Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management

Re-thinking Commercial Real Estate Market Segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.market segmentation, commercial real estate, financial performance measurement, cluster analysis, neural network analysis, risk diversification

Multivariate Statistical Analysis of Phyllite Samples Based on Chemical (XRF) and Mineralogical Data by XRD

It is presented the results obtained of a multivariate statistical analysis concerning the chemical and phase composition, as a characterization purpose, carried out with 52 rock phyllite samples selected from the provinces of Almería and Granada (SE Spain). Chemical analysis was performed by X-ray fluorescence (XRF). Crystalline phase analysis was performed by X-ray powder diffraction (XRD) and the mineralogical composition was then deduced. Quantification of weight loss (100° and 1000°C) was carried out by thermal analysis. The aims of this investigation were to analyze and compare the chemical and mineralogical composition of all these samples and to find similarities and differences between them to allow a classification. Several correlations between results of the characterization techniques have been also investigated. All the data have been processed using the multivariate statistical analysis method. The XRF macroelements (10) and microelements (39) data generate one macrogroup with two new subgroups (1 and 2), and an isolated sample. In subgroup 1 of macroelements, a positive correlation was found between XRF results and geographic location characterized by lower MgO content, which is associated to its geological origins. When multivariate statistical analysis is applied to results obtained by XRD, two groups appear: the first one with a sample with zero percentage of iron oxide and the second one with the rest of the samples, which is classified in two groups. A correlation is observed between the alkaline content (XRF) and illite (XRD), CaO and MgO with dolomite and indirectly between the weight loss after heating at 1000°C and the contents of phase minerals that lose structural water (illite + chlorite) or carbon dioxide (dolomite). The present investigation has interest and implications for geochemistry and analytical chemistry concerning earth rocks and silicate raw material

Estimation of Total Phenols, Flavanols and Extractability of Phenolic Compounds in Grape Seeds Using Vibrational Spectroscopy and Chemometric Tools

Near infrared hyperspectral data were collected for 200 Syrah and Tempranillo grape seed samples. Next, a sample selection was carried out and the phenolic content of these samples was determined. Then, quantitative (modified partial least square regressions) and qualitative (K-means and lineal discriminant analyses) chemometric tools were applied to obtain the best models for predicting the reference parameters. Quantitative models developed for the prediction of total phenolic and flavanolic contents have been successfully developed with standard errors of prediction (SEP) in external validation similar to those previously reported. For these parameters, SEPs were respectively, 11.23 mg g−1 of grape seed, expressed as gallic acid equivalents and 4.85 mg g−1 of grape seed, expressed as catechin equivalents. The application of these models to the whole sample set (selected and non-selected samples) has allowed knowing the distributions of total phenolic and flavanolic contents in this set. Moreover, a discriminant function has been calculated and applied to know the phenolic extractability level of the samples. On average, this discrimination function has allowed a 76.92% of samples correctly classified according their extractability level. In this way, the bases for the control of grape seeds phenolic state from their near infrared spectra have been stablished.España MINECO AGL2017-84793-C2España, Universidad de Sevilla VPPI-II.2, VPPI-II.

Texture descriptors applied to digital mammography

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version

Work Meaning Patterns in Early Career

Work meaning patterns combine dimensions such as work centrality, expressive versus economic work goals, entitlement versus obligation societal norms into a holistic picture of the over time evolution of the meaning of work. Data from a longitudinal study in eight European countries are used to empirically establish major work meaning patterns and to study their stability during the early career. Further, some potential determinants of these work meaning patterns are analyzed and their consequences for the later career are considered. Statistical analyses include: cluster analysis, multiple discriminant analysis, analysis of covariance combined with multiple classification analysis, analysis of variance, and chi square analysis. Five cross-national work meaning patterns are identified for machine operators in their third year of labour market participation. One third of the sample remain in the same work meaning pattern over a time period of two years, while two third change their pattern membership. Respondents\u27 age, country, prior work environment, and their prior work socialization behaviours and outcomes have an impact on work meanings held two years later. In addition the work meaning pattern shared by the respondent allows to predict subsequent career enhancing strategies and effort expenditure at the job

Data interpretation in forensic sediment and soil geochemistry

Automated geochemical techniques enable reproducible elemental assays of small quantity samples and have been used in recent years in many forensic criminal investigations in England and Wales. Two case studies are presented that highlight the problems of testing the presence of pre-, syn-, or post-crime event sample mixing. The number of elements or compounds analyzed can have a bearing on statistical discriminant techniques that may provide false-positive or false-negative associations or exclusions. Chemical analyses of soils and sediments using both atomic absorption spectrometry and Dionex (DX500 Sunnyvale, CA, USA), and inductively coupled plasma mass spectrometry enabled the identification and classification of discrete groups by hierarchical cluster analysis and canonical discriminant function analysis. These groupings, however, prove fragile to small variations within samples of even the most common minerals. Copyright © Taylor & Francis Group, LLC

Selection Models for the Internal Quality of Fruit, based on Time Domain Laser Reflectance Spectroscopy

Time domain laser reflectance spectroscopy (TRS) was applied for the first time to evaluate internal fruit quality. This technique, known in medicine-related knowledge areas, has not been used before in agricultural or food research. It allows the simultaneous measurement of two optical characteristics of the sample: light scattering inside the tissues and light absorption. Models to estimate non-destructively firmness, soluble solids and acid contents in tomato, apple, peach and nectarine were developed using sequential statistical techniques: principal component analysis, multiple stepwise linear regression, clustering and discriminant analysis. Consistent correlations were established between the two parameters measured with TRS, i.e. absorption and transport scattering coefficients, with chemical constituents (soluble solids and acids) and firmness, respectively. Classification models were created to sort fruits into three quality grades (‘low’, ‘medium’ and ‘high’), according to their firmness, soluble solids and acidity