Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach

In real estate, there are various variables for forecasting when doing future land price analysis in addition to the macro and micro perspectives used in the current research. Examples of such variables are economic growth rate, unemployment rate, regional development and important locations, and transportation. Therefore, in this study, data on real estate and national price fluctuation rates were used to predict how future land prices will fluctuate, and macro and micro perspective variables were actively utilized to conduct land analysis based on Big Data analysis. I sought to understand what kinds of variables directly affect the fluctuation of the land and use it for future land price analysis. In addition to the two above mentioned variables, one factor of landscape is also considered to be related closely to the real estate market. News articles were used to confirm real estate land prices, and landscape earth relevance analysis was conducted through web crawl by text mining. I then predicted the future of the landscape area. In other words, I predicted how land prices will fluctuate in the future by actively utilizing macroscopic and microscopic variables in real estate land price forecasting. I would like to use this method to improve accuracy in the real estate market, which is difficult to predict, and I hope it will be useful in the future real estate market.1. Introduction 1 2. Related Research 4 2.1 The Concept of Real Estate Big Data 4 2.2 Real Estate Market Regional Analysis 4 2.3 Real Estate Market Analysis Technique 5 2.4 Real Estate Big Data Domestic Case 6 2.4.1 Housing Supply Statistics Information System (HIS) 7 2.4.2 Construction Administration System (Seum-teo) 7 2.4.3 Real Estate Transaction Management System 7 2.4.4 Korea Land and Housing Corporation (SEE:REAL) 8 2.4.5 Republic of Korea Real Estate Statistics System (R-ONE) 9 2.4.6 Real Estate Aptjin 10 2.4.7 Ziptoss 11 2.5 Possibility of Using Real Estate Big Data 12 3. Real Estate Market Future Land Price Prediction 14 3.1 Macroscopic View of the Real Estate Market 15 3.1.1 Growth Rate of Young People Participating in the Company 16 3.1.2 Economic Growth Rate / Unemployment Rate 17 3.1.3 Interest Rate 18 3.1.4 Comprehensive Real Estate Tax Charge 19 3.1.5 Government Policy 20 3.1.6 Land Construction Regulation 21 3.1.7 Foreign Currency Reserves 22 3.2 Microscopic View of the Real Estate Market 23 3.2.1 External Capital Inflow 24 3.2.2 Local Development or Public Facilities, Important Facilities 24 3.2.3 Commercial Growth 25 3.2.4 Traffic 27 3.2.5 Education 28 3.2.6 Violent Crime Rate 29 4. Analysis of Factors from Macroscopic and Microscopic Perspectives for Predicting Future Land Prices in the Real Estate Market 30 4.1 R Program Data Analysis 31 4.2 Python Program Data Analysis 33 4.3 Analysis of the Relationship between Foreign Exchange Reserves and Land Prices 36 5. Impact on Landscape Districts of Real Estate 39 5.1 Crawl Analysis between Real Estate Market and Landscape 43 6. Conclusion 46Maste