Doctoral thesis of philosophy forecasting commercial property market performance beyond the primary reliance on econometric models

Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy Treshani Perera BSc (Hons) in Quantity Surveying – University of Moratuwa BSc (Hons) in Applied Accounting – Oxford Brookes University MAIQS, ACCA Affiliate School of Property Construction and Project Management College of Design and Social Context RMIT University August 2018 DECLARATION I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed Treshani Perera August 2018 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models i ACKNOWLEDGEMENT First and foremost, I wish to thank my principal supervisor, Dr Wejendra Reddy, for his invaluable guidance, advice and assistance with the many challenges faced during my Ph.D research candidature This thesis has benefited greatly from many stimulating discussions we had, and his insightful comments and feedback I wish to give an equal acknowledgement to Professor David Higgins who helped to get this research started and continuously supported as an external advisor David was instrumental with industry liaison for data collection His supervision was very motivational, always providing the encouragement to reach the research goals I also owe a special gratitude to my co-supervisors, Dr Woon-Weng Wong who provided useful input into the quantitative modelling and Professor Ron Wakefield, for their encouragement and constructive advice throughout the research The nature and enormity of research meant this thesis would not have been possible without the support of industry personnel I would also like to acknowledge the support of the many property and financial market experts who gave their time and contributed their knowledge in the semi-structured interview research phase Their expert advice and recommendations were valuable in shaping this research and were significant in result validation Special thanks go to Mark Wist (property consultant) who provided constructive comments and suggestions to improve my research I have been very fortunate to receive the RMIT International Ph.D Scholarship I am grateful to RMIT University for the sponsorships and technical support that made this doctoral study achievable I acknowledge the assistance of Mr Robert Sheehan from Sharp Words Consultancy for his editorial comments I also appreciate the support provided by the staff and fellow Ph.D colleagues at the School of Property, Construction and Project Management, RMIT University I thank Professor Kerry London for her meaningful introduction to the world of research philosophy I also acknowledge Professor Chris Eves, Dr Eric Too, Associate Professor Ashton De Silva, Dr Mehrdad Arashpour and Dr Michelle Turner, for their constructive feedback during my Ph.D study Finally, I acknowledge the support, patience and understanding of all my family members My late father’s blessings and treasured love have always motivated me to achieve the fruit of my academic endeavour I owe my deepest gratitude to my husband Dimuthu This thesis would not have been possible without his unwavering support, love, encouragement and tolerance Also, I feel blessed to conceive my little sweetheart at the end of my Ph.D journey I would also like to thank my mother and brother Shehan for their motivational support, love and encouragement This thesis is dedicated to my mother who inspires me and to whom I owe forever for everything I have achieved To all, I thank you for your support, guidance and encouragement It is highly appreciated Treshani Perera Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models ii TABLE OF CONTENTS Table of Contents iii List of Figures v List of Tables viii List of Equations x List of Abbreviations xii Abstract xv CHAPTER INTRODUCTION 1.1 Background to the Research 1.2 Statement of the Problem 1.3 Research Aim and Objectives 1.4 Research Methodology 1.5 Contribution to Knowledge 1.6 Scope and Limitations 1.7 Thesis Layout and Structure 1.8 Publications and Presentations 11 CHAPTER LITERATURE REVIEW – COMMERCIAL PROPERTY MARKET FORECASTING AND THE OUTLOOK ON DOWNSIDE RISK EXPOSURE 13 2.1 Introduction 13 Commercial Property Market Forecasting 15 2.2 Commercial Property Market Structure and Features 15 2.3 Principles of Forecasting 28 2.4 Commercial Property Market Forecasting 50 Downside Risks in the Commercial Real Estate Environment 73 2.5 Downside Risks in Commercial Property Market 73 2.6 Decision Maker’s Imperatives for Downside Risks 106 2.7 Summary 119 CHAPTER METHODOLOGY – PRAGMATIC RESEARCH DESIGN 122 3.1 Introduction 122 3.2 Overview of Methodology 123 3.3 Philosophical Orientation 125 3.4 Strategies of Inquiry 127 3.5 Methods of Data Collection and Analysis 132 3.6 Validity and Reliability 141 3.7 Ethical Considerations 142 3.8 Summary 143 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models iii CHAPTER QUANTITATIVE ANALYSIS – EVALUATING COMMERCIAL PROPERTY MARKET FORECAST ACCURACY 145 4.1 Introduction 145 4.2 Secondary Data Arrangement 147 4.3 Testing for Forecast Accuracy 153 4.4 Analysing Outliers of Forecast Errors 166 4.5 Testing for Relationships between Variables 177 4.6 Summary 190 CHAPTER QUALITATIVE ANALYSIS – DETERMINING THE CURRENT STATUS OF COMMERCIAL PROPERTY MARKET FORECASTING AND THE LEVEL OF DOWNSIDE RISK EXPOSURE 193 5.1 Introduction 193 5.2 Semi-Structured Interview Plan 194 5.3 Australian Commercial Property Market Forecasting Practice 196 5.4 Downside Risk Exposure in the Australian Commercial Property Market 223 5.5 Summary 243 CHAPTER EVALUATION FORECAST DECISION MAKING MODEL DEVELOPMENT: INDUSTRY 246 6.1 Introduction 246 6.2 Best Practices for Improving Forecast Accuracy 246 6.3 ADSV Decision Making Model for Integrating Downside Risks to Improve Forecast Accuracy 253 6.4 Expert Panel Comments, Feedback and Recommendations 256 6.5 Summary 260 CHAPTER SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 262 7.1 Introduction 262 7.2 Summary 262 7.3 Achievement of Research Objectives 264 7.4 Contribution to Knowledge 284 7.5 Recommendations 285 7.6 Further Research Directions 287 REFERENCES 290 APPENDICES 318 Appendix 1: Journal Publications 318 Appendix 2: Supplementary Appendix to Literature Review 356 Appendix 3: Semi-Structured Interview Guideline 360 Appendix 4: Participant Information and Consent Form 362 Appendix 5: Test Results of Augmented Dickey Fuller Test 366 Appendix 6: Test Results of Vector Autoregression in between Property and Economic Forecast Errors 376 Appendix 7: Semi-Structured Interview Respondents Profile 380 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models iv LIST OF FIGURES Figure 1.1: Modelling the Economic Environment Figure 1.2: Research Design Figure 1.3: Thesis Layout and Structure 10 Figure 2.1: Property Investment Options for Investors 16 Figure 2.2: Links between Space and Capital Markets 17 Figure 2.3: The Relationship of Space, Capital and Property Markets 18 Figure 2.4: The Real Estate System 19 Figure 2.5: Property Cycle 20 Figure 2.6: Asset Class Return for the Year Ended 31st December 2017 22 Figure 2.7: Property Sector Weights as at 31st March 2017 23 Figure 2.8: Australian Commercial Property Market Total Returns 1987-2017 23 Figure 2.9: Commercial Property Sector 12-Month Total Returns 24 Figure 2.10: All ADIs’ Commercial Property Exposures 24 Figure 2.11: Property Yield compression over the Timeline 25 Figure 2.12: Ex Post and Ex Ante Forecasting Periods 30 Figure 2.13: Scientific Methods of Forecasting 30 Figure 2.14: Forecasting Methods and Their Relationships 33 Figure 2.15: Steps in Formulating an Econometric Model 42 Figure 2.16: The Tangent Illustration for MAPE and MAAPE 47 Figure 2.17: A Theoretical Structure for the Determination of Office Rents 54 Figure 2.18: A Cobweb Market Adjustment Process 60 Figure 2.19: Judgemental Intervention in the Property Market Forecasting Process 63 Figure 2.20: Comparison between Normal Distribution and Power Law Distribution 70 Figure 2.21: Modelling the Economic Environment 71 Figure 2.22: Illusions of Certainty 74 Figure 2.23: Knowledge Transition in Cynefin Model 79 Figure 2.24: The Black Swan’s Surprising Aspect: Micro Perspective 85 Figure 2.25: Distinguishing the Knowns and Unknowns: Black Swan Event Framework 86 Figure 2.26: The Dispersion of Worldwide Natural Catastrophes in 2017 91 Figure 2.27: Natural Catastrophes and Manmade Disasters: Number of Events 1970-2016 92 Figure 2.28: Total Reported Natural Disasters around Australia between 1970-2017 93 Figure 2.29: Estimated Risk Appetite 94 Figure 2.30: Ranking of Demographic Focussed Megatrends 98 Figure 2.31: Major Driving Forces in Real Estate 98 Figure 2.32: Trends for Sustainable Development in Property and Construction 104 Figure 2.33: Modelling Uncertainty in Real Asset Development Projects 106 Figure 2.34: KuU Risk Assessment with Associated Probabilities 107 Figure 2.35: Nonlinearity of Fragility and Antifragility 112 Figure 2.36: Simple-Complex Model Considerations 114 Figure 2.37: Disaster Risk Index: Global Map 117 Figure 2.38: Summary of Structural Changes in the Property Market 121 Figure 3.1: The Research Process 123 Figure 3.2: The Research Onion 124 Figure 3.3: Sequential Exploratory Design 130 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models v Figure 3.4: The Ways of Mixing Methods 131 Figure 3.5: Methods of Data Collection 133 Figure 3.6: Types of Sampling 135 Figure 4.1: The Sequential Approach for the Quantitative Analysis 146 Figure 4.2: The Australian GDP Growth Rate and Employment to Population Ratio 148 Figure 4.3: Cash Rate – Forecasts vs Actuals 153 Figure 4.4: Bond Rate – Forecasts vs Actuals 153 Figure 4.5: AUD/USD – Forecasts vs Actuals 154 Figure 4.6: Australian Eq – Forecasts vs Actuals 154 Figure 4.7: Rental Movement-Prime – Forecasts vs Actuals 154 Figure 4.8: Yield-Prime – Forecasts vs Actuals 154 Figure 4.9: Total Vacancy – Forecasts vs Actuals 155 Figure 4.10: Net Absorption – Forecasts vs Actuals 155 Figure 4.11: Economic Forecast Accuracy Based on Scaled-independent Metrics –3M Vs 6M 158 Figure 4.12: Rental Movement-Prime Forecast Accuracy Based on Scaled-independent Metrics 159 Figure 4.13: Yield-Prime Forecast Accuracy Based on Scaled-independent Metrics 161 Figure 4.14: Total Vacancy Forecast Accuracy Based on Scaled-independent Metrics 162 Figure 4.15: Net Absorption Forecast Accuracy Based on Scaled-independent Metrics 163 Figure 4.16: The Comparison of Economic and Property Forecast Accuracy 163 Figure 4.17: A Conceptual Structure of Property Market Forecast Determinants 166 Figure 4.18: Box-and-Whisker Plot for Rental Movement – Prime Forecast Errors 169 Figure 4.19: Box-and-Whisker Plot for Yield – Prime Forecast Errors 170 Figure 4.20: Box-and-Whisker Plot for Total Vacancy Forecast Errors 171 Figure 4.21: Box-and-Whisker Plot for Net Absorption Forecast Errors 172 Figure 4.22: Box-and-Whisker Plot for Economic and Property Forecast Errors 173 Figure 4.23: Stacked Area Diagram for Economic Forecast Percentage Errors 175 Figure 4.24: Stacked Area Diagram for Property Forecasts Errors, in Percentages 175 Figure 4.25: Line Diagram for Property Forecasts Errors in Percentages 176 Figure 4.26: Granger Causality of Property and Economic Forecast Errors 188 Figure 5.1: Semi-structured Interview Respondents Structures 194 Figure 5.2: Experience Level of the Respondents 196 Figure 5.3: Driving Factors for Setting-up Commercial Property Forecast Objectives 197 Figure 5.4: Respondents’ Sources of Forecast Data Collection 203 Figure 5.5: Types of Input Data for Forecasting 206 Figure 5.6: Methodological Orientation of Commercial Property Market Forecasting 216 Figure 5.7: Forecast Output Validation 218 Figure 5.8: Level of Confidence of Forecast Models 219 Figure 5.9: Forecast Model Accuracy Measurement in Current Practice 220 Figure 5.10: Greed vs Fear in Forecasting 226 Figure 5.11: Structural Changes in the Property Market 235 Figure 5.12: Example of Commercial Property Conversions by Location 237 Figure 5.13: Structural Changes Related to Demographics 238 Figure 5.14: Physical and Digital Presence in Business Lifecycle 241 Figure 5.15: The ‘Give and Take’ Effect in the Commercial Property Market 242 Figure 6.1: The Onion Model for Improving Forecast Accuracy 247 Figure 6.2: Stress Testing Methodology in Property Market Practice 250 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models vi Figure 6.3: Investment Mangers’ Risk Response Strategies 252 Figure 6.4: ADSV Decision making Model for Integrating Downside Risks to Improve Forecast Accuracy 255 Figure 6.5: Middle Line of Forecasting within Limits 258 Figure 7.1: Modelling the Economic Environment 267 Figure 7.2: Conceptual Framework of Structural Changes in Property Market 268 Figure 7.3: The Comparison of Economic and Property Forecast Accuracy 270 Figure 7.4: Granger Causality of Property and Economic Forecast Errors 272 Figure 7.5: The Onion Model of Improving Forecast Accuracy 279 Figure 7.6: ADSV Decision making Model to Improve Forecast Accuracy 281 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models vii LIST OF TABLES Table 2.1: Four Quadrant Investment Market and Property Investment Products 16 Table 2.2: Global Real Estate Transparency 2016 22 Table 2.3: Contingency Table for Directional Accuracy Test 50 Table 2.4: What Do We Model and Forecast in Real Estate? 51 Table 2.5: Principal Determinants for Net Effective Rent Forecasts 55 Table 2.6: Principal Determinants for Equivalent Yields Forecasts 57 Table 2.7: Commercial Property Market Rent and Rental Income Models 61 Table 2.8: Commercial Property Market Yield, Capital Return Models 62 Table 2.9: The Evaluation of Accuracy of Competing Models 65 Table 2.10: Model/ Data Dichotomy of Four Classes of Uncertainties 77 Table 2.11: The Transition of Levels of Uncertainty from Determinism to Total Ignorance 78 Table 2.12: Knowledge as Measurement and Theory 80 Table 2.13: Probability of Exceeding Multiples of Sigma 87 Table 2.14: A Fractal Law with a Tail Exponent (α) of 88 Table 2.15: Types of Disasters 89 Table 2.16: Comparison of World Natural Catastrophes in the First Half of 2017 90 Table 2.17: Sigma Event Selection Criteria for 2016 92 Table 2.18: Place and Space Risks of Black Swan Events 95 Table 2.19: Four Quadrants of Decision making 114 Table 3.1: Methodological Framework for Achieving Research Objectives 122 Table 3.2: Alternative Knowledge Claim Positions 125 Table 3.3: Major Differences between Deductive and Inductive Approaches to Research 127 Table 3.4: Alternative Strategies of Inquiry 129 Table 3.5: Quantitative, Qualitative and Mixed Methods Procedures 132 Table 3.6: The Differences between Sampling in Quantitative and Qualitative Research 135 Table 3.7: Procedures in Quantitative and Qualitative Data Analysis 137 Table 4.1: The List of Economists in the AFR’s Quarterly Survey of Economists (2001-2011) 148 Table 4.2: Market Share of Australian CBD Office Markets - July 2011 149 Table 4.3: Unit Root in Level 152 Table 4.4: Unit Root in 1st Difference 152 Table 4.5: Economic Forecast Accuracy Based on Scaled-dependent Metrics – 3M Vs 6M 156 Table 4.6: Rental Movement-Prime Forecast Accuracy Based on Scaled-dependent Metrics 159 Table 4.7: Yield-Prime Forecast Accuracy Based on Scaled-dependent Metrics 160 Table 4.8: Total Vacancy Forecast Accuracy Based on Scaled-dependent Metrics 161 Table 4.9: Net Absorption Forecast Accuracy Based on Scaled-dependent Metrics 162 Table 4.10: The Comparison of Forecast Accuracy of Naïve Vs Expert Forecasts 164 Table 4.11: Normality Test Output for Forecast Errors 165 Table 4.12: Descriptive Statistics of FE for Economic Variables 167 Table 4.13: IQR rule for Outliers in Economic Forecast Errors 168 Table 4.14: Descriptive Statistics of FE for Rental Movement-Prime 168 Table 4.15: Descriptive Statistics of FE for Yield-Prime 169 Table 4.16: Descriptive Statistics of FE for Total Vacancy 171 Table 4.17: Descriptive Statistics of FE for Net Absorption 172 Table 4.18: IQR Rule for Economic and Property Outliers Given in Error Percentage 174 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models viii Table 4.19: Multicollinearity Statistics of Independent Variables 178 Table 4.20: Interpretation of Multiple Regression Outputs 178 Table 4.21: Multiple Regression for Forecasts: Economic Vs RMP_D 179 Table 4.22: Multiple Regression for Forecasts: Economic Vs YP_D 180 Table 4.23: Multiple Regression for Forecasts: Economic Vs TV_D 181 Table 4.24: Multiple Regression for Forecasts: Economic Vs NA6_D 182 Table 4.25: Multiple Regression for Forecasts: Property Vs Economic 183 Table 4.26: Correlation Matrix of Key Property Forecasts 183 Table 4.27: Multiple Regression for Forecast Errors: Economic Vs Property 184 Table 4.28: Correlation Matrix of Property Forecast Errors 184 Table 4.29: Granger Causality between Rental Movement and Economic Forecast Errors 186 Table 4.30: Granger Causality between Property Yield-Prime and Economic Forecast Errors 187 Table 4.31: Granger Causality between Property Total Vacancy and Economic Forecast Errors 187 Table 4.32: Granger Causality between Property Total Vacancy and Economic Forecast Errors 187 Table 4.33: Correlation Matrix of Key Property and Economic Forecast Errors 189 Table 5.1: Semi-structured Interview Participants’ Profile 195 Table 5.2: Respondent’s Objectives for Commercial Property Market Forecasting 197 Table 5.3: Respondent’s Short-term, Medium-term and Long-term Forecasting Approaches 198 Table 5.4: Reasons for Obtaining Secondary Sources of Forecast Data 204 Table 5.5: Forecast Determinants of Dependent Property Variables (Y) 210 Table 5.6: Additional Determinants in Commercial Property Market Forecasting 213 Table 7.1: Methodological Framework 262 Table 7.2: Driving Factors for Setting-up Commercial Property Forecast Objectives 273 Table 7.3: The Summarised View of the ADSV Model Outcome 282 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models ix Appendices Brisbane_YS_FE% Adelaide_YS_FE% Melbourne_YS_FE% Perth_YS_FE% -4.294978 -5.325189 -5.730289 -4.506471 0.0038 0.0004 0.0002 0.0023 √ √ √ √ Total Vacancy Forecast Error Unit root in level Variable ADF t-stat -4.485325 Canberra_TV_FE -3.050426 Sydney_TV_FE -3.315860 Brisbane_TV_FE -2.310456 Adelaide_TV_FE -1.203150 Melbourne_TV_FE -2.888789 Perth_TV_FE st Unit root in Difference Canberra_TV_FE -3.874272 Sydney_TV_FE -6.618607 Brisbane_TV_FE -10.28506 Adelaide_TV_FE -10.09477 Melbourne_TV_FE -6.119851 Perth_TV_FE Stationary/ Non-Stationary p-value 0.0022 0.0464 0.0271 0.1784 0.6518 0.0636 √ X X X X X 0.0097 0.0000 0.0000 0.0000 0.0001 √ √ √ √ √ Direct Vacancy Forecast Error Unit root in level Variable ADF t-stat Canberra_DV_FE -3.062420 -3.011657 Sydney_DV_FE -3.653729 Brisbane_DV_FE -1.152721 Adelaide_DV_FE -1.162865 Melbourne_DV_FE -2.889764 Perth_DV_FE Unit root in 1st Difference -4.669446 Canberra_DV_FE -3.984576 Sydney_DV_FE -3.781380 Brisbane_DV_FE -8.868618 Adelaide_DV_FE -12.06887 Melbourne_DV_FE -6.385489 Perth_DV_FE Stationary/ Non-Stationary p-value 0.0453 0.0501 0.0151 0.6732 0.6689 0.0634 0.0019 0.0077 0.0139 0.0000 0.0000 0.0000 X X X X X X √ √ X √ √ √ * Total Vacancy Forecast Error% Unit root in level Variable ADF t-stat Canberra_TV_FE% -1.398534 -3.075664 Sydney_TV_FE% -3.675534 Brisbane_TV_FE% -2.037378 Adelaide_TV_FE% Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models Stationary/ Non-Stationary p-value 0.5596 0.0441 0.0127 0.2698 X X X X 371 Appendices -3.456615 Melbourne_TV_FE% -3.222548 Perth_TV_FE% Unit root in 1st Difference -5.646086 Canberra_TV_FE% -4.108678 Sydney_TV_FE% -7.538509 Brisbane_TV_FE% -11.63463 Adelaide_TV_FE% -8.693073 Melbourne_TV_FE% -6.459754 Perth_TV_FE% 0.0203 0.0329 X X 0.0003 0.0060 0.0000 0.0000 0.0000 0.0000 √ √ √ √ √ √ Direct Vacancy Forecast Error% Unit root in level Variable ADF t-stat Canberra_DV_FE% -0.007694 -3.411775 Sydney_DV_FE% -3.410369 Brisbane_DV_FE% -1.202625 Adelaide_DV_FE% -1.439668 Melbourne_DV_FE% -3.395763 Perth_DV_FE% st Unit root in Difference -5.755196 Canberra_DV_FE% -5.513924 Sydney_DV_FE% -5.768655 Brisbane_DV_FE% -9.916759 Adelaide_DV_FE% -9.607754 Melbourne_DV_FE% -6.926313 Perth_DV_FE% Stationary/ Non-Stationary p-value 0.9453 0.0222 0.0245 0.6520 0.5423 0.0230 0.0003 0.0003 0.0003 0.0000 0.0000 0.0000 X X X X X X √ √ √ √ √ √ Net Absorption – months Forecast Error Unit root in level Variable ADF t-stat -4.971411 Canberra_NA6_FE -3.511240 Sydney_ NA6_FE -4.880381 Brisbane_ NA6_FE -2.940988 Adelaide_ NA6_FE -2.767912 Melbourne_ NA6_FE -3.834870 Perth_ NA6_FE Unit root in 1st Difference Canberra_NA6_FE -4.734624 Sydney_ NA6_FE Brisbane_ NA6_FE -5.707737 Adelaide_ NA6_FE -5.336931 Melbourne_ NA6_FE Perth_ NA6_FE Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models Stationary/ Non-Stationary p-value 0.0008 0.0186 0.0010 0.0583 0.0807 0.0095 √ X √ X X X 0.0015 0.0002 0.0004 - √ √ √ - 372 Appendices Net Absorption – months Forecast Error% Unit root in level Variable ADF t-stat -4.411706 Canberra_NA6_FE% -3.808440 Sydney_ NA6_FE% -5.638137 Brisbane_ NA6_FE% -4.454494 Adelaide_ NA6_FE% -4.126427 Melbourne_ NA6_FE% -4.067098 Perth_ NA6_FE% Unit root in 1st Difference Canberra_NA6_FE% -6.931169 Sydney_ NA6_FE% Brisbane_ NA6_FE% Adelaide_ NA6_FE% Melbourne_ NA6_FE% Perth_ NA6_FE% Stationary/ Non-Stationary p-value 0.0028 0.0100 0.0002 0.0025 0.0051 0.0058 √ X √ √ √ √ 0.0000 - √ - Rental Movement-Prime Forecast Unit root in level Variable ADF t-stat Canberra_RMP_F -0.501798 -3.370618 Sydney_RMP_F -3.193206 Brisbane_RMP_F -3.464979 Adelaide_RMP_F -2.788739 Melbourne_RMP_F -2.313824 Perth_RMP_F Unit root in 1st Difference -3.287124 Canberra_RMP_F -1.960824 Sydney_RMP_F -3.848712 Brisbane_RMP_F -4.857865 Adelaide_RMP_F -2.647433 Melbourne_RMP_F -1.003303 Perth_RMP_F Stationary/ Non-Stationary p-value 0.8706 0.0297 0.0374 0.0220 0.0796 0.1803 X X X X X X 0.0312 0.2983 0.0108 0.0015 0.1023 0.7233 X X X √ X X * * * * Rental Movement-Secondary Forecast Unit root in level Variable ADF t-stat Canberra_RMS_F -0.523098 -1.750435 Sydney_RMS_F -3.530659 Brisbane_RMS_F -2.945071 Adelaide_RMS_F -2.450919 Melbourne_RMS_F -1.473965 Perth_RMS_F Unit root in 1st Difference -1.740262 Canberra_RMS_F -4.773475 Sydney_RMS_F Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models Stationary/ Non-Stationary p-value 0.8650 0.3918 0.0193 0.0587 0.1430 0.5246 0.3957 0.0016 X X X X X X X √ * 373 Appendices Brisbane_RMS_F Adelaide_RMS_F Melbourne_RMS_F Perth_RMS_F -3.713851 -5.206083 -2.830002 -4.128166 0.0149 0.0006 0.0739 0.0058 X √ X √ * * Yield-Prime Forecast Unit root in level Variable ADF t-stat Canberra_YP_F -1.529741 -3.075760 Sydney_YP_F -1.484760 Brisbane_YP_F -1.378107 Adelaide_YP_F -2.334328 Melbourne_YP_F -3.770238 Perth_YP_F Unit root in 1st Difference -3.007363 Canberra_YP_F -3.447868 Sydney_YP_F -4.633498 Brisbane_YP_F -3.118452 Adelaide_YP_F -5.733452 Melbourne_YP_F -5.827383 Perth_YP_F Stationary/ Non-Stationary p-value 0.4984 0.0449 0.5214 0.5730 0.1712 0.0104 0.0513 0.0220 0.0017 0.0413 0.0002 0.0001 X X X X X X X √ X √ √ * * * Yield-Secondary Forecast Unit root in level Variable ADF t-stat Canberra_YS_F -1.722857 -2.349439 Sydney_YS_F -2.445581 Brisbane_YS_F -1.935380 Adelaide_YS_F -1.230561 Melbourne_YS_F -1.459735 Perth_YS_F Unit root in 1st Difference -2.951860 Canberra_YS_F -3.117963 Sydney_YS_F -2.172958 Brisbane_YS_F -5.509190 Adelaide_YS_F -3.281494 Melbourne_YS_F -3.543371 Perth_YS_F Stationary/ Non-Stationary p-value 0.4053 0.1675 0.1428 0.3109 0.6409 0.5336 X X X X X X 0.0571 0.0413 0.2211 0.0003 0.0298 0.0174 X X X √ X X * * * * * Total Vacancy Forecast Variable Canberra_TV_F Sydney_TV_F Brisbane_TV_F Adelaide_TV_F Unit root in level ADF t-stat -0.644462 -2.550369 -2.014650 -1.489692 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models Stationary/ Non-Stationary p-value 0.8400 0.1194 0.2786 0.5190 X X X X 374 Appendices -1.318465 Melbourne_TV_F -2.337986 Perth_TV_F Unit root in 1st Difference -4.620459 Canberra_TV_F -3.028334 Sydney_TV_F -2.861056 Brisbane_TV_F -4.107521 Adelaide_TV_F -3.635417 Melbourne_TV_F -3.014456 Perth_TV_F 0.6011 0.1706 X X 0.0018 0.0493 0.0679 0.0053 0.0144 0.0536 √ X X √ X X * * * * Direct Vacancy Forecast Unit root in level Variable ADF t-stat Canberra_DV_F -1.103590 -2.523725 Sydney_DV_F -2.824774 Brisbane_DV_F -1.077681 Adelaide_DV_F -1.306269 Melbourne_DV_F -2.651079 Perth_DV_F Unit root in 1st Difference -4.465033 Canberra_DV_F -2.626361 Sydney_DV_F -0.506115 Brisbane_DV_F -4.537615 Adelaide_DV_F -4.500314 Melbourne_DV_F -2.558838 Perth_DV_F Stationary/ Non-Stationary p-value 0.6943 0.1250 0.0783 0.7045 0.6067 0.1007 X X X X X X 0.0025 0.1044 0.8674 0.0021 0.0023 0.1176 √ X X √ √ X * * * Net Absorption – months Forecast Unit root in level Variable ADF t-stat Canberra_NA6_F -2.863956 -2.164872 Sydney_ NA6_F -3.709320 Brisbane_ NA6_F -2.815791 Adelaide_ NA6_F -2.878995 Melbourne_ NA6_F -2.346070 Perth_ NA6_F Unit root in 1st Difference -4.679211 Canberra_NA6_F -4.074263 Sydney_ NA6_F -4.815428 Brisbane_ NA6_F -4.633982 Adelaide_ NA6_F -5.546059 Melbourne_ NA6_F -4.844028 Perth_ NA6_F Stationary/ Non-Stationary p-value 0.0675 0.2239 0.0123 0.0739 0.0656 0.1684 0.0017 0.0060 0.0014 0.0021 0.0003 0.0012 X X X X X X √ √ √ √ √ √ * represents the variable that are not stationary even with the 1st difference Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 375 Appendices APPENDIX 6: TEST RESULTS OF VECTOR AUTOREGRESSION IN BETWEEN PROPERTY AND ECONOMIC FORECAST ERRORS The following Tables shows the full statistical output for vector autoregression (VAR) in between property and economic forecast errors as before discussed in the sub-section 4.5.2 Standard errors are given in ( ) & t-statistics in [ ] in the following VAR estimates A value of t-statistic exceeding ±1.645, 1.96 or 2.58 indicates statistical significance at the 10%, 5% and 1% level of significance respectively Uni directional and bi-directional causality are highlighted in each property variable VAR estimates between Rental Movement and Economic Forecast Errors RMP_FE Cash Rate _FE Bond Rate _FE Australian Eq_FE AUD/USD _FE RMP_FE (-1) 0.312823 (0.26535) [ 1.17893] 0.030247 (0.02941) [ 1.02854] -0.017570 (0.02669) [-0.65837] -46.86623 (23.4183) [-2.00127] -0.002762 (0.00473) [-0.58384] Cash Rate_FE (-1) 2.272597 (3.99945) [ 0.56823] -0.422950 (0.44325) [-0.95420] -0.343220 (0.40224) [-0.85328] -175.0736 (352.975) [-0.49599] -0.120476 (0.07131) [-1.68938] Bond Rate_FE (-1) -0.532636 (3.86077) [-0.13796] -0.270931 (0.42788) [-0.63319] -0.505600 (0.38829) [-1.30212] -215.6190 (340.735) [-0.63280] 0.095793 (0.06884) [ 1.39151] Australian Eq_FE (-1) 0.006810 (0.00277) [ 2.45695] 0.000432 (0.00031) [ 1.40737] 0.000137 (0.00028) [ 0.49265] -0.160317 (0.24461) [-0.65540] -4.56E-05 (4.9E-05) [-0.92242] AUD/USD_FE (-1) -10.11016 (14.9827) [-0.67479] 3.219240 (1.66051) [ 1.93871] 3.603848 (1.50686) [ 2.39163] 2815.016 (1322.31) [ 2.12886] -0.467798 (0.26716) [-1.75103] C -0.835756 (1.25848) [-0.66410] -0.008978 (0.13947) [-0.06437] -0.033245 (0.12657) [-0.26266] -39.59001 (111.068) [-0.35645] 0.008069 (0.02244) [ 0.35959] 0.560910 0.341365 247.2394 4.972318 2.554876 -44.60516 6.325646 6.615366 -0.955714 6.126840 0.549852 0.324777 3.036815 0.551073 2.442979 -9.408782 1.926098 2.215818 -0.010423 0.670634 0.703049 0.554573 2.500811 0.500081 4.735118 -7.855227 1.731903 2.021624 -0.008412 0.749294 0.641740 0.462610 1925772 438.8362 3.582537 -116.2890 15.28613 15.57585 4.375052 598.6289 0.426486 0.139730 0.078608 0.088661 1.487276 19.82397 -1.727997 -1.438276 0.008883 0.095591 R-squared Adj R-squared Sum sq resids S.E equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D dependent Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion 448.2734 42.75068 -143.5582 21.69477 23.14337 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 376 Appendices VAR estimates between Property Yield-Prime and Economic Forecast Errors YP_FE Cash Rate _FE Bond Rate _FE Australian Eq_FE AUD/USD _FE YP_FE (-1) 0.128137 (0.21946) [ 0.58388] -0.672924 (0.21802) [-3.08647] -0.196168 (0.33335) [-0.58847] -242.1115 (307.872) [-0.78640] 0.025859 (0.06911) [ 0.37419] Cash Rate_FE (-1) -0.076792 (0.27317) [-0.28111] -0.348725 (0.27139) [-1.28497] -0.205687 (0.41495) [-0.49570] -415.2594 (383.228) [-1.08358] -0.054937 (0.08602) [-0.63865] Bond Rate_FE (-1) 0.131720 (0.28111) [ 0.46857] -0.293261 (0.27928) [-1.05008] -0.658659 (0.42701) [-1.54250] -58.40864 (394.367) [-0.14811] 0.031956 (0.08852) [ 0.36099] Australian Eq_FE (-1) -0.000625 (0.00022) [-2.79540] 0.000372 (0.00022) [ 1.67605] 0.000233 (0.00034) [ 0.68619] -0.046604 (0.31341) [-0.14870] -2.38E-05 (7.0E-05) [-0.33878] AUD/USD_FE (-1) 0.659126 (1.03437) [ 0.63722] 1.582971 (1.02762) [ 1.54042] 1.648609 (1.57121) [ 1.04926] 96.23877 (1451.11) [ 0.06632] -0.520556 (0.32572) [-1.59816] C 0.024051 (0.09414) [ 0.25547] 0.023169 (0.09353) [ 0.24772] 0.002356 (0.14300) [ 0.01648] -12.62905 (132.072) [-0.09562] 0.018725 (0.02965) [ 0.63165] 0.454319 0.244441 2.138464 0.405583 2.164687 -6.208498 1.285105 1.583349 0.017355 0.466601 0.695995 0.579071 2.110657 0.402937 5.952503 -6.084156 1.272016 1.570260 0.027031 0.621059 0.506420 0.316581 4.934239 0.616082 2.667633 -14.15155 2.121215 2.419459 8.43E-05 0.745238 0.302926 0.034820 4208728 568.9889 1.129875 -143.8880 15.77769 16.07593 -23.96554 579.1615 0.266649 -0.015409 0.212053 0.127718 0.945368 15.74606 -1.025902 -0.727658 0.012338 0.126745 R-squared Adj R-squared Sum sq resids S.E equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D dependent Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion 7.095700 1.064006 -135.3885 17.40932 18.90054 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 377 Appendices VAR estimates between Total vacancy and Economic Forecast Errors TV_FE Cash Rate _FE Bond Rate _FE Australian Eq_FE AUD/USD _FE TV_FE (-1) -0.403975 (0.19367) [-2.08588] -0.196984 (0.14984) [-1.31466] 0.203105 (0.17929) [ 1.13284] 303.1045 (144.483) [ 2.09785] 0.078850 (0.02683) [ 2.93885] Cash Rate_FE (-1) 0.139868 (0.43328) [ 0.32281] -0.155043 (0.33521) [-0.46252] -0.056645 (0.40110) [-0.14122] -174.6961 (323.239) [-0.54046] -0.043996 (0.06003) [-0.73297] Bond Rate_FE (-1) -0.359836 (0.45450) [-0.79173] -0.446983 (0.35163) [-1.27119] -0.868128 (0.42074) [-2.06333] -366.0165 (339.064) [-1.07949] -0.004272 (0.06296) [-0.06784] Australian Eq_FE (-1) -0.000646 (0.00037) [-1.74814] 0.000367 (0.00029) [ 1.28512] 0.000301 (0.00034) [ 0.87953] 0.049868 (0.27564) [ 0.18092] -4.43E-06 (5.1E-05) [-0.08664] AUD/USD_FE (-1) 1.198613 (1.87517) [ 0.63920] 2.331800 (1.45075) [ 1.60731] 2.198471 (1.73591) [ 1.26647] 1887.984 (1398.92) [ 1.34960] -0.649982 (0.25978) [-2.50207] C 0.020901 (0.15714) [ 0.13301] 0.004758 (0.12158) [ 0.03913] -0.013009 (0.14547) [-0.08942] 4.141037 (117.232) [ 0.03532] 0.009794 (0.02177) [ 0.44991] 0.591082 0.420699 5.321114 0.665903 3.469141 -14.57269 2.285855 2.582645 0.004168 0.874900 0.532240 0.337339 3.184971 0.515184 2.730832 -9.953535 1.772615 2.069406 0.007246 0.632873 0.542649 0.352085 4.560085 0.616447 2.847606 -13.18362 2.131513 2.428304 -0.008667 0.765838 0.486108 0.271986 2961472 496.7790 2.270242 -133.6383 15.51537 15.81216 4.384532 582.2285 0.646449 0.499137 0.102123 0.092251 4.388280 21.00662 -1.667402 -1.370611 0.011393 0.130350 R-squared Adj R-squared Sum sq resids S.E equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D dependent Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion 13.15833 1.732785 -132.6520 18.07245 19.55640 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 378 Appendices VAR estimates between Net absorption and Economic Forecast Errors NA6_FE Cash Rate _FE Bond Rate _FE Australian Eq_FE AUD/USD _FE NA6_FE (-1) -0.120872 (0.22517) [-0.53679] 1.09E-05 (8.5E-06) [ 1.27591] 7.25E-07 (1.0E-05) [ 0.07281] -0.011226 (0.00847) [-1.32505] 4.55E-08 (1.9E-06) [ 0.02435] Cash Rate_FE (-1) 20139.47 (9147.27) [ 2.20169] -0.239055 (0.34634) [-0.69022] -0.306061 (0.40476) [-0.75615] -350.1900 (344.170) [-1.01749] -0.105169 (0.07586) [-1.38639] Bond Rate_FE (-1) -25170.42 (9529.57) [-2.64130] -0.373659 (0.36082) [-1.03558] -0.549964 (0.42168) [-1.30423] -134.8536 (358.554) [-0.37610] 0.080011 (0.07903) [ 1.01243] Australian Eq_FE (-1) 25.01398 (7.55131) [ 3.31254] 0.000362 (0.00029) [ 1.26644] 0.000194 (0.00033) [ 0.58029] -0.039895 (0.28412) [-0.14042] -3.48E-05 (6.3E-05) [-0.55590] AUD/USD_FE (-1) 12494.62 (42076.3) [ 0.29695] 3.909020 (1.59314) [ 2.45366] 2.905939 (1.86185) [ 1.56078] 1527.296 (1583.14) [ 0.96473] -0.613204 (0.34894) [-1.75734] C -1620.272 (3389.90) [-0.47797] -0.008019 (0.12835) [-0.06247] -0.079613 (0.15000) [-0.53075] -75.99734 (127.546) [-0.59584] -0.002278 (0.02811) [-0.08102] 0.532802 0.320439 2.05E+09 13636.14 2.508924 -182.2699 22.14940 22.44347 -1500.695 16541.60 0.569200 0.373382 2.932298 0.516307 2.906776 -9.183825 1.786332 2.080408 0.004476 0.652238 0.562519 0.363663 4.004871 0.603390 2.828785 -11.83349 2.098057 2.392133 -0.060315 0.756405 0.478987 0.242163 2895593 513.0650 2.022544 -126.5086 15.58925 15.88332 -21.51173 589.3652 0.285458 -0.039333 0.140669 0.113084 0.878897 16.63181 -1.250801 -0.956726 -0.005945 0.110924 R-squared Adj R-squared Sum sq resids S.E equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D dependent Determinant resid covariance (dof adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion 1.18E+10 1.34E+09 -299.2203 38.73180 40.20218 Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 379 Appendices APPENDIX 7: SEMI-STRUCTURED INTERVIEW RESPONDENTS PROFILE Reference LEND#1 Speciality of the Organisation Bank#1 is a major financial institution in Respondents’ Role Top management Australia in terms of market capitalisation and customers The structure of the bank is designed • Property market future risk assessment to align divisions with key customer segments • Forecasting the performance of The respondent is from the institutional division property markets that delivers a broad range of financial services to commercial, corporate, institutional and government clients LEND#2 Bank#2 is also a major financial institution in Middle management Australia Bank delivers personal banking, business banking and wealth management that • Stress testing include • Credit risk modelling the services for investment, superannuation and insurance solutions to retail, • Behavioural modelling corporate and institutional clients The banking products cater to customers’ needs with specialist expertise in agribusiness, property, health, government, education, and community PRO- PRO-FUND#1 is an A-REIT that invests in, FUND#1 develops, manages and trades Australian office and industrial property On behalf of third party clients, which are mainly domestic and international pension funds, PRO-FUND#1 transacts, develops, and manages Australian office, industrial and retail property The owned portfolio consists of high quality CBD office Middle management • Research and analysis of property markets • Forecasting the performance of property markets • Strategic advice for property investment properties, held long-term, and leased to derive stable and secure ongoing income streams PRO- PRO-FUND#2 is a leading, diversified A- FUND#2 REIT, with an integrated development and asset management capability Development activities deliver innovative and high-quality commercial assets and residential projects Middle management • Research and analysis of property markets • Strategic advice for property investment Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 380 Appendices Reference Speciality of the Organisation Respondents’ Role • Strategic advice for developments PRO- PRO-FUND#3 is an A-REIT which is a FUND#3 specialised operating platform for the retail real Middle management estate assets They manage, develop, and own • Research and analysis of retail market high quality regional shopping centres in • Behavioural assessment Australia and New Zealand PRO- PRO-FUND#4 is an A-REIT that owns, FUND#4 manages, and develops in specialised sectors such as childcare, healthcare, education and government tenanted facilities leased on a long- Top management • Forecasting the performance of properties for valuation term basis INV-MGT- INV-MGT-FUND#1 is a leading investment FUND#1 house with a strength in real estate and infrastructure, and specialist expertise in fixed income, equities and multi-asset solutions providing insights into ever-changing markets Middle management • Research and analysis of property markets • Forecasting the performance of property markets • Strategic advice for property investment INV-MGT- INV-M#2 is a specialist in the active FUND #2 management of Australian equities with an extensive investment management experience and insight into the Australian market A range of Australian equity portfolios include core, concentrated, income focused, long/short, small Middle management • Forecasting the performance of properties for valuation • Strategic advice for property investment companies, and listed property securities INV-MGT- FIN-ANALYST#2 is a boutique investment FUND#3 manager specialising in Australian Equities and Property Securities – including Australian and global REITs A range of funds include a large number of high net worth and retail clients in Middle management • Strategic advice for property investment • Asset allocation decision making Australia who predominantly invest through financial planners and platforms Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 381 Appendices Reference Speciality of the Organisation is a leading Respondents’ Role PRO- PRO-CONSULT#1 global ANALYST professional services firm that specialises in real #1 estate analysis The firm assists real estate investors, developers, corporates, and occupiers to achieve their business objectives across all asset classes including residential and commercial property Full-suite of services comprise research, strategic consulting, project development services, energy, and Middle management • Research and analysis of property markets • Forecasting the performance of property market • Strategic advice for property investment • Disseminating property market outlook sustainability PRO- PRO-CONSULT#2 is a global source of ANALYST commercial and residential real estate analyst #2 They cover the full spectrum from providing strategic advice, consultancy, and transacting deals to managing assets and projects in the Middle management • Research and analysis of property markets • Forecasting the performance of property market world’s key locations • Strategic advice for property investment • Disseminating property market outlook • Advisory role to the Government PRO- PRO-CONSULT#3 is a global property agency ANALYST and consultancy ensures the highest standards #3 of quality and integrity in global property transactional, management services commercial for and and advisory residential property Top management • Research and analysis of property markets • Forecasting the performance of property markets • Development of Forecasting models • Disseminating property market outlook PRO- ECON#2 is a real estate agency specialising in ANALYST commercial property providing end-to-end #4 property solutions for occupiers and investors around the world Research and consultancy services provide clients with global and local Middle management • Research and analysis of property markets • Forecasting the performance of property market Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 382 Appendices Reference Speciality of the Organisation Respondents’ Role market knowledge, forecasting and trend analysis to make the best long-term decisions • Strategic advice for property investment • Disseminating property market outlook PRO- PRO-CONSULT#4 provides independent ANALYST advice to clients across the financial services #5 industry including the areas of asset and investment consulting, research, and portfolio Top management • Forecasting the performance of all investment asset classes construction advice Complex investment, • Strategic advice on investments organisational and governance issues are solved • Capital market analysis through rigorous diligence, research, subsequently analysis advice to and reach practical solutions and recommendations PRO- PRO-CONSULT#5 provides independent ANALYST impartial retail property consultant services for #6 landlords and tenants A wide range of services • Retail lease valuation include retail property lease rent disputes, • Retail performance evaluation and current market rent, expert witness, specialist Top management benchmarking retail valuations, breach of lease (loss and • Retail portfolio risk assessment causation) reports • Strategic advice based on SWOT analysis PRO- PRO- ANALYST#6 assists institutional and ANALYST professional investor and advise them to #7 identify and realise value through a personalised and discreet property consulting structure Rigorous research and analysis is overlaid with macro considerations on client-specific detail PRO- PRO-CONSULT#7 is a leading full-service real ANALYST estate services and investment organisation in #8 the world A broad range of integrated services include facilities, transaction and project management, investment property management, Top management • Forecasting the performance of properties for valuation • Strategic advice for property investment Top management • Forecasting the performance of properties for valuation management, appraisal and valuation, property leasing, strategic consulting, Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 383 Appendices Reference Speciality of the Organisation Respondents’ Role property sales, mortgage, and development services PRO- PRO- ANALYST#8 is an independent provider ANALYST of research-driven insights and tools for asset #9 owners, institutional investors active and passive managers and risk managers A broad product line supports clients’ needs across all Top management • Forecasting the performance of properties for valuation • Forecasting for investment decision making major asset classes • Strategic advice for property investment ECON#1 ECON#1 is a leading provider of research and Top management forecasting about key industries The most comprehensive assessment of the market outlook is provided in both short-term and • Assessment for cyclical and structural shifts medium-term Reports are provided across a • Risk assessment range of sectors including: macroeconomics, • Evaluate financial consequences of building forecasting, residential property, alternative courses of action commercial property ECON#2 ECON#2 is a business school of a leading Middle management university in Melbourne is deliver programs responsive to the latest industry requirements and are actively engaged in research and • Research and analysis on property markets and economics • Development of econometric models consultation to business and government • Disseminating property market outlook as a commentator ECON#3 The respondent is an economist employed in Middle management ECON#3 is a multinational digital advertising company specialised in residential and commercial property and provide services to • Data Science management and driving data-lead decisions real estate agents and their clients across the • Advanced analytical modelling globe • Behavioural modelling • Forecasting the performance of property market Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 384 Appendices Reference ECON#4 Speciality of the Organisation Respondents’ Role ECON#4 is a leading independently owned Middle management investment research provider dedicated to the delivery of investment research solutions for financial advisors and their clients Key areas of expertise are in managed fund research, equities • Research and analysis on investments for all asset classes • Strategic advice for investment research and market research Forecasting Commercial Property Market Performance: Beyond the Primary Reliance on Econometric Models 385