DEGREE PROJECT, IN REAL ESTATE AND CONSTRUCTION MANAGEMENT BUILDING AND REAL ESTATE ECONOMICS MASTER OF SCIENCE, 30 CREDITS, SECOND LEVEL STOCKHOLM, SWEDEN 2016 Examining the Deviation to Net Asset Value for Swedish Listed Property Companies Applying a rational and irrational approach Tomas Shaw Matilda Wåhlin TECHNOLOGY DEPARTMENT OF REAL ESTATE AND CONSTRACTION MANAGEMENT ROYAL INSTITUTE OF TECHNOLOGY DEPARTMENT OF REAL ESTATE AND CONSTRUCTION MANAGEMENT Master of Science thesis Title Examining the Deviation to Net Asset Value for Swedish Listed Property Companies Authors Tomas Shaw, Matilda Wåhlin Department Real Estate and Construction Management Master Thesis number TRITA-FOB-ByF-MASTER-2016:29 Archive number 435 Supervisor Herman Donner Keywords Deviation to NAV, Panel data, Real Estate Abstract Net asset value (NAV) is commonly used to represent the value of a property company. For listed property companies a secondary valuation occurs simultaneously as the company’s stocks are traded on the stock market. Historically, a deviation between the NAV and the market capitalisation has been found for property companies implying that the stock market values the company differently. This thesis examines the deviation to NAV for 14 Swedish listed property companies during 2006-2015. The examination explains the deviation from the basis of a rational and an irrational approach. The thesis investigates empirically which factors that have affected the deviation by the use of a panel data regression analysis. The rational approach investigates the impact of company-specific, share-specific and corporate governance variables. The results of the thesis show that the rational variables can explain the deviation to NAV to some extent. The main contribution comes from company- specific variables. Larger companies, companies focused on fewer locations, companies with a better reputation among asset managers and companies with a higher amount of insider ownership are negatively correlated to the discount to NAV. These company characteristics thus suggest a decrease in discounts to NAV (increase in premiums). At the same time companies with a higher loan to value, focus on property type and systematic risk increase the discount to NAV (decrease in premiums). The final rational model produces an adjusted R-square of 37.4% for the Swedish listed property market during the investigated period. The irrational approach investigates the impact of noise traders. The results show that the contribution of market sentiment is significant. The confidence indicator for the households has the greatest impact on the discount to NAV and an inclusion of the variable increases the adjusted R-square to 53.6%. An investigation into the justification of using the Noise Trader Theory is conducted and concludes that the use of a proxy for market sentiment is justified. Examensarbete Titel Substansrabatter och substanspremier hos svenska börsnoterade fastighetsbolag Författare Tomas Shaw, Matilda Wåhlin Institution Fastigheter och Byggande Examensarbete Master nivå TRITA-FOB-ByF-MASTER-2016:29 Arkiv nummer 435 Handledare Herman Donner Nyckelord Substansrabatt, Substanspremie, Panel data Sammanfattning Substansvärdet (NAV) används ofta för att representera värdet av ett fastighetsbolag. För börsnoterade fastighetsbolag sker samtidigt en sekundär värdering eftersom deras aktier köps och säljs på aktiemarknaden. Historiskt sett har fastighetsbolagens substansvärden skilt sig från börspriserna av deras aktier vilket tyder på att aktiemarknaden värderar bolagen annorlunda och det uppstår då en substansrabatt eller substanspremie. Denna uppsats utvärderar detta fenomen för 14 svenska börsnoterade fastighetsbolag under åren 2006-2015 utifrån en rationell och en irrationell utgångspunkt. Uppsatsen testar empiriskt vilka faktorer som påverkar skillnaden under perioden genom en regressionsanalys med paneldata. Den rationella utgångspunkten undersöker effekterna av variabler knutna till företaget, aktien samt företagets bolagsstyrning. Resultatet visar att rationella variabler kan förklara substansrabatter och substanspremier till en viss grad. Det största bidraget kommer från de företagsspecifika variablerna. Större företag, företag fokuserade på ett mindre antal orter, företag med ett bättre rykte och företag vars styrelse har ett stort aktieinnehav tenderar att ha en minskad substansrabatt alternativt en ökad substanspremie. Å andra sidan tenderar företag med hög belåningsgrad, ett fåtal fastighetstyper och hög systematisk risk att ha en ökad substansrabatt alternativt en minskad substanspremie. Den slutliga modellen av rationella variabler genererar ett justerat R-square om 37,4% för svenska börsnoterade fastighetsbolag. Den irrationella utgångspunkten i denna uppsats undersöker variabler knutna till ett irrationellt handlande. Resultatet visar signifikant utfall för irrationellt handlande, där en konfidensindikator för hushållen visar störst inverkan och genererar ett justerat R-square om 53,6%. Uppsatsen undersöker möjligheten att använda irrationellt handlande som förklaringsvariabler till varför substansrabatter och substanspremier uppstår. Resultatet visar att det är motiverat att inkludera irrationella förklaringsvariabler. Acknowledgement This master thesis is written as the final part of the master program in Real Estate and Construction Management and the Civil Engineering programme at KTH, the Royal Institute of Technology. The deviation to NAV for listed property companies has been studied broadly on an international level. The aim of this thesis is to investigate this phenomenon in the Swedish context. Hopefully, the contribution of the report could be of interest to academia, property companies as well as investors in terms of investment strategies and stock market behaviour. First and foremost, we would like to thank our supervisor Herman Donner for his inspiration, positive guidance and long discussions throughout the process of writing this thesis. We also want to thank Han-Suck Song and Mats Wilhelmsson for additional guidance as well as for their contribution in discussions. Feel free to contact us regarding questions about the thesis at [email protected] (Tomas Shaw) and [email protected] (Matilda Wåhlin). Stockholm 2nd of June 2016, Tomas Shaw and Matilda Wåhlin Table of Contents 1. Introduction ................................................................................................................... 1 1.1 Background ................................................................................................................................... 1 1.2 The Aim of the Thesis ................................................................................................................... 4 1.3 Research Question ........................................................................................................................ 4 1.4 Limitations .................................................................................................................................... 4 2. Theoretical Framework .................................................................................................. 5 2.1 Modelling Financial Markets ........................................................................................................ 5 2.2 The Property Market Versus the Stock Market ............................................................................ 7 2.3 The Calculation of the NAV Spread ............................................................................................... 8 2.4 Literature Review .......................................................................................................................... 9 2.5 Expertise Review ......................................................................................................................... 24 3. Method ........................................................................................................................ 26 3.1 Methodology .............................................................................................................................. 26 3.2 The Model ................................................................................................................................... 26 3.3 Regression Analysis ..................................................................................................................... 29 3.4 Reliability .................................................................................................................................... 33 3.5 Validity ........................................................................................................................................ 34 4. Data Description .......................................................................................................... 35 4.1 Selection ..................................................................................................................................... 35 4.2 Variables ..................................................................................................................................... 37 4.3 Descriptive Statistics ................................................................................................................... 41 5. Results and Discussion ................................................................................................. 48 5.1 Results – Rational Approach ......................................................................................................