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© Crosby, Lizieri, McAllister 2009 www.henley.reading.ac.uk/ rep School of Real Estate & Planning June 17, 2022 Means, Motive and Means, Motive and Opportunity? Disentangling Opportunity? Disentangling Client Influence in Client Influence in Performance Measurement Performance Measurement Appraisals Appraisals Neil Crosby, Colin Lizieri and Pat Neil Crosby, Colin Lizieri and Pat McAllister McAllister

Neil Crosby, Colin Lizieri and Pat McAllister

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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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Page 1: Neil Crosby, Colin Lizieri and Pat McAllister

© 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

Page 2: Neil Crosby, Colin Lizieri and Pat McAllister

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.”

Page 3: Neil Crosby, Colin Lizieri and Pat McAllister

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)

Page 4: Neil Crosby, Colin Lizieri and Pat McAllister

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?

Page 5: Neil Crosby, Colin Lizieri and Pat McAllister

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 …

Page 6: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 7: Neil Crosby, Colin Lizieri and Pat McAllister

Client Influence and Appraisal

Fund Type and Capital ChangeFund Type and Capital Change

H2 2007: OEF values fell 222bp more than average

Page 8: Neil Crosby, Colin Lizieri and Pat McAllister

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?

Page 9: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 10: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 11: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 12: Neil Crosby, Colin Lizieri and Pat McAllister

Client Influence and Appraisal

Different Sector Allocations?Different Sector Allocations?

Page 13: Neil Crosby, Colin Lizieri and Pat McAllister

Client Influence and Appraisal

Different Sector Allocations?Different Sector Allocations?

Page 14: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 15: Neil Crosby, Colin Lizieri and Pat McAllister

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

Page 16: Neil Crosby, Colin Lizieri and Pat McAllister

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.