Floor Area Research Articles

Multi-scale correlation analysis between geometric parameters and solar radiation in high density urban environment——Case study in Nanjing

This study explores the impact of multi-scale urban morphology on solar radiation in high-density environments, focusing on Nanjing as a case study. Using Grasshopper, the research decomposed urban models into four scales—district, block group, block, and plot—and analyzed eight morphological indicators, including site coverage (SC), floor area ratio (FAR), and block surface ratio (BSR). Solar radiation was simulated for the entire year, the summer solstice, and the winter solstice. Key findings reveal that different scales exhibit varying influences on solar radiation. At the district scale, BSR shows the highest correlation with annual solar radiation (R2 = 0.96), while at the neighborhood cluster scale, the minimum distance between buildings (DBmin) is most correlated (R2 = 0.7). At the block scale, BSR significantly correlates with summer solar radiation (R2 = 0.55). At the plot scale, no single indicator strongly correlates with solar radiation, but a combination of indicators is more relevant. These findings enable rapid solar performance assessments in urban design, promoting efficient solar energy use.

Microscale Temperature-Humidity Index (THI) Distribution Estimated at the City Scale: A Case Study in Maebashi City, Gunma Prefecture, Japan

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.

Myostatin Changes in Females with UI after Magnetic Stimulation: A Quasi-Experimental Study.

Background and Objectives: Urinary incontinence (UI) is the involuntary loss of urine caused by a weakness in the pelvic floor muscles (PFMs) that affects urethral closure. Myostatin, which prevents the growth of muscles, is a protein expressed by human skeletal muscle cells. Indeed, it has been observed that myostatin concentration rises during skeletal muscle inactivity and that suppressing serum myostatin promotes muscle growth and strength. Furthermore, therapeutic interventions that reduce myostatin signalling may lessen the effects of aging on skeletal muscle mass and function. For this reason, the aim of the study was to assess if flat magnetic stimulation technology affects serum myostatin levels, as myostatin can block cell proliferation at the urethral sphincter level. Materials and Methods: A total of 19 women, 75% presenting stress urinary incontinence (SUI) and 25% urgency urinary incontinence (UUI), were enrolled. A non-invasive electromagnetic therapeutic system designed for deep pelvic floor area stimulation was used for eight sessions. Results: The ELISA (enzyme linked immunosorbent assay) test indicated that the myostatin levels in blood sera had significantly decreased. Patients' ultrasound measurements showed a significant genital hiatus length reduction at rest and in a stress condition. The Pelvic Floor Bother Questionnaire consistently revealed a decrease in mean scores when comparing the pre- and post-treatment data. Conclusions: Effective flat magnetic stimulation reduces myostatin concentration and genital hiatus length, minimizing the severity of urinary incontinence. The results of the study show that without causing any discomfort or unfavourable side effects, the treatment plan significantly improved the PFM tone and strength in patients with UI.

Data-driven analysis of heating and cooling base temperatures for buildings: Case studies in South Korea

To investigate statistical differences in the heating (Tbh) and cooling base temperatures (Tbc) across various building types, vintages, and sizes, we conducted case studies in Seoul, South Korea, based on data from more than 10,000 buildings. To assess the relative impacts of internal and external loads on Tbh and Tbc, we used a core-to-perimeter zone ratio approximated by the average floor area, defined as the floor area per building floor. Employing the energy signature method, change-point regression, and calendarization, we determined the best-fit change-point model and the corresponding Tbh and Tbc for each building. Our findings revealed statistically significant differences in Tbhs and Tbcs, with variations of up to 6 and 4 °C, respectively, across six building types. Notably, for most building types, Tbh and Tbc decreased as the average floor area increased, possibly influenced by a higher core zone to total area ratio. This highlights the predominant impact of internal loads over external loads. The influence of the average floor area on Tbh and Tbc outweighed that of building vintage, particularly for lodging, medical, and sales facilities, where Tbh and Tbc decreased in the more recent vintage buildings. However, this influence was less pronounced for facilities for senior citizens and children and education and research facilities, possibly due to factors such as diverse building shapes and the thermal preferences of occupants. Although this study focuses on South Korea, our data-driven approach provides statistical evidence for refining base temperature and improving traditional degree-day methods. Our findings suggest the need to establish base temperatures that consider various building types, vintages, and average floor areas, especially for large metropolitan cities where these factors vary widely.

Investigating the influence of current trends and behaviours on household structures and housing consumption patterns

As a major contributor to overall carbon emissions and energy consumption, the housing sector has great potential to reduce energy consumption, whether by reducing the number of appliances, heating temperature or floor space. Consumption patterns encompass how people choose and consume products that satisfy their needs and wants. However, wants, and to some extent needs, are influenced by various factors and existing material and non-material (infra)structures, especially in the housing sector. Focusing on the floor area, this article aims to identify potentials towards lower consumption lifestyles by applying the Avoid-Shift-Improve framework in the residential sector. Through a conceptual review, the article explores what shapes current patterns of space use and outlines potential future pathways. Starting from the macro level, the article examines existing and emerging (societal) trends with (potential) impacts on housing consumption. It then looks at the structural development of households affected by the studied trends. At the micro level, the article provides an overview of the potential impact of individual behaviour on space use patterns within different categories of housing behaviour. The article identifies the potential for social and technical change in the housing sector and concludes that promoting non-materialistic narratives (avoid), offering alternative and innovative solutions to satisfy people’s spatial needs (shift) and designing flexible buildings (improve) appear to be effective ways for fostering behavioural change towards more efficient use of space.

Energy Consumption and Outdoor Thermal Comfort Characteristics in High-Density Urban Areas Based on Local Climate Zone—A Case Study of Changsha, China

This study aims to investigate the characteristics of energy consumption and outdoor thermal comfort within the high-density urban fabric of Changsha. Two different types of building (residential and office), as well as three building forms (point, slab, and enclosed) were analyzed under the local climate zone scheme. Utilizing the ENVI-met 5.6.1 and EnergyPlus 23.2.0 software, simulations were conducted to assess the thermal comfort and energy consumption of 144 architectural models. Then, multiple regression and spatial regression were applied to predict the energy consumption characteristics of the study area. The results showed the following: (1) In the high-density urban area of Changsha, the central business district and historic old town adjacent to the Xiangjiang River are identified as areas with high energy use intensity. (2) Among the residential categories, the point-types LCZ-3 and LCZ-6, as well as the slab-type LCZ-4, exhibit the lowest energy use intensity. In contrast, the enclosed office buildings, LCZ-2 and LCZ-5, are characterized by the highest energy use intensity. (3) Urban form parameters such as floor area ratio and building shape coefficient have a significant impact on EUIwinter, while EUIsummer is highly related to the normalized difference vegetation index and building shape coefficient (BSC). (4) The slab-type LCZ-4 stands out with its notably lower cooling and heating energy use intensity, coupled with excellent thermal comfort, making it particularly well-suited for the climatic conditions of Changsha.

From expansion to efficiency: Machine learning-based forecasting of Japan's building material stocks under demographic declines

Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the historical evolution and project future scenarios of building material stock (MS) and their spatial distribution across Japan's three major metropolitan areas. Through a comprehensive material flow and stock analysis, the historical accumulation of building materials from 2009 to 2020 was quantified, revealing a dominance of concrete and an increasing overall stock. The contributions of various driving factors to changes in construction areas were explored, identifying population dynamics as the predominant influence. Leveraging shared socioeconomic pathway scenarios (SSPs), this research forecasted building floor area and MS until 2050 under five distinct SSPs. The results indicated an overall reduction across all scenarios, yet with a continued concentration in high-density urban cores. The substantial gap between the highest and lowest projected MS scenarios highlighted opportunities for material conservation and emission reductions through sustainable practices. Sustainable urban development in densely populated regions necessitates a balance between infrastructure provision and environmental conservation, while in sparsely populated areas, the focus shifts to the efficient management and utilization of vacant properties and materials to cope with the impacts of significant population declines. By offering insights into the building floor area and MS implications of Japan's demographic changes, this study underscores the necessity of sustainable urban planning and resource management strategies to navigate the challenges posed by demographic shifts, ultimately contributing to sustainable development and environmental conservation goals.

Optimal Machine Learning Model to Predict Demolition Waste Generation for a Circular Economy

A suitable waste-management strategy is crucial for a sustainable and efficient circular economy in the construction sector, and it requires precise data on the volume of demolition waste (DW) generated. Therefore, we developed an optimal machine learning model to forecast the quantity of recycling and landfill waste based on the characteristics of DW. We constructed a dataset comprising information on the characteristics of 150 buildings, demolition equipment utilized, and volume of five waste types generated (i.e., recyclable mineral, recyclable combustible, landfill specified, landfill mix waste, and recyclable minerals). We applied an artificial neural network, decision tree, gradient boosting machine, k-nearest neighbors, linear regression, random forest, and support vector regression. Further, we derived the optimal model through data preprocessing, input variable selection, and hyperparameter tuning. In both the validation and test phases, the “recyclable mineral waste” and “recyclable combustible waste” models achieved accuracies (R2) of 0.987 and 0.972, respectively. The “recyclable metals” and “landfill specified waste” models achieved accuracies (R2) of 0.953 and 0.858 or higher, respectively. Moreover, the “landfill mix waste” model exhibited an accuracy of 0.984 or higher. This study confirmed through Shapley Additive exPlanations analysis that the floor area is the most important input variable in the four models (i.e., recyclable mineral waste, recyclable combustible waste, recyclable metals, and landfill mix waste). Additionally, the type of equipment employed in demolition emerged as another crucial input variable impacting the volume of recycling and landfill waste generated. The results of this study can provide more detailed information on the generation of recycling and landfill waste. The developed model can provide precise data on waste management, thereby facilitating the decision-making process for industry professionals.

Indoor Scene Reconstruction with Fine-Grained Details Using Hybrid Representation and Normal Prior Enhancement.

The reconstruction of indoor scenes from multi-view RGB images is challenging due to the coexistence of flat and texture-less regions alongside delicate and fine-grained regions. Recent methods leverage neural radiance fields aided by predicted surface normal priors to recover the scene geometry. These methods excel in producing complete and smooth results for floor and wall areas. However, they struggle to capture complex surfaces with high-frequency structures due to the inadequate neural representation and the inaccurately predicted normal priors. This work aims to reconstruct high-fidelity surfaces with fine-grained details by addressing the above limitations. To improve the capacity of the implicit representation, we propose a hybrid architecture to represent low-frequency and high-frequency regions separately. To enhance the normal priors, we introduce a simple yet effective image sharpening and denoising technique, coupled with a network that estimates the pixel-wise uncertainty of the predicted surface normal vectors. Identifying such uncertainty can prevent our model from being misled by unreliable surface normal supervisions that hinder the accurate reconstruction of intricate geometries. Experiments on the benchmark datasets show that our method outperforms existing methods in terms of reconstruction quality. Furthermore, the proposed method also generalizes well to real-world indoor scenarios captured by our hand-held mobile phones. Our code is publicly available at: https://github.com/yec22/Fine-Grained-Indoor-Recon.

Influence of daylight on real estate housing prices. A multiple regression model application in Turin

Daylight is crucial in architecture, influencing human health, well-being, energy efficiency, and the post-COVID-19 perception of residential spaces. People often consider daylight among the most important features when buying a home, which potentially affects real estate asset pricing. Within this framework, this study explored the impact of daylight on real estate asset pricing, particularly focusing on its role in the Italian market. The research employed two approaches: (i) simulations of 100 units to determine a large set of daylight metrics and (ii) a statistical approach, applying Hedonic Analysis principles to investigate how house attributes influence pricing. A data sample of 100 housing units was selected in Turin (Italy), including variables such as location, floor area, construction year, façade type, and daylight metrics. By using Multiple Regression Analysis, after Exploratory Analysis and outlier management, we identified significant variables influencing housing listing prices, such as energy class, conservation status, elevator presence, terraces/balconies, and frame type. Notably, two daylight metrics (annual sunlight exposure and useful daylight illuminance) were found to be significant, while others, like average daylight factor and spatial daylight autonomy, were not. The final model was validated by means of a control sample, demonstrating the relevance of daylight in real estate pricing. This research contributes to the literature, providing an in-depth exploration of the impact of daylight on hedonic analysis in a relatively underexplored research space. Precisely, the work contributes to bridging the literature gap about the detection of the influence of daylight on real estate market pricing processes by means of regression analysis.

How do driving factors affect the diurnal variation of land surface temperature across different urban functional blocks? A case study of Xi'an, China

A comprehensive and in-depth understanding of the formation mechanisms of the urban thermal environment is the basis for thermal environment regulation, however, there is insufficient knowledge regarding how driving factors influence daytime and nighttime land surface temperature (LST) within urban functional blocks (UFBs). We selected Xi'an, China as a case study, integrating remote sensing data including ECOSTRESS, Landsat-8, and Gaofen-1, along with geographic data including road network, areas of interest, points of interest, building footprint, and mobile phone signaling. It divided 10 types of UFBs, inverted daytime and nighttime LST, calculated 5 types of driving factors, and finally analyzed the contribution and marginal effects of driving factors on day-night LST using boosted regression tree. The results showed that LST and its driving factors differed significantly in different times and UFBs. Industrial blocks and urban villages had higher LST in the daytime, while residential blocks, commercial blocks, and public service blocks had higher LST in nighttime. Industrial blocks were the dominant blocks that drove the overall LST up during the day, while residential blocks were the dominant blocks at night. Location distance (-) and population density (+) affected LST in all UFBs during day and night, NDVI (-), building density (+), and floor area ratio (-) were key factors for most UFBs during daytime, and NDVI (+), surface albedo (-), and point density of interest (+) were key factors during nighttime. Most of the driving factors had significant influence thresholds, but there were small differences across UFBs. This paper aims to elucidate the mechanisms by which driving factors influence urban LST during day and night across different UFBs, thereby providing new support for more targeted thermal environment regulation and diurnal trade-offs at the block scale.

Effects of solar energy absorbed by south wall on carbon emission from space heating in hot summer-cold winter region

Carbon emission caused by space heating (CEH) in hot summer and cold winter climate zone has increased dramatically. Taking advantage of the solar energy absorbed by south wall (SEASW) can effectively reduce CEH, however, the accurate quantification of its impact remains a challenge. The study investigates the potential of SEASW in mitigating CEH. Key parameters affecting CEH, such as sunny day ratio, insulation form, and outer surface heat transfer coefficient of south wall (h), were analyzed. Results show the reduction in carbon emissions caused by the solar energy absorbed by each unit area of the south-facing wall (ΔCreduction) can be as high as 18.61 kgCO2⋅m−2 for the wall employing internal insulation and smaller h. A prediction model of ΔCreduction was further developed by using multivariate nonlinear regression, which can estimate the reduction in CEH for different fuels as heating source by a south wall in different sizes. Besides, the potential of SEASW on the reduction of carbon emission per unit floor area can reach 12.2 %. This study underscores the importance of SEASW as a carbon-neutral heating strategy, particularly for regions characterized by short winter heating periods and lower heating loads compared to colder climates. It provides valuable insights for sustainable energy practices and policy formulation.

FAR regulations and the spatial size of Brazilian cities

ABSTRACT This paper evaluates the influence of the maximum allowed floor area ratio (FAR) on the spatial size of Brazilian cities. We built a novel database on building height restrictions for the 325 largest cities in the country and combined it with recent satellite data. Our estimations show that, as predicted by the monocentric model, tighter constraints are associated with larger urban areas, and this result is robust to several specifications, sensitivity tests and different estimation strategies. Additionally, back-of-the-envelope calculations show that a higher stringency of the building-height regulation generates an annual cost of about US$9.67 million per average Brazilian city.

Beyond efficiency: Evaluation of the electricity saving potential of green residential buildings

While green building standards are widely promoted for their energy-saving potential, limited research has explored their actual impact on electricity consumption in residential buildings. This study addresses this gap, comparing certified green buildings to standard ones using a multifaceted approach that combines high-frequency smart meter data with comprehensive socio-demographic data and detailed descriptions of building properties. Metrics investigated included total household consumption, energy use intensity (EUI), and per capita consumption, in each case differentiating between overall appliance use and dedicated heating, ventilation and air conditioning (HVAC) consumption. While green buildings achieved a 16% reduction in EUI for HVAC compared to standard buildings and thus lowered peak electricity demand, total annual household electricity consumption remained similar. The variance in electricity consumption among households in the sample indicates that occupant behaviour is a significant factor. Counter-intuitively, the research found that occupants of green buildings did not exhibit greater environmental awareness or report more energy-efficient appliance use habits compared to those in standard buildings. It also highlights limitations of EUI as the primary metric for ’efficiency’, rather than total consumption. Furthermore, because consumption is normalized by floor area, this metric may in fact inadvertently incentivize larger apartments that might require even more energy overall. These findings demonstrate the need for a modified area-normalized EUI target incorporated in the building codes as well as a more comprehensive approach to develop complementary policy measures.

The influence of occupant control behaviour on thermal comfort under different heating charging modes in residential buildings in cold regions

In China's central heating, there are two modes for calculating heating costs, which are divided into Charging by flow mode which charges according to the amount of use and Charging by area mode which charges according to the floor area. The Charging by flow mode has been increasingly adopted by numerous urban central heating buildings. Thus it is worth investigating whether occupants experience varying levels of thermal comfort under these two modes. To address this, a field test and subjective questionnaire survey were conducted on residential buildings in cold regions of China during the heating season. The study assessed 134 residential occupants utilizing radiator heating, comprising 66 in Charging by area and 68 in Charging by flow modes. A collection of 1206 valid data points was obtained, with 609 in Charging by area mode and 597 in Charging by flow mode. The findings reveal noteworthy disparities in the duration, area, and strength of heating equipment usage between the two modes. While there are no marked variances in the interior and exterior environmental conditions under both modes, residents in the Charging by flow mode experience enhanced thermal comfort, acceptability and expectation, as well as better air quality satisfaction. Perceived control can greatly enhance individuals' thermal sensation in temperatures below 18 °C and above 24 °C. The impact of perceived control on thermal expectation is linear with temperature adjustments. The heightened degree of sensing control in Charging by flow mode lowers residents' expectations of high temperatures, broadens the range of acceptable low temperatures and accomplishes energy conservation and carbon reduction while ensuring optimal comfort.

Resource Use Efficiency of Pig Production raised with and without Open Area during Summer Season

A total of 20 piglets (10 castrated males and 10 females) at 3 months of age with initial body weight of 18.94 ± 1.04 Kg were randomly subjected to minimum limit of floor area recommendations of housing standards (IS: 3916-1966) range of 0.9-1.8 square meter for weaner to finisher. In treatments Tc, pigs were provided with only 0.9 m2 covered floor area per pig without access to open area and in control (To) with covered floor area of 0.9 m2 plus additional equal open floor area (total 1.8 m2 per pig). Result showed that resource use efficiency in terms of saving of 90 % space, 41.23 % labor and 22.05 % water per pig in Tc were significantly (p < 0.01) better than To. However, production cost per kg live weight in Tc was 5.38 % higher than To.

Energy consumption of UAE public schools. Mapping of a diversified sector assessing typology, conditions, and educational systems

The UAE school sector impacts several Sustainable Development Goals of the UAE Vision 2021. The sector is designed according to a standardized system of Model buildings, repeated several times throughout the country, comprising facilities that are often obsolete at both functional and technological level; hence, frequently presenting poor energy performances. The paper presents the first comprehensive review of the energy consumption state-of-the-art of the public-school sector in the Emirate of Sharjah, United Arab Emirates, including the first necessary dataset for future upgrade actions. The study was completed by means of a deep mapping, functional, typological, and pedagogical analysis. The collected data of real energy consumption of 69 schools highlighted an average energy consumption of 174 kWh/m2 year, with very consistent results for each educational cycle, even if a larger variance was expected due to difference in size, typology, and working hour. More significant differences can be observed when comparing the result of school with different typologies, stressing the relevance of further investigating the standardized Model system. To quantify their correlation to the buildings’ energy consumption, linear regression analyses were conducted on a number of notable factors, such as building age, net floor area, roof area, shape coefficient, number of students, number of classrooms and labs, presence of gyms and multipurpose halls, and implementation of digital technologies. Schools’ capacity/occupancy ratio, net floor area and roof area, and the use of energy-demanding devices for novel smart learning techniques were found having the highest impact. The tested multiple regression model, which displays a marginal error of 5–30 % when applied to single buildings and 9 % when used to assess larger clusters, could facilitate the estimation of the energy consumption of schools and support stakeholders’ decision making. The outcomes of the study can assist governmental agencies in elaborating suitable refurbishment policies for the energy and functional upgrade of the educational sector, both at the building and urban scale, and school administrator in tackling appropriate actions toward the schools use and resources optimization.

Spatiotemporal heterogeneity of the relationship between urban morphology and land surface temperature at a block scale

Understanding the relationship between urban morphology and land surface temperature (LST) is essential for mitigating urban heat island (UHI). This study investigates the spatiotemporal heterogeneity of the relationship between urban morphology and LST across 5 experiment sites located in different temperature zones of China at a block scale. Normalized difference vegetation index (NDVI), building density (BD), floor area ratio (FAR), average building height (ABH) and open space ratio (OSR) are adopted to indicate the physical urban morphology. The results show positive relationship between BD and LST, and negative relations between LST and NDVI, ABH and OSR from a global perspective. The relations of FAR to LST are mixed. However, variable relationship between urban morphological indicators (UMIs) and LST are significantly observed at a block scale. 12 scenes that generate local relations of UMIs and LST differing from that of the surroundings are identified. Typical strategies for land development, vegetation phenology, large natural elements and human activities may be the most important causative factors to the heterogenous relationship between urban morphology and LST. Findings derived in the study would promote studies on mechanisms of the effects of urban morphology on LST and contribute to practices of UHI mitigation.

Periodic stays in a 'playcage' as an environmental enrichment measure for laboratory rats housed in individually ventilated cages: Short report.

Male Zucker Diabetic Sprague Dawley rats were housed in standard individually ventilated cages with floor area of 1500 cm2, and were placed in a 'playcage' (a large open cage) for three visits per week from the age of 16-18 weeks. The playcage was introduced in an attempt to reverse the compulsive behaviour that the rats displayed in the individually ventilated cages, with the purpose to increase their well-being and to provide cognitive as well as physical stimulation. After two weeks of periodic stays in the playcage, the rats' repetitive behaviour in their home cage ceased, and the rats displayed signs of happiness and excitement when they were in the playcage. The observations strongly indicate that periodic stays in a larger playcage can be an alternative environmental enrichment for laboratory rats when housing in a larger home cage is not an option.

Implementing housing policies for a sufficient lifestyle

The German buildings sector is currently facing a double challenge: meeting emission reduction targets and providing affordable housing in rapidly growing cities. Living space per person has a major impact on household energy consumption and it has increased significantly in recent decades. New construction increases the demand for often energy-intensive building materials, and energy demand during the use phase of buildings is positively related to the size of the space. Optimising the allocation of living space therefore offers great potential for addressing both challenges. Sufficiency policies such as flat exchanges, a financial bonus for moving and the provision of moving advice aim to reduce the total floor area in the housing sector by using existing space more efficiently. However, the effectiveness of such measures is not yet fully understood. As the sufficiency approach relies heavily on individual choices, residents’ involvement, perception and acceptance are crucial for the success of such measures. An investigation in selected German housing companies shows that the potential of these measures is not fully exploited under the current political and social framework in Germany. Social acceptance, new norms and business models for landlords are needed for the effective use of these measures. Policy relevance How effective is the implementation of sufficiency measures for space utilisation in the German rental housing sector? The success factors and limiting barriers of the measures along with the acceptability and effectiveness of these measures are investigated by interviewing housing companies (some of them social housing providers) that have the potential to implement such policies in their housing stock. Improvements are needed in the social and technical infrastructure to increase the effectiveness of the measures. Raising awareness, rethinking communications and designing effective financial incentives are recommended to make the measures attractive to the public. Involving all stakeholders, keeping the issue on the political agenda and engaging in dialogue with policymakers are considered effective steps in the process of achieving space sufficiency in the housing sector.