Bank Efficiency, Risk-based Capital, And Real Estate Exposure

8/12/2019 Bank Efficiency, Risk-based Capital, And Real Estate Exposure

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REAL ESTATE LENDINGUNIVERSITA DI TOR VERGATA

PAPER DISCUSSION- Francesco Di Leo

PAPER: William L. Weber and Michael Devaney (Bank Efficiency,Risk-Based Capital, and Real Estate Exposure: The Credit CrunchRevisited , Real estate Economics, 1999 V27 (1): 1-25


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INTRODUCTION AND AIM (1/2)

During 1990-1993 banks shifted their assets reducing real estate loans.

The research focuses on how the implementation of risk basedcapital (RBC) standards effects real estate lending and banks assets .

RBC established a system of weights that requires more equity capitalfor risky assets.

The aim of the paper is to answer to these questions:

Credit Crunch depends on demand or supply side real estate lending?

How is possible to measure the supply side real estate lendingefficiency?


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LITTERATURE REVIEW (1/4)Tuccillo (1991) - calls the reallocation of banks assets due to reducingreal estate loans Credit crunch

Keeton (1994) : describes the asset shift as an increase in bankingsecurity holdings focusing more on credit rationing to smallbusiness

Hancock and Wilcox (1994)-Peack and Rosengren (1994) : bad realestate loans determine crisis and new constrains coming from bankingauthority

Seiders (1989)-Tuccillo (1991) : aggressive interventions byregulators may have increase banks liquidity problems

Berger, Hunter and Timme (1993) : studying banks efficiency , onebank is a multi output firm that use some inputs (physical capital, laborand deposits), combining them in a productive process.


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LITTERATURE REVIEW (2/4)Fergus and Goodman (1994): real estate environment of the early1990s depends on 1980s disintermediation, changes in tax law,aggressive lending policies. The Financial Institution Reform, Recoveryand Enforcement Act (FIRREA Act of 1989) penalized long term creditchannels, the Resolution Trust Corporation (RTC) settled to helpdispose of the assets failed thrift cause anxiety among lenders due to

inventories of problem properties. Moreover the Office of Comptroller ofthe Currency (OCC) put some pressure on CEOs and board directorsabout real estate lending policies and many believed that it was asignal of a general crackdown. Overall they suggest in a dynamicmarket, Credit Crunch depends on the difference between present

landing policies and standard credit practice at the same time ofthe business cycle.


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LITTERATURE REVIEW (3/4)Peak and Rosengren (1995): Credit Crunch is a supply-induced

restriction in the availability of credit. They analyzed capitalconstrained bank. Lenders that fail to meet minimum capital standards,need more equity capital or have to reduce higher risk assets categories or have to shrink in size . They found evidence in early1990s, analyzing New England banks that despite severe downturn in

real estate markets, the proportion of real estate lending in bankportfolio didnt explain credit shrinkage.

Berger and Udell (1994) : analyzing six different explanation for theincrease of bank security holdings, four depends on supply side two ondemand. They test that RBS standards influenced more capitalrequirements.

Shepard (1953)-Fire (1998) : defined an output distance function starting from a multi-output and multi input production technology(methodology literature)


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LITTERATURE REVIEW (4/4)Grosskorp (1986) : discussed in detail the construction of technology,

under different assumption of returns of scale and analyzing differentinputs and outputs disposability (methodology literature)

Sealey and Lindley(1977)-English et al.(1993): used an assetapproach using balance sheet data to define outputs (methodologyliterature)

Spong (1994) : summarized risk weight asset categories and RBCstandards (methodology literature)

Hughes at all (1996) : scale economies increase as bank grow larger(methodology literature)


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SAMPLE (1/2). Data are taken from FFIEC Call Reports. They consider US banks with

over $1 billion in assets in 1990 and 1993, because banks with thissize reported the value of assets in each risk category . There arefour weight-risk assets categories, 0%,20%,50%,100%.

Banks with research standards are 247 in 1990 and 267 in 1993 Inputs are labor (number of full time equivalent workers employed),

physical capital (value of premises and fixed assets) , deposits

(interest and non-interest bearing)

Outputs are: 1)Cash, Treasury Securities, Government Agency Securities: 0% risk

weighted assets

2)Securities issued by State and Local Governments, other USGovernments securities: 20% risk weighted asset 3)Real Estate loans to one-to-four family, some multi-family properties,

MBS, State and local bond issuances: 50% risk weighted assets 4)All other loans and industrial development revenue bonds, asset

value of premises, fixed assets, real estate owned, intangible assets:100% risk weighted assets


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SAMPLE (2/2)


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METHODOLOGY (1/7)They use a non-parametric linear programming (LP) methods to

measure bank efficiency when banks produce multiple outputs usingmultiple inputs and are subject to regulatory constraints.

Vector of M outputs

Vector of N inputs

Production possibilities set

The output distance function

Each k bank belong to K bank observed

kth observation on inputs and outputs

Assuming CRS ( Constant Rates of Scale ) and strong disposability ofoutputs and inputs:Linear production possibility for bank k

weighted average outputs and inputs of all banks at t time


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METHODOLOGY (2/7)Construction of P(x) and the Output Distance Function Suppose three k using same inputs (x1, x2, x3) to produce two output (u1,u2)

Observation of outputs= a,b,cfb cg = extension of production technology with strong outputsbc = production technology weighting two outputs for two banks0fbcg = Production Possibility Frontiera = point were an hypothetical bank produce inside the Production Possibilityh= point were the hypothetical bank expanding production became efficient

OTE= Overall Technical Efficiency, is the value of the Output Distance Function

max 1 when bank produce on the Frontier,


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METHODOLOGY (3/7)The LP method used to estimate the distance function define the bestpractice reference technology for each bank:

m and n are constrains

bank under observation atperiod t

intensity variables serve to form convex of

combinations of x an u for K banksThis model consider CRS, adding VRS (Variable Rate of Scale) in the technologywe add the constraint:


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METHODOLOGY (4/7)The Scale efficiency is SCALE=TCRS/TVRSInefficiency means bank is not operating in the range of CRS

Regulation may influence production possibility set. Given:(E)=amount of Equity (Aj)=value of Asset j (SumAj)=total assets(wj)=weight of asset by regulation (L)=minimum leverage ratio put fromregulatory authority (R)=minimum risk-weighted capital ratio set byauthority

Leverage-ratio constraint

Risk-weighted ratio constraint

Let M outputs as a subset of j assetsLeverage-ratio constraint

Risk-weighted ratio constraint

The tworegulatoryconstraints linerized:


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METHODOLOGY (5/6) Given the two regulatory constraints and the VRS technology

(TVRS) subject to the two regulatory constraints they calculate thePTE (Pure Technical Efficiency) that is the reciprocal solution ofthis LP problem:

Level of Regulatory Efficiency

Level of Overall Technical Efficiency

Level of Scale Efficiency

In a given period (t) each measure of efficiency is constructed assuming a constant

bank output mixAt t+1 can change: bank output mix, bank position on the frontier, frontier (shift)


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METHODOLOGY (6/7) There are three banks employing thesame inputs, the outputs are a,b,cThe Unregulated ProductionPossibility Frontier =0fbcgEquation of risk-weighted capital-ratioconstraint ( segment RR ):

Bank a OTE=0a/0h PTE=0h/0i REG=0i/0hExample:SECURITY (u1) RISK WEIGTHED (w1=0,2)REAL ESTATE LOANS (u2) RISK WEIGTED

(w2=0,5)Bank a u=1,1 Bank b u=2,1 Bank c u=1,2

In absence of regulation:Banks c and b OTE=1 bank a

OTE=0a/0h=0,67 potentialu=1/OTE=1/067=1,5 u=(1,5,1,5)

With regulation constraints:Hypothesis: R=0,08 other assets dontexist u1


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METHODOLOGY (7/7) Example (continuing)

Solving LP problem with regulatory constraint, (bank a) can have an efficiency yield of 0,7-production=1/0,7=1,44 so output could just expand to u=(1,44 1,44)

REG=0,67/0,7=0,95 and lost of potential output=((1/0,95)-1)%=5,3%E have to expand to E>0,084 maintained its original output mix, if bank decide tomaintained the same E have to change is outputs mix to u1/u2 1,67/1,33


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RESULTS (1/13)

Since OTE is multiplicative in its components, they reported the geometric means of thecomponents of OTEOTE improved from 53% to 61%realizing OTE=1 potential output in 1990 is ((1/0,53)-1)%=89% and ((1/0,61)-1)%=64% in1993The FDIC Improvement Act took effect in December 1991, the data from 1990 show thatbank change their assets (outputs) before the Act takes effect. Efficiency in 1990 was dueto PTE* REG(speculative)Scale efficiency decline in 1993, none of the banks operated in DRS


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RESULTS (2/13)


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RESULTS (3/13)


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RESULTS (4/13) Table 3 examines average bank real estate loans and the potential

gains reducing PTI, reducing scale inefficiency by operating at CRSand reducing RTI. Bank real estate portfolio is divided into: construction, farm loans,

one-to-four family residential, multi-family residential, other loans (i.e.commercial)

Potential Real estate loan portfolio is when bank realize OTE=1 1/OTE measures maximum expansion of each category of

assets (output)

Measure of OTE doesnt require information on inputs and outputsprices(constant, but not for banking industry-expansion of RE loansto its potential: sell marginal output: reduce gain)

Gains are independent from changes in real estate portfolio due toallocative inefficiencies in the mix of inputs and outputs


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RESULTS (5/13)Potential Real Estate loan portfolio= Mean of each REcategories/OTE Each actual real estate loan portfolio do not increase to the samefactor, because each bank has a different OTE level and a differentamount of each kind of RE loan

Actual Real Estate lending increase from 1990 to 1993 by 12% butpotential real estate lending fell, because banks reallocated their loanportfolio

The potential gains in real estate lending from reducing allinefficiencies is 1/OTE and OTE=PTE*REG*SCALE

The authors measure Potential gains in real estate lending with PTE=1 REG=1 SCALE=1RE=actual real estate lending


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RESULTS (6/13)

REG=1 in 1990 is speculative because FDCIA take place in 1991soGAINreg depends in that year only on PTE, but although consideringREG=1 it could be that constraints didnt have effects on real estatelending for the downturn in the business cycle.REG=1 GAINreg declines

From 1990 to 1993 the average bank of the sample grew from $2.5

billion to $2.8 billion(merger and normal grew)SCALE=1 GAINscale increasedThe LP method construct the best-practice technology and not the trueunobserved technology, so it could be that the observed frontier of 1990lies further from the true frontier and increasing bank size compensate

declining in scale so that there was a positive GAINscale


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RESULTS (8/13)


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RESULTS (9/13)The authors examine the three alternatives that bank have torespect capital constraints regressing the log change inregulatory efficiency on(as in Peek and Rosengren):1)log change in equity(E)2)log change in total assets (A)3)log change in real estate lending(RE)4)log change in per capita disposable income (Y): to controlbusiness cycle


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RESULTS (10/13)

Last part of the study they examine regional differences in bankefficiency disaggregated the sample by Federal Reserve District(FRD). This because credit crunch had different impact in USstates: severe impact in New England, southwest and west-cost.

They reported for each district, distinguished data collected in1990 than in 1993, number of the bank observed, OverallTechnical Efficiency and each of its components measured bygeometrical mean and Gain in real estate loans(expectation of realestate lending if banks realize OTE=1).Non-parametric Wilcoxon signed-rank test is used to testsignificant differences across states


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RESULTS (11/13)


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RESULTS (12/13)


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RESULTS (13/13)


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VALUE ADDED RESPECT TO LITERATURE(1/1)

They give an empirical evidence of the theoretical research of Peak andRosengren (1995): Credit Crunch is a supply-induced restriction.

Is a originally and complex microeconomic study that investigate supplyside behavior by banks .

They find evidence that studying bank production function there arerelation between bank crisis, bank efficiency, capital equipment,regulatory constraints, ALM and credit risk management.

They were able to measure potential real estate lending efficiencyoverall and analyzing each component of the efficient productionfrontier .


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IMPLICATIONS AND FURTHER DEVOLPMENT(1/1)

Estimated regression suggests that the decline in inefficiency due toRBC standards results from increase in bank equity capital and aslower growth in real estate lending .

The RBC standards constrained residential mortgage lending themost , but an active secondary market in residential mortgage allowsbank to originate and then remove mortgage from the balance sheet.Moreover there was numerous non-bank sources of financingmortgage.

Construction and small commercial real estate borrower are more

bank-dependent so the RBC may have more impact in constructionand commercial real estate.

The research is dated , new studies have to compare the CreditCrunch period starting in 2007, and is important to introduce

variables measuring the babble in real estate price and how iti fl l l di li

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Bank Efficiency, Risk-based Capital, And Real Estate Exposure