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). 

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. 

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.

Det är under all kritik att så många doktorander mår dåligt och inte färdigställer sina avhandlingar. Vi mer seniora akademiker har ett medmänskligt ansvar att etablera strukturer som tar hand om våra kollegor. Omtanke om våra doktoranders utveckling behövs då vi designar strukturer för doktorerande så väl som handledande. I denna artikel argumenterar jag för att omtanken om doktorandens utveckling bör vara en utgångspunkt i handledningsarbetet. Med hjälp av omtanke blir det enklare att: hitta rätt nivå för krav och mål, erkänna brister i handledarskapet, prata problem i satta maktrelationer, hantera utmaningar från den våldsamma akademin, med mera. Att bry sig om doktorandens utveckling innebär många olika saker under resans gång. Även om det i artikeln konkretiseras vad omtanke om doktorandens utveckling kan innebära, så är poängen snarare att det blir enklare att inse vad god handledning är, ifall omtanken om doktorandens väl är en utgångpunkt i handledningsarbetet.

The financing of energy efficiency measures and renovations is key to reaching energy efficiency targets for the housing sector. The purpose of this article is to add the Swedish case of how capital market funds have become accessible and used by public and private housing companies, in particular for energy efficiency measures. The core of this article are interviews with representatives of Swedish housing companies made during the spring of 2021 with the purpose of mapping how public and larger private housing companies finance renovation and energy efficiency measures, and to what extent funds from the capital market are used for these purposes. In this article, we have found that capital market funds are commonly used by the Swedish public and the largest private housing companies. Bonds are less costly compared to bank loans, and green bonds are 0.02–0.03 percentage points less costly than conventional bonds. Furthermore, control systems that investigate the values of building portfolios as security for bonds are poor. A conclusion is that governmental control systems over the capital market issuing bonds for the housing market could be needed to avert future housing bubbles. © 2022, The Author(s).

Although renovation costs can lead to rent increases in energy retrofitting, it is often assumed that reductions in energy costs will counterbalance the rent increase. In Swedish multifamily housing, energy costs for heating are however generally included as a fixed component in the monthly rent, meaning that the rent increase after energy retrofitting corresponds to the net change in rent level as well as energy costs for heating. This makes Sweden a methodologically advantageous setting for studying tenants' cost burden of energy retrofitting. The aim of this study was thus to investigate how energy performance improvement has affected rent increases in Swedish renovation projects between 2013 and 2019. Utilising a national database of multifamily housing, it was found that energy retrofitting entailed a cost relief for tenants in renovation projects with smaller investments. However, in renovation projects with larger investments, energy retrofitting entailed a cost burden for tenants. Moreover, public housing companies had conducted a high share of the extensive energy retrofits, leading to low-income tenant groups being disproportionately subjected to cost burdens of energy retrofitting. On the contrary, light energy retrofits with a cost relief for energy efficiency had been rather evenly distributed across income groups. These results indicate ongoing conflicts with the ability-to-pay principle in the energy transition of Swedish multifamily housing, and suggest that if low-investment energy retrofits are not sufficient for upcoming objectives and requirements, subsidies could be needed to compensate low-income tenants for the cost burden of extensive energy retrofitting. © 2022 The Author(s)

The presence of hazardous materials inhibits material circularity. The existing residential buildings are exposed to the risk of the unforeseen presence of asbestos-containing materials during the demolition or renovation process. Estimating the potential occurrence of contaminated building components can therefore facilitate semi-selective demolition and decontamination planning. The study aims to investigate the prediction possibility of seven frequently detected asbestos-containing materials by using artificial neural networks based on a hazardous material dataset from pre-demolition audit inventories and national building registers. Through iterative model evaluation and careful hyperparameter tuning, the prediction performance for each asbestos-containing material was benchmarked. A high level of accuracy was obtained for asbestos pipe insulation and ventilation channel, yet barely any patterns were found for asbestos floor mats. Artificial neural networks show potential for classifying specific asbestos components and can enhance the knowledge of their detection patterns. However, more quality data are needed to bring the models into practice for risk assessment for not yet inventoried residential buildings. The proposed screening approach for in situ asbestos-containing materials has high applicability for the quality assurance of recycled materials in circular value chains.

Identifying in situ hazardous materials can improve demolition waste recyclability and reduce project uncertainties concerning cost overrun and delay. With the attempt to characterize their detection patterns in buildings, the study investigates the prediction potential of machine learning techniques with hazardous waste inventories and building registers as input data. By matching, validating, and assuring the quality of empirical data, a hazardous material dataset for training, testing, and validation was created. The objectives of the explorative study are to highlight the challenges in machine learning pipeline development and verify two prediction hypotheses. Our findings show an average of 74% and 83% accuracy rates in predicting asbestos pipe insulation in multifamily houses and PCB joints or sealants in school buildings in two major Swedish cities Gothenburg and Stockholm. Similarly, 78% and 83% of recall rates were obtained for imbalanced classification. By correlating the training sample size and cross-validation accuracy, the bias and variance issues were assessed in learning curves. In general, the models perform well on the limited dataset, yet collecting more training data can improve the model's generalizability to other building stocks, meanwhile decreasing the chance of overfitting. Furthermore, the average impact on the model output magnitude of each feature was illustrated. The proposed applied machine learning approach is promising for in situ hazardous material management and could support decision-making regarding risk evaluation in selective demolition work. © 2022 The Author(s)