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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.

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.

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.

Despite the quantity of collaborations, the vocational network of the housing production in Ankara during its first five years (1923–1928) remains dispersed. The aim of this study is to identify all the actors of housing production and their collaborations which shaped Ankara's urban development as the new capital city.

The study engages with the literature and archival documents to identify the actors of the housing production, i.e. architects, master-builders, public institutions, private companies, contractors and entrepreneurs, and their resultant vocational network in the housing production in Ankara during 1923–1928.

Due to different agendas, such as speculation, financial interests or patriotism, the construction industry in Ankara had become an arena where many paths intersected, forming an intertwined vocational network. The profession of contractor became popular, and local architects, engineers and even individuals of various other professions began to work as mediators for foreign companies and public institutions, which required support especially in large-scale projects.

The dispersed information revealed that the actors of the housing production remained mostly anonymous, or only the famous architects were commemorated; however, others could be found within the lines of the established literature on Ankara and/or in archival documents. This research not only focuses on “salient” actors but also highlights the “silent” actors of the housing production and prepares charts to clarify the vocational network in Ankara during its first five years to contribute to the future studies on Ankara and its housing.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.

The purpose of this study is to examine the buyers’ preferences influencing the purchase of privately developed affordable housing in Kolkata and to determine whether unsold houses result from misalignment with these preferences.

The literature review and user-opinion survey identified 119 independent variables that indicate buyers’ preferences. A questionnaire survey of 383 households in affordable housing units from 32 housing complexes in Kolkata recorded buyers’ preferences and satisfaction against the independent variables grouped under five levels of characteristics. The product weights of variables derived from the rank sum method and percentage satisfaction give the Utility Score. Multivariate regression and univariate linear regressions were conducted to determine the significance of each Level of characteristics and each variable, identifying the significant variables that would affect the sale of affordable houses.

The multivariate regression analysis has indicated that 68.56% of the variation in the percentage of unsold houses was explained by the five utility scores, which affirms that misalignment with buyers’ preferences significantly affects the sale of privately developed affordable houses. Furthermore, building and neighbourhood-level utility show the highest significance as predictors, while city-level and miscellaneous utility have moderate significance, but housing complex-level utility lacks statistical significance.

This study addresses a research gap in privately developed affordable housing in Kolkata, enhancing understanding of buyer preferences in this segment.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This study aims to clarify the following research questions: to what extent do people consider natural disaster risks as important for residential selection? what personal demographics and attitudes toward natural disaster risks are associated with the relative importance of natural disasters for residential selection? and to what extent do the associated personal attributes influence the relative importance of natural disasters for residential selection?

An internet-based survey was performed to collect 2,000 responses from residents of Osaka Prefecture, Japan, to gauge people’s relative importance of safety against natural disasters regarding residential preference. The obtained results were analysed using two types of statistical analysis, specifically chi-square test and multivariable logistic regression analyses.

It was found that 37.3% of the respondents in Osaka Prefecture, Japan, considered the “safety against natural disasters” relatively important when selecting a residential location. The statistical analysis also demonstrated that those having a relatively higher level of disaster awareness and preparedness were 1.41 times more likely to prefer to live in a place that is safer from natural disasters. Thus, it was suggested that disaster education aimed at raising the level of people’s disaster awareness could be effective to increase the number of people who choose to live in a safer place from natural disasters.

Living in an area that is safer from natural disasters can effectively minimize human and property damage. Recently, several measures have been taken in Japan to guide people to live in a safer place. The clarification of the extent to which people consider natural disaster risks as important for residential selection and the understanding of the categories of the people who are likely to do so is important to develop more effective natural disaster measures; however, there has been less attention on such investigation. Therefore, this study conducted an internet-based survey and examined it.

The intention of this work is to generate a tool to facilitate the visualization of urban parameters, critically discussing the current form of urban planning in Brazil and thus facilitate popular participation in decisions, considering that since 2001, it is foreseen by law that the elaboration processes of municipal urban plans in Brazil must have popular participation in order to be legitimized.

The method consists of three-dimensionally modeling the constructive potential within the lots, using the parameters of the Ribeirão das Neves city master plan (State of Minas Gerais, Brazil), using computer software, to predict the impact on landscape that the parameters generate and comparing different software programs.

With the proposed tool, it became clear that many of the city's parameters alone cannot reach the guidelines of the master plan, and the comparison of two software programs generated options for different local realities. It is a methodology that is able to provide excellent support for urban planning laws in Brazil to be more effective and less delayed.

The software was configured with a script developed by the Geoprocessing Laboratory of a university in Brazil and was used for the first time to completely analyze a municipal master plan for the metropolitan region of Belo Horizonte, generating a tool able to be used in master plans' review.

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.