Modelling and Forecasting Property Rents and Returns (summary section only)

Recently, focus of real estate investment has expanded from the building-specific level to the aggregate portfolio level. The portfolio perspective requires investment analysis for real estate which is comparable with that of other asset classes, such as stocks and bonds. Thus, despite its distinctive features, such as heterogeneity, high unit value, illiquidity and the use of valuations to measure performance, real estate should not be considered in isolation. This means that techniques which are widely used for other assets classes can also be applied to real estate. An important part of investment strategies which support decisions on multi-asset portfolios is identifying the fundamentals of movements in property rents and returns, and predicting them on the basis of these fundamentals. The main objective of this thesis is to find the key drivers and the best methods for modelling and forecasting property rents and returns in markets which have experienced structural changes. The Finnish property market, which is a small European market with structural changes and limited property data, is used as a case study. The findings in the thesis show that is it possible to use modern econometric tools for modelling and forecasting property markets. The thesis consists of an introduction part and four essays. Essays 1 and 3 model Helsinki office rents and returns, and assess the suitability of alternative techniques for forecasting these series. Simple time series techniques are able to account for structural changes in the way markets operate, and thus provide the best forecasting tool. Theory-based econometric models, in particular error correction models, which are constrained by long-run information, are better for explaining past movements in rents and returns than for predicting their future movements. Essay 2 proceeds by examining the key drivers of rent movements for several property types in a number of Finnish property markets. The essay shows that commercial rents in local markets can be modelled using national macroeconomic variables and a panel approach. Finally, Essay 4 investigates whether forecasting models can be improved by accounting for asymmetric responses of office returns to the business cycle. The essay finds that the forecast performance of time series models can be improved by introducing asymmetries, and the improvement is sufficient to justify the extra computational time and effort associated with the application of these techniques.

Topics in Spatial Econometrics - With Applications to House Prices

Topics in Spatial Econometrics — With Applications to House Prices Spatial effects in data occur when geographical closeness of observations influences the relation between the observations. When two points on a map are close to each other, the observed values on a variable at those points tend to be similar. The further away the two points are from each other, the less similar the observed values tend to be. Recent technical developments, geographical information systems (GIS) and global positioning systems (GPS) have brought about a renewed interest in spatial matters. For instance, it is possible to observe the exact location of an observation and combine it with other characteristics. Spatial econometrics integrates spatial aspects into econometric models and analysis. The thesis concentrates mainly on methodological issues, but the findings are illustrated by empirical studies on house price data. The thesis consists of an introductory chapter and four essays. The introductory chapter presents an overview of topics and problems in spatial econometrics. It discusses spatial effects, spatial weights matrices, especially k-nearest neighbours weights matrices, and various spatial econometric models, as well as estimation methods and inference. Further, the problem of omitted variables, a few computational and empirical aspects, the bootstrap procedure and the spatial J-test are presented. In addition, a discussion on hedonic house price models is included. In the first essay a comparison is made between spatial econometrics and time series analysis. By restricting the attention to unilateral spatial autoregressive processes, it is shown that a unilateral spatial autoregression, which enjoys similar properties as an autoregression with time series, can be defined. By an empirical study on house price data the second essay shows that it is possible to form coordinate-based, spatially autoregressive variables, which are at least to some extent able to replace the spatial structure in a spatial econometric model. In the third essay a strategy for specifying a k-nearest neighbours weights matrix by applying the spatial J-test is suggested, studied and demonstrated. In the final fourth essay the properties of the asymptotic spatial J-test are further examined. A simulation study shows that the spatial J-test can be used for distinguishing between general spatial models with different k-nearest neighbours weights matrices. A bootstrap spatial J-test is suggested to correct the size of the asymptotic test in small samples.

Real Estate Investment and Uncertainty: Econometric Modelling using Finnish Data

The purpose of this paper is to test for the effect of uncertainty in a model of real estate investment in Finland during the hihhly cyclical period of 1975 to 1998. We use two alternative measures of uncertainty. The first measure is the volatility of stock market returns and the second measure is the heterogeneity in the answers of the quarterly business survey of the Confederation of Finnish Industry and Employers. The econometric analysis is based on the autoregressive distributed lag (ADL) model and the paper applies a 'general-to-specific' modelling approach. We find that the measure of heterogeneity is significant in the model, but the volatility of stock market returns is not. The empirical results give some evidence of an uncertainty-induced threshold slowing down real estate investment in Finland.

Myotonic dystrophy type 2 (DM2) : Diagnostic methods and molecular pathology

Myotonic dystrophies type 1 (DM1) and type 2 (DM2) are the most common forms of muscular dystrophy affecting adults. They are autosomal dominant diseases caused by microsatellite tri- or tetranucleotide repeat expansion mutations in transcribed but not translated gene regions. The mutant RNA accumulates in nuclei disturbing the expression of several genes. The more recently identified DM2 disease is less well known, yet more than 300 patients have been confirmed in Finland thus far, and the true number is believed to be much higher. DM1 and DM2 share some features in general clinical presentation and molecular pathology, yet they show distinctive differences, including disease severity and differential muscle and fiber type involvement. However, the molecular differences underlying DM1 and DM2 muscle pathology are not well understood. Although the primary tissue affected is muscle, both DMs show a multisystemic phenotype due to wide expression of the mutation-carrying genes. DM2 is particularly intriguing, as it shows an incredibly wide spectrum of clinical manifestations. For this reason, it constitutes a real diagnostic challenge. The core symptoms in DM2 include proximal muscle weakness, muscle pain, myotonia, cataracts, cardiac conduction defects and endocrinological disturbations; however, none of these is mandatory for the disease. Myalgic pains may be the most disabling symptom for decades, sometimes leading to incapacity for work. In addition, DM2 may cause major socio-economical consequences for the patient, if not diagnosed, due to misunderstanding and false stigmatization. In this thesis work, we have (I) improved DM2 differential diagnostics based on muscle biopsy, and (II) described abnormalities in mRNA and protein expression in DM1 and DM2 patient skeletal muscles, showing partial differences between the two diseases, which may contribute to muscle pathology in these diseases. This is the first description of histopathological differences between DM1 and DM2, which can be used in differential diagnostics. Two novel high-resolution applications of in situ -hybridization have been described, which can be used for direct visualization of the DM2 mutation in muscle biopsy sections, or mutation size determination on extended DNA-fibers. By measuring protein and mRNA expression in the samples, differential changes in expression patterns affecting contractile proteins, other structural proteins and calcium handling proteins in DM2 compared to DM1 were found. The dysregulation at mRNA level was caused by altered transciption and abnormal splicing. The findings reported here indicate that the extent of aberrant splicing is higher in DM2 compared to DM1. In addition, the described abnormalities to some extent correlate to the differences in fiber type involvement in the two disorders.

Contraception in Namibia : usage, methods and the impact of education

Användningen av preventivmedel har blivit en allt viktigare fraga i utvecklingsländerna idag, speciellt i Namibia dar fruktsamheten och HIV-prevalensen är höga. Kondomen är det enda allmänt tillgängliga preventivmedlet som skyddar mot könssjukdomar, medan ocksä injektioner, p-piller och andrà metoder kan användas för att förhindra graviditet. Användningen av preventivmedel har upptäckts korrelera med vissa sociodemografiska faktorer, bland annat utbildningsnivå och förmögenhet. Malet med denna undersökning var att studera användningen av preventivmedel, avsikter att använda preventivmedel samt kunskap om HIV/AIDS och andra könssjukdomar bland kvinnor i Namibia. Detta gjordes frän ett historiskt perspektiv genom att studerà användningsmönster frän 1990 till slutet av 2000-talet. Dessutom undersöktes sociodemografiska faktorers, speciellt utbildningens, inverkan på användningen av preventivmedel, likasä sambandet mellan skolningsnivå och preventivmedelsanvändning pä regionnivå. Undersökningen gjordes utgäende frän statistiska Namibia Demographic and Health Survey -material samlade 1992, 2000 och 2006-2007. Prevalenser och användningen av specifika metoder studerades skilt för olika bakgrundsvariabler 1992, 2000 och 2006-2007, och enligt utbildningsnivå och region är 2006-2007. Utbildning mattes skilt pä individ- och aggregatnivå. Sambandet mellan preventivmedelsanvändning och utbildning undersöktes med hjälp av logistisk regression, i vilken sociodemografiska bakgrundsfaktorer kontrollerades i sex modeller. Resultaten visade att användningen av preventivmedel har fördubblats sedan början av 1990-talet. Skillnader mellan kvinnor med olika utbildningsnivåer existerade redan i början av 1990-talet, likaså mellan olika yrkesgrupper. Undersökningen visade att högre utbildning ökar på reventivmedelsanvändningen, också då sociodemografisk bakgrundfaktorer, även utbildning och användning av preventivmedel på aggregatnivå, kontrollerades. Undersökningen antyder att utbildning på aggregatnivå inte ensam påverkar användningen av preventivmedel hos en individ. De sistnämnda resultaten var dock inte statistiskt signifikanta och kan inte generaliseras över namibiska kvinnor i allmänhet.

Methods in general model localization

The aim of this study was to evaluate and test methods which could improve local estimates of a general model fitted to a large area. In the first three studies, the intention was to divide the study area into sub-areas that were as homogeneous as possible according to the residuals of the general model, and in the fourth study, the localization was based on the local neighbourhood. According to spatial autocorrelation (SA), points closer together in space are more likely to be similar than those that are farther apart. Local indicators of SA (LISAs) test the similarity of data clusters. A LISA was calculated for every observation in the dataset, and together with the spatial position and residual of the global model, the data were segmented using two different methods: classification and regression trees (CART) and the multiresolution segmentation algorithm (MS) of the eCognition software. The general model was then re-fitted (localized) to the formed sub-areas. In kriging, the SA is modelled with a variogram, and the spatial correlation is a function of the distance (and direction) between the observation and the point of calculation. A general trend is corrected with the residual information of the neighbourhood, whose size is controlled by the number of the nearest neighbours. Nearness is measured as Euclidian distance. With all methods, the root mean square errors (RMSEs) were lower, but with the methods that segmented the study area, the deviance in single localized RMSEs was wide. Therefore, an element capable of controlling the division or localization should be included in the segmentation-localization process. Kriging, on the other hand, provided stable estimates when the number of neighbours was sufficient (over 30), thus offering the best potential for further studies. Even CART could be combined with kriging or non-parametric methods, such as most similar neighbours (MSN).

Occupational chronic solvent encephalopathy in Finland 1995 - 2007 : incidence and diagnostic methods

The occurrence of occupational chronic solvent encephalopathy (CSE) seems to decrease, but still every year reveals new cases. To prevent CSE and early retirement of solvent-exposed workers, actions should focus on early CSE detection and diagnosis. Identifying the work tasks and solvent exposure associated with high risk for CSE is crucial. Clinical and exposure data of all the 128 cases diagnosed with CSE as an occupational disease in Finland during 1995-2007 was collected from the patient records at the Finnish Institute of Occupational Health (FIOH) in Helsinki. The data on the number of exposed workers in Finland were gathered from the Finnish Job-exposure Matrix (FINJEM) and the number of employed from the national workforce survey. We analyzed the work tasks and solvent exposure of CSE patients and the findings in brain magnetic resonance imaging (MRI), quantitative electroencephalography (QEEG), and event-related potentials (ERP). The annual number of new cases diminished from 18 to 3, and the incidence of CSE decreased from 8.6 to 1.2 / million employed per year. The highest incidence of CSE was in workers with their main exposure to aromatic hydrocarbons; during 1995-2006 the incidence decreased from 1.2 to 0.3 / 1 000 exposed workers per year. The work tasks with the highest incidence of CSE were floor layers and lacquerers, wooden surface finishers, and industrial, metal, or car painters. Among 71 CSE patients, brain MRI revealed atrophy or white matter hyperintensities or both in 38% of the cases. Atrophy which was associated with duration of exposure was most frequently located in the cerebellum and in the frontal or parietal brain areas. QEEG in a group of 47 patients revealed increased power of the theta band in the frontal brain area. In a group of 86 patients, the P300 amplitude of auditory ERP was decreased, but at individual level, all the amplitude values were classified as normal. In 11 CSE patients and 13 age-matched controls, ERP elicited by a multimodal paradigm including an auditory, a visual detection, and a recognition memory task under single and dual-task conditions corroborated the decrease of auditory P300 amplitude in CSE patients in single-task condition. In dual-task conditions, the auditory P300 component was, more often in patients than in controls, unrecognizable. Due to the paucity and non-specificity of the findings, brain MRI serves mainly for differential diagnostics in CSE. QEEG and auditory P300 are insensitive at individual level and not useful in the clinical diagnostics of CSE. A multimodal ERP paradigm may, however, provide a more sensitive method to diagnose slight cognitive disturbances such as CSE.

Methods and tools for mass spectrometric lipidome analysis

The analysis of lipid compositions from biological samples has become increasingly important. Lipids have a role in cardiovascular disease, metabolic syndrome and diabetes. They also participate in cellular processes such as signalling, inflammatory response, aging and apoptosis. Also, the mechanisms of regulation of cell membrane lipid compositions are poorly understood, partially because a lack of good analytical methods. Mass spectrometry has opened up new possibilities for lipid analysis due to its high resolving power, sensitivity and the possibility to do structural identification by fragment analysis. The introduction of Electrospray ionization (ESI) and the advances in instrumentation revolutionized the analysis of lipid compositions. ESI is a soft ionization method, i.e. it avoids unwanted fragmentation the lipids. Mass spectrometric analysis of lipid compositions is complicated by incomplete separation of the signals, the differences in the instrument response of different lipids and the large amount of data generated by the measurements. These factors necessitate the use of computer software for the analysis of the data. The topic of the thesis is the development of methods for mass spectrometric analysis of lipids. The work includes both computational and experimental aspects of lipid analysis. The first article explores the practical aspects of quantitative mass spectrometric analysis of complex lipid samples and describes how the properties of phospholipids and their concentration affect the response of the mass spectrometer. The second article describes a new algorithm for computing the theoretical mass spectrometric peak distribution, given the elemental isotope composition and the molecular formula of a compound. The third article introduces programs aimed specifically for the analysis of complex lipid samples and discusses different computational methods for separating the overlapping mass spectrometric peaks of closely related lipids. The fourth article applies the methods developed by simultaneously measuring the progress curve of enzymatic hydrolysis for a large number of phospholipids, which are used to determine the substrate specificity of various A-type phospholipases. The data provides evidence that the substrate efflux from bilayer is the key determining factor for the rate of hydrolysis.