Taiwan is a crowded island with a large population but limited soil resource. The government makes the most effective use of the land by making various integrative developments in order to create the maximum use of social welfare. The idea of land reorganization thus came out. Take the metropolis of Kaohsiung for example, the implementation of land readjustment not only led to economic prosperity, but also created a spillover-effect like the growth of land utilization, construction, population and industries inside and outside the reorganization areas. It also brought enormous benefits to the government, land owners and the whole citizens as well. The fluctuation of real estate market price in Taiwan is easily affected by its economic situation, people's fear of the expectation on the increase of price index, the domestic idle capital flood, and the dramatic rising of the stock market, which lead to the enormous growth of land value, so there should be a long-term balance among economic factors (GDP, interest rate, and exchange rate) and land value. As for the researches of the interrelationship between the analysis of the land value change of real estate and economic factors, most scholars chose cities in northern Taiwan as an example, fewer researches had been made for Kaohsiung City in southern Taiwan. This paper, different from others, analyzes the public tender data of lands in every reorganization area in Kaohsiung City from the past few years. This paper aims at the long-term relationship of cointegration between the public tender data over the years of lands of readjustment area in Kaohsiung City and economic factors. The sample date is a long-term relationship from the year of 1962 to 2004 on such four parameters as Land value, GDP, interest rate, and exchange rate, which are the objects of this study, adopting the unit root test and Johansen¡¦s Maximum Likelihood Estimation (MLE) as studying tools. As this study finds out, the phenomenon of cointegration really exists among these four parameters. The land value has negative correlation with interest rate, and positive correlation with the GDP, and exchange rate.

This thesis covers two subjects regarding the real estate market in Prague. In the first part, we look for factors that influence the differences between offer and realized prices of residential properties. From our dataset, we identify the area and the time spent on market as the variables with the largest impact on the price differences. Additionally, we find that price differences are spatially correlated and tend to influence each other. Finally, accessibility of the apart- ment to given POI's seems to have a small but significant effect as well. In the second part, we build a neural network to predict the transaction prices per meter squared. After thorough architecture adjustment and hyperparameter tuning, we propose a model which is able to improve the current best prediction on the dataset by more than 12 %.