한국해양대학교

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

Title
Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach
Alternative Title
Land Price Forecasting Research by Macro and Micro Factors and Real Estate Market Utilization Plan Research by Landscape Factors: Big Data Analysis Approach
Author(s)
이상향
Keyword
MicroscopicMacroscopicReal Estate MarketLandscapeBig Data
Issued Date
2021
Publisher
한국해양대학교 대학원
URI
http://repository.kmou.ac.kr/handle/2014.oak/12606
http://kmou.dcollection.net/common/orgView/200000376276
Abstract
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.
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데이터정보학과 > Thesis
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