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NAV Discounts in European Listed Property Companies: A Panel Regression Approach

NAV Discounts in European Listed Property Companies: A Panel Regression Approach. Rebecca Goodall and Michael White. ERES 2011, Eindhoven. NAV discounts/premiums =. Price per share NAV per share. -1. 09 September 2014. 2. The commercial significance of discounts.

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NAV Discounts in European Listed Property Companies: A Panel Regression Approach

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  1. NAV Discounts in European Listed Property Companies: A Panel Regression Approach Rebecca Goodall and Michael White ERES 2011, Eindhoven

  2. NAV discounts/premiums = Price per share NAV per share -1 09 September 2014 2

  3. The commercial significance of discounts The “puzzle” is not that puzzling. Morri and Ward (2005) argue that there are good grounds for deviation (i.e. a premium or a discount). 2) Schutte and Unlu (2009) explain that ‘price stability is critical for the decision making at the corporate level because managers rely on forecasted prices to make various long-term decisions regarding capital structure (issuance of equity versus debt), payout policy (dividend and share purchases), and corporate acquisitions/divestitures’ 3) If share price anomalies/frictions are to blame can management tackle “investibility” issues and reduce the discount? 09 September 2014 3

  4. NAV debate stock take No comprehensive explanation Contradictions Not enough time series data Data sets used 09 September 2014 4

  5. Building on previous research 1) Brounen and ter Laak (2005) ‘Discount = f(Constant, Size, Leverage, Mean Return, Freefloat, Total Risk, Systematic Risk)’ 2) Barkham and Ward (1999) conclude that market sentiment (noise trading) is the prime driver of the discount. So how do different shares react to noise? 3) Schutte and Unlu (2009) show that sell-side analyst coverage can reduce noise So do firms with more coverage have a lower discount? 09 September 2014 5

  6. The model Discount =f(Constant, Market Cap, Trading Volume, Free float, EPRA index membership, Focus, Market Cap of Exchange, Analyst Coverage) If a share is easy to invest in and is also being marketed through the sell-side it should be more popular with investors ceteris paribus and the share price should rise, leading to a lower NAV discount? 09 September 2014 6

  7. Market capitalisation 09 September 2014 7

  8. EPRA index membership 09 September 2014 8

  9. Sell-side analyst coverage 09 September 2014 9

  10. Data and Methodology Data for the analysis stage come from the Kempen universe plus some additional sources This is still an on-going process at this stage Kempen comprises over 50 property companies across Europe The final dataset will consist of data over time and will permit a panel regression approach to be adopted. Panel unit root tests and cointegration tests will be employed The structure of the dataset will permit testing for index membership effects, and allow identification of any cyclical impacts 09 September 2014 10

  11. Conclusion Discount =f(Constant, Market Cap, Trading Volume, Free float, EPRA index membership, Focus, Market Cap of Exchange, Analyst Coverage) Results will be available for ERES 2012!!! Contact details: Michael.white@ntu.ac.uk, rebecca.goodall@ntu.ac.uk 09 September 2014 12

  12. NAV Discounts in European Listed Property Companies: A Panel Regression Approach Rebecca Goodall and Michael White ERES 2011, Eindhoven

  13. Regional and use-type focus 09 September 2014 14

  14. Further variables Trading volume Free-float Market cap of the exchange the company is listed on

  15. Trading volume and free-float 09 September 2014 16

  16. Market cap of exchange 09 September 2014 17

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