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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.
Highlights
Korea’s record for improving access to quality housing has been significant
Busan stands for Busan Metropolitan City, Daegu stands for Daegu Metropolitan City, Incheon stands for Incheon Metropolitan City, Gwangju stands for Gwangju Metropolitan City, Daejeon stands for Daejeon Metropolitan City, Ulsan stands for
GICO redistributes that investment amount of Wirye A2-11 (675,800 million Korean Won (KRW)) in order to maximize the number of selected houses
Summary
Korea’s record for improving access to quality housing has been significant. This has been partially due to the introduction of minimum living standards (e.g., the number of rooms and floor space being differentiated by the size and composition of households) and by direct government support for housing construction. The first one considered the optimal investment portfolio for land development projects [19], and the second one considered the optimal project combination for urban regeneration projects [20] Both studies used both the genetic algorithm and the branch and bound method to obtain combinatorial optimization solutions in real estate cases. Park et al [21] proposed an optimization model for another type of real estate problem involving investment scheduling for public rental housing projects. Their initial study has the limitation of not considering real-world cases.
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