Global ETD Search

1	Statistiques spatiales et étude immobilière / Spatial Statistics and Real Estate Study Srikhum, Piyawan 12 November 2012 (has links) La présence de dépendance spatiale des prix immobiliers impose aux méthodes d’estimation de prendre en compte cet élément. Les deux approches de la statistique spatiale sont l’économétrie spatiale et la géostatistique. La géostatistique estime directement la matrice de variance-covariance en supposant que la covariance entre les observations dépend inversement de la distance séparant leur localisation. L’économétrie spatiale définit et intègre la matrice d’interaction spatiale dans un modèle de régression hédonique. Si ces deux méthodes sont possibles pour étudier la dépendance spatiale des prix immobiliers dans des contextes variés, il n’existe cependant pas de règles très claires quant au choix de la méthode à sélectionner. Cette thèse procède à un examen détaillé de ces deux approches afin de pouvoir en distinguer les ressemblances et les différences, les avantages et les inconvénients. Des exemples d’application de chaque approche dans une étude immobilière sont présentés. La géostatistique est utilisée pour analyser la stationnarité du variogramme, ainsi que la sensibilité du variogramme aux paramètres de l’estimation hédonique. Le modèle d’économétrie spatiale est utilisé pour tenter d’identifier économétriquement le quartier dominant du marché immobilier d’une ville / Geostatistics and spatial econometrics are two spatial statistical approaches used to deal with spatial dependence. Geostatistics estimates directly the variance-covariance matrix by assuming that the covariance among observations depends inversely on the distance between their locations, called the covariogram. Spatial econometrics defines and integrates the spatial interaction matrix in a hedonic regression model. In real estate, price estimation should take into account these spatial characteristics because property prices are correlated. Hence, these two approaches are commonly used to study the spatial dependence of the real estate prices in many contexts. However, a definite rule in selection these statistic approaches has not been established. This thesis examined these two approaches in order to distinguish the similarities, differences, advantages, and disadvantages of each methodology. Some examples of their applications in a real estate study. The geostatistics is used to analyze the stationarity of the variogram and its sensitivity depending on the parameters added in hedonic estimation. The spatial econometric is used to define econometrically the real estate market dominant area Econométrie spatiale Non stationnarité spatiale Quartier dominant Evaluation de biens immobiliers Spatial statistics Spatial econometrics Real estate price estimation Non-stationary variogram
2	The Price of Uranium : an Econometric Analysis and Scenario Simulations Kroén, Johannes January 2019 (has links) The purpose of this thesis is to analyze: (a) the determinants of the global price of uranium; and (b) how this price could be affected by different nuclear power generation scenarios for 2030. To do this a multivariable regression analysis will be used. Within the model, the price of uranium is the dependent variable and the independent variables are generated nuclear power electricity representing demand (GWh), price of coal as a substitute to generated nuclear power electricity, and the price of oil representing uranium production costs. The empirical results show that generated nuclear electricity and the oil price, to be statistically significant at the 5 percent level. The coal price was not however a statistically significant. The scenarios for 2030 are three possible nuclear power generation demand cases; high, medium and low demand. The results for the high demand generated a price of 255 US$/kg and the medium demand 72US$/kg. Price of uranium uranium nuclear power scenarios uranium price in 2030 econometric model price estimation uranium nuclear power and uranium scenario simulations econometric analysis Economics Nationalekonomi
3	Real-Time Automatic Price Prediction for eBay Online Trading Raykhel, Ilya Igorevitch 30 November 2008 (has links) (PDF) While Machine Learning is one of the most popular research areas in Computer Science, there are still only a few deployed applications intended for use by the general public. We have developed an exemplary application that can be directly applied to eBay trading. Our system predicts how much an item would sell for on eBay based on that item's attributes. We ran our experiments on the eBay laptop category, with prior trades used as training data. The system implements a feature-weighted k-Nearest Neighbor algorithm, using genetic algorithms to determine feature weights. Our results demonstrate an average prediction error of 16%; we have also shown that this application greatly reduces the time a reseller would need to spend on trading activities, since the bulk of market research is now done automatically with the help of the learned model. machine learning artificial intelligence ebay auction trading price prediction price estimation laptop notebook genetic algorithm nearest neighbor k-NN Computer Sciences
4	Mental Arithmetic in Consumer Judgments : Mental Representations, Computational Strategies and Biases. / Arithmétique Mentale dans les Jugements des Consommateurs : Représentations Mentales, Stratégies de Calcul et les Biais. Sokolova, Tatiana 23 June 2015 (has links) Dans ma thèse, j’étudie les représentations mentales et les processus cognitifs qui sous-tendent le calcul mental sur le marché. Cette thèse contribue à la recherche de prix psychologique en décrivant de nouveaux facteurs qui influencent les jugements de prix des consommateurs. En particulier, je découvre facteurs qui rendent les consommateurs plus ou moins susceptibles d’arrondir les prix vers le bas (Essai 1) et les facteurs qui déterminent leur tendance à se fixer sur les différences de pourcentage (Essai 3). En outre, cette recherche fournit de nouvelles perspectives à la littérature de budgétisation mentale en identifiant des stratégies de calcul mental qui conduisent à des estimations panier de prix plus précis (Essay 2). Dans l'ensemble, ma recherche va contribuer à notre compréhension des jugements de prix des consommateurs et proposer des contextes et des stratégies conduisant à des évaluations de prix plus précis. / In my dissertation I look at mental representations and cognitive processes that underlie mental arithmetic in the marketplace. This research contributes to behavioral pricing literature by outlining novel factors that influence consumers’ price difference judgments. Particularly, I uncover factors that make consumers more or less likely to fall prey to the left-digit anchoring bias (Essay 1) and factors that determine their tendency to rely on relative thinking in price difference evaluations (Essay 3). Further, this research provides new insights to the mental budgeting literature by identifying mental computation strategies that lead to more accurate basket price estimates (Essay 2). Overall, I expect my research to contribute to our understanding of consumers’ price judgments and suggest contexts and strategies leading to more accurate price evaluations. Prix Psychologique Cognition Numérique Calcul Mental Jugements de Prix Stratégies de Calcul Budget Behavioral pricing Numerical cognition Mental arithmetic Price magnitude Left-Digit bias Basket Price estimation Computational strategies Budget Shoppers Consumer Welfare Relative Thinking
5	Prisestimering på bostadsrätter : Implementering av OCR-metoder och Random Forest regression för datadriven värdering / Price estimation in the housing cooperative market : Implementation of OCR methods and Random Forest regression for data-driven valuation Lövgren, Sofia, Löthman, Marcus January 2023 (has links) This thesis explores the implementation of Optical Character Recognition (OCR) – based text extraction and random forest regression analysis for housing market valuation, specifically focusing on the impact of value factors, derived from OCR-extracted economic values from housing cooperatives’ annual reports. The objective is to perform price estimations using the Random Forest model to identify the key value factors that influence the estimation process and examine how the economic values from annual reports affect the sales price. The thesis aims to highlight the often-overlooked aspect that when purchasing an apartment, one also assumes the liabilities of the housing cooperative. The motivation for utilizing OCR techniques stems from the difficulties associated with manual data collection, as there is a lack of readily accessible structured data on the subject, emphasizing the importance of automation for effective data extraction. The findings indicate that OCR can effectively extract data from annual reports, but with limitations due to variation in report structures. The regression analysis reveals the Random Forest model’s effectiveness in estimating prices, with location and construction year emerging as the most influential factors. Furthermore, incorporating the economic values from the annual reports enhances the accuracy of price estimation compared to the model that excluded such factors. However, definitive conclusions regarding the precise impact of these economic factors could not be drawn due to limited geographical spread of data points and potential hidden value factors. The study concludes that the machine learning model can be used to make a credible price estimate on cooperative apartments and that OCR methods prove valuable in automating data extraction from annual reports, although standardising report format would enhance their efficiency. The thesis highlights the significance of considering the housing cooperatives’ economic values when making property purchases. OCR Optical Character recognition Random Forest regression price estimation housing cooperatives machine learning OCR Optisk teckenigenkänning Random Forest regression Prisestimering Bostadsrätter Maskininlärning Other Computer and Information Science Annan data- och informationsvetenskap
6	Price Prediction of Vinyl Records Using Machine Learning Algorithms Johansson, David January 2020 (has links) Machine learning algorithms have been used for price prediction within several application areas. Examples include real estate, the stock market, tourist accommodation, electricity, art, cryptocurrencies, and fine wine. Common approaches in studies are to evaluate the accuracy of predictions and compare different algorithms, such as Linear Regression or Neural Networks. There is a thriving global second-hand market for vinyl records, but the research of price prediction within the area is very limited. The purpose of this project was to expand on existing knowledge within price prediction in general to evaluate some aspects of price prediction of vinyl records. That included investigating the possible level of accuracy and comparing the efficiency of algorithms. A dataset of 37000 samples of vinyl records was created with data from the Discogs website, and multiple machine learning algorithms were utilized in a controlled experiment. Among the conclusions drawn from the results was that the Random Forest algorithm generally generated the strongest results, that results can vary substantially between different artists or genres, and that a large part of the predictions had a good accuracy level, but that a relatively small amount of large errors had a considerable effect on the general results. price prediction price estimation vinyl records vinyl prices regression machine learning machine learning algorithms algorithm comparison dataset vinyl dataset k-nearest neighbors linear regression neural network random forest discogs ai artificial intelligence prisuppskattning vinyl vinylskivor maskininlärning artificiell intelligens Computer Sciences Datavetenskap (datalogi)
7	ML implementation for analyzing and estimating product prices / ML implementation för analys och estimation av produktpriser Kenea, Abel Getachew, Fagerslett, Gabriel January 2024 (has links) Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. This project aims to use machine learning to assist in the pricing of products by estimating the prices to be inserted. Multiple machine learning models have been tested, and an artificial neural network has been implemented for estimating prices effectively. Through additional experimentation, the design of the network was fine-tuned to make it compatible with the project’s needs. The libraries used for implementing and managing the machine learning models are mainly ScikitLearn and TensorFlow. As a result, the trained model has been saved into a file and integrated with an API for accessibility. Machine Learning ML Regression Deep Learning Artificial Neural Network ANN TensorFlow ScikitLearn CUDA cuDNN Estimation Prediction AI Artificial Intelligence Price Tracking Price Logging Price Estimation Supervised Learning Random Forest Decision Trees Batch Learning Hyperparameter Tuning Linear Regression Multiple Linear Regression Maskininlärning Djup lärning Artificiellt Neuralt Nätverk Regression TensorFlow SciktLearn ML ANN Estimation Uppskattning CUDA cuDNN AI Artificiell Intelligens pris loggning pris estimation prisspårning Batchinlärning Hyperparameterjustering Linjär Regression Multipel Linjär Regression Supervised Learning Random Forest Decision Trees Computer Engineering Datorteknik

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