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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 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]

The setting of farmland prices in the market not only reflects existing agricultural activities but also expected potential for development. This study decomposes farmland prices into values representative of current agricultural production and the prospective development potential at the county level in South Korea. The income value of farmland is derived by analysing agricultural revenue and production cost, and the sale value of farmland is estimated by reviewing transaction prices filed with the administrative authority. The difference between income value and sale value is adopted as the development value in this study. The results of the estimation show that the proportion of development value in the price of farmland is remarkably high, with a median proportion of 0.78, indicating that the threat of converting land to non-agricultural use is non-trivial because it remains a financially attractive alternative. In addition, the magnitude of the portion of the development value in the price of farmland varies considerably across counties depending on the distance to nearby metropolitan cities. This implies that agricultural policy should be designed in a locally optimised manner to effectively restrain the conversion of farmland for urban use. [ABSTRACT FROM AUTHOR]