摘要：Abstract The upward of the Spanish real estate sector (2000–2007) caused the excessive growth of many companies mainly through acquisitions. This study aimed to identify behaviour patterns for takeover bids in the Spanish real estate industry, which was particularly hard hit by the last financial crisis. Considering that in bubble growing/burst periods, economic and financial variables are considered the most useful measures (market variables can have reliability problems), a set of 20 economic and financial variables was analysed, along with their relationship with listed companies’ participation in this type of operations over the period 2000–2012. Both acquiring and target companies were included in the 354 cases studied here. A two-stage methodology was used. Firstly, the principal component method was applied to identify the variables with greatest explanatory capacity. That was followed by the construction of a decision tree-based predictive model, more specifically a CHAID, which categorised the set of companies analysed to establish behaviour patterns. The findings of this study show that the five principal components found to afford the greatest explanatory capacity were: (a) liquidity, solvency and borrowing capacity; (b) size; (c) economic performance; (d) operating capacity; and (e) financial performance. Taken together, the first two components explained 70% of dependent variable behaviour, primarily relative to buyers. Overall, the model proposed explained on the order of 80% of dependent variable behaviour. The percentage not explained by the model was attributed essentially to strategic issues, financial speculation and private interests, among other factors present in decision-making.
关键词：Real estate industry; Takeover bids; Economic-financial variables; Principal components analysis; Chi-square Automatic Interaction Detector (CHAID); Sector inmobiliario; Ofertas públicas de adquisición de acciones; Variables económico-financieras; Método de componentes principales; Chi-square Automatic Interaction Detector (CHAID)
