Avaliação imobiliária: entre a ciência da avaliação e a arte da apreciação

Recentemente a avaliação imobiliária levou ao colapso de instituições financeiras e à crise no Subprime. A presente investigação pretende contribuir para perceber quais os factores preponderantes na avaliação imobiliária. O trabalho aborda a problemática da assimetria de informação, os diferentes métodos de avaliação imobiliária e a importância das externalidades. Empiricamente há diversos casos analisados através do uso da metodologia da Regressão Linear, Análise de Clusters e Análise de Componentes Principais da Análise Factorial. O primeiro caso analisado é direccionado à avaliação das externalidades, onde os resultados indicam que as externalidades positivas principais são as seguintes: as vistas de marina são mais valorizadas que as vistas de mar, as vistas frontais são mais valorizadas que as vistas laterais e existem diferenças de valorização ao nível do piso de acordo com o tipo de habitação (residência ou férias). O segundo estudo analisa como o método do rendimento ajuda na explicação da realidade portuguesa, no qual foram obtidos três clusters de rendas e três clusters de yields para cada uma das amostras. Os resultados demonstram que: (a) ambos os clusters, das yields e das rendas são formados por diferentes elementos (b) que o valor da oferta é explicado pelo método do rendimento, pelo cluster das yields e pela densidade populacional. No terceiro estudo foram inquiridos 427 indivíduos que procuravam apartamento para residência. A partir da Análise de Componentes Principais da Análise Factorial efectuada obtiveram-se sete factores determinantes na procura de apartamento: as externalidades negativas, as externalidades positivas, a localização de negócios no rés-do-chão do edifício de apartamentos, os interesses racionais de proximidade, as variáveis secundárias na utilização do edifício, as variáveis de rendimento e as variáveis de interesses pessoais. A principal conclusão é que como é uma área transdisciplinar, é difícil chegar a um único modelo que incorpore os métodos de avaliação e as diferentes dinâmicas da procura. O avaliador, deve analisar e fazer o seu scoring, tendo em conta o equilíbrio entre a ciência da avaliação e a arte da apreciação.

NMR in the characterization of heavy residual procedural streams

The main objective of this work was to monitor a set of physical-chemical properties of heavy oil procedural streams through nuclear magnetic resonance spectroscopy, in order to propose an analysis procedure and online data processing for process control. Different statistical methods which allow to relate the results obtained by nuclear magnetic resonance spectroscopy with the results obtained by the conventional standard methods during the characterization of the different streams, have been implemented in order to develop models for predicting these same properties. The real-time knowledge of these physical-chemical properties of petroleum fractions is very important for enhancing refinery operations, ensuring technically, economically and environmentally proper refinery operations. The first part of this work involved the determination of many physical-chemical properties, at Matosinhos refinery, by following some standard methods important to evaluate and characterize light vacuum gas oil, heavy vacuum gas oil and fuel oil fractions. Kinematic viscosity, density, sulfur content, flash point, carbon residue, P-value and atmospheric and vacuum distillations were the properties analysed. Besides the analysis by using the standard methods, the same samples were analysed by nuclear magnetic resonance spectroscopy. The second part of this work was related to the application of multivariate statistical methods, which correlate the physical-chemical properties with the quantitative information acquired by nuclear magnetic resonance spectroscopy. Several methods were applied, including principal component analysis, principal component regression, partial least squares and artificial neural networks. Principal component analysis was used to reduce the number of predictive variables and to transform them into new variables, the principal components. These principal components were used as inputs of the principal component regression and artificial neural networks models. For the partial least squares model, the original data was used as input. Taking into account the performance of the develop models, by analysing selected statistical performance indexes, it was possible to conclude that principal component regression lead to worse performances. When applying the partial least squares and artificial neural networks models better results were achieved. However, it was with the artificial neural networks model that better predictions were obtained for almost of the properties analysed. With reference to the results obtained, it was possible to conclude that nuclear magnetic resonance spectroscopy combined with multivariate statistical methods can be used to predict physical-chemical properties of petroleum fractions. It has been shown that this technique can be considered a potential alternative to the conventional standard methods having obtained very promising results.

Describing vocal performance of Portuguese cantadeiras of Alto Minho

Portugal has strong musical traditions, which have been perpetrated by decades through folkloristic activities. In folk groups from Alto Minho (north of Portugal), folk singing is mostly performed by cantadeiras, amateur female solo singers who learn this style orally. Their vocal characteristics are distinctive when compared with other regions of the country; however, deep understanding of these vocal practices is still missing. The present work aims at studying Alto Minho cantadeira’s vocal performance in a multidimensional perspective, envisioning social, cultural and physiological understanding of this musical style. Thus, qualitative and quantitative data analyses were carried out, to: (i) describe current performance practices, (ii) explore existent perceptions about most relevant voice features in this region, (iii) investigate physiological and acoustic properties of this style, and (iv) compare this style of singing with other non-classical singing styles of other countries. Dataset gathered involved: 78 groups whose members were telephone interviewed, 13 directors who were asked to fill in a questionnaire on performance practices, 1 cantadeira in a pilot study, 16 cantadeiras in preliminary voice recordings, 77 folk group members in listening tests, and 10 cantadeiras in multichannel recordings, including audio, ELG, air flow and intra-oral pressure signals. Data were analysed through thematic content analysis, descriptive and inferential statistics, hierarchical principal components, and multivariate linear regression models. Most representative voices have a high pitched and loud voice, with a bright timbre, predominance of chest register without excessive effort, and good text intelligibility with regional accent. High representativeness levels were obtained by few cantadeiras; these sing with high levels of subglottal pressure and vocal fold contact quotient, predominance of high spectrum energy and vocal loudness, corroborating indications of prevalence of pressed phonation. These vocal characteristics resemble belting in musical theatre and share similarities with country (USA) and ojikanje (Croatia) singing. Strategies that may contribute to the preservation of this type of singing and the vocal health of current cantadeiras are discussed, pointing at the direction of continuous education among folk groups, following practices that are already adopted elsewhere in Europe.

The alternating least-squares algorithm for CDPCA

Clustering and Disjoint Principal Component Analysis (CDP CA) is a constrained principal component analysis recently proposed for clustering of objects and partitioning of variables, simultaneously, which we have implemented in R language. In this paper, we deal in detail with the alternating least-squares algorithm for CDPCA and highlight its algebraic features for constructing both interpretable principal components and clusters of objects. Two applications are given to illustrate the capabilities of this new methodology.

Characterization of the methanol recovery process at Prio Biocombustíveis S. A.

In a industrial environment, to know the process one is working with is crucial to ensure its good functioning. In the present work, developed at Prio Biocombustíveis S.A. facilities, using process data, collected during the present work, and historical process data, the methanol recovery process was characterized, having started with the characterization of key process streams. Based on the information retrieved from the stream characterization, Aspen Plus® process simulation software was used to replicate the process and perform a sensitivity analysis with the objective of accessing the relative importance of certain key process variables (reflux/feed ratio, reflux temperature, reboiler outlet temperature, methanol, glycerol and water feed compositions). The work proceeded with the application of a set of statistical tools, starting with the Principal Components Analysis (PCA) from which the interactions between process variables and their contribution to the process variability was studied. Next, the Design of Experiments (DoE) was used to acquire experimental data and, with it, create a model for the water amount in the distillate. However, the necessary conditions to perform this method were not met and so it was abandoned. The Multiple Linear Regression method (MLR) was then used with the available data, creating several empiric models for the water at distillate, the one with the highest fit having a R2 equal to 92.93% and AARD equal to 19.44%. Despite the AARD still being relatively high, the model is still adequate to make fast estimates of the distillate’s quality. As for fouling, its presence has been noticed many times during this work. Not being possible to directly measure the fouling, the reboiler inlet steam pressure was used as an indicator of the fouling growth and its growth variation with the amount of Used Cooking Oil incorporated in the whole process. Comparing the steam cost associated to the reboiler’s operation when fouling is low (1.5 bar of steam pressure) and when fouling is high (reboiler’s steam pressure of 3 bar), an increase of about 58% occurs when the fouling increases.