• 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.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.271-280
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• 2017

Real-estate values and related economics are often the first read newspaper category. We are concerned about the opinions of experts on the forecast for real estate prices. The Box-Jenkins ARIMA model is a commonly used statistical method to predict housing prices. In this article, we tried to predict housing prices by combining independent component analysis (ICA) in multivariate data analysis and the Box-Jenkins ARIMA model. The two independent components for both the selling price index and the long-term rental price index were extracted and used to predict the future values of both indices. In conclusion, it has been shown that the actual indices and the forecast indices using ICA are more comparable to the forecasts of the ARIMA model alone.

• Journal of Construction Engineering and Project Management
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• v.2 no.4
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• pp.32-41
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• 2012

In recent times, multi-centralization and decentralization as well as large Capital area and suburbanization in the spatial structure of capital area. With rapid growth, urbanization and industrialization are unsystematic, and growth inequality between regions caused negative effects such as discordant centralization and decentralization, fluctuating land value, and gap between living conditions. Accordingly, this study analyzed urban spatial indexes by the self-governed body in the capital area such as Seoul, Incheon, and Gyeonggi province for the analysis of the regional inequality phenomenon. We examined the characteristics of temporal and spatial changes in urban spatial structure in the capital area by utilizing the distribution pattern and density of city indexes such as population, employment, etc, and then drew the commonality of those factors through factor analysis. We evaluated the drawn results through the city standard index by each city, conducted factor score analysis, and identified the interaction between each factor and Housing Purchase Price Composite Indices index, housing rent price index(Housing Jeonse Price Composite Indices), land price fluctuation rate, diffusion ratio of house, and financial independence.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.301-306
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• 2013

The Ratio of APT jeonse to purchase price was still rising. The interaction of APT Purchase and Jeonse price indices by region analysis in order to analyze this phenomenon, and results were summarized as follows. First, because the regional APT purchase and jeonse prices appears the rise and fall differently by region, regional polarization was deepening. Second, the recently real estate market was analyzed the province's booming real estate and the downturn of the metropolitan area. So, the ratio of APT jeonse to purchase price was continued to rise. Finally, the Ratio of APT jeonse to purchase price changing rate is (+) increased if the APT purchase price changing rate is larger then the APT purchase price changing rate and smaller then is (-) decreased.

• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.45-63
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• 2019

Until recently, most people have invested in a traditional portfolio consisting of stocks, bonds and real estates based on the three-division method of properties in Korea. However, this study analyzed the impact of the composition of a portfolio combining representative real estate indirect investment products such as Reits and real estate funds on the investment performance. For this purpose, the empirical analysis using the mean variance model, which is the most appropriate method for the portfolio composition, was used. For variables used in this study, mixed asset portfolios were classified into Portfolio A through Portfolio G depending on the composition of assets, and the price indices selected as Kospi, Krx bond, Reits Trus Y7, Hanwha-Lasal fund, and Office (Seoul). The results are as follows; first Portfolio D, which combined bonds, stocks, Reits and Real Estate funds, and Portfolio G, which added the office, the actual real estate, were shown to have the lowest risk. second, Portfolio B composed of bonds, stocks and Reits and Portfolio D with added real estate funds had the lowest risk while Portfolio F composed of bonds, stocks, offices and real estate funds, and Portfolio G with added Reits were the most profitable. As a result, it has been analyzed that it was more effective to compose a portfolio including Reits and real estate funds, which were real estate indirect investment products that eliminated the illiquidity limitation of real estates than real estates, the traditional three-division method of properties. Therefore, it is possible to minimize the risk of investors and reduce the cost of ownership of the real estate by solving the illiquidity problem that is the biggest disadvantage of the direct investment, In addition, it is considered that it is more necessary to reinvigorate the real estate indirect investment market where small amounts can be invested.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.89-99
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• 2014

Correlation of KOSPI from stock market and Apartment Prices in Seoul HPPCI from real estate market has been found from this research. Furthermore, from the comparison of those indicators' flows, certain precedence was found as well. The purpose of this research is to analyze correlation and precedence among KOSPI, Apartment price in Seoul, HPPCI and CLI. As for predicting KOSPI of stock market and real estate market, it is necessary to find out preceding indices and analyzing their progresses first. For 27 years from the January 1987 to December 2013, KOSPI has been grown by 687%, while CLI showed 443%, Apartment of Seoul showed 391%, HPPCI showed 263% of growth rate in order. As the result of correlation analysis among Apartment of Seoul, CLI, KOSPI and HPPCI, KOSPI and HPPCI showed high correlation coefficient of 0.877, and Apartment of Seoul and CLI showed that of 0.956 which is even higher. Result from the analysis, CLI shows high correlation with stock and real estate market, it is a good option to watch how CLI flows to predict stock and real estate market.

• Korea Real Estate Review
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• v.28 no.4
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• pp.63-73
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• 2018

The purpose of this study is to identify the key factors affecting official land values and the appropriateness of the assessed land price, to find out what determines the real estate price and to assess the appropriateness of the valuation. This study explored whether actual transaction prices of forest land located in six Gu districts in southern and northern parts of Han river are appropriate using independent sample t-analysis and logistic regression analysis. Results showed that regional differences and shape were adequate for development restriction areas, whether biotope was designated, whether or not to be preserved, differences in pitch, and differences in use, and differences in bearing and approach. Thorough analysis of unique factors that determine forest land prices must be carried out in advance and the findings should be applied to the examination and assessment of official land values. The forest land appraisal system is closely related to the public's economic activity, thus it is necessary to apply forest land value determinants considered to be significant by market participants to the forestland appraisal system. I look forward to seeing variables related to the appropriateness of forest land transactions drawn from this study being used as indices for settlement of forest land transaction orders and market stabilization.

• Korea Real Estate Review
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• v.27 no.4
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• pp.7-20
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• 2017

This study attempts to diagnose and categorize the characteristics of old industrial parks, and eventually link the results to the regeneration of industrial complexes. For this reason, we performed a factor analysis by utilizing 15 indices of 89 industrial parks, excluding 5 large equipment industry sites. The 15 indices were classified into 5 factors. Factor 1 can be described as a category of 'urbanization possibility' for the indices of building age, plot ratio of less than $1,650m^2$, and urbanization ratio of the surrounding area. Factor 2 can be described as a category of 'productive efficiency' for the indices of land productivity, amount of exports by land, employment productivity, and repair costs of industrial areas. Factor 3 can be described as a category of 'infrastructure amenity' for the indices of road ratio, plot ratio attached to the road, and parks and recreation ratio. Factor 4 can be described as a category of 'location potentiality' for the indices of land price, infrastructure age, and distance to the highway, while factor 5 can be described as a category of 'availability of supporting facilities' for the indices of parking lot ratio and supporting facility land ratio. By using these 5 factor scores, we were able to extract industrial parks included in the lower 25% of the factor score and searched for what kind of factor problem they have for each industrial park. Based on these results, this research will provide sufficient information on the decline of industrial parks with respect to their demerits. The results of this study show significant implications and contribute to the establishment of policies for regional competitiveness, as well as job creation, in the process of industrial regeneration.

• East Asian Journal of Business Economics (EAJBE)
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• v.8 no.1
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• pp.57-69
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• 2020

Purpose - This study examines what are the asset market fluctuations in Europe and how each economic variable affects major variables, and explore the dynamics of housing and stock market through Greece. The variables under consideration are balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), M3, real rate of interest (IR_REAL) and household credits (LOAN). We investigate the functional and causal relationships between housing and stock market. Research design, data, and methodology - Vector error correction model (VECM) is used to figure out the dynamic relationships among variables. This study also contains the augmented Dickey-Fuller unit root, cointegration, Granger causality test, and impulse response function and variance decomposition analysis by EViews 11.0. Results - The statistical tests show that all variables under consideration have one unit root and there is a longterm equilibrium relationship among variables for Greece. GDP, IR_REAL, M3, STOCK and LOAN can be considered as causal factors to affect real estate market, while GDP, LOAN, M3, BCA and HOUSING can bring direct effects to stock market in Greece. Conclusions - It can be judged that the policy that affects the lending policy of financial institutions may be more effective than the indirect variable such as monetary interest rate.