Renovations of the housing rental stock have become a political concern since they have been claimed to drive gentrification and affect tenants’ everyday lives as well as long-term housing conditions. Furthermore, new actors have entered the market, partly as a result of high supply on the international capital markets creating a flow of capital into market segments. This has led to a critique of private equity in the housing sector, and raised the question of the extent to which ownership of the rental stock matters for housing affordability. Yet there seems to be little systematic research on this topic. This study uses a unique dataset covering the entire rental housing stock in Sweden to address whether there are differences in renovation investments between different ownership groups. The purpose of this article is to increase understanding of how ownership affects renovation processes, and specifically to analyse to what extent, and how, private and public actors differ in renovation and rent setting decisions. Our results demonstrate that public housing companies raised rents less and renovated more, particularly in the lower-income segments of the multi-family building stock between 2014 and 2020. © 2023 The Author(s). 

Identifying potential moisture damage is crucial for maintenance practices and assurance of well-being of oc cupants. However, due to limited information availability and standardization, assessing damage prevalence on the building stock scale remains understudied. By combining investigation records and building databases, this study leverages data analytic techniques and machine learning modeling to characterize damage pathology and predict its occurrence in Swedish buildings. The interrelationships between damage-specific attributes and their associations with building parameters of several damage types were analyzed using feature selection, forming the basis for developing predictive models. Results show that multilabel classifiers outperform binary classifiers for every damage type, with lead tree ensemble models achieving minimum average AUCPR and F2 of 0.85 for microbial growth, 0.87 for deformation, 0.91 for odor, and 0.95 for water leakage. The identified patterns were interpreted and verified against descriptive statistics. The binary relevance models estimate that one-third of school buildings, 20 % of commercial and office buildings, and 15 % of residential dwellings in regional building stock contain moisture damage. These findings advance the quantification of moisture damage by providing new knowledge and approaches for appraising moisture damage likelihood at aggregated and individual building levels, thereby aiding in moisture safety evaluations and preventive maintenance efforts

Moisture damages lead to significant costs and impact indoor environments negatively. Identifying their occurrence patterns is crucial for the implementation of preventive or mitigative measures, though it remains highly challenging due to the complex interplay of multidimensional variables. This study aims to identify primary moisture damage profiles in Swedish buildings and evaluate their prevalence. Using data-driven analytical and visualization techniques, 2,100 complex moisture-related damage records between 2014 and 2020 with information on building parameters and damage specifics from Sweden were examined in multivariate analysis. The association analysis reveals varied relationships among factors related to damage, with damage types distributed proportionally across damaged components, causing components, sources, building phase, and responsible actors. The presence patterns of damage differ significantly by building types, yet they are generally reported more frequently in buildings built during the 1960–1980 and 2000–2020. Prevalent moisture damages in Swedish buildings include microbial growth and deformation at the building envelopes and roof, odor in indoor environments caused by wind-driven rain, and indoor or outdoor humidity. Additionally, these damages appear more often in buildings constructed with non-ventilated crawlspaces, wood, concrete, brick structures, façade, exterior walls, and non-ventilated cold attics. The characterized moisture damage patterns and estimated frequency enhance the understanding of their occurrence and associating factors.

Residents’ perceived safety is key to improving livelihoods and reducing disparities between neighborhoods in Sweden. Neighborhood interventions may be more cost-effective than individual-level interventions in addressing major societal issues such as unequal levels of safety between neighborhoods. However, most studies investigating the impact of neighborhood characteristics on perceived safety suffer from either poor data quality, too few respondents per statistical unit, large units of analysis, or a lack of longitudinally collected data. This study aims to fill this gap by combining property-specific longitudinal sociodemographic data with customer satisfaction survey data (N = 147,965) collected between 2013–2014 and 2016–2021 in Gothenburg. Using two multilevel models, we examined the relationship between perceived safety and both property-level and area-level structural characteristics, testing three hypotheses. Consistent with prior research, we find that sociodemographic and urban environmental characteristics influenced perceptions of safety. The multilevel analyses reveal that proximity to the square is associated with lower levels of perceived safety, particularly among residents living within 0–100 m of the square in socioeconomically disadvantaged neighborhoods. Moreover, the results show that living in taller buildings of 10–16 floors is associated with lower levels of safety. 

Predicting modeling and analysis of moisture damages in Swedish buildings Moisture damage in buildings poses significant challenges, leading to costly repairs and negative impacts on indoor environments. Understanding the patterns and prevalence of such damage is crucial for implementing effective preventive and mitigative measures. This report synthesizes findings from the comprehensive study on moisture damage in Swedish buildings, highlighting the complex interplay of multidimensional variables that contribute to these issues. Analyzing 2,100 moisture-related damage records from empirical damage investigations from 2014 to 2020, the study employs data-driven analytical techniques to identify primary moisture damage profiles and evaluate their prevalence. Multivariate analyses reveal diverse associations among factors related to moisture damage, with significant variations in damage types across different building components, phases, and actors responsible. The findings indicate that moisture damage is most prevalent in buildings constructed between 1960-1980 and 2000-2020, with common issues including microbial growth, deformation of building envelopes and roofs, odors caused by humidity-related problems and wind-driven rain. Buildings with non-ventilated crawlspaces and non-ventilated attics are particularly susceptible. By characterizing moisture damage patterns and their associated factors, these findings enhance our understanding of moisture damage occurrences and informs strategies for relevant actors to improve their practices on moisture damage prevention along building lifecycles. Furthermore, various predictive modeling techniques and machine learning algorithms were explored to predict the presence of prevalent moisture damage types, including microbial growth, deformation, odor, and water leakage. The binary and multilabel classification models achieve high prediction rates of over 0.9 average AUCPR and F2 scores by training with building-related features in cross-validation and validation. The lead binary relevance models estimate that approximately one-third of school buildings, 20% of commercial and office buildings, and 15% of residential dwellings in the uninvestigated Swedish building stock may contain unreported moisture damage. These findings advance quantitative research on moisture damage, providing new insights and tools for assessing the extent and patterns of in-situ moisture damage, thereby aiding existing moisture safety evaluations, and building maintenance strategies. Specifically, the results can help building owners, insurance companies, and damage investigators predict and address potential moisture damage in buildings. They also support developers, designers, and contractors in making informed decisions about construction solutions, helping to avoid designs with a high risk of damage. By preventing moisture damage in both existing and new buildings, we can significantly reduce costs and the environmental impact of remediation and material replacement, which currently result in substantial expenses and CO2 emissions.

PVC flooring accounts for a significant share of PVC use in the construction sector and has great potential for recycling. Nevertheless, the actual recycling rate of PVC flooring spillage in 2018 was less than 20%, according to the national system for the separate collection and recycling of material residues from the installation of PVC floorings, developed by flooring manufacturer Tarkett AB and now used by all manufacturers in the flooring industry. To improve the sorting and recycling process of old PVC flooring it is necessary to identify where the material is located and evaluate its recycling potential. Such information is crucial for demolition waste recycling companies and flooring manufacturers to improve recycling practices for PVC flooring and then use the recycled PVC materials in the new flooring production. The challenge is to find out in which buildings there is PVC flooring and when it was installed which will indicate when it is planned to be dismantled and replaced. Since the PVC flooring manufactures do not keep track on where their products are laid such information is lacking. The best source of information that was made available for the researchers appeared to be the public building owners´ maintenance plans. Therefore, it was decided to focus on the presence of PVC flooring in public preschools as an example. By combining data from maintenance plans with national building registers, the PVC flooring in the Swedish preschools have been forecasted. The project results show an example how limited data sources can be used to predict presence of materials in larger stocks and is therefore expected to contribute to a climate-neutral supply chain with recycled PVC flooring. Based on the results of this study, dialogue, recommendations and guidelines can be developed for the flooring industry, the waste and recycling industry and the Swedish real estate and construction sector.

During and post pandemic more people spent time in their second homes, which is expected to have led to higher energy use for heating. The knowledge of energy performance, heating systems, energy renovation and use patterns of second homes is still poor. The aim of the research is therefore to compile available information from building registers but also to empirically investigate user patterns, heating source and the renovation and energy efficiency measures carried out in second homes. A first step is to synthesize existing knowledge and develop a method for a broad mapping in a next step. The methods used are analysing statistics from national building registers and collecting information from owners/users through a pre-survey that is developed and tested. In this paper statistics on Swedish second homes and results from a pre-survey responded by 92 second homes owners/users are reported. From statistics, the energy performance and the main heating source for second homes with an EPC are identified. Despite the limited sample, the results from the pre-survey give an indication of user patterns, energy renovation measures carried out, and also whether the owners care about cultural values. Based on the experience from the pre-survey, a national survey has been initiated in Sweden.

The undesirable legacy of radioactive concrete (blue concrete) in post-war dwellings contributes to increased indoor radon levels and health threats to occupants. Despite continuous decontamination efforts, blue concrete still remains in the Swedish building stock due to low traceability as the consequence of lacking systematic documentation in technical descriptions and drawings and resource-demanding large-scaled radiation screening. The paper aims to explore the predictive inference potential of learning Bayesian networks for evaluating the presence probability of blue concrete. By integrating blue concrete records from indoor radon measurements, pre-demolition audit inventories, and building registers, it is possible to estimate buildings with high probabilities of containing blue concrete and encode the dependent relationships between variables. The findings show that blue concrete is estimated to be present in more than 30% of existing buildings, more than the current expert assumptions of 18–20%. The probability of detecting blue concrete depends on the distance to historical blue concrete manufacturing plants, building class, and construction year, but it is independent of floor area and basements. Multifamily houses and buildings built between 1960 and 1968 or nearby manufacturing plants are more likely to contain blue concrete. Despite heuristic, the data-driven approach offers an overview of the extent and the probability distribution of blue concrete-prone buildings in the regional building stock. The paper contributes to method development for pattern identification for hazardous building materials, i.e., blue concrete, and the trained models can be used for risk-based inspection planning before renovation and selective demolition. © 2023 The Author(s)

Indoor radon represents a health hazard for occupants. However, the indoor radon measurement rate is low in Sweden because of no mandatory requirements. Measuring indoor radon on an urban scale is complicated, machine learning exploiting existing data for pattern identification provides a cost-efficient approach to estimate indoor radon exposure in the building stock. Extreme gradient boosting (XGBoost) models and deep neural network (DNN) models were developed based on indoor radon measurement records, property registers, and geogenic information. The XGBoost models showed promising results in predicting indoor radon intervals for different types of buildings with macro-F1 between 0.93 and 0.96, whereas the DNN models attained macro-F1 between 0.64 and 0.74. After that, the XGBoost models trained on the national indoor radon dataset were transferred to fit building registers in metropolitan regions to estimate the indoor radon intervals in non-measured and measured buildings by regions and building classes. By comparing the prediction results and the statistical summary of indoor radon intervals in measured buildings, the model uncertainty and validity were determined. The study ascertains the prediction performance of machine learning models in classifying indoor radon intervals and discusses the benefits and limitations of the data-driven approach. The research outcomes can assist preliminary large-scale indoor radon distribution estimation for relevant authorities and guide onsite measurements for prioritized building stock prone to indoor radon exposure. 

Hazardous materials in buildings cause project uncertainty concerning schedule and cost estimation, and hinder material recovery in renovation and demolition. The study aims to identify patterns and extent of polychlorinated biphenyls (PCBs) and asbestos materials in the Swedish building stock to assess their potential presence in pre-demolition audits. Statistics and machine learning pipelines were generated for four PCB and twelve asbestos components based on environmental inventories. The models succeeded in predicting most hazardous materials in residential buildings with a minimum average performance of 0.79, and 0.78 for some hazardous components in non-residential buildings. By employing the leader models to regional building registers, the probability of hazardous materials was estimated for non-inspected building stocks. The geospatial distribution of buildings prone to contamination was further predicted for Stockholm public housing to demonstrate the models’ application. The research outcomes contribute to a cost-efficient data-driven approach to evaluating comprehensive hazardous materials in existing buildings.