In Korea, about 70% of all household assets are real estate. Therefore, there is significant demand for knowing the value of the real estate. Recently, there had been an increasing number of research to predict the types of buildings such as apartments with machine learning methods and real transact...
In Korea, about 70% of all household assets are real estate. Therefore, there is significant demand for knowing the value of the real estate. Recently, there had been an increasing number of research to predict the types of buildings such as apartments with machine learning methods and real transaction data. However, none of these studies considers machine learning algorithms as a method to predict land and building types with real transaction data. The demand for real estate valuation is more common for land and building real estate types, which the general public finds difficult to grasp. Therefore, this study aims to predict the real estate price (land and building types) with machine learning methods and real transaction data.
In Part I, real estate prices are predicted using random forest (RF), Light GBM, XGBoost, artificial neural network (ANN), deep neural network (DNN), and Seoul real transaction data. In Part II, the model for predicting only the value of land is analyzed. In Korea, land and buildings are classified as separate real estate; therefore, it is necessary to find out what the price of land and building is in the total actual transaction price. This study predicts the price of land by subtracting only the building price from the total actual transaction price. For the model for predicting the entire land and building, the method by XGBoost is found to be the best. In particular, it shows better results when adding coordinates and condition variables. The method by DFITS is the best for removing outliers. Moreover, the prediction accuracy of the model is improved when the analysis is divided into two regions with similar prices. As for the model for estimating the value of only land, the case of allocating building prices based on the survey results of appraisers is found to be the best.
Predicting real estate prices will help market participants make rational decisions in real estate transactions. Also, the results of this study will have implications on the improvement of the two reference books when evaluating building prices in the current practice of appraisal. One limitation of this study is that there is no consideration of environmental variables. As real estate is greatly affected by the environment of the region, it is necessary to analyze this by adding related variables later.
In Korea, about 70% of all household assets are real estate. Therefore, there is significant demand for knowing the value of the real estate. Recently, there had been an increasing number of research to predict the types of buildings such as apartments with machine learning methods and real transaction data. However, none of these studies considers machine learning algorithms as a method to predict land and building types with real transaction data. The demand for real estate valuation is more common for land and building real estate types, which the general public finds difficult to grasp. Therefore, this study aims to predict the real estate price (land and building types) with machine learning methods and real transaction data.
In Part I, real estate prices are predicted using random forest (RF), Light GBM, XGBoost, artificial neural network (ANN), deep neural network (DNN), and Seoul real transaction data. In Part II, the model for predicting only the value of land is analyzed. In Korea, land and buildings are classified as separate real estate; therefore, it is necessary to find out what the price of land and building is in the total actual transaction price. This study predicts the price of land by subtracting only the building price from the total actual transaction price. For the model for predicting the entire land and building, the method by XGBoost is found to be the best. In particular, it shows better results when adding coordinates and condition variables. The method by DFITS is the best for removing outliers. Moreover, the prediction accuracy of the model is improved when the analysis is divided into two regions with similar prices. As for the model for estimating the value of only land, the case of allocating building prices based on the survey results of appraisers is found to be the best.
Predicting real estate prices will help market participants make rational decisions in real estate transactions. Also, the results of this study will have implications on the improvement of the two reference books when evaluating building prices in the current practice of appraisal. One limitation of this study is that there is no consideration of environmental variables. As real estate is greatly affected by the environment of the region, it is necessary to analyze this by adding related variables later.
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#Machine Learning Real Estate Prices Land and Building Price Apportionment Appraisal Real Estate Submarket Realization Ratio
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