variable (ROA) and as a distress indicator

variable (Loss); the predicted signs are negative and positive, respectively. We then include

several balance sheet constructs to capture audit risk. Consistent with prior research (e.g.,

Hay et al., 2006), we include Receiv; as receivables may be subject to higher risk of error, the

predicted sign is positive.6 Real estate firms maintain moderate amounts of receivables,

reflecting amounts due from tenants. We also include Lev; as more leveraged firms face

                                                            5  Similar to prior research, this variable is based on the Thomson Reuters Worldscope data item 01801,

Auditor Fees, which comprises fees paid to the firm’s auditor for the statutory audit of the financial statements (statutory audit fees) as well as fees paid to the firm’s auditor for other services (non-audit fees). We examine alternative definitions in section 6.3.  

6 While prior research proposes inventory as another risk construct leading to higher audit fees, we exclude this variable as real estate firms do not typically hold material amounts of inventory. Nonetheless, results are unchanged if we include inventory (scaled by total assets) in the regression.  

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greater financing constraints, the predicted sign is positive. Real estate firms typically

employ substantial leverage, suggesting it is a very relevant construct for this industry. Next,

we include Distress and Qualified to capture extreme negative performance; as firms with

negative equity or qualified audit opinions are more likely in distress, the predicted signs for

both variables are positive. Finally, we include Volatility to reflect overall market risk; as

more volatile stock returns reflect riskier firms, the predicted sign is again positive.

The second group of variables includes audit characteristics, which would affect audit

pricing for real estate firms. We include BigN to capture perceived higher quality and/or

reputational effects of larger audit firms (i.e., the “large audit firm premium”; see Francis,

1984); the predicted sign is positive. Finally, we include Yearend to capture the higher fees

charged when audits occur during periods of constrained auditor resources (i.e., during the

audit “busy season”; see Ireland and Lennox, 2002); the predicted sign is positive.

In the sections that follow, we refer back to this base model, augmenting it by adding

experimental variables to test our hypotheses.

4 Primary tests

4.1 The effect of reporting model on audit fees: evidence using European real estate firms upon mandatory IFRS adoption

Our first test examines whether audit fees decrease when European real estate firms

switch from amortized cost to fair value. Specifically, we use mandatory adoption of IAS 40

in Europe as a natural experiment to conduct a difference-in-differences analysis. We

compare how audit fees change upon mandatory IFRS adoption for two sets of European real

estate firms: those domiciled in countries requiring that property assets be reported at

amortized cost under pre-IFRS domestic standards (treatment group), and those requiring that

property assets be reported at fair value under pre-IFRS domestic standards or early IFRS

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adoption (control group).7 If audit fees vary with the firms’ primary reporting model (H1A),

we should find a significantly larger change in audit fees for the treatment group (which

transitions from a cost regime to a fair value regime) compared to the control group (which

remains on a fair value regime throughout the analysis period).

We then explore a possible reason for differential audit fees across reporting regimes.

If impairment testing (which only firms reporting under amortized cost are subject to) drives

up audit fees (H1B), we should observe higher audit fees for firm-year observations with

recognized impairment losses compared to those without such charges.

To implement our difference-in-differences analysis within the linear regression

framework of equation (1), we estimate the following augmented model:

LogFeesit = ξ0 + ξ1LogTAit + ξ2Foreignit + ξ3NSegmit + ξ4ROAit + ξ5Lossit + ξ6Receivit

+ ξ7Levit + ξ8Distressit + ξ9Qualifiedit + ξ10Volatilityit + ξ11BigNit + ξ12Yearendit

+ ξ13HCit + ξ14IFRSit + ξ15HCit×IFRSit + ξ16Impair_Dit + ωit (2)

where:

HCit is an indicator variable equal to 1 if firm i is domiciled in a country that required property assets to be carried at amortized cost under pre-IFRS domestic standards, and 0 otherwise (i.e., is domiciled in a country that required property assets to be carried at fair value under pre-IFRS domestic standards or under early IFRS adoption). Countries requiring amortized cost include Austria, Belgium, Finland, France, Germany, Italy, Norway, Poland, Spain, and Switzerland; those requiring fair value include Denmark, the Netherlands, Sweden, and the United Kingdom;

IFRSit is an indicator variable equal to 1 if firm i is reporting under IFRS (reporting property assets at fair value) in year t, and 0 otherwise (i.e., is reporting under pre-IFRS domestic standards in year t); and

Impair_Dit is an indicator variable equal to 1 if firm i reports impairment charges during year t, and 0 otherwise.

All other variables are as defined previously (see Appendix A). Equation (2) excludes

IFRS_Adopt as the years captured by that variable are excluded from the analysis. The

                                                            7 We exclude the year of first-time IFRS adoption and the immediately preceding year to avoid the increased

audit effort due to the implementation of a new accounting framework. Our inferences are unchanged when these transition years are included in the analysis. 

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experimental variables are HC, IFRS, their interaction of HC × IFRS, and Impair_D. HC

captures the difference in audit fees between treatment firms (applying amortized cost) and

control firms (applying fair value) before IFRS adoption. IFRS captures the effect of

switching to IFRS for the control firms (i.e., for firms applying fair value accounting before

IFRS adoption). HC × IFRS captures the incremental effect on audit fees of moving from an

amortized cost to a fair value regime; its coefficient ξ15 tests H1A. Impair_D captures the

effect on audit fees of reporting impairment charges; its coefficient ξ16 tests H1B.

Table 1 presents the sample selection, with the final sample including real estate firms

domiciled in the European Economic Area with the necessary data for the period 2001–2008.

After deleting outliers and observations from the IFRS adoption year and the preceding year,

the sample includes 480 firm-year observations representing 172 unique firms. Columns (1)

and (2) of Table 2 present means and medians for this sample. On average, sample firms

have 1.7% foreign revenues, 1.9 segments, 24% of observations reporting losses, a leverage

ratio of 1.6, and 56.9% of observations being audited by Big N auditors.

Table 3 presents the empirical results, which are based on robust standard errors.8 In

Column (1), we report a base model, which is supplemented with the experimental variables

in Columns (2) and (3). Throughout all models, the coefficients on most of the control

variables are statistically significant with the expected signs. For the base model in Column

(1), we find that audit fees are increasing in total assets (0.551, t-stat = 23.80), number of

segments (0.123, t-stat = 3.84), receivables (1.763, t-stat = 4.45), volatility (1.742, t-stat =

2.57), use of Big N auditor (0.253, t-stat = 2.57), and audit occurring within the busy season

(0.263, t-stat = 3.08), and decreasing in ROA (–0.472, t-stat = 2.06).

                                                            8 An alternative approach would be to cluster standard errors by firm and/or year. Our tests, however, do not

have a sufficiently large number of clusters across the year and/or firm dimensions, which is a prerequisite for using clustered standard errors (Petersen, 2009). We perform a sensitivity test and control for clustering by using standard errors clustered by year and firm, which leads to qualitatively similar results.

18  

Introducing our first set of experimental variables in Column (2), the coefficient for

HC is insignificant, consistent with no difference in audit fees between treatment firms and

control firms before IFRS adoption. Likewise, the coefficient for IFRS, which captures the

effect on audit fees of switching to IFRS for the control firms that used fair value accounting

before IFRS, is also insignificant. However, the coefficient on the interaction HC × IFRS is

significantly negative (–0.759, t-stat = 2.84), indicating that treatment firms’ reduction in

audit fees upon switching to IFRS is significantly stronger than that experienced by the

control firms during the same period. This finding suggests that, ceteris paribus, firms

reporting under a cost-based accounting regime experience a significant decrease in audit fees

when switching to a fair value-based accounting regime, providing support for H1A.

Turning to the effect of impairment testing on audit fees, Column (3) reports a

significantly positive coefficient for Impair_D (0.259, t-stat = 2.31), indicating that firm-year

observations in which impairment losses occur are associated with higher audit fees. This

result provides support for H1B, and suggests that the risk and effort associated with

impairment testing procedures are major drivers of audit fees, and likely contributes to the

results above for H1A. In interpreting the result on H1B, we note two items. First, impairment

testing occurs only for firms reporting under a pre-IFRS amortized cost-based accounting

regime or adopting the cost model under IAS 40; fair value model firms do not conduct

impairment testing. Second, Impair_D captures only recognized impairment charges. This

suggests that any incremental audit efforts associated with impairment tests that do not lead

to recognized impairment charges are not captured by this variable, likely leading Impair_D

to understate the full effect of impairment testing on audit fees.

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4.2 The effect of fair value characteristics on audit fees: evidence using European real estate firms after mandatory IFRS adoption

To test our second set of hypotheses, we adjust our setting by retaining our focus on

the European real estate industry, but turning to the post-IFRS adoption period (2005–2008)

during which all firms report under IAS 40. This setting is beneficial as all sample firms

report under a uniform fair value regime while nonetheless exhibiting variation across each of

the four fair value attributes we wish to examine: exposure, complexity, recognition versus

disclosure, and use of an alternative external monitor.

Accordingly, we augment the basic audit fee model of equation (1) as follows:

LogFeesit = β0 + β1LogTAit + β2Foreignit + β3NSegmit + β4ROAit + β5Lossit + β6Receivit

+ β7Levit + β8Qualifiedit + β9Volatilityit + β10BigNit + β11Yearendit

+ β12FV_TA_REit + β13FV_Complexit + β14FV_Recogit + β15FV_Extit + øit (3)

where:

FV_TA_REit is 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 at amortized cost, it is the ratio of disclosed property fair values to the sum of total assets less recognized property at amortized cost plus disclosed fair value of property. Second, FV_TA_RE 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);

FV_Complexit is 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 eleven sectors: land, residential, office, retail, parking, industrial, gastronomy, health care, education, leisure, and other. Second, FV_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);9

FV_Recogit is 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); and

                                                            9 Alternative measures of FV_Complex (e.g., reducing from 11 to 4 main sectors, or based on reference to

median sample complexity values) yield similar results.

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FV_Extit is 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.

All other variables are as defined previously (see Appendix A). Note that we exclude the

control variables IFRS_Adopt, ADR, and Distress due to a lack of variation in this sample.

The four experimental variables correspond to H2A through H2D. First, we include

FV_TA_RE, which captures real estate firms’ exposure to assets reported (i.e., either

recognized or disclosed) at fair value. If higher exposure to fair value reporting for assets

requires additional effort by the auditor (e.g., to validate the fair values), the predicted sign is

positive: that is, audit fees will be higher for firms having greater exposure to assets reported

at fair value. Alternatively, if higher exposure reduces audit effort (e.g., by simplifying

procedures necessary to validate the fair values, or due to reductions attributable to more

costly audit procedures related to assets carried at cost, such as auditing component

depreciation and impairment testing), the predicted sign is negative. Accordingly, the sign is

not predicted; and β12 is our primary test of H2A. Next, we include FV_Complex, which

captures the complexity of the fair value measurement through the heterogeneity of the firm’s

property portfolio. If greater complexity leads to additional audit effort, the predicted sign is

positive; and β13 is our test of H2B. We then include FV_Recog, which captures the

recognition of fair values (fair value changes) on the balance sheet (income statement). If

recognition leads to incremental audit effort, the predicted sign is positive; and β14 is our test

of H2C. Finally, we include FV_Ext, which captures differences in audit fees due to firms

employing external appraisers to provide fair value estimates of their property assets. If the

use of this alternative monitor reduces the effort necessary by the auditor, the predicted sign

is negative; and β15 is our test of H2D.

Table 1 reveals that the sample of firms having the necessary hand-collected data

includes 159 firm-year observations representing 96 unique firms. Columns (3) and (4) of

Table 2 present means and medians for this sample. On average, property assets represent

21  

72.3% of total assets, 81.1% of observations reflect recognition (versus disclosure only) of

property fair values, and 88.7% employ external appraisers. Among the control variables,

9.7% of assets are international, firms average 2.7 segments, on average ROA performance is

6.4%, and 79.2% employ large auditors.

Table 4 presents the empirical results. Similar to our previous analysis, Column (1)

presents results including only the control variables, while Column (2) additionally

incorporates the experimental variables. Focusing on Column (2), among the control

variables, we find as predicted that audit fees are increasing in total assets (0.635, t-stat =

13.38), receivables (2.853, t-stat = 2.29), leverage (0.158, t-stat = 3.07), volatility (2.248, t-

stat = 2.12), use of Big N auditor (0.322, t-stat = 1.90), and whether the audit occurs during

the busy season (0.522, t-stat = 2.81). The remaining control variables are insignificant.10

Among the four experimental variables, we first find that FV_TA_RE is significantly

negative (–0.413, t-stat = 2.28). This variable captures the shift in average audit fees for

firms having above average exposure to operating assets requiring fair value measurement.

Accordingly, the significantly negative coefficient supports H2A, and is consistent with audit

firms charging lower audit fees for firms reporting higher proportions of property assets at

fair value, relative to those reporting lower proportions. Second, FV_Complex is

significantly positive (0.247, t-stat = 2.24). This variable captures the mean shift in audit fees

for firms having above average complexity in their property portfolios. The significantly

positive coefficient supports H2B, and is consistent with audit firms charging higher fees

when auditing more difficult-to-value, i.e., more complex, property portfolios. Third,

FV_Recog is significantly positive (0.383, t-stat = 2.18). This result supports H2C, and is

consistent with audit firms charging higher audit fees for fair values that are recognized on

the primary financial statements versus disclosed only in the footnotes. Finally, FV_Ext is

                                                            10 The variance inflation factors (VIF) across all of our specifications do not exceed 4, suggesting

multicollinearity is not an issue (Neter et al., 1985). 

22  

insignificant (0.025, t-stat = 0.14). Thus, we fail to find evidence supporting H2D that use of

an alternative external monitor reduces audit fees.

Overall, the results are consistent with audit fees decreasing in the firm’s proportion

of total assets reported at fair value, increasing in the difficulty to measure the fair values, and

increasing if the fair value is recognized (versus only disclosed). In the next section, we

exploit other settings to provide additional evidence on our two sets of hypotheses.

5 Alternative settings

In this section, we reassess the results of our primary analyses by examining two

alternative settings to test our predictions. We first compare UK versus US real estate firms

to reassess H1A and H1B. We then use UK investment trusts to reassess H2A and H2B. Finally,

we use UK manufacturing, real estate, and investment trust firms to reassess H1A and H2B.

5.1 The effect of reporting model on audit fees: evidence using UK and US real estate firms

We compare audit fees for UK versus US real estate firms over the period 2001–2008,

which provides an advantageous alternative setting. First, it maintains the within-industry

setting, and thus the nature of the assets being examined. Second, the UK and US both have

highly developed real estate industries, reflected in a large number of publicly traded real

estate firms within each country. Third, this setting exploits the primary difference between

the UK and US real estate industries: the financial reporting model. Specifically, real estate

firms in the UK report property assets on the balance sheet at fair value: either as required

under UK domestic GAAP prior to mandatory IFRS adoption, or as implemented under

IAS 40 by this industry in the UK. In contrast, US real estate firms report property assets on

the balance sheet at amortized cost, as US GAAP prohibits firms from reporting tangible

assets, including real estate, at fair value. Further, industry practice in the US is such that

23  

voluntary disclosure of real estate fair values is extremely rare.11 However, this setting has

one major disadvantage: the US is generally held to have a more litigious audit environment

than most other countries (including the UK). Nonetheless, prior research fails to designate

the real estate industry as a high-risk setting (see, for example: Hogan and Jeter, 1998; Shu,

2000; Brown et al., 2005), suggesting that litigation differences are an unlikely (primary)

source of audit pricing differences across the US and UK for this sector.12

To reexamine the effect of fair value on audit fees, we re-state the base model of

equation (1) using the following model:

LogFeesit = γ0 + γ1LogTAit + γ2IFRS_Adoptit + γ3Foreignit + γ4NSegmit + γ5ROAit + γ6Lossit

+ γ7Receivit + γ8Levit + γ9Distressit + γ10Qualifiedit + γ11Volatilityit

+ γ12BigNit + γ13Yearendit + γ14FV_UKit + γ15Impair_Dit + χit (4)

where:

FV_UKit is an indicator variable equal to 1 if firm i reports real estate assets on the balance sheet at fair value (i.e., is domiciled in the UK) in year t, and 0 otherwise (i.e., reports real estate assets on the balance sheet at amortized cost, and is domiciled in the US); and

Impair_Dit is an indicator variable equal to 1 if a US firm i reports an impairment of plant, property and equipment in year t, and 0 otherwise.

All other variables are as previously defined (see Appendix A). Equation (4) excludes the

variable ADR, which does not show variation in this sample. Following the findings of Table

3, if reporting real estate assets at fair value (as done in the UK) reduces audit effort, then the

predicted sign on the coefficient for FV_UK is negative; and γ14 is an alternative test of H1A.

Further, if impairments lead to incremental audit effort for amortized cost firms, then the

predicted sign on the coefficient for Impair_D is positive; and γ15 is an alternative test of H1B.

                                                            11 Whereas professional analysts commonly construct net asset value estimates, which generally reflect fair-

value-type measures of property assets less the fair value of debt, these calculations reflect user-derived, not firm-supplied, estimates (Kibel and Kozyr, 2007).

12 However, 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.

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To facilitate the estimation, we select UK and US firms with a propensity score

match, which uses predicted values from a probit regression of a country indicator variable

on all control variables in Equation (4). This matching procedure maximizes the similarity of

matched pairs in terms of our control variables, resulting in two sub-samples similar on all

non-fair value dimensions shown to affect audit fees. To the extent that Equation (4) captures

all major factors affecting audit pricing, propensity score matching is expected to mitigate the

influence of country-specific characteristics, which are unrelated to the research question.

Table 1 reveals that the matching procedure leads to a final sample of 623 UK and

616 US firm-years, representing 172 unique UK and 152 unique US firms. Columns (5)

through (8) of Table 2 present descriptive statistics. Untabulated univariate comparisons

reveal that relative to the UK firms, the US firms are significantly larger, have fewer foreign

assets, lower receivables, and are more likely to conduct the audit during the busy season.

Table 5 presents the empirical results, with Column (1) providing the regression

including only control variables, and Column (2) incorporating the experimental variables.

Focusing on Column (2), results for the control variables remain consistent with the previous

tables. Regarding the experimental variables, the coefficient on FV_ UK is significantly

negative (–0.613, t-stat = 10.75), and that for Impair_D is significantly positive (0.279, t-stat

= 3.99). We find similar (untabulated) results alternatively matching on year and total assets,

as well as estimating a fully-interacted model. These results suggest that, controlling for

other determinants of audit fees, UK real estate firms (which report property assets at fair

value) exhibit lower average audit fees relative to US real estate firms (which report property

assets at amortized cost). This finding is consistent with our previous support for H1A.

Further, the results suggest that impairment testing leads to higher audit fees for amortized

cost firms, providing additional support for H1B.

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5.2 The effect of fair value characteristics on audit fees: evidence using UK investment trusts

We now use a sample of UK investment trusts to reassess how exposure to fair value

(H2A) and complexity in fair value measurement (H2B) affect audit fees. This setting offers

several advantages. First, limiting the sample to UK firms mitigates variation in audit fees

that can arise in cross-country settings due to differing institutional features versus the fair

value attributes we seek to examine. Second, this industry potentially offers a strong setting

to examine the attribute of complexity in deriving fair value, by allowing for a cleaner

separation of fair values reflecting observable inputs (analogous to level 1 fair values) versus

those based on less reliable valuation inputs (analogous to level 2 and 3 fair value).13

Accordingly, we re-examine the effect of fair value characteristics on audit fees re-

stating the base audit fee model of equation (1) as:

LogFeesit = σ0 + σ1LogTAit + σ2IFRS_Adoptit + σ3Foreignit + σ4NSegmit + σ5ROAit

+ σ6Lossit + σ7Receivit + σ8Levit + σ9Volatilityit + σ10BigNit + σ11Yearendit

+ σ12FV_TA_ITit (+ σ12FV_INVit) + σ13FV2/3it + υit (5)

where:

FV_TA_ITit is an indicator variable equal to 1 if firm i’s year t proportion of total assets measured at fair value is above the sample mean (indicating greater exposure to assets reported at fair value), and 0 otherwise (indicating less exposure to assets reported at fair value);

FV_INVit is an indicator variable equal to 1 if firm i’s year t proportion of its investment portfolio measured at fair value is above the sample mean (indicating greater exposure to assets reported at fair value), and 0 otherwise (indicating less exposure to assets reported at fair value); and

                                                            13 In the US, Statement of Financial Accounting Standards (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. Accordingly, SFAS 157 reflects measurement challenges by capturing the nature of the inputs used to assess fair value. Similar guidance has been developed under IFRS; see the common framework for fair value measurement and disclosure by the International Accounting Standards Board (IASB) and the US Financial Accounting Standards Board (FASB) (IASB, 2009, 2010a). Both boards have published updated guidance in the form of IFRS 13: Fair Value Measurement (IASB, 2011) and FASB Accounting Standards Update 2011-04: Fair Value Measurement (FASB, 2011), respectively, in May 2011.

26  

FV2/3it is an indicator variable equal to 1 if firm i’s year t proportion of fair-valued investments measured using level 2/3 inputs is above the sample mean (indicating greater valuation complexity of the fair value portfolio), and 0 otherwise (indicating less valuation complexity of the fair value portfolio).

All other variables are as previously defined (see Appendix A). Relative to equation (1), we

exclude ADR, Distress, and Qualified as those variables have no variation in this sample. We

use FV_TA_IT to proxy for fair value exposure of investment trusts’ total assets, and FV_INV

as an alternative proxy for fair value exposure, measured relative to firms’ investment assets.

Consistent with Table 4, if audit fees are lower for firms having greater exposure to assets

reported at fair value due to costly audit procedures unique to assets reported at cost, then we

predict negative coefficients for FV_TA_IT as well as FV_INV, and σ12 is an alternative test

of H2A. Similarly, if audit fees are higher for firms with more complex fair value

measurements (i.e., greater exposure to assets reported at level 2 or 3 fair values), then we

predict a positive coefficient for FV2/3; and σ13 is an alternative test of H2B.

Table 1 presents the sample. Due to hand-collection costs, we include only UK

investment trusts having 10 or more available observations during 1993–2008, which leads to

a sample of 236 firm-years. The descriptive statistics, presented in Columns (9) and (10) of

Table 2, reveal that the firms average 90.6% of total assets at fair value, 96.2% of

investments at fair value, and 8.5% of fair-value investments using level 2 or 3 inputs.

Table 6 presents multivariate results. In Column (1), results on the control variables

are again consistent with our previous tables. Focusing on the experimental variables, we

find that FV_TA_IT is significantly negative (–0.316, t-stat = 2.93). Consistent with our

previous analyses, this finding suggests that firms having above-average exposure to assets

reported at fair value experience lower audit fees, supporting H2A. In addition, we find that

FV2/3 is significantly positive (1.109, t-stat = 7.56). This result is consistent with H2B,

revealing that firms with greater exposure to more difficult-to-value investments (i.e., those

for which market prices are unavailable) experience higher audit fees.

27  

To further assess our results, we re-estimate equation (5) using an alternative proxy

for exposure to assets reported at fair value. Specifically, we replace FV_TA_IT with the

variable FV_INV, an indicator variable equal to 1 if firm i’s year t proportion of investment

portfolio measured at fair value is above the sample mean (indicating greater exposure to

investments reported at fair value), and 0 otherwise. FV_INV refines the proxy for exposure

to assets reported at fair value by holding the asset type constant as investments. If greater

exposure to investments at fair value leads to reduced audit fees, the predicted sign is

negative. Results are reported in Column (2) of Table 6; consistent with expectations, the

coefficient on FV_INV is significantly negative (–0.681, t-stat = 3.32), and the coefficient on

FV2/3 is again significantly positive (1.127, t-stat = 7.81). Overall, the results suggest that

UK investment trusts exhibit lower audit fees with greater exposure to fair value, and higher

audit fees with greater exposure to more difficult-to-value fair values.

5.3 The effect of reporting model and fair value characteristics on audit fees: evidence using UK manufacturing, investment trust, and real estate firms

As a final alternative setting, we report untabulated results of an analysis that speaks

to our two sets of hypotheses simultaneously. We again use the UK setting, which holds

constant the country-level institutions, and allows identification of large sub-samples of firms

that report operating assets principally under either the amortized cost or fair value models.

The principal assets for UK manufacturing firms (SIC = 3xxx) are long-term tangible assets

(e.g., PP&E), which are reported at amortized cost. The principal assets for real estate firms

(SIC = 65xx, 6798) are property assets; these assets are reported at fair value on the balance

sheet using level 2/3 inputs. The principal assets for investment trusts (SIC = 6726) are

financial assets; these assets are also reported at fair value on the balance sheet, but

28  

(generally) using market prices (i.e., level 1 inputs).14 Thus, we use the shift in audit fees

across firms reporting assets principally at amortized cost (i.e. manufacturing firms) versus

those reporting assets principally at fair value to provide an alternative test of H1A. Further,

comparing audit fees of investment trusts that use observable fair value inputs with those for

real estate firms that principally use unobservable inputs provides an alternative test of H2B.

The disadvantage of this setting is that it relies on an industry mean shift.

We augment the base regression of equation (1) by introducing two new experimental

variables: FV1_InvestmentTrustit, an indicator variable equal to 1 if firm i primarily reports

its assets on the balance sheet at fair value based on market prices (i.e., level 1 inputs, thus is

in the investment trust industry) in year t, and 0 otherwise; and FV2/3_RealEstateit, an

indicator variable equal to 1 if firm i primarily reports its assets on the balance sheet at fair

value based on less reliable valuation inputs (i.e., level 2 or 3 inputs, thus is in the real estate

industry), and 0 otherwise. Consistent with H1A and our previously reported results, we

expect that audit fees of firms reporting assets principally at fair value are lower than those of

firms reporting assets principally at amortized cost; thus the coefficients on both

FV1_InvestmentTrust and FV2/3_RealEstate are predicted to be negative. Consistent with

H2B, if audit effort and risk is higher for fair values reflecting less complex valuations, we

predict the coefficient on FV2/3_RealEstate > coefficient on FV1_InvestmentTrust.

To align our three samples across the amortized cost and fair value groupings on

characteristics shown to affect audit fees, we employ matching based on propensity scores

and, alternatively, total assets, leading to 1,381 firm-years in each of the three industries. We

augment the base fee model of equation (1) with our two experimental variables. Results on

the control variables are unchanged from previous findings. Of interest, the coefficients of

                                                            14 Note that the UK adopted IFRS effective 2005 (2006 for non-December fiscal year-end firms). However,

balance sheet reporting for the assets we focus on is generally consistent across UK domestic standards and IFRS. Both allow firms to revalue PP&E (though few firms choose to do so; accordingly, most UK firms report such assets at historical cost); both require real estate assets to be reported on the balance sheet at fair value; and both require (most) financial assets to be reported on the balance sheet at fair value.

29  

FV1_InvestmentTrust and FV2/3_RealEstate are negative (–1.948 and –0.509, respectively)

and significant (t-statistics = 35.61 and 11.29, respectively), providing support for H1A. The

results suggest that firms reporting assets on the balance sheet principally at fair value level 1

or fair value level 2/3 exhibit lower audit fees (on average) compared to firms reporting

assets principally at amortized cost. We further find support for H2B by documenting that

audit fees for firms reporting fair values based on level 2 or 3 inputs are significantly higher

relative to firms reporting fair values based on market prices, as the coefficient for

FV2/3_RealEstate is significantly less negative than that for FV1_InvestmentTrust (t-test =

42.30). These results hold across both matching procedures.

6 Sensitivity tests

6.1 Replications excluding the financial crisis year of 2008

Our primary sample is comprised of real estate firms over the period 2001–2008.

Similar to most firms, the real estate industry was affected by the global financial crisis,

which was most severe during the years of 2008 and 2009. To assess the robustness of our

results to the inclusion of a sample year potentially affected by the crisis, we replicate the

primary analyses of Tables 3 and 4 by excluding 2008 firm-year observations from the

samples. Results of these replications are reported in Table 7, and remain consistent with our

primary analyses. Further, we replicate the results of Tables 5 and 6 excluding 2008

(untabulated); results are again consistent with those reported.

6.2 Country effects

We also examine the robustness of the Table 3 and 4 results to controlling for country

effects. Specifically, previous research indicates that cross-country differences in audit

30  

quality depend on institutional characteristics such as the level of investor protection or

liability standards (Francis and Wang, 2008). Therefore, as a sensitivity test we include the

anti-self-dealing index from Djankov et al. (2008) to capture the level of legal protection and

the burden-of-proof index from La Porta et al. (2006) to proxy for the liability standard (that

is, the procedural difficulty in recovering funds from liable stakeholders). Results of this

analysis are qualitatively similar to those reported previously, suggesting that country-

specific institutional settings are not driving our results.

6.3 Alternative measurement of the dependent variable

Consistent with prior research, our tests measure total auditor fees using the Thomson

Reuters Worldscope database (data item 01801, Auditor Fees). These data comprise fees

paid to the firm’s auditor for the statutory audit of the financial statements as well as fees

paid to the firm’s auditor for other services. Since our interest lies in the relation of fair value

and statutory audit fees only, we assess the sensitivity of our results to the presence of non-

audit fees in the dependent variable. Due to data limitations, we can conduct this sensitivity

test only for the UK investment trusts (Table 6) with the available data from Bureau van

Dijk’s Financial Analysis Made Easy (FAME) database, which disaggregates total auditor

fees into statutory audit fees and fees paid to the auditor for non-audit services. Data

availability constraints restrict the sample to 125 observations over the period 1997–2008.

In untabulated analyses, we observe highly significant positive correlations (Pearson

correlation coefficient = 0.94) between the natural logarithm of auditor fees and the natural

logarithm of statutory audit fees. Using firm-years with available data from FAME and

substituting as the dependent variable the log of statutory audit fees for the log of total auditor

fees, we find similar coefficient signs, albeit somewhat lower significance levels. Of note,

the coefficients on our experimental variables, FV_IT_TA and FV_INV remain significantly

31  

negative, while the coefficient on FV2/3 remains significantly positive, indicating that

support for H2A and H2B is also obtained using a more precise measure of our dependent

variable. To the extent that the observed high correlation between auditor fees and statutory

audit fees is also present in other settings, this suggests that the other tests are also unaffected

by this type of measurement error in the dependent variable.

6.4 Interviews with real estate audit partners

To validate our expectations, we conduct phone interviews with several audit partners

specializing in the real estate industry. The interviewees represent each of the Big 4

accounting firms, as well as a regional European audit firm. All audit partners are based in

Europe. The interviews confirm that, while their initials expectations were that fair value

would lead to increased audit fees, experience suggests otherwise. The two primary

contributors to higher audit fees under amortized cost for real estate firms appear to be

component depreciation, and (particularly) impairment testing. The interviews further

confirmed our predictions regarding complexity leading to more audit effort, recognition

leading to more audit effort relative to disclosure only, and use of external appraiser leading

to lower audit effort (assuming the appraiser’s work is of high quality).

7 Conclusion

This paper builds on the literatures examining the determinants of audit fees and the

effects of fair value reporting by investigating whether fair value reporting and its salient

characteristics affect observed audit fees. We examine both the effect of the primary

reporting model used (fair value versus amortized cost) as well as four characteristics of fair

value reporting: exposure to assets reported (either recognized or disclosed) at fair value; the

32  

complexity of the fair value measurement; whether the fair value is recognized versus

disclosed; and the use of non-auditor external monitors to derive the fair value estimates.

Using the European real estate industry as our primary setting, we provide evidence

that audit fees are lower for firms reporting their primary operating asset under a fair value

regime, compared to firms reporting their primary operating asset under an amortized cost

regime. We additionally find that impairments appear to be a major driver for higher audit

fees for amortized cost firms. We then document that audit fees are decreasing in firms’

exposure to fair values, and increasing both in the complexity of measuring fair value and if

fair values are recognized (versus only disclosed in the footnotes).

We provide corroborating results in three alternative settings. First, we find that audit

fees are lower for UK real estate firms (which report property assets at fair value) relative to a

matched sample of US real estate firms (which report property assets at historical cost); in

this setting, impairments again appear to be a major contributor to higher audit fees for

amortized cost firms. Second, we find using UK investment trusts that audit fees are lower

for firms with higher exposure to investment assets reported at fair value, and higher for firms

with investments reflecting more complex measurement (i.e., level 2/3 versus level 1 inputs).

Finally, in a cross-industry analysis, we find audit fees are highest for UK manufacturers

(which report long-term assets generally at amortized cost), then for UK real estate firms

(which report long-term assets at fair value, using low-reliability valuation inputs), and then

for UK investment trusts (which report long-term assets at fair value with market inputs).

Overall, the results suggest that greater exposure to assets reported at fair value can

lower contracting costs, such as audit fees. This insight complements previous findings that

fair values lead to reduced information asymmetries, by suggesting that movement to fair

value can both improve decision making and reduce contracting costs. However, the findings

also highlight that salient characteristics of the fair value reporting can attenuate the

33  

benefits―particularly, the complexity of deriving the fair values, and whether they are

recognized in the primary financial statements. These results may assist standard setters in

their ongoing deliberations about the role of fair value reporting in general-purpose financial

statements, as they suggest that fair value reporting can enhance both the decision and

contracting usefulness of financial statements, and thus potentially foster both objectives of

financial reporting.

34  

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37  

Appendix A Variable definitions Dependent Variables

LogFeesit The log of total auditor fees paid by firm i for year t.

Control Variables

LogTAit The log of firm i’s total assets at the end of year t.

IFRS_Adoptit 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.

Foreignit International assets divided by total assets for firm i for year t.

NSegmit Number of firm i’s operating segments for year t.

ADRit An indicator variable equal to 1 if firm i is cross-listed in the United States for year t, and 0 otherwise.

ROAit Firm i’s net income, net of impairment losses, divided by total assets, both measured for year t.

Lossit An indicator variable equal to 1 if firm i reports negative net income for year t, and 0 otherwise.

Receivit Firm i’s receivables divided by total assets, both measured for year t.

Levit Firm i’s total debt divided by market value of equity for year t.

Distressit An indicator variable equal to 1 if firm i reports negative book value of equity for year t, and 0 otherwise.

Qualifiedit An indicator variable equal to 1 if firm i receives a qualified audit opinion for year t or t-1, and 0 otherwise.

Volatilityit The standard deviation of monthly stock returns for firm i over year t.

BigNit 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.

Yearendit 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.

Experimental Variables

Table 3

HCit An indicator variable equal to 1 if firm i is domiciled in a country that required property assets to be carried at amortized cost under pre-IFRS domestic standards, and 0 otherwise (i.e., is domiciled in a country that required property assets to be carried at fair value under pre-IFRS domestic standards or under early IFRS adoption). Countries requiring amortized cost include Austria, Belgium, Finland, France, Germany, Italy, Norway, Poland, Spain, and Switzerland; those requiring fair value include Denmark, the Netherlands, Sweden, and the United Kingdom.

IFRSit 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_Dit An indicator variable equal to 1 if firm i reports an impairment of property, plant and equipment or fixed financial assets during year t, and 0 otherwise.

38  

Table 4

FV_TA_REit 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 amortized cost, it is the ratio of disclosed property fair values to the sum of total assets less recognized property at amortized cost plus disclosed fair value of property. Second, FV_TA_RE 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). Calculated from hand-collected data.

FV_Complexit 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 eleven sectors: land, residential, office, retail, parking, industrial, gastronomy, health care, education, leisure, and other. Thus, higher values reflect more complex portfolios by reflecting diversity across these sectors. Second, FV_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).

FV_Recogit 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).

FV_Extit 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_UKit 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_Dit An indicator variable equal to 1 if a US firm i reports an impairment of plant, property and equipment in year t, and 0 otherwise.

Table 6

FV_TA_ITit The proportion of firm i’s total assets measured at fair value. Calculated from hand-collected data. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean.

FV_INVit The proportion of firm i’s investment portfolio measured at fair value. Calculated from hand-collected data. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean.

FV2/3t The proportion of firm i’s fair-valued investments measured using level 2/3 inputs. Calculated from hand-collected data. Multivariate tests use indicator variables equaling to 1 if firm i’s fair value exposure is higher than the sample mean.

39  

Table 1 Sample selection Firm-Years Unique Firms European

real estate

UK real

estate

US real

estate

UK investment

trusts

European real

estate

UK real

estate

US real

estate

UK investment

trusts Initial sample 2,717 725 2,001 3,719 519 186 336 576

Remaining after deleting: Missing data for test variables 1,157 637 1,492 3,408 310 173 320 538 Year of first-time IFRS adoption and preceding year 490 175 Outliers (1st and 99th percentiles of audit fees) 480 623 1,461 3,305 172 172 315 531

Table 3 sample European real estate firms 480 172 Table 4 sample: European real estate firms after mandatory

IFRS adoption (with available hand-collected data)

159 96

Table 5 sample: matched UK and US real estate firms 623 616 172 152 Table 6 sample: UK investment trusts with available

hand-collected data 236 28

This table presents the sample selection for the three analyses of Tables 3, 4 and 5. The Table 3 sample includes publicly traded European real estate firms (SIC = 65xx, 6798); the sample period is 2001–2008, and excludes the firm’s first year of IFRS adoption and the immediately preceding year. The Table 4 sample includes European real estate firms reporting under IFRS over the period 2005–2008 with available hand-collected data. The Table 5 sample includes publicly traded real estate firms domiciled in the UK (which report property assets at fair value) during the period 2001–2008, matched by year using propensity scores with publicly traded real estate firms domiciled in the US (which report property assets at amortized cost). The Table 6 sample includes UK investment trusts (SIC = 6726) over the period 1993–2008 with available hand-collected data.

40  

Table 2 Descriptive statistics

Table 3 Table 4 Table 5 Table 6 European

real estate firms (N = 480)

European real estate firms

(N = 159)

UK real estate firms

(N = 623)

US real estate firms

(N = 616)

UK investment trusts

(N = 236) Mean Median Mean Median Mean Median Mean Median Mean Median (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Dependent Variable

LogFeesit 4.915 4.836 6.220 5.999 4.871 4.836 5.638 5.684 3.478 3.219

Control Variables

LogTAit 12.065 11.955 14.248 14.165 11.881 11.819 12.201 12.317 13.233 13.489 Foreignit 0.017 0.000 0.097 0.000 0.043 0.000 0.011 0.000 0.010 0.000 NSegmit 1.923 1.000 2.717 3.000 1.799 1.000 1.854 1.000 1.792 1.000 ROAit –0.011 0.023 0.064 0.058 –0.052 0.019 –0.086 0.014 0.020 0.019 Lossit 0.240 0.000 0.107 0.000 0.327 0.000 0.339 0.000 0.030 0.000 Receivit 0.059 0.020 0.037 0.017 0.061 0.020 0.041 0.014 0.009 0.005 Levit 1.632 0.822 1.034 0.741 1.541 0.712 2.842 0.788 0.165 0.139 Distressit 0.038 0.000 # # 0.037 0.000 0.054 0.000 # # Qualifiedit 0.010 0.000 # # 0.024 0.000 0.037 0.000 # # Volatilityit 0.091 0.070 0.078 0.067 0.103 0.077 0.163 0.076 0.056 0.048 BigNit 0.569 1.000 0.792 1.000 0.562 1.000 0.580 1.000 0.826 1.000 Yearendit 0.771 1.000 0.887 1.000 0.708 1.000 0.820 1.000 0.606 1.000

Experimental Variables

Impair_Dit 0.181 0.000 # # 0.213 0.000 FV_TA_REit 0.723 0.823 FV_Complexit 1.631 1.577 FV_Recogit 0.811 1.000 FV_Extit 0.887 1.000 FV_TA_ITit 0.906 0.950 FV_INVit 0.962 1.000 FV2/3it 0.085 0.005

41  

This table reports descriptive statistics. All variables are defined in Appendix A. The descriptive statistics for the variables FV_TA_RE, FV_Complex, FV_TA_IT, FV_INV, and FV2/3 are reported for the raw values, before applying a binary coding as described in Appendix A. # indicates insufficient variation within the indicated sample, and therefore the variable is excluded from multivariate tests and no descriptive statistics provided. Columns (1) – (2) present statistics for the sample used in Table 3. The sample includes publicly traded European real estate firms (SIC = 65xx, 6798); the sample period is 2001–2008 and excludes the firm’s first year of IFRS adoption and the immediately preceding year. Columns (3) – (4) present statistics for the sample used in Table 4. The sample includes European real estate firms reporting under IFRS over the period 2005–2008 with available hand-collected data. Columns (5) – (8) present statistics for the sample used in Table 5. The sample includes publicly traded real estate firms domiciled in the UK (which report property assets at fair value) during the period 2001–2008, matched using propensity scores with publicly traded real estate firms domiciled in the US (which report property assets at historical cost). Columns (9) – (10) present statistics for the sample used in Table 6. The sample includes UK investment trusts (SIC = 6726) over the period 1993–2008 with available hand-collected data.

42  

Table 3 The effect of reporting model on audit fees: evidence using European real estate firms upon mandatory IFRS adoption

Base model

Effect of IFRS adoption

Effect of impairments

Variable

Predicted sign

Coeff (t-stat)

Coeff (t-stat)

Coeff (t-stat)

(1) (2) (3)

Intercept + / – –2.633 *** (11.00) –2.693 *** (11.33) –2.630 *** (11.33)

LogTA + 0.551 *** (23.80) 0.555 *** (24.74) 0.549 *** (24.92)

Foreign + 0.089 (0.23) 0.097 (0.24) 0.136 (0.35)

NSegm + 0.123 *** (3.84) 0.108 *** (3.40) 0.101 *** (3.18)

ROA – –0.472 ** (2.06) –0.362 * (1.88) –0.395 * (1.94)

Loss + 0.157 (1.38) 0.159 (1.57) 0.095 (0.88)

Receiv + 1.763 *** (4.45) 1.797 *** (4.42) 1.800 *** (4.48)

Lev + 0.004 (0.65) 0.001 (0.13) –0.002 (0.23)

Distress + 0.181 (0.96) 0.281 * (1.74) 0.265 (1.60)

Qualified + –0.311 (1.73) –0.432 (2.15) –0.466 (2.29)

Volatility + 1.742 ** (2.57) 1.939 *** (3.11) 1.956 *** (3.27)

BigN + 0.253 ** (2.57) 0.272 *** (2.92) 0.270 *** (2.92)

Yearend + 0.263 *** (3.08) 0.358 *** (4.12) 0.364 *** (4.21)

Experimental Variables

HC + 0.043 (0.23) 0.006 (0.03)

IFRS + / – 0.066 (0.61) 0.079 (0.73)

HC× IFRS + / – –0.759 *** (2.84) –0.756 *** (2.84)

Impair_D + 0.259 ** (2.31)

Adj-R2 72.8% 74.6% 74.9%

43  

This table examines the effect of the firm’s reporting model for its primary operating asset on observed audit fees, using mandatory adoption of IFRS in Europe as a natural experiment. The sample includes publicly traded European real estate firms (SIC = 65xx, 6798); the sample period is 2001–2008 and excludes the firm’s first year of IFRS adoption and the immediately preceding year. Across all columns, N = 480. The dependent variable is LogFees. All variables are defined in Appendix A. The coefficient on HC x IFRS is used to test H1A; the coefficient on Impair_D is used to test H1B. ***, **, * represent significance at the 1%, 5% and 10% levels for the indicated one or two-tailed tests. t -statistics are based on robust standard errors and are reported in parentheses.

44  

Table 4 The effect of fair value characteristics on audit fees: evidence using European real estate firms after mandatory IFRS adoption

Base model

With experimental variables

Variable

Predicted Sign

Coeff (t-stat)

Coeff (t-stat)

(1) (2)

Intercept + / – –4.100 *** (7.21) –4.130 *** (7.54)

LogTA + 0.636 *** (16.01) 0.635 *** (13.38)

Foreign + 0.206 (0.89) 0.236 (1.08)

NSegm + 0.036 (0.83) 0.013 (0.27)

ROA – –1.141 (0.75) –1.104 (0.76)

Loss + –0.396 (1.46) –0.419 (1.73)

Receiv + 4.037 *** (3.67) 2.853 ** (2.29)

Lev + 0.121 ** (2.17) 0.158 *** (3.07)

Qualified + 0.094 (0.40) –0.265 (1.14)

Volatility + 3.291 *** (3.01) 2.248 ** (2.12)

BigN + 0.442 ** (2.52) 0.322 * (1.90)

Yearend + 0.428 ** (2.25) 0.522 *** (2.81)

Experimental Variables

FV_TA_RE + / – –0.413 ** (2.28)

FV_Complex + 0.247 ** (2.24)

FV_Recog + 0.383 ** (2.18)

FV_Ext – 0.025 (0.14)

Adj-R2 72.7% 75.1% This table examines the effect of fair value characteristics on audit fees. The sample includes European real estate firms reporting under IFRS over the period 2005–2008 with available hand-collected data. Across all columns, N = 159. The dependent variable is LogFees. All variables are defined in Appendix A. The coefficients on FV_TA_RE, FV_Complex, FV_Recog, and FV_Ext are used to test H2A, H2B, H2C, and H2D, respectively. ***, **, * represent significance at the 1%, 5% and 10% levels for the indicated one or two-tailed tests. t-statistics are based on robust standard errors and are reported in parentheses.

45  

Table 5 Alternative setting for the effect of reporting model on audit fees: evidence using UK and US real estate firms

Base

model With experimental

variables Variable

Predicted sign

Coeff (t-stat)

Coeff (t-stat)

(1) (2)

Intercept + / – –1.199 *** (6.15) –0.695 *** (3.49)

LogTA + 0.468 *** (24.87) 0.451 *** (25.05)

IFRS_Adopt + –0.190 (3.08) 0.164 ** (2.56)

Foreign + 0.382 (1.53) 0.694 *** (2.82)

NSegm + 0.047 ** (2.21) 0.036 * (1.78)

ROA – –0.085 * (1.82) –0.074 (1.41)

Loss + 0.365 *** (5.82) 0.351 *** (5.74)

Receiv + 1.830 *** (4.07) 2.037 *** (4.51)

Lev + –0.000 (0.18) –0.001 (0.65)

Distress + 0.206 (1.25) 0.148 (0.91)

Qualified + 0.146 (0.49) 0.109 (0.39)

Volatility + 0.017 (0.69) –0.003 (0.13)

BigN + 0.442 *** (5.56) 0.466 *** (6.17)

Yearend + 0.332 *** (5.80) 0.260 *** (5.17)

Experimental Variables    

FV_UK – –0.613 *** (10.75)

Impair_D + 0.279 *** (3.99)

Adj-R2 61.1% 66.2% This table examines an alternative setting to assess the effect of the firm’s reporting model for its primary operating asset on observed audit fees. The sample includes publicly traded real estate firms domiciled in the UK (which report property assets at fair value) during the period 2001–2008, matched by year using propensity scores with publicly traded real estate firms domiciled in the US (which report property assets at historical cost). Across both columns, N = 1,239. The dependent variable is LogFees. All variables are defined in Appendix A. The coefficient on FV_UK is used to test H1A; the coefficient on Impair_D is used to test H1B. ***, **, * represent significance at the 1%, 5% and 10% levels for the indicated one or two-tailed tests. t -statistics are based on robust standard errors and are reported in parentheses.

46  

Table 6 Alternative setting for the effect of fair value characteristics on audit fees: UK investment trusts

Variable

Predicted sign

Coeff (t-stat)

Coeff (t-stat)

(1) (2) Intercept + / – –1.626 *** (3.49) –1.374 *** (3.00)

LogTA + 0.361 *** (8.96) 0.366 *** (9.03)

IFRS_Adopt + 0.530 *** (3.95) 0.521 *** (3.81)

Foreign + 4.491 *** (5.06) 3.048 *** (2.79)

NSegm + 0.051 (1.23) 0.031 (0.71)

ROA – 4.705 (1.45) 3.432 (1.41)

Loss + 0.795 *** (3.06) 0.690 *** (2.79)

Receiv + 0.989 (0.27) 1.763 (0.51)

Lev + 0.541 ** (2.20) 0.654 ** (2.40)

Volatility + 2.290 (1.31) 2.506 (1.61)

BigN + –0.062 (0.47) 0.108 (0.90)

Yearend + –0.132 (1.43) –0.212 (2.19)

Experimental Variables  

FV_TA_IT – –0.316 *** (2.93)

FV_INV – –0.681 *** (3.32)

FV2/3 + 1.109 *** (7.56) 1.127 *** (7.81)

Adj-R2 64.6% 65.3% This table presents an alternative setting to examine the effect of fair value characteristics on audit fees. The sample includes UK investment trusts over the period 1993–2008 with available hand-collected data. Across both columns, N = 236. The dependent variable is LogFees. All variables are defined in Appendix A. The coefficients on FV_TA_IT and FV_INV are used to test H2A; the coefficient on FV2/3 is used to test H2B. ***, **, * represent significance at the 1%, 5% and 10% levels for the indicated one or two-tailed tests. t -statistics are based on robust standard errors and are reported in parentheses.

47  

Table 7 Sensitivity analyses: replications excluding the financial crisis year of 2008

Table 3 Table 4

Variable

Predicted Sign

Coeff (t-stat)

Coeff (t-stat)

(1) (2) Intercept + / – –2.813 *** (11.56) –4.106 *** (7.61)

LogTA + 0.565 *** (24.80) 0.629 *** (14.25)

Foreign + 0.912 ** (2.20) 0.291 (1.35)

NSegm + 0.087 *** (2.73) –0.002 (0.04)

ROA – –0.374 * (1.76) –1.246 (0.75)

Loss + 0.181 (1.64) –0.075 (0.31)

Receiv + 1.840 *** (4.47) 2.105 (1.58)

Lev + –0.002 (0.40) 0.156 *** (3.21)

Distress + 0.230 (1.18)

Qualified + –0.423 (1.84) –0.356 (1.39)

Volatility + 2.129 *** (3.22) 3.613 *** (2.78)

BigN + 0.235 ** (2.45) 0.367 ** (2.14)

Yearend + 0.356 *** (4.07) 0.538 *** (2.77)

Experimental Variables      

HC + –0.008 (0.04)

IFRS + / – 0.123 (1.07)

HC× IFRS + / – –0.721 ** (2.40)

Impair_D + 0.238 ** (2.02)

FV_TA_RE + / – –0.439 ** (2.34)

FV_Complex + 0.290 ** (2.58)

FV_Recog + 0.464 ** (2.55)

FV_Ext – –0.047 (0.25)

N 453 149

Adj-R2 75.4% 77.2% This table provides sensitivity analyses replicating the results of Tables 3 and 4 by excluding the sample year 2008, which occurs at the beginning of the global financial crisis. The dependent variable is LogFees. All variables are defined in Appendix A. ***, **, * represent significance at the 1%, 5% and 10% levels for the indicated one or two-tailed tests. t -statistics are based on robust standard errors and are reported in parentheses.