Establishment of an Integrated Support System for Vacant House Maintenance

Overview

Vacant homes are rapidly increasing from 1.06 million units in 2015 to an estimated 1.53 million units in 2023, driven by population decline, aging, and regional development imbalances. Projections indicate that by 2050, vacant homes will reach approximately 3.24 million units, accounting for 13% of all housing stock. This situation necessitates moving beyond existing support for demolition and renovation to develop systematic, data-driven countermeasures.

Calls for solutions to the vacant housing problem persist from local governments, the National Assembly, and the media. The government is also actively preparing countermeasures, including operating a cross-ministerial vacant housing support team involving four ministries.

Accordingly, this project aims to systematically address the vacant housing problem by establishing an integrated support platform. This platform will manage data from individual local governments at the national level, analyze vacant housing remediation plans, predict the risk of vacant housing occurrence, and support vacant housing transactions. It seeks to contribute to balanced national development and the resolution of regional depopulation issues.

Project description

Through an integrated vacant house management platform, it enables real-time management of newly occurring vacant houses and renovation results by local governments, establishing a nationwide monitoring system for vacant house status. It supports the derivation of regeneration and utilization plans for vacant houses that reflect their current status and regional demand for renovation and utilization. It provides information on available vacant houses for transaction and buyer-centric data to predict the risk of vacant house occurrence using vacant house databases and statistical data, while also supporting public vacant house transactions.

Key technological features

• Extracting value from large datasets of structured or unstructured data and analyzing results for vacant house statistical analysis
• AI Model (Boosting): Utilizing an algorithm that combines multiple weak learners sequentially to enhance prediction or classification performance, as one of the machine learning ensemble techniques, to build a vacant house occurrence risk prediction function

Expected outcomes

Starting in 2025, this project plans to build a system to support the establishment of systematic vacant house policies by national and local governments through nationwide vacant house data analysis and the proposal of maintenance plans. This project is also expected to achieve budget savings, such as reduced survey costs and consulting fees for maintenance planning, by improving the vacant house identification rate, as well as reduced social costs through the early prediction of areas prone to vacant houses.

Launch year: 2025

Attribution

Case study courtesy of the National Information Society Agency, Republic of Korea, Apolitical's Content Partner. Sourced from Use Cases of Public AI Service, Vol.1,  https://eng.nia.or.kr/site/nia_eng/ex/bbs/View.do?cbIdx=31975&bcIdx=28997&parentSeq=29010