What sells in a crisis? Determinants of sale probability over a cycle and through a crash
Notice bibliographique
Résumé
Purpose The purpose of this paper is to understand what affects the liquidity of individual commercial real estate assets over the course of the economic cycle by exploring a range of variables and a number of time periods to identify key determinants of sale probability. Design/methodology/approach Analyzing 12,000 UK commercial real estate transactions (2003 to 2013) the authors use an innovative sampling technique akin to a perpetual inventory approach to generate a sample of held assets for each 12 month interval. Next, the authors use probit models to test how market, owner and property factors affect sale probability in different market environments. Findings The types of properties that are most likely to sell changes between strong and weak markets. Office and retail assets were more likely to sell than industrial both overall and in better market conditions, but were less likely to sell than industrial properties during the downturn from mid-2007 to mid-2009. Assets located in the City of London more likely to sell in both strong and weak markets. The behavior of different groups of owners changed over time, and this indicates that the type of owner might have implications for the liquidity of individual assets over and above their physical and locational attributes. Practical implications Variation in sale probability over time and across assets has implications for real estate investment management both in terms of asset selection and the ability to rebalance portfolios over the course of the cycle. Results also suggest that sample selection may be an issue for commercial real estate price indices around the globe and imply that indices based on a limited group of owners/sellers might be susceptible to further biases when tracking market performance through time. Originality/value The study differs from the existing literature on sale probability as the authors analyzed samples of transactions drawn from all investor types, a significant advantage over studies based on data restricted to samples of domestic institutional investors. As well, information on country of origin for buyers and sellers allows us to explore the influence of foreign ownership on the probability of sale. Finally, the authors not only analyze all transactions together, but the authors also look at transactions in five distinct periods that correspond with different phases of the UK commercial real estate cycle. This paper considers the UK real estate market, but it is likely that many of the findings hold for other major commercial real estate markets.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».