Establishing greater reliability in property appraisal is critical because it promotes the public interest. Reliability is composed of competence and ethics. In particular, the ethics of property appraisal has individual and corporational aspects. This study aimed to evaluate how the ethical climate of a firm is affected by the scale of appraisal firms, thereby validating the effect of the ‘Large Appraisal Firm System’. Hence, the ethical climate of appraisal firms, an indicator of inherent ethical value recognition, was investigated, divided into recognition of social responsibility and exclusion of self-interest. The study results showed that the ethical management systems of appraisal firms encourage the appraisers of such firms to be conscious of their social responsibility and to exclude self-interest in their work. The degree of such influence, however, differs according to the scale of appraisal firms. Based on these results, further research on the maintenance and operation of the ethical management systems of appraisal firms is required to enhance the reliability of property appraisal.
This study examined the motive and incentive for the disposal and purchase of corporate real estate assets based on the various firm characteristics. It was empirically found that firms with a higher leverage ratio, lower cash holdings, and lower sales growth are more likely to dispose of their real estate assets. This implies that financial constraints, internal reserves, and growth opportunities are important factors affecting the corporate decisions regarding the disposal of real estate assets. Meanwhile, it was found that firms with a lower leverage ratio have a higher probability of purchasing real estate assets, suggesting that a stable financial structure enables firms to acquire more of such assets. Using the transaction amount of corporate real estate assets, consistent results were found. While varied opinions on the utilization of corporate real estate assets have been raised, this study broadened the understanding of such by performing rigorous analyses. The result of this study would have practical implications in terms of the introduction of regulations or the establishment of business strategies related to corporate real estate assets.
Previous studies, especially that by Lee (2014), showed how time series volatility models can be applied to the house price series. As the regional housing market trends, however, have shown significant differences of late, analysis with national data may have limited practical implications.
This study applied volatility models in analyzing and forecasting regional house prices. The estimation of the AR(1)-ARCH(1), AR(1)-GARCH(1,1), and AR(1)-EGARCH(1,1,1) models confirmed the ARCH and/or GARCH effects in the regional house price series. The RMSEs of out-of-sample forecasts were then compared to identify the best-fitting model for each region. The monthly rates of house price changes in the second half of 2017 were then presented as an example of how the results of this study can be applied in practice.
This study aimed to measure street centralities with the street width, and to analyze their effects on the residential and non-residential land prices in Seoul, South Korea. Most of the studies on urban economics and policy focusing on the urban spatial structure have evolved in terms of their perspective from monocentric to polycentric models. Recently, their themes shifted to measuring street centralities and capturing their effects on urban phenomena. To expand the existing studies and discussion, this study analyzed the street centralities with the street width, and how they changed the land prices. Multilevel regression models generated a few key findings relevant to the relationship between street centralities and land prices. While a higher detour volume and closeness to wider streets commanded premium residential land prices, higher visibility and detour volume to wider streets were associated with higher non-residential land prices. These findings suggest a robust connection between street configuration and near-land prices. Thus, the results of this study suggest a few insightful policy implications for urban planners, urban designers, real estate developers, and appraisers.
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 purpose of this study was to compare the taxation equity of non-residential collective real estate based on its standard market prices set by National Tax Service and those for taxation set by the Ministry of Government Administration and Home Affairs with that of the apartment houses in Seoul, South Korea.
The study findings were as follows. First, the analysis results of the standard market price rates of non-residential collective real estate pointed to a huge gap in the assessment rate (AR) of the taxation standards among the Gu offices. Second, there was a big coefficient of dispersion (COD) in the standard market prices of non-residential collective real estate, which confirmed the presence of horizontal inequity. Finally, there was regressive vertical inequity, which leads to the undervaluation of high-value assets, in the standard market prices of non-residential collective real estate.
The evaluation of the standard market prices of non-residential collective real state should thus reflect the market prices and the addition and assessment of the land and buildings to achieve taxation equity. Based on these findings, it is hoped that this study will make a significant contribution to the improvement of the official announcement system for non-residential real estate based on real transactions during the shift to such system.
Under the ownership pre-sale system in the South Korean apartment market, developers can sell apartment ownerships as soon as they start to construct an apartment complex. In the South Korean apartment market, people call this kind of ownership “Bun-yang right.” There is a time difference between ownership sale and apartment completion under the ownership pre-sale system. The pre-completion apartment ownerships can be resold to third parties until the apartment complexes are completed, which is called “Geon-mae” of the Bun-yang right. Using survival analysis, this research analyzed the elapsed time between ownership purchase and resale to a third party using 48,316 apartment units nationwide in the 192 complexes supplied from 2000 to 2016. Specifically, this study analyzed the influence of the real estate policy, contract term, location, apartment complex, and unit characteristics on the elapsed time between ownership purchase and resale to a third party.
The empirical analysis revealed that the real estate policy and contract term characteristics have a significant effect on the elapsed time between ownership purchase and resale to a third party. Also, this study confirmed that the product characteristics, such as the location and apartment complex and unit characteristics, have an influence on the elapsed time between ownership purchase and resale to a third party.