Assessment of potential tenants and decision support systems: from systematic literature review to future research guidelines

Abstract

With increasing competition in the rental market, landlords and real estate professionals must make informed commercial real estate (CRE) tenant selection decisions. Traditional evaluation methods, such as credit checks and rental history analysis, often lack comprehensiveness. This study examines the applicability of decision support systems (DSS) for tenant assessment through a systematic literature review (2020–2024). By integrating data analytics, machine learning, and advanced decision-making tools, DSS can offer a structured, data-driven approach to improving assessment accuracy. The findings reveal key trends, research gaps, and DSS’s evolving role in increasing decision-making objectivity. Additionally, the study explores managerial implications, including ethical concerns, data integration challenges, and the impact of emerging technologies.