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This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

This research addresses the professional logics of street-level managers (SLMs) and bureaucrats (SLBs) working in the Brazilian National Social Security Agency (INSS) through their perceptions of distributive justice and discretion. Since SLMs have the authority to influence SLBs' actions, we investigate whether these two groups hold similar viewpoints.

We integrate the administrative data and survey responses (n = 678) with earlier thematic content analysis (n = 350) in three stages: mean-testing, regression analyses and complementary qualitative analysis, integrated through a mixed-methods matrix.

Whilst no significant differences emerge in distributive justice ideas between groups, SLMs demand wider benefit-granting discretion, praising professionalism whilst adopting managerial posture and jargon.

The study adds to the theoretical discussions concerning SLM’s influence on SLB’s decision-making, suggesting that other factors outweigh it. The finding concerning the managers’ demand for wider discretion asks for further in-depth approaches.

Findings supply valuable insights for policymakers and managers steering administrative reforms, by questioning whether some roles SLMs play are limited to symbolic levels. Further, SLBs’ heterogenous formations might be more relevant to policy divergence than managerial influence and perhaps an underutilised source of innovation.

By approaching street-level management professional logics within a Global South welfare state through a mixed-methods approach, this study offers a holistic understanding of complex dynamics, providing novel insights for public sector management.