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Purpose: Properties with specific orientations are preferred in South Korea, depending on the real estate market. This preference is usually considered during property transactions and in designing buildings. Despite the importance of property orientation, the magnitude of preference for favored orientation has rarely been empirically estimated in the literature. This study attempts to estimate the value of favored orientation in a quantitative manner and interpret the results. Design/methodology/approach: Using a geographically weighted regression model, this study obtains nationwide property price data and estimates the strength of orientation preference, that is, the premium for favored orientation. Among the various property types, residential sites and forests were investigated because the orientation of these two property types is known to influence their sales prices in the Korean real estate market. Findings: The results show that premiums for south-facing residential sites exist in the market, varying locally and ranging from zero to 13.2%, over residential sites with non-south orientations. The results for forests are mixed in that a south-facing forest commands a maximum of 33.1% premium in a certain region, over a forest with a non-south direction, while it also commands a maximum of 33.8% negative premium (discount) in another region, indicating significant local variations in premiums. Originality/value: These findings are expected to be utilized in fields such as property valuation, house architecture and design. [ABSTRACT FROM AUTHOR]

Abandoned houses have become a common feature of the local landscapes: the rising number of abandoned houses is a major challenge facing many counties in South Korea. Their presence negatively influences the neighborhood by undermining its aesthetic quality, depreciating the perception of safety in the neighborhood properties, and deepening the fiscal deficit of local financing. The detection of abandoned houses is the first step toward adequate housing management by local governments. This study aims to provide a cost-effective and prompt approach to identifying abandoned houses in rural areas. Multi-source data, that is, images and building registry data are utilized and a multi-input neural network is designed to adopt these heterogeneous datasets. Trained by the two source datasets, the proposed network achieves 86.2% accuracy in classifying abandoned houses, which is an acceptable performance level in administrative practice. The database of abandoned houses identified in this manner is expected to promote effective housing management by governments and ultimately contribute to mitigating vacancies in rural areas. [ABSTRACT FROM AUTHOR]

In contrast to the brilliant success of deep learning approach in dealing with unstructured data such as image and natural language, it has not shown noticeable achievements in handling structured data, that is, tabular format data. Categorical data types form a considerable portion of structured data, and a neural network, the most universal implementation algorithm for deep learning, is inefficient in processing these data types. This is a reason for the poor performance of the neural network applied to the structured data. In this study, a neural network is used to estimate land prices in the Gyunggi province, South Korea. To enhance the performance of the network when most input variables are categorical, the architecture of the neural network is specified using the entity embedding layers, a technique to reveal the continuity inherent in categorical data. This study demonstrates that information in the categorical data can be efficiently extracted by the entity embedding technique. The network architecture proposed in this study can be applied in valuation practices where categorical data are abundant. In addition, the interpretation of the resultant embedding layers can enhance the explainability of the deep learning approach, promoting its rapid adoption in the real estate industry. [ABSTRACT FROM AUTHOR]

In contrast to the outstanding performance of the machine learning approach, its adoption in industry appears to be relatively slow compared to the speed of its proliferation in a variety of business sectors. The low interpretability of a black-box-type model, such as a machine learning-based valuation model, is one reason for this. In this study, house prices in Seoul and Jeollanam Province, South Korea, were estimated using a neural network, a representative model to implement machine learning, and we attempted to interpret the resultant price estimations using an interpretability tool called a partial dependence plot. Partial dependence analysis indicated that locally optimized valuation models should be designed to enhance valuation accuracy: a land-oriented model for Seoul and a building-focused model for the Jeollanam Province. The interpretable machine learning approach is expected to catalyze the adoption of machine learning in the industry, including property valuation. [ABSTRACT FROM AUTHOR]

Purpose: It is important to forecast local trading volumes as well as global trading volumes because the real estate market is always characterized as a localized market. The house trading volume at the local level is forecast through appropriate models to enhance the predictive accuracy. Design/methodology/approach: Four representative housing submarkets in South Korea are selected, and their trading volumes are forecast. A well-established time-series model and a deep learning algorithm are employed: the autoregressive integrated moving average (ARIMA) model and the recurrent neural network (RNN), respectively. The trading volumes in adjacent areas are utilized as covariates, and an ensemble prediction is applied additionally to improve the model performance. Findings: The results indicate no significant difference in prediction performance between the ARIMA model and the RNN, which can be attributed to the insufficient amount of data used. It is discovered that the spillover effects of trading volumes across the study areas can be exploited to improve the predictive accuracy, and that the diversity of the predicted values from the candidate models can be used to increase the forecasting accuracy further. Originality/value: Whereas property prices have been investigated extensively, the discussion on forecasting trading activity of properties is limited in the literature. The results of this study are expected to promote more interest in adopting a local perspective and using a diversity of predicted values when forecasting house trading volumes. [ABSTRACT FROM AUTHOR]

Purpose: Prior studies on the application of deep-learning techniques have focused on enhancing computation algorithms. However, the amount of data is also a key element when attempting to achieve a goal using a quantitative approach, which is often underestimated in practice. The problem of sparse sales data is well known in the valuation of commercial properties. This study aims to expand the limited data available to exploit the capability inherent in deep learning techniques. Design/methodology/approach: The deep learning approach is used. Seoul, the capital of South Korea is selected as a case study area. Second, data augmentation is performed for properties with low trade volume in the market using a variational autoencoder (VAE), which is a generative deep learning technique. Third, the generated samples are added into the original dataset of commercial properties to alleviate data insufficiency. Finally, the accuracy of the price estimation is analyzed for the original and augmented datasets to assess the model performance. Findings: The results using the sales datasets of commercial properties in Seoul, South Korea as a case study show that the augmented dataset by a VAE consistently shows higher accuracy of price estimation for all 30 trials, and the capabilities inherent in deep learning techniques can be fully exploited, promoting the rapid adoption of artificial intelligence skills in the real estate industry. Originality/value: Although deep learning-based algorithms are gaining popularity, they are likely to show limited performance when data are insufficient. This study suggests an alternative approach to overcome the lack of data problem in property valuation. [ABSTRACT FROM AUTHOR]