Application of Small Area Estimation for Estimating Households Living in Adequate Housing at the Subdistrict Level in DKI

Abstract

Access to adequate housing is a right of all Indonesian citizens guaranteed by the 1945 Constitution and is part of the Sustainable Development Goals (SDGs), specifically Goal 11. DKI Jakarta is the province with the second-lowest percentage of households living in adequate housing in Indonesia. Estimation at the subdistrict level is needed to support the policy on affordable vertical housing development initiated by the DKI Jakarta Department of Public Housing and Settlement Areas. Direct estimation at the subdistrict level based on the Susenas sampling design would result in inaccurate estimators. To address this issue, this study applies the Small Area Estimation (SAE) method using the Empirical Best Linear Unbiased Prediction (EBLUP) model and the Hierarchical Bayes (HB) Beta model, which leverage auxiliary variables to improve precision. The findings reveal that the HB Beta model provides the best estimates in measuring the percentage of households living in adequate housing in DKI Jakarta in 2024, producing accurate estimates across all subdistricts