International evidence on the predictability of returns to securitized real estate assets: econometric models versus neural networks

The performance of various statistical models and commonly used financial indicators for forecasting securitised real estate returns are examined for five European countries: the UK, Belgium, the Netherlands, France and Italy. Within a VAR framework, it is demonstrated that the gilt-equity yield ratio is in most cases a better predictor of securitized returns than the term structure or the dividend yield. In particular, investors should consider in their real estate return models the predictability of the gilt-equity yield ratio in Belgium, the Netherlands and France, and the term structure of interest rates in France. Predictions obtained from the VAR and univariate time-series models are compared with the predictions of an artificial neural network model. It is found that, whilst no single model is universally superior across all series, accuracy measures and horizons considered, the neural network model is generally able to offer the most accurate predictions for 1-month horizons. For quarterly and half-yearly forecasts, the random walk with a drift is the most successful for the UK, Belgian and Dutch returns and the neural network for French and Italian returns. Although this study underscores market context and forecast horizon as parameters relevant to the choice of the forecast model, it strongly indicates that analysts should exploit the potential of neural networks and assess more fully their forecast performance against more traditional models.

Evidence for insertional codemixing: mixed compounds and French nominal groups in Brussels Dutch

In this paper we analyse mixed compounds, such as legume+winkel ‘vegetable shop, greengrocery’ and winter+paletot ‘winter coat’ which contain a French and a Dutch element, and French nominal groups, such as carte d’identité ‘identity card’, and journal parlé ‘radio news’, which bilingual speakers from Brussels frequently insert into Brussels Dutch utterances. Using Muysken’s (2000) typology of bilingual speech, we claim that the mixed compounds and the nominal groups display the characteristics of insertional code-mixing. In addition, some evidence for the existence of a continuum between borrowing and code-switching can be obtained from these examples. As the multimorphemic units that are inserted into Dutch are neither single words, nor full constituents, their status in the lexicon raises interesting issues for researchers interested in the interface between syntax and the lexicon (see also Backus 2003). We try to argue that nominal groups such as carte d’identité and journal parlé are probably best seen as lexical templates or constructional idioms (Booij, 2002b). The insertion of French constructional idioms in Brussels Dutch represents an innovation in the lexical patterns that are available to speakers of this language, which is highly relevant for theories of language change.

Isotype: design and contexts 1925–1971

The work in graphic communication carried out by Otto Neurath and his associates – now commonly known simply as Isotype – has been the subject of much interest in recent years. Conceived and developed in the 1920s as ‘the Vienna method of pictorial statistics’, this approach to designing information had from its inception the power to grow and spread internationally. Political developments in Europe played their part in its development, and production moved to the Netherlands (1934) and to England (1940), where the Isotype Institute continued to produce work until 1971. Bringing together the latest research, this book is the first comprehensive, detailed account of its subject. The Austrian, Dutch, and English years of Isotype are described here freshly and extensively. There are chapters on the notable extensions of Isotype to Soviet Russia, the USA, and Africa. Isotype work in film and in designing for children is fully documented and discussed. Between these main chapters the book presents interludes documenting Isotype production visually. Three appendices reprint key documents. In its international coverage and its extensions into the wider terrain of history, this book opens a new vista in graphic design.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.