Developing Rules for Rental Subsidy: An Empirical Housing Study in Taiwan
Publication: Journal of Urban Planning and Development
Volume 150, Issue 4
Abstract
Homeownership rates have declined, underscoring the significant challenges our society faces in affording homes. This research aims to develop an effective and precise tool for swiftly evaluating and filtering out unqualified applications and establish consistent review criteria for cities and townships across Taiwan. The proposed approach involves the creation of a tool that utilizes particle swarm optimization-based fuzzy hyperrectangular composite neural networks. This paper, chosen without political bias and based on a randomly selected year, uses a data set of 36,086 entries from across Taiwan, with each application containing 10 distinct features for further analysis. The result achieves an impressive accuracy rate of 98.6% and produces 66 recommended rules for determining eligibility for rental subsidies. The contributions of this study are twofold: (1) the rapid auditing tool benefits both government agencies and applicants, streamlining the application process; and (2) the 66 rules generated by the tool offer valuable guidance to internal auditors, expediting audits and reducing personal biases. This promotes a more standardized and efficient workflow.
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Data Availability Statement
All data, models, and codes generated or used during the study appear in the published article.
Acknowledgments
The authors extend their gratitude for the partial support provided for this research by the Taiwan Ministry of Science and Technology (MOST)/National Science and Technology Council (NSTC) under grant numbers MOST-108-2221-E-008-002-MY3, MOST-109-2622-E-008-018-CC2, MOST-110-2622-E-008-018-CC2, MOST-110-2221-E-008-052-MY3, NSTC-111-2622-E-008-017, and NSTC-111-2221-E-008-027-MY3. It is important to note that any opinions, findings, conclusions, and recommendations presented in this paper solely belong to the authors and do not necessarily reflect the perspectives of the MOST/NSTC.
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Journal of Urban Planning and Development
Volume 150 • Issue 4 • December 2024
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This work is made available under the terms of the Creative Commons Attribution 4.0 International license, https://creativecommons.org/licenses/by/4.0/.
History
Received: Oct 26, 2023
Accepted: May 22, 2024
Published online: Aug 5, 2024
Published in print: Dec 1, 2024
Discussion open until: Jan 5, 2025
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