The aim of this study is to get some approach which can help in improving the predictions of daily temperature of Umeå. Weather forecasts are available through various sources nowadays. There are various software and methods available for time series forecasting. Our aim is to investigate the daily maximum temperatures of Umeå, and compare the performance of some methods in forecasting these temperatures. Here we analyse the data of daily maximum temperatures and find the predictions for some local period using methods of autoregressive integrated moving average (ARIMA), exponential smoothing (ETS), and cubic splines.  The forecast package in R is used for this purpose and automatic forecasting methods available in the package are applied for modelling with ARIMA, ETS, and cubic splines. The thesis begins with some initial modelling on univariate time series of daily maximum temperatures. The data of daily maximum temperatures of Umeå from 2008 to 2013 are used to compare the methods using various lengths of training period. On the basis of accuracy measures we try to choose the best method. Keeping in mind the fact that there are various factors which can cause the variability in daily temperature, we try to improve the forecasts in the next part of thesis by using multivariate time series forecasting method on the time series of maximum temperatures together with some other variables. Vector auto regressive (VAR) model from the vars package in R is used to analyse the multivariate time series. Results: ARIMA is selected as the best method in comparison with ETS and cubic smoothing splines to forecast one-step-ahead daily maximum temperature of Umeå, with the training period of one year. It is observed that ARIMA also provides better forecasts of daily temperatures for the next two or three days. On the basis of this study, VAR (for multivariate time series) does not help to improve the forecasts significantly. The proposed ARIMA with one year training period is compatible with the forecasts of daily maximum temperature of Umeå obtained from Swedish Meteorological and Hydrological Institute (SMHI).

Bewertungen von Immobilien sollen den Marktwert einschätzen und sind notwendig für Kauf-, Verkaufs- und Bauentscheidungen, für die Kreditvergabe und für die Besteuerung. Trotz dieser eindeutigen Aufgabenstellung existierten unterschiedliche Verfahren, mit welchen Marktwerte ermittelt werden können. Ein Bewertungsverfahren soll einerseits mit ökonomischer Theorie vereinbar sein und andererseits Bewertungen generieren, die beobachtete Transaktionspreise gut vorhersagen. Die Dissertation analysiert die drei wichtigsten Bewertungsansätze Sachwert-, Vergleichswert- und Ertragswertverfahren, zeigt das jeweils zugrundeliegende Marktmodell und evaluiert die kodifizierten Verfahren nach der Wertermittlungsverordnung (WertV) anhand von beobachteten Transaktionen. Darüber hinaus gibt die Dissertation einen Überblick zu Immobilienpreisindizes und zu hedonischen Methoden. Für die ökonometrischen Analysen wurden umfangreiche Daten zum Berliner Immobilienmarkt verwendet. / Appraisals should assess the market value of properties and are necessary for buying, selling or building decisions, for lending and for taxation. Despite this unambiguous task different techniques exist for ascertaining market values. An valuation approach should be in accordance with economic theory and should generate appraisals, which are reliable estimates for transaction prices. This dissertation analyzes the three most important valuation approaches, i.e. cost, sales comparison, and income approach, shows the underlying market models and evaluates the valuation techniques that are codified in the German Regulation on Valuation (WertV). For the latter evaluations, appraisals are compared with observed transaction prices. In addition, the dissertation gives an overview on real estate price indices and on the hedonic approach. Extensive data on Berlin's real estate market are used for the econometric analysis.