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Means, Motive and Opportunity? Disentangling Client Influence in Performance Measurement Appraisals. Neil Crosby, Colin Lizieri and Pat McAllister. The Research Problem. Are Clients Able To Systematically Bias Real Estate Appraisal Outcomes? - PowerPoint PPT Presentation
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© Crosby, Lizieri, McAllister 2009 www.henley.reading.ac.uk/rep
School of Real Estate & Planning
April 21, 2023
Means, Motive and Means, Motive and Opportunity? Disentangling Opportunity? Disentangling
Client Influence in Client Influence in Performance Measurement Performance Measurement
AppraisalsAppraisals
Neil Crosby, Colin Lizieri and Pat Neil Crosby, Colin Lizieri and Pat McAllisterMcAllister
Client Influence and Appraisal
The Research ProblemThe Research Problem
Are Clients Able To Systematically Bias Real Estate Appraisal Outcomes?
“In the most unstable and unprecedented market circumstances for very many years, UK valuers have demonstrated their ability to respond speedily to exceptional changes in sentiment despite the thinness of the evidence available to them.”
Client Influence and Appraisal
Client Influence on Appraisals:Client Influence on Appraisals: Prior Research Prior Research
• A Sensitive Topic – Raising Ethical, Reputational, Negligence and Criminal Issues
• Questionnaire Based Research – Smolen and Hambleton, (1997), Worzala,
Lenk and Kinnard (1998), Gallimore and Wolverton (2000)
• Interview Based Research – Levy and Schuck (2005), Baum et al. (2000)
• Experimental Research– Hansz (2000), Diaz and Hansz (2001)
Client Influence and Appraisal
Study ContextStudy Context
• Sharp Market Correction in Second Half of 2007.
• Decrease In Transaction Levels → So Limited Evidence Of Changes In Pricing Levels.
• Different Types of Incentives for Different Categories of Client?– Open-ended funds required rapid marking to
market due to NAV-based redemptions– Closed-ended funds, insurance companies,
listed funds more concerned with LTV ratios, bonuses, share prices?
Client Influence and Appraisal
Research Questions and Research Questions and MethodMethod
• Did Different Client Categories Exhibit Different Patterns of Changes To Capital Values in this Period?
• Did OEFs Experience the Largest Falls? Was This Consistent Across All Sectors?
• Were Appraisers Pushed By OEFs or Were They Pulled By Other Client Groups?
• Anecdotal Evidence – But Not Proof
• Opportunity to Test for Evidence in the Appraisals Themselves …
Client Influence and Appraisal
Data and AnalysisData and Analysis• IPD UK Data on Quarterly Capital Returns
Disaggregated by PAS Segment and Type of Client.
• Confidentiality Issues: – No Asset-Level Data – Some PAS Segments Masked– Implication for Statistical Testing
• Calculate Capital Growth Series for Client Types• Examine Data at Sector Level• To Control for Different PAS Weightings,
Hypothetical Benchmark Indices for Client Categories Created
• Statistical Analysis Conducted at PAS Segment Level
Client Influence and Appraisal
Fund Type and Capital ChangeFund Type and Capital Change
H2 2007: OEF values fell 222bp more than average
Client Influence and Appraisal
Why Could OEF Values Why Could OEF Values Fall Faster?Fall Faster?
• Information Effect?– More frequent valuations overcomes anchoring
effects?– “Catch up” might provide some evidence of this
• Valuer Firm Effect?– But same firms valuing OEFs and other fund types– No evidence from IPD of systematic firm level
biases• Compositional Effect?
– Is it just about portfolio mix? Some sectors fall faster?
• Asset Quality Effect?– Do the different client categories own different
qualities of asset? • Client Influence Effect?
– Can clients push/influence appraisers to mark prices down, or hold them up?
Client Influence and Appraisal
Fund Type Fall H2 2007 Fall H1 2008 Fall from Peak
Closed Ended Funds -9.03% -10.50% -23.07%
Open Ended Funds -13.25% -7.95% -25.43%
Pension Funds -9.95% -8.70% -22.50%
Insurance Companies -10.66% -8.82% -23.54%
Property Companies -10.31% -7.40% -21.68%
All Funds -11.03% -8.86% -23.84%
Timing: Falls and “Catch-Timing: Falls and “Catch-Up”?Up”?
All: 100(.8997)(.9114) = 82.00OEF: 100(.8675)(.9205) = 79.85
Client Influence and Appraisal
Segment VariationsSegment Variations
Decrease in CV: June-December 2007West End Offices
0.002.004.006.008.0010.0012.0014.00
CEF OEF IC PF Market
% d
ecre
ase
in C
V
Client Influence and Appraisal
Segment VariationsSegment Variations
Decrease in CV: June-December 2007Standard Shops RestUK
0.002.004.006.008.0010.0012.0014.00
OEF IC PF PropCo Market
% d
ecre
ase
in C
V
Client Influence and Appraisal
Different Sector Allocations?Different Sector Allocations?
Client Influence and Appraisal
Different Sector Allocations?Different Sector Allocations?
Client Influence and Appraisal
Preliminary Statistical Preliminary Statistical AnalysisAnalysis
• Limitations Caused By Nature of Data
• Can Analyse By PAS Segment & Fund Type– Observations = PAS*Type – e.g. City Offices held
by OEF– 45 observations in total
• Preliminary Non-Parametric Analysis– Strong statistical association between fund type
& performance – 2 significant at 0.001 level.
• Basic Regression Analyses– Analyse falls in value by time period, e.g. H2
2007– RHS: PAS and fund type attributes– Strong common movement across segments
Client Influence and Appraisal
Preliminary Statistical AnalysisPreliminary Statistical Analysis
Dependent Variable: Capital Value Change, H2 2007
Variable Coeff T-Stat Probability
Constant -0.099 -29.88 0.000
Open Ended Fund -0.027 -3.25 0.002
Closed Ended Fund 0.021 +1.69 0.098
Retail Warehouse -0.047 -7.59 0.000
“Other” Property Type 0.045 +1.81 0.078
Adjusted R-Squared 50.4%
Standard Error 0.025
F Statistic 12.18 Prob F: 0.000N Obs = 45, White robust standard errors and covariance
Client Influence and Appraisal
ConclusionsConclusions
• Anecdotally, Clients Can Exert Influence on Appraisers to Adjust Their Valuations;
• To Date, Evidence Only from Anecdote or Second Hand, Survey, Interview or Experimental Methods;
• Sharp Market Adjustment From 2007 and Differing Client Needs Brings Opportunity for Direct Test;
• Evidence Consistent with Model of Client Influence;
• Individual, Disaggregated, Data Would Permit More Robustness in Statistical Testing.