European REITs NAV discount: do investors believe in property appraisal?

Abstract

Purpose – The purpose of this paper is to analyse the NAV discount of European REITs listed in France, the Netherlands and the UK between 2003 and 2014, considering elements of both “rational” and “noise trader” approaches. Design/methodology/approach – The analysis examines the hypothesis that discounts (premiums) are the result of leverage, size, liquidity, risk, performance, investment activity and sentiment. The regressions are initially run against the traditional NAV discount, subsequently using the unlevered NAV discount measure introduced by Morri et al. (2005) in order to clean out the bias generated by the level of leverage. The NAV discount is then adjusted for investor sentiment (appraisal reduction) with the aim of better identifying firm-specific factors, considering distortions induced by sentiment. Findings – Higher liquidity commands lower discounts for French REITs, while Dutch and British REITs, which trade in markets characterized by a higher number of average daily transactions, do not seem to feature discounts resulting from liquidity. For all three samples, operational risk and performance are significant in explaining the NAV discount, the former having a positive relationship with the discount, and the latter a negative one. When measured using the average sector discount, sentiment has a profound effect on the discount, accounting alone for 10-15 per cent of the explanatory power of the model. Practical implications – REITs listed in different markets behave differently. When the discount is adjusted in order to remove the bias resulting from the level of debt, the relationship between leverage and the unlevered discount becomes less pronounced in all cases. Originality/value – The paper considers a new approach to NAV discount puzzle that takes into account market sentiment and appraisals.

New Insights into the NAV Spread Puzzle of Listed Real Estate: Idiosyncratic and Systematic Evidence

“…However, the results are ambiguous. Bond and Shilling (2003), Brounen and Laak (2005), Ke (2015) as well as Morri and Baccarin (2016) report that increasing leverage increases discounts to NAV, while others (Barkham and Ward, 1999;Rehkugler et al, 2012) find no significant relationship. To the contrary, Clayton and MacKinnon (2000), Morri et al (2005) and Nellessen and Zuelch (2011) report a positive and significant coefficient.…”

“…Barkham and Ward (1999), Clayton and MacKinnon (2002) and Brounen and Laak (2005) find a negative relationship between NAV discounts and liquidity using different liquidity proxies. Clayton and MacKinnon (2002) as well as Morri and Baccarin (2016) use bid-ask-spreads as proxies for liquidity. In a recent study, Morri and Baccarin (2016) confirm latter findings, but only for French REITs, while Dutch and U.K. REIT's liquidity is not significantly related to NAV discounts.…”

“…Several studies show that company-specific risk is expected to widen the discount to NAV. Bond and Shilling (2003), Morri et al (2005) and Morri and Baccarin (2016) use the beta factor as risk proxy and find that increasing risk increases the discount. Ke (2015) uses the standard deviation of returns as a risk measure and shows that "companies with higher risk have higher discounts".…”

“…In particular, they find that NAV spreads can be explained by sentiment and recommend that future research should consider both rational fundamental factors and irrational sentiment factors. Barkham and Ward (1999), Clayton and MacKinnon (2000), Ke (2015), Morri and Baccarin (2016) proxy noise trader sentiment by the average property sector discount. Each study finds a significant influence on NAV spreads and that sentiment increases the explanatory power of the applied models.…”

“…Previous studies have analyzed subpanels before: REIT status (Bond and Shilling, 2003;Ke, 2015), regions (Morri and Baccarin, 2016), sectoral focus and strategy (Capozza and Korean ,1995;Brounen and Laak, 2005) and provides diverging results. The subpanel analysis allows to draw conclusions from certain regional, sectoral or regulatory characteristics when explaining NAV spreads.…”