In real estate, there are various variables for the forecasting of future land prices, in addition to the macro and micro perspectives used in the current research. Examples of such variables are the economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this paper, data on real estate and national price fluctuation rates were used to predict the ways in which future land prices will fluctuate, and macro and micro perspective variables were actively utilized in order to conduct land analysis based on Big Data analysis. We sought to understand what kinds of variables directly affect the fluctuation of the land, and to use this for future land price analysis. In addition to the two variables mentioned above, the factor of the landscape was also confirmed to be closely related to the real estate market. Therefore, in order to check the correlation between the landscape and the real estate market, we will examine the factors which change the land price in the landscape district, and then discuss how the landscape and real estate can interact. As a result, re-explaining the previous contents, the future land price is predicted by actively utilizing macro and micro variables in real estate land price prediction. Through this method, we want to increase the accuracy of the real estate market, which is difficult to predict, and we hope that it will be useful in the real estate market in the future.

中文翻译：

---

宏观和微观因素的地价预测研究和景观因素的房地产市场利用计划研究：大数据分析方法

在房地产中，除了当前研究中使用的宏观和微观观点之外，还有各种变量可用于预测未来的土地价格。这样的变量的例子是经济增长率，失业率，区域发展和重要地点以及交通。因此，本文使用房地产和全国价格波动率的数据来预测未来土地价格的波动方式，并积极利用宏观和微观视角变量来进行基于大数据分析的土地分析。我们试图了解哪些变量会直接影响土地的波动，并将其用于未来的土地价格分析。除了上述两个变量之外，景观因素也被确认与房地产市场密切相关。因此，为了检查景观与房地产市场之间的相关性，我们将研究影响景观区土地价格变化的因素，然后讨论景观与房地产如何相互作用。结果，重新解释了先前的内容，通过在房地产土地价格预测中积极利用宏观和微观变量来预测未来的土地价格。通过这种方法，我们希望提高房地产市场的准确性，这是很难预测的，并且希望它在将来对房地产市场有用。我们将研究改变景观区土地价格的因素，然后讨论景观和房地产如何相互作用。结果，重新解释了先前的内容，通过在房地产土地价格预测中积极利用宏观和微观变量来预测未来的土地价格。通过这种方法，我们希望提高房地产市场的准确性，这是很难预测的，并且希望它在将来对房地产市场有用。我们将研究改变景观区土地价格的因素，然后讨论景观和房地产如何相互作用。结果，重新解释了先前的内容，通过在房地产土地价格预测中积极利用宏观和微观变量来预测未来的土地价格。通过这种方法，我们希望提高房地产市场的准确性，这是很难预测的，并且希望它在将来对房地产市场有用。