Essays about: "Bostadspriser"
Showing result 11 - 15 of 25 essays containing the word Bostadspriser.
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11. The Housing Bubble Situation in Third-level Cities in China : ACcase Study of Yangzhou
University essay from KTH/Fastigheter och byggandeAbstract : Housing bubbles could have a great impact on the economy of a country, especially for a country as large as China. Therefore, it is necessary to evaluate the housing bubble situation of a region. READ MORE
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12. The Effect of Market Power on Housing Prices
University essay from Göteborgs universitet/Institutionen för nationalekonomi med statistikAbstract : The Effect of Market Power on Housing Prices Abstract: In this paper we wish to examine whether market power of housing suppliers is predictive of the price of housing in the municipality of Stockholm. This is, in part, done by developing a measure of market power - market concentration - and regressing measures of price on it. READ MORE
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13. Entrepreneur or Fool : A comparative study of the housing market in Stockholm and London
University essay from Södertörns högskola/NationalekonomiAbstract : During the past few years, the housing price has increased for both Sweden and the United Kingdom, and according to previous studies there can be many reasons for this development. The purpose of this study is to investigate the degree of differences and similarities between two housing markets. READ MORE
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14. Stabilisation of higher wooden houses in volume building technology
University essay from Lunds universitet/Avdelningen för Konstruktionsteknik; Lunds universitet/Institutionen för bygg- och miljöteknologiAbstract : Byggnationen av bostadshus med trästomme har enligt Statistiska centralbyrån ökat med 85% efter den senast lägsta uppmätningen 2011. Statistiska centralbyrån menar också att det är bostadshus bestående av 4-8 våningar som byggs mest i Sverige idag. READ MORE
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15. Predicting house prices with machine learning methods
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : In this study, the machine learning algorithms k-Nearest-Neighbours regression (k-NN) and Random Forest (RF) regression were used to predict house prices from a set of features in the Ames housing data set. The algorithms were selected from an assessment of previous research and the intent was to compare their relative performance at this task. READ MORE