This study analyzes transformations in institutions of affordable housing production connected to neoliberalization, which have impacted affordability rates and income gaps in Copenhagen and Istanbul. Also, different types of affordable housing began to develop, such as modular housing of Almenbolig+ in Copenhagen and housing for very low-income people in Istanbul. Increased affordability rates are the results of the transformations of welfare state-based practices to market-based approaches. The aim of this paper is to analyze and compare how affordable housing institutions have transformed from being financed through public funds to private funding sources since the periods of financial crisis in 2001 and 2008 in a Western and non-Western country. Using the concept of neoliberal localization, this comparative case study employs variation finding tool to analyze the transformations under three causal mechanisms. The first mechanism is state withdrawal of providing finance to affordable housing production; second, collaboration with private sector for financing affordable housing while increasing authority of municipalities. The third mechanism is restructuring institutions to open a base for public-private partnerships. Primary data was provided from interviews, while secondary data was gathered from planning policies, legislations, OECD and country statistics. The outcome of this study reveals information about changes in affordable housing institutions and neoliberal effects on affordable housing production due to contributions of local governments contingent on requirements of private funding.

Homeownership rates have declined in several countries including Denmark and Turkey since 2010. A majority of the decline in homeownership has been observed among low income holders. This variation finding comparative case study compares similar patterns of neoliberal housing policies to examine wealth inequalities based on homeownership despite fundamental differences in housing markets and welfare state provision. The comparison of Denmark and Turkey reveals similar adoption of policies that support financialization as a strategy to recover from financial crises. This paper examines how states have supported financialization with policies that allowed deregulations in the housing market to create an enabling environment for construction and real estate-specific growth, and how neoliberal housing policies positioned homeownership, a wealth symbol, as the core tenet of asset-based welfare that increased wealth inequalities. The outcome of this paper shows that neoliberal housing policies have generated new forms of inequality between low and high-income earners to access housing in both countries in different ways to produce a similar outcome.

Housing expenses have the highest share in household budgets of low-income groups in Copenhagen leading to affordability problems. To provide affordable housing (AH) for low-income groups, the municipality developed a zoning plan in 2015 aimed to reserve up to 25% of land for new AH production. This qualitative case study compares the affordability of homes produced by private sector and nonprofit housing associations for low-income young groups at the risk of poverty and families in North and South Harbor areas of Copenhagen. Also, this paper explores local housing partnership agreements since 2009 and zoning plan developments in Copenhagen since 2015. Findings present the role of local governments in shaping socio-economic structure and partnership agreements that led to the development of AH by land use zoning in Copenhagen. In North Harbor, there were no AH units for families and young groups until 2020 when they were produced according to this zoning plan. The discussion presents the AH development in the higher property value harbor areas as a small but promising solution and a leading example for other Scandinavian countries.

Following Turkey’s 2018 currency crisis, the government introduced four key housing re-regulations which reduced mortgage interest rates, investment-based citizenship incentives, rent increase limitations, and monetary easing to stimulate economic recovery. This study examines how state-led financialization of housing disproportionately favours middle- and high-income groups while limiting access for low-income households. Focusing on Istanbul, the analysis combines policy review with secondary data from 2007 to 2025 and reveals that the state housing agency TOKİ increasingly prioritised middle-income housing production over housing for low-income in Istanbul. Homeownership among the lowest-income groups fell by 25% from 2007 to 2024. Although originally designed to support low-income groups, TOKİ’s housing output for very low-income groups declined after 2019. Furthermore, foreign investment surged due to relaxed citizenship laws, rose housing prices, while 750,000 units remained vacant as landlords anticipated future capital gains. This paper argues that Turkey’s post‑crisis housing strategy has deepened state‑led financialization reinforcing inequality and weakening the social function of housing for low‑income households .

Continuous intraoperative monitoring generates physiologic data at a density no human observer can fully exploit in real time, and a growing body of machine-learning work has sought to convert that data stream into actionable, minutes-ahead prediction of adverse intraoperative events. This review surveys four areas in which real-time predictive modeling has been applied to anesthetic care: prediction of intraoperative hypotension from arterial waveform and vital-sign features, closed-loop and reinforcement-learning-based control of hypnotic drug delivery, machine-learning-derived indices of nociception and anesthetic depth, and extension of intraoperative data streams into postoperative complication prediction such as acute kidney injury. Hypotension-prediction models built on waveform morphology and multicenter time-series data show strong retrospective discrimination, and nociception-level monitoring has the most direct randomized evidence of improved postoperative pain outcomes, while reinforcement-learning-based drug delivery remains largely confined to retrospective and simulated validation. Across all four domains, the central unresolved question is not whether these models can predict but whether acting on their output changes clinical outcomes, and we highlight generalizability across institutions, alert fatigue, and accountability for autonomous dosing decisions as the principal barriers to wider adoption. We conclude that precision anesthesia is a genuinely emerging paradigm rather than a settled one, with the clearest near-term priority being prospective, multicenter trials that test clinical impact rather than further retrospective model refinement.