Optimal Project Planning for Public Rental Housing in South Korea

Abstract

Although Korea has made notable progress in the availability of public rental housing, Korea’s public rental housing representing 6.3% of the country’s total housing is still below the 8% OECD average from 2016. The Seoul Metropolitan Area (composed of Seoul City, Incheon City, and Gyeonggi Province) has nearly 50% of the country’s population, but 11% of the nation’s territory, meaning the area suffers from an acute shortage of public rental housing. This is a serious problem which is hampering the sustainability of Korean society in general. We will examine the possibility of improving this public housing problem using certain algorithms to optimize decision making and resource allocation. This study reviews two pioneering studies on optimal investment portfolio for land development projects and optimal project combination for urban regeneration projects, and then optimizes a public housing investment combination to maximize the amount of public rental houses in Gyeonggi province using optimization techniques. Through the optimal investment combination, public rental houses were found to be more efficiently and sustainably planned for the community.

Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)

“…In Korea, where this study takes place, the level of housing costs in a household budget of 15% indicates that housing affordability is not a severe problem. However, the quality of housing for low-income groups is poor, and the supply of PRH stock is deficient [9,10]. Along with the overheating housing market in city centers, it is no surprise that the burden of housing costs is in a steep incline, and the need for PRH is growing.…”

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

“…The use of artificial intelligence in a variety of engineering problems is increasing. As an example, the fuzzy set theory and its application [22,23], meta-heuristic algorithms [24][25][26][27][28][29][30][31], hybrid algorithms [32][33][34][35], pattern recognition, and machine learning [36][37][38][39][40] have been adopted. The GMDH type of neural network as a subset of artificial intelligence was proposed by Ivakhnenko [41], and it has a suitable capability for the training of network statistics.…”

“…The use of artificial intelligence in a variety of engineering problems is increasing. As an example, the fuzzy set theory and its application [22,23], meta-heuristic algorithms [24][25][26][27][28][29][30][31], hybrid algorithms [32][33][34][35], pattern recognition, and machine learning [36][37][38][39][40] have Appl. Sci.…”

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