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This study aims to examine the impact of housing construction on single-family housing values and the implications for urban development.

To achieve this objective, the author used the difference-in-difference methodology to examine the effect of multifamily and single-family housing construction on surrounding single-family homes in Stockholm, Sweden. The author analysed data from approximately 480 housing construction projects between 2009 and 2014 and 17,000 single-family detached house transactions between 2005 and 2018.

The research found that multifamily construction projects did not affect the value of surrounding single-family homes, while single-family home construction had a negative impact. The author attributes this result to single-family housing projects typically located in areas with initially positive externalities, while multifamily housing projects are often located on the edge of areas with negative externalities before construction.

The research is limited by its focus on a specific geographic area and time frame, and future research could expand the scope to include other cities and regions and different periods. Additionally, further research could examine the impact of housing construction on other economic factors beyond housing values.

The research has practical implications for urban planners and policymakers. They should consider the potential negative impact of new single-family home construction on existing single-family housing areas while balancing the need for new housing in urban areas. By carefully evaluating construction locations, policymakers can create more sustainable, livable and equitable urban environments that benefit all members of society.

This research paper contributes to the field of housing economics by examining the impact of housing construction on single-family housing values in the context of urban development and climate change mitigation. Using a difference-in-difference methodology, the study provides evidence of the price effect of multifamily and single-family housing construction on surrounding single-family homes, which has important policy implications for urban planners and policymakers. By identifying the negative impact of single-family home construction on surrounding areas and highlighting the need for careful evaluation of construction locations, the research provides valuable insights for creating sustainable, livable and equitable urban environments that benefit all members of society.

As an important indicator of the quality of life of individuals, residential environments are continuing to evolve, due to the rapidly changing production–consumption relations. However, in this evolving process, the effect of the differentiated residential environments on the individuals' residential satisfaction remains unclear. This paper aims to measure the effects of the varying residential environments on the overall quality of urban life (QoUL) in Kayseri, one of the most developed cities in Central Anatolia.

It is based on empirical data on the quality of life in the different residential environments of Kayseri. The research method used stratified purposeful sampling, and the household survey data were analyzed using factor analysis, multiple regression and ANOVA statistical methods.

The most influential factors on the overall QoUL of individuals living in different Kayseri residential neighborhoods were satisfaction with neighborhood and city-level urban services, neighborhood relations and belonging factor groups. The critical finding obtained in this study is that residential satisfaction in low-rise and compact form housing areas in Kayseri is higher compared to residential satisfaction in high-rise neighborhoods. This result reveals that the high-rise building typology that is dominant in Turkey's big cities should be seriously questioned, and urban development policies should be re-evaluated.

The study was designed to produce baseline data so that future changes in residential conditions as perceived by the residents of Kayseri could be monitored to support decisions for residential areas.

Comparative case studies, particularly on low-rise versus high-rise environments, are scarce. As a result, this research contributes to the field of comparative studies on residential environments.

This study aims to examine living preference and tenure among millennials, with a particular focus on the impact of ethnic and cultural diversity on housing outcomes including observed homeownership inequalities.

Using the individual panel data from three waves in American Housing Survey, 2015–2019, this study compares the likelihood of co-residing among Asian and Hispanic millennials with non-Hispanic white millennial peers. Furthermore, this study estimates the effect of co-residence on homeownership across generational and ethnic backgrounds.

This study finds a preference for coresident adult familial households among foreign-born Asian and Hispanic millennials, and US-born Hispanic millennials when compared to their non-Hispanic white millennial peers. The results are robust after considering neighborhood selection bias, affordability and education. The effect of co-residence on ownership is significant and positive, suggesting this living arrangement contributes to homeownership across all generational and ethnic groups.

Housebuilders should be aware of Asian and Hispanic millennials’ increased appetite for extended family living arrangements and consider increasing the physical size of affordable or workforce-oriented rental housing and new single family construction to accommodate more adult co-living arrangements.

This study provides a more comprehensive understanding of the role ethnic and cultural diversity has on millennial adult living preferences and its generational differences, which is not just “boomeranging” as identified by previous literature, contributing to the growing interest in the housing research on the effect of ethnic diversity and culture on millennials’ homeownership rates.

By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.

The purpose of the study is to observe the feasibility of missing middle housing’s (MMH) realization under density-based zoning, form-based zoning and a combination of both while simultaneously providing affordable housing, improving quality of life and making efficient use of land.

This study takes a theorist approach and designs three hypothetical cottage court projects that comply with all relevant official local zoning ordinances to showcase design feasibility, followed by an analytical component in the form of a financial model constructed using official local economic and demographic conditions.

MMH, and in particular cottage clusters, can be implemented under rigorous density-based, form-based and hybrid (density-based + form-based) zoning ordinances and provide affordable housing (Atlanta, GA), improve quality of life (Blackpool, UK) and make efficient use of land (Jinan, China). All hypothetical projects are financially feasible under reasonable conditions.

To the best of the author’s knowledge, this paper is the first in the body of knowledge to discuss how the MMH can be integrated into urban density-based zoning rather than converting density-based zoning into form-based so that the MMH can fit. The paper also takes a cross-national perspective and discusses the feasibility of MMH in the resolution of housing issues in the USA, China and the UK. The study also concludes that the issue of housing unaffordability in the UK was caused by high construction cost relative to median income.

This paper aims to examine how neighborhood characteristics (income, population composition) and individual building attributes (ownership) affect the recovery period of single-family housing and determine their correlations with property abandonment and changes in residential land use after natural disaster.

This empirical study focuses on Valley Fire, one of the California’s most destructive wildfires in 2015, and uses assessor, community, demographic and sales data to measure recovery of a panel of single-family houses located in Lake County in California between 2012 and 2020. Several regression and correlation models will be developed to test different hypotheses.

This study found that: Recovery period is longer than what expected in most existing literature; ownership status significantly affects recovery period; income level is not a significant factor for shortening the recovery period; and minorities may need more assistance for constant recovery. Findings of this research will help identify at risk communities to avoid uneven housing recovery and lower the rate of property abandonment.

Housing recovery is key to revitalizing communities following major natural disasters. The sociodemographic characteristics of each neighborhood have significant impact on the duration of recovery and possible property abandonment. Understanding how home and neighborhood characteristics affect recovery will help planners prevent long-lasting adverse effects of natural disasters.

This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.

This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.

The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.

Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

This study aims to introduce the results of a research carried out to develop a prototype of a highly energy-efficient modular detached house, called CASA+ CASA means HOUSE in spanish, adapted to the climatic features of central-southern Chile. The project enables a sustainable alternative to facilitate the reconstruction of the residential areas after the impact of the 2010 earthquake.

The methodology is based on an “integrated design process” of a case study that proposes a constructive response that quantitatively and qualitatively improves the initial data of traditional dwellings. The characteristics of the new system have been simulated with specific software to validate the final decisions, considering the cost–benefit ratio.

Simulation tools were used to assess and improve the system’s energy performance with respect to present options and to analyse its economic and construction viability. We obtained several economically competitive housing prototypes that substantially reduced energy consumption and the CO₂ footprint by between 20% and 80%.

The prototype has not been developed, as we are waiting for funding, but all its energy features have been simulated.

Furthermore, this experience also identified similar modifications made to the design of the houses, which revealed general possibilities for improving energy performance.

The origin of this research is a public call for international researchers to improve the quality of the new homes to be built in Chile after the strong earthquake of 2010. The result of the research has been put at the direct service of Chilean society and in other international projects for the construction of low-energy social housing.

These are the result of a long research aimed at establishing a new architectural model that, in addition to improving the architectural quality of the product, obtains significant improvements in energy consumption and CO₂ emissions. The most particular aspect is the practical vocation and its implementation with real construction with the support of construction companies.

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.