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This thesis employs a hedonic regression to measure the impact of Airbnb, the digital platform for short term rentals, on residential prices in Prague. The model is based on the unique transaction dataset of all apartment sales from the first quarter of 2014 to the third quarter of 2018 in Prague. Also, Airbnb listings dataset is used and other datasets containing Prague city data enabling involvement of the property specifications and several neighborhood characteristics influencing the sale price in the model. The main variable of interest included in the regression is Airbnb activity, proxied by the number of Airbnb listings within 300 m of the property at the time of the sale. The results show that a 1% increase in Airbnb activity leads to a 0.0423% increase in sale prices. Moreover, in the city center, the estimated impact is almost twice as high, a 1% increase in Airbnb activity leads to a 0.0816% increase in sale prices. The third hypothesis tested in this thesis shows that the impact of Airbnb has increased in 2017 and 2018. All the estimated results slightly vary, depending on the proxy for Airbnb activity. Nevertheless, estimates in all regressions are statistically significant.

This study assess Prague real estate market from price level bubble existence point of view. For this purpose construct loglinear regression models for estimating price level of purchase and renting for several segments on the real estate market. These estimated price levels afterwards compare to theoretical price levels set by user cost concept, which employs fundamental factors such as capital costs, depreciation rates, personal income and property taxes, additional asset risk and expected capital gain. Study concludes that Prague real estate market does not currently experience positive price bubble, even one of the segments assess as underpriced.

This study deals with asset price bubbles and possible approaches for their identification. Afterwards focuses on real estate pricing mechanism and defines user cost or alternatively imputed rent as sum of all cash-flows connected to owning real estate property. These cash-flows consist of capital cost, depreciation rate, personal income and property taxes, additional asset risk and expected capital gain. Then we construct loglinear regression models for estimating recent price levels of purchase and renting for several segments on the real estate market. These estimated price levels after that compare with user costs. Comparison provides us with information about potential mispricing on Prague real estate market in examined segments. Study concludes that from long run point of view, all subjected segments are slightly underpriced.

This text was written as a diploma thesis of master's degree study course "Regional and political geography." It focuses on the development between the last two censuses, of the fastest growing metropolitan areas in the United States. The economic crisis of the late 2000s has been a major turning point of the decade. The goal of the research was to create a developmental typology of the fastest growing metros. The text analyzes "classifiers" data in order to decide what developmental types have been among the metros. A major factor contributing to high population gains in the fastest growing metros was the migration induced by the housing bubble.