Registration of heritage values in buildings is important to safeguard heritage values that can be lost in renovation and energy efficiency projects. However, registering heritage values is a cumbersome process. Novel artificial intelligence tools may improve efficiency in identifying heritage values in buildings compared to costly and time-consuming traditional inventories. In this study, OpenAI's large language model GPT was used to detect various aspects of cultural heritage value in facade images [N = 15 195]. Using GPT derived data and building register data, machine learning models were trained to classify multi-family and non-residential buildings in Stockholm, Sweden. Validation against a heritage expert-created inventory shows a macro F1-score of 0.71 using a combination of register data and features retrieved from GPT, and a score of 0.60 using only GPT-derived data. The methods presented can contribute to higher-quality datasets, more time- and cost-efficient inventories, and support decision making by adding explicit rigour

Until 2026, the Energy Performance of Buildings Directive is implemented in the European Union member states, requiring all member states to have National Building Renovation Plans. In Sweden, there is no comprehensive national register of buildings with heritage values. The purpose of this research was to assist Swedish authorities in developing comprehensive information on heritage values. Buildings in street view images from all over Sweden (N = 154,710) have been analysed using multimodal Large Language Models (LLM) to assess visible aspects indicative of heritage value. Zero-shot predictions by LLMs were used for identifying buildings with potential heritage values for 5.0 million square meters of heated floor area. The results of the predictions and lessons learned are related to the development of the Swedish Building Renovation Plan as part of governance. Risks with authorities’ use of LLM-based data are addressed, with a focus on issues of transparency, error detection and sycophancy

Den primära tilltänkta användningen är att översikten av kulturvärden i det svenska byggnadsbeståndet ska användas av Riksantikvarieämbetet som underlag till EPBD-processen (Direktiv om byggnaders energiprestanda)1, där Boverket utifrån regeringsuppdrag efterfrågar en identifiering av byggnader med kulturvärden i det svenska byggnadsbeståndet. Denna rapport kommer att vara en utgångspunkt för Riksantikvarieämbetets dialog med Boverket inom ramen EPBD-processen.

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). 

I följande forskningsöversikt behandlas forskning som genomförts i Sverige om renovering och renovräkning de senaste 15 åren, 2010-2025. Rapporten grundar sig främst på peer-reviewad forskning, men inkluderar även så kallad ”grå litteratur” från tankesmedjor, hyresmarknadens parter samt myndigheter. Fokus för analysen har varit att identifiera där konsensus eller meningsskiljaktigheter finns i forskningen, samt att redogöra för de policy-förslag som diskuteras. I litteraturen återfinns konsensus om att bruksvärdessystemet i sin nuvarande tappning har bidragit till en ökade andel renoveringar bestående av just bruksvärdeshöjande åtgärder, och att dessa renoveringar har påverkat hyresgäster negativt. Detta genom att sådana åtgärder tillåter hyreshöjningar medan regelbundet underhåll inte gör det. Resultatet blir att underhållsåtgärder såsom stambyten i hyreshus ofta innehåller omfattande standardhöjningar i lägenheterna som föranleder signifikanta hyreshöjningar. I denna fråga finns det främst skillnader mellan allmännyttiga och privata fastighetsägare, men även skillnader mellan större privata ägare och mindre sådana. Majoriteten av forskningen på ämnet förordar en förändring av hyressättningssystemet, men inte en avreglering. Istället pekar litteraturen ut alternativa regleringar, ofta ett ökat hyresgästinflytande. Detta går i linje med den forskningen som utgår från hyresgästernas upplevelse av större renoveringsprojekt, där upplevelsen av att inte kunna påverka processen, att inte ha möjligheten att bo kvar eller att vara tvingad till att godkänna renovering är genomgående. Renoveringarna utövar ett bortträngningstryck, det som ibland kallas renovräkning, på befintliga hyresgäster genom att vara ett större ingrepp i de boendes vardag och med sin påföljande hyreshöjande effekt. Bruksvärdessystemet i kombination med besittningsskyddet för befintliga hyresgäster har även skapat incitament till successionsrenoveringar, där lägenheter renoveras vid tomställning. Det finns en avsaknad av kvantitativ forskning på bortträningseffekter, men den gråa litteraturen pekar på att vissa grupper i större utsträckning än andra tvingas flytta vid renovering. Detta syns även i kvantitativa undersökningar av flyttmönster eller flyttanledningar, där vissa hushåll flyttar på grund av följande hyreshöjningar. Slutligen kan policyförslag som ger olika renoveringsalternativ till hyresgästerna lyftas. Hyresgäster kan ges olika alternativ med olika medföljande hyreshöjningar. En fördel med en sådan väg framåt är att det ger möjligheten att förhindra att fler hyresgäster tvingas flytta till följd av en renovering, samtidigt som det låter fastighetsföretag öka sina intäkter tillräckligt för att finansiera renoveringen.

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.