The impact of cross-border real estate investment on commercial real estate capitalisation rates

This study examines the impact of foreign real estate investment on the US office market capitalization rates. The geographic unit of analysis is MSA and the time period is 2001-2013. Drawing upon a database of commercial real estate transactions provided by Real Capital Analytics, we model the determinants of market capitalization rates with a particular focus on the significance of the proportion of market transactions involving foreign investors. We have employed several econometric techniques to explore the data, potential estimation biases, and test robustness of the results. The results suggest statistically significant effects of foreign investment across 38 US metro areas. It is estimated that, all else equal, a 100 basis points increase in foreign share of total investment in a US metropolitan office market causes about an 8 basis points decrease in the market cap rate.

Assessing the accuracy and dispersion of real estate investment forecasts

Existing empirical evidence has frequently observed that professional forecasters are conservative and display herding behaviour. Whilst a large number of papers have considered equities as well as macroeconomic series, few have considered the accuracy of forecasts in alternative asset classes such as real estate. We consider the accuracy of forecasts for the UK commercial real estate market over the period 1999-2011. The results illustrate that forecasters display a tendency to under-estimate growth rates during strong market conditions and over-estimate when the market is performing poorly. This conservatism not only results in smoothed estimates but also implies that forecasters display herding behaviour. There is also a marked difference in the relative accuracy of capital and total returns versus rental figures. Whilst rental growth forecasts are relatively accurate, considerable inaccuracy is observed with respect to capital value and total returns.

Functionality of several cake ingredients: a comprehensive approach

The roles of some cake ingredients – oil, a leavening agent, and inulin – in the structure and physicochemical properties of batter and cakes were studied in four different formulations. Oil played an important role in the batter stability, due to its contribution to increasing batter viscosity and occluding air during mixing. The addition of the leavening agent was crucial to the final height and sponginess of the cakes. When inulin was used as a fat replacer, the absence of oil caused a decrease in the stability of the batter, where larger air bubbles were occluded. Inulin dispersed uniformly in the batter could create a competition for water with the flour components: gluten was not properly hydrated and some starch granules were not fully incorporated into the matrix. Thus, the development of a continuous network was disrupted and the cake was shorter and softer; it contained interconnected air cells in the crumb, and was easily crumbled. The structure studies were decisive to understand the physicochemical properties.

Does foreign investment affect US office real estate prices?

his article examines the impact of foreign real estate investment on U.S. office market capitalization rates. The geographic unit of analysis is MSA and the time period is 2001–2013. Drawing upon a database of commercial real estate transactions, the authors model the determinants of market capitalization rates with a particular focus on the significance of the proportion of market transactions involving foreign investors. Employing several econometric techniques to analyze the data, the results suggest statistically significant effects of foreign investment across 38 U.S. metro areas. It is estimated that, all else equal, a 100 basis point increase in foreign share of total investment in a U.S. metropolitan office market causes about an 8 basis point decrease in the market cap rate.

A review of Islamic mutual funds’ financial performance and investment style in Muslim and non-Muslim countries

Islamic finance has grown beyond its reputation of providing small-scale banking options and now provides investment and financing options for complex large-scale commercial transactions. Islamic investments are one area that has attracted the attention of investors due to its performance, especially during the economic downturn. The Shari’ah compliance nature of Islamic funds provides an opportunity for those Muslim investors to be part of the global investment sector who have previously been reluctant to invest in conventional mutual funds. The fact that the funds’ managers are prohibited from investing in activities such as weapons production, alcohol production and interest-bearing finance operations, makes Islamic mutual funds also attractive for those Non-Muslim investors who wish to invest ethically. Today there are hundreds of Islamic equity indices offered by Dow Jones, FTSE, MSCI and S&P. Despite the growing importance of Islamic funds, there have been limited studies exploring the performance of Islamic funds worldwide. Due to very limited data sets and not too rigorous analytical methods, these existent studies have neither investigated Islamic funds’ financial performance in noticeable detail nor analysed the investment style of more than six funds. For instance, relevant questions such as the financial performance of Islamic mutual funds’ beyond their investment styles or a difference in performance between funds from Muslim and non-Muslim countries have nearly not been investigated at all. Very recently, a study by Hoepner, Rammal and Rezec (2011) analysed the financial performance and investment style of 262 Islamic equity funds from 20 countries in five regions (Africa, Asia-Pacific, Europe, Gulf Cooperative Council-GCC, and North America). As comparison, previous studies did not even analyse 60 funds. Hoepner et al.’s study sampled a period of two decades and was therefore able to test the performance of the funds during economic booms as well as economic downturns. The findings of the study provide new insights into the performance of Islamic mutual funds in Muslim and Western markets and during financial crisis.

The determinants of inward foreign direct investment in business services across European regions

The determinants of inward foreign direct investment in business services across European regions, Regional Studies. The role of forward linkages with manufacturing sectors and other service sectors as attractors of business services foreign direct investment (FDI) is studied at the regional level. Using data on 146 NUTS-2 regions, it is found that regions specialized in those (manufacturing) sectors that are high potential users of business services attract more FDI in the business services than other regions. Results are robust to the inclusion of the traditional determinants of foreign investments at the regional level as well as to controls for spatial dependence. The results suggest that regional policies aimed at attracting foreign investors in the business service industry might prove ineffective in the absence of a pre-existing local intermediate demand

Long-run restrictions and survey forecasts of output, consumption and investment

We consider the extent to which long-horizon survey forecasts of consumption, investment and output growth are consistent with theory-based steady-state values, and whether imposing these restrictions on long-horizon forecasts will enhance their accuracy. The restrictions we impose are consistent with a two-sector model in which the variables grow at different rates in steady state. The restrictions are imposed by exponential-tilting of simple auxiliary forecast densities. We show that imposing the consumption-output restriction yields modest improvements in the long-horizon output growth forecasts, and larger improvements in the forecasts of the cointegrating combination of consumption and output: the transformation of the data on which accuracy is assessed plays an important role.

Investigation of institutional investors’ responsible investment practices in UK

The focus of Corporate Governance is shifting from the role of directors to active ownership. Based on their fiduciary duty to other shareholders, it is believed that institutional investors have an important role to play in this regard. However, the Pension Funds and the Sovereign Wealth Organisations are not driven by the same set of objectives. In addition, Environmental Social and Governance (ESG) issues in investment decision-making are now becoming more important and they are capable of becoming the mainstream in the future. However, there are widespread variations in perception of fiduciary responsibilities, ESG issues appraisal, as well as the strategies adopted by institutional investors on shareholder engagement as responsible investors. Responsible Investment market is largely driven by institutional investors and they are expected to continue to lead the way. This research work investigates the role of the main asset owners and their advisors in responsible investment practices in the UK. It adopts a qualitative approach using semi-structured interviews, questionnaire and meetings observations. Gathered data is analysed using grounded theory and the findings highlight the perception of the various investor groups to corporate governance. The research work contributes to the body of knowledge by assessing the corporate governance perspectives of the various classes of institutional investors which may have practical implications for other countries.

Using proxies to calculate the carbon impact of investment into electricity network assets

Replacement and upgrading of assets in the electricity network requires financial investment for the distribution and transmission utilities. The replacement and upgrading of network assets also represents an emissions impact due to the carbon embodied in the materials used to manufacture network assets. This paper uses investment and asset data for the GB system for 2015-2023 to assess the suitability of using a proxy with peak demand data and network investment data to calculate the carbon impacts of network investments. The proxies are calculated on a regional basis and applied to calculate the embodied carbon associated with current network assets by DNO region. The proxies are also applied to peak demand data across the 2015-2023 period to estimate the expected levels of embodied carbon that will be associated with network investment during this period. The suitability of these proxies in different contexts are then discussed, along with initial scenario analysis to calculate the impact of avoiding or deferring network investments through distributed generation projects. The proxies were found to be effective in estimating the total embodied carbon of electricity system investment in order to compare investment strategies in different regions of the GB network.

Exploring methods to evaluate the carbon impact of network investment deferral

Replacement, expansion and upgrading of assets in the electricity network represents financial investment for the distribution utilities. Network Investment Deferral (NID) is a well discussed benefit of wider adoption of Distributed Generation (DG). There have been many attempts to quantify and evaluate the financial benefit for the distribution utilities. While the carbon benefits of NID are commonly mentioned, there is little attempt to quantify these impacts. This paper explores the quantitative methods previously used to evaluate financial benefits in order to discuss the carbon impacts. These carbon impacts are important for companies owning DG equipment for internal reporting and emissions reductions ambitions. Currently, a GB wide approach is taken as a means for discussing more regional and local methods to be used in future work. By investigating these principles, the paper offers a novel approach to quantifying carbon emissions from various DG technologies.

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

Biases in comparative analyses of extinction risk: mind the gap

1. Comparative analyses are used to address the key question of what makes a species more prone to extinction by exploring the links between vulnerability and intrinsic species’ traits and/or extrinsic factors. This approach requires comprehensive species data but information is rarely available for all species of interest. As a result comparative analyses often rely on subsets of relatively few species that are assumed to be representative samples of the overall studied group. 2. Our study challenges this assumption and quantifies the taxonomic, spatial, and data type biases associated with the quantity of data available for 5415 mammalian species using the freely available life-history database PanTHERIA. 3. Moreover, we explore how existing biases influence results of comparative analyses of extinction risk by using subsets of data that attempt to correct for detected biases. In particular, we focus on links between four species’ traits commonly linked to vulnerability (distribution range area, adult body mass, population density and gestation length) and conduct univariate and multivariate analyses to understand how biases affect model predictions. 4. Our results show important biases in data availability with c.22% of mammals completely lacking data. Missing data, which appear to be not missing at random, occur frequently in all traits (14–99% of cases missing). Data availability is explained by intrinsic traits, with larger mammals occupying bigger range areas being the best studied. Importantly, we find that existing biases affect the results of comparative analyses by overestimating the risk of extinction and changing which traits are identified as important predictors. 5. Our results raise concerns over our ability to draw general conclusions regarding what makes a species more prone to extinction. Missing data represent a prevalent problem in comparative analyses, and unfortunately, because data are not missing at random, conventional approaches to fill data gaps, are not valid or present important challenges. These results show the importance of making appropriate inferences from comparative analyses by focusing on the subset of species for which data are available. Ultimately, addressing the data bias problem requires greater investment in data collection and dissemination, as well as the development of methodological approaches to effectively correct existing biases.

A comprehensive meta-analysis of common genetic variants in autism spectrum conditions

Background Autism spectrum conditions (ASC) are a group of neurodevelopmental conditions characterized by difficulties in social interaction and communication alongside repetitive and stereotyped behaviours. ASC are heritable, and common genetic variants contribute substantial phenotypic variability. More than 600 genes have been implicated in ASC to date. However, a comprehensive investigation of candidate gene association studies in ASC is lacking. Methods In this study, we systematically reviewed the literature for association studies for 552 genes associated with ASC. We identified 58 common genetic variants in 27 genes that have been investigated in three or more independent cohorts and conducted a meta-analysis for 55 of these variants. We investigated publication bias and sensitivity and performed stratified analyses for a subset of these variants. Results We identified 15 variants nominally significant for the mean effect size, 8 of which had P values below a threshold of significance of 0.01. Of these 15 variants, 11 were re-investigated for effect sizes and significance in the larger Psychiatric Genomics Consortium dataset, and none of them were significant. Effect direction for 8 of the 11 variants were concordant between both the datasets, although the correlation between the effect sizes from the two datasets was poor and non-significant. Conclusions This is the first study to comprehensively examine common variants in candidate genes for ASC through meta-analysis. While for majority of the variants, the total sample size was above 500 cases and 500 controls, the total sample size was not large enough to accurately identify common variants that contribute to the aetiology of ASC.