• Korea Real Estate Review
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• v.27 no.3
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• pp.71-86
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• 2017

The purpose of this study was to apply the deep running method to real estate price index predicting and to compare it with the time series analysis method to test the possibility of its application to real estate market forecasting. Various real estate price indices were predicted using the DNN (deep neural networks) and LSTM (long short term memory networks) models, both of which draw on the deep learning method, and the ARIMA (autoregressive integrated moving average) model, which is based on the time seies analysis method. The results of the study showed the following. First, the predictive power of the deep learning method is superior to that of the time series analysis method. Second, among the deep learning models, the predictability of the DNN model is slightly superior to that of the LSTM model. Third, the deep learning method and the ARIMA model are the least reliable tools for predicting the housing sales prices index among the real estate price indices. Drawing on the deep learning method, it is hoped that this study will help enhance the accuracy in predicting the real estate market dynamics.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2011.04a
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• pp.285-289
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• 2011

he purpose of this study is to assess the effects of real estate policy on apartment price index in Seoul. To meet the research goal, this research reviewed real estate policy of the government from January of 1986 to August of 2010, and then it collected monthly apartment price index in 25 local districts of Seoul from January of 2003 to August of 2010. After 25 districts were grouped into 2 areas (14 districts in Gangnam and 11 districts in Gangbuk), the data of two areas were analyzed by using the SAS program, Cluster analysis with Ward method showed 3 clusters on each area, and with 6 clusters in total, the effects of real estate policy in the period were examined by using residual analysis. The analysis indicated two major shocks (one was from May to October of 2003, and the other was from March of 2006 to January of 2007), and the results showed that the intervention of government in the market had the asymmetric effects in bullish and bearish times. It implies that the market volatility is substantially influenced by irrational sentiments. Thus, it's suggested to devise the consumer sentiment index suitable in real estate market.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.2
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• pp.205-213
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• 2010

This study was to analyze the past regime's real estate policy and the time-series data on real estate price index from 1986 to 2009 in 24 years. Also, the real estate index and macroeconomic variables, the impact on house price index variable conducted to regression analysis and to analyze whether and how much is affected. Analyzed as follows: First, Korea's real estate policy was the post-policy and the past regime's real estate policy was inconsistent with each other. Second, in the normal phase whenever real estate issues, the measures of the strengthening regulation and of the economic recovery were only to repeat periodically. Third, the timing and means of policy enforcement was an inappropriate and Real estate market was getting worse at the time whenever a real estate policies performed. Fourth, The apartments prices index of the housing types rose the highest and were the most popular for 24 years. Increase or decrease the amount of the price index for apartments, Roh Tae-woo(65.0%) - Kim Dae-jung (42.5%) - Roh Moo-hyun (32.8%) were in order. Fifth, the results of the regression analysis carried out: The impact on housing prices among independent variables were followed by Cap Construction- one per capita income - Housing consumer price index - Accompanying Composite Index - Trailing Composite Index - Home subscription Subscriber account - Leading Composite Index.

• Korean Journal of Construction Engineering and Management
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• v.11 no.5
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• pp.41-52
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• 2010

Even though REITs (Real Estate Investment Trusts) are listed on the stock market, REITs have characteristics that allow them to invest in real estate and financing for real estate development. Therefore REITs is related with stock market and construction business and real estate business. Using time-series analysis, this study analyzed REITs in relation to construction businesses, real estate businesses, and the stock market, and derived influence factor of REITs. We used the VAR (vector auto-regression) and the VECM (vector error correction model) for the time-series analysis. This study classified three steps in the analysis. First, we performed the time-series analysis between REITs and construction KOSPI(The Korea composite stock price index) and the result showed that construction KOSPI influenced REITs. Second, we analyzed the relationship between REITs and construction commencement area of the coincident construction composite index, office index and housing price index in real estate business indexes. REITs and the housing price index influence each other, although there is no causal relationship between them. Third, we analyzed the relationship between REITs and the construction permit area of the leading construction composite index. The construction permit area is influenced by REITs, although there is no causal relationship between these two indexes, REITs influenced the stock market and housing price indexes and the construction permit area of the leading composite index in construction businesses, but exerted a relatively small influence in construction starts coincident with the composite office indexes in this study.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.59-70
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• 2013

As people invest most of their asset in real estate, there is high interest in changing in housing and real estate prices in the future for a digital economy. Various variables are affecting the housing and real estate market. Among them, four variables : households, productive population, interest rate and index price are chosen and analyzed representatively. This study is aimed to build decision model of apartment prices in Seoul empirically. From the analysis result the stock index is the only variable which is significant statistically to apartments in Seoul. From this study, the households and productive population show the same direction as shown in the previous studies before but not significant statistically. Among the independent variables, the stock index is chosen as a major variable of determinant of Seoul apartment price. From the result of the research, prediction of stock market should be preceded to forecast the movement of housing and real estate market in the future.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.1
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• pp.121-128
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• 2019

This study analyses the relationship between the real estate market and economic growth in Vietnam, a country with a fledgling real estate market. Research data included economic growth rate and growth rate of the real estate market in Vietnam. The research used quarterly data for the period from 2005: Q1 to 2018: Q1. With the characteristics of Vietnam, there has been no real estate index up to now; therefore, the research used data on growth rates of the real estate market. In addition, the real estate market in Vietnam is still young, so the data series is very short, which is a limitation of this research. With qualitative and quantitative methods especially with the Vector Auto Regressive (VAR) model; the results of the study indicate new findings, unlike previous studies, including: (1) The real estate market positively impacts Vietnam's economic growth, most noticeably in the second quarter lag and the fourth quarter lag, and then its trend impacts inversely; (2) The real estate market and economic growth in Vietnam have fluctuated over time with many risks that are affected by the past shocks of these factors. From these findings, we proposed some managerial implications for managing the real estate market with economic growth in Vietnam sustainably.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.171-181
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• 2022

The real estate price index plays key roles as quantitative data in real estate market analysis. International organizations including OECD publish the real estate price indexes by country, and the Korea Real Estate Board announces metropolitan-level and municipal-level indexes. However, when the index is set on the smaller spatial unit level than metropolitan and municipal-level, problems occur: missing values. As the spatial scope is narrowed down, there are cases where there are few or no transactions depending on the unit period, which lead index calculation difficult or even impossible. This study suggests a supervised learning-based machine learning model to compensate for missing values that may occur due to no transaction in a specific range and period. The models proposed in our research verify the accuracy of predicting the existing values and missing values.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.144-155
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• 2015

This paper is to understand investment's efficiency and performance of commercial real estate assets diversified by use and district. To do so, this paper divides two different commercial real estate markets(office build market and retail real estate market) in Seoul city by district into "GBD(Gangnam Business District), YBD(Yeouido Business District), and CBD(Central Business District)" and "GBD(Gangnam Business District), SBD(Shinchon Business District), and CBD(Central Business District)" respectively, configures these districts each other to structure portfolios as its portion varies based on Markowitz's Mean-Variance principle, and looks at risk-return relationship of portfolios to find out efficiency, performance, and optimal investment chosen based upon Sharpe's Performance Index. As a result, the portfolio configured by "10 to 30% of office building asset at CBD" and "70 to 90% of retail real estate asset at CBD" is shown to be the most optimal, suggesting the highest quarterly Sharpe's performance index of 2.7118~2.7776 with quarterly rate of return of 1.826%~1.838% and quarterly standard deviation of 0.573~0.589. Furthermore, it is obvious that diversified portfolio configured by use(office-retail) shows better investment performance than that by district with same type of asset(office-office or retail-retail). Finally, results driven from this research will play an important role to stimulate real estate and construction markets through enlarging ideas as to diversified investment by use and district on real estate indirect investment products.

• Asian Journal of Business Environment
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• v.12 no.1
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• pp.25-33
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• 2022

Purpose: The purpose of this paper is to determine the effects of the Real Estate Transactions Tax (RETT) on the economic cycles of Saudi Arabia. A secondary purpose is to determine the effects of RETT on the construction and real estate sectors of Saudi Arabia. Research design, data and methodology: The data used is retrieved from the General Authority of Statistics, Saudi Central Bank and the World Bank Open Data. Econometric models of multiple linear regression with dummy variables have been conducted to achieve the objectives and to quantitatively verify the hypotheses. Results: With the VAT exemption in real estate transactions and its substitution with RETT, a positive effect on the economy and the real estate sector has been observed. However, this tax reform has not produced any significant effects in the construction sector. Conclusions: The main conclusion of the present research is that the real estate market has a major influence on economic cycles. After the tax reform, a reduction in the contribution of taxes on real estate transactions to GDP was detected. For the construction sector, after the tax reform, it is estimated that there will be an insignificant reduction in the contribution of the real estate price index, and of the taxes on real estate transactions, to GDP.