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There is a lack of understanding in the literature on the relation between parks and house price in relation to crime in Scandinavian context. This paper aims to investigate the effect of the amenity value of accessibility to parks on apartment prices with reference to crime rates in parks in Stockholm.

This paper analyses the effects of park proximity and crime in parks on apartment prices by using geographic information systems and hedonic modelling.

Findings show that the proximity of parks as an environmental amenity has an effect on apartment prices. The results also demonstrate that the impact of parks on apartment prices is different in the different segments of the apartment market in Stockholm. Moreover, various types of parks may differ in their impact, for instance, grass parks and park blocks are more desirable in Stockholm than landscape parks and neighbourhood parks. The effects of crimes in parks influence apartment prices negatively.

This paper provides a new methodology by using the shortest distance to a park as a main variable. The shortest distance to a park variable is considered a better choice than using park as an aggregate measure. To the best of the author’s knowledge, this is the first study that investigates the effect of specific park types, for instance, grass parks, neighbourhood parks, landscape parks and park blocks, in Stockholm housing market.

This study aims to analyse the effect of gun-related violence on housing values, controlling for the area's crime levels and locational factors. Previous studies that aimed to find a causal connection between crime and housing values used instrument variables to solve the endogeneity problem. Here, the authors have instead been able to take advantage of the fact that shootings have occurred in random time and space. This has made it possible to estimate models to create windows around the shooting (event) and to estimate the causal effects of the shootings. Thus, the authors aim to contribute to the regression discontinuity design method in this context to estimate the short-term effects.

Using the regression discontinuity design method, the authors can estimate the short-term effects of shootings.

Findings from the analysis indicate that shootings directly affect those who are impacted by shootings and indirectly affect the environments where shootings occur. The indirect effect of shootings is momentary as it is capitalised directly in housing values in the immediate area. The effect also appears to be relatively long-term and persistent as housing values have not returned to the price level before the shooting 100–200 days after the shooting. The capitalisation effect is higher the closer one gets to the central parts of the city. On the other hand, the capitalisation effect is not higher or lower in areas with a higher crime rate per capita.

The article contributes to the previous literature in several ways. First and foremost, it provides an explicit analysis of shootings in built-up areas and their hypothesised effect on property prices through the impact on attractiveness and perceived safety. As far as the authors know, no study has analysed this issue on the international level or in Sweden. In this way, the authors aim to develop a study that can provide critical knowledge about one of the adverse effects of shootings. The authors also contribute to the literature by utilising unique data material, which allows the authors to merge information from the police about the exact location of shootings in the Stockholm area with data on sales of apartments in the same residential areas. In addition to the exact location of the shootings (coordinates), the authors also have access to data about whether the shootings led to injuries or deaths. Thus, the authors have separated the effect of shootings and fatal shootings, which has not been done before. Finally, the authors set out to highlight the results as a contribution to the debate on shootings.

The purpose of this study is to analyse the trends regarding housing segregation over the past 10–20 years and determine whether housing segregation has a spillover effect on neighbouring housing areas. Namely, the authors set out to determine whether proximity to a specific type of segregated housing market has a negative impact on nearby housing markets while proximity to another type of segregated market has a positive impact.

For the purposes of this paper, the authors must combine information on segregation within a city with information on property values in the city. The authors have, therefore, used data on the income of the population and data on housing values taken from housing transactions. The case study used is the city of Stockholm, the capital of Sweden. The empirical analysis will be the estimation of the traditional hedonic pricing model. It will be estimated for the condominium market.

The results indicate that segregation, when measured as income sorting, has increased over time in some of the housing markets. Its effects on housing values in neighbouring housing areas are significant and statistically significant.

A better understanding of the different potential spillover effects on housing prices in relation to the spatial distribution of various income groups would be beneficial in determining appropriate property assessment levels. In other words, awareness of this spillover effect could improve existing property assessment methods and provide local governments with extra information to make an informed decision on policies and services needed in different neighbourhoods.

On housing prices emanating from proximity to segregated areas with high income differs from segregated areas with low income, policies that address socio-economic costs and benefits, as well as property assessment levels, should reflect this pronounced difference. On the property level, positive spillover on housing prices near high-income segregated areas will cause an increase in the number of higher income groups and exacerbate segregation based on income. Contrarily, negative spillover on housing prices near low-income areas might discourage high-income households from moving to a location near low-income segregated areas. Local government should be aware of these spillover effects on housing prices to ensure that policies intended to reduce socioeconomic segregation, such as residential and income segregation, produce desirable results.

Furthermore, a good estimation of these spillover effects on housing prices would allow local governments to carry out a cost–benefit analysis for policies intended to combat segregation and invest in deprived communities.

The main contribution of this paper is to go beyond the traditional studies of segregation that mainly emphasise residential segregation based on income levels, i.e. low-income or high-income households. The authors have analysed the spillover effect of proximity to hot spots (high income) and cold spots (low income) on the housing values of nearby condominiums or single-family homes within segregated areas in Stockholm Municipality in 2013.

This paper aims to analyze how nearby property prices are affected by new construction projects in Stockholm. If there is an impact on property prices, the authors endeavor to investigate whether the effects vary among different areas within the municipality, for different groups of inhabitants and for different types of housing (i.e. public versus private housing).

The authors use a difference-in-difference specification in a hedonic model, and the sample consists of more than 90,000 observations over the period 2005-2013.

The results are robust and indicate that house prices in nearby areas increase following the completion of infill development. The results also indicate that infill development has a positive spillover effect on nearby dwelling prices only in areas with lower incomes, more public housing units and more inhabitants born abroad.

It provides an analysis on how nearby property prices are affected by new construction projects by creating a restricted control area, so as to make the treatment group and the control group more homogeneous. Thus, it mitigates any potential problems with spatial dependency, which can cause biased standard errors.

This paper aims to construct rental housing indices and identify market segmentation for more effective property-management strategies.

The hedonic model was employed to construct the rental indices. Using the k-means++ and REDCAP (Regionalisation with Dynamically Constrained Agglomerative Clustering and Partitioning) approaches, the authors conducted clustering analysis and identified different market segmentation. The empirical study relied on the database of 80,212 actual rental transactions in Beijing, China, spanning 2016–2018.

Rental housing market segmentation may distribute across administrative boundaries. Properly segmented indices could provide a better account for the heterogeneity and spatial continuity of rental housing and as well be crucial for effective property management.

Residential rent might not only vary over space but also interplays with housing price. It would be worth studying how the rental market functions together with the owner-occupied sector in the future.

Residential rental indices are of great importance for policymakers to be able to evaluate housing policies and for property managers to implement competitive strategies in the rental market. Their constructions largely depend on the analysis of market segmentation, a trade-off between housing spatial heterogeneity and continuity.

This paper fills the gap in knowledge concerning segmented rental indices construction, particularly in China. The spatial constrained clustering approach (REDCAP) was also initially introduced to identify regionalised market segmentation due to its superior performance.

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.

The purpose of this paper is to investigate the impact of financial literacy, risk attitude, and saving motives on the attenuation of mutual fund investors’ disposition bias. Specifically, the authors focus on individual characteristics explaining the investors’ propensity to sell shares in a poorly performing mutual fund.

The study relies on survey data collected from 1,564 Swedish households in 2013. The authors test the hypotheses considering three different portfolio compositions and portfolio performances. Each composition corresponds to a dependent variable and a separate model which are estimated using ordinal logistic regression.

The authors find that different forms of financial literacy affect attenuation of the disposition effect. Specifically, the authors find that knowledge about mutual funds and knowledge about current market conditions affect the attenuation of the disposition effect, whereas the authors find no support for the effect of “technical financial knowledge” (e.g. the ability to calculate compound interest rates). The authors also find no support for the effects of risk attitude and saving motives on the attenuation of the disposition bias.

The findings suggest a need for a more fine-grained conceptualization of the financial literacy concept and its effect on investors’ disposition bias. Since an important implication of the findings is that financial literacy could potentially help people overcome behavioral bias, the study provides insights for policymakers as well as into the discussion on the design of consumer education programs.

This study aims to measure the occurrence of gentrification and to relate gentrification with housing values.

The authors have used Getis-Ord statistics to identify and quantify gentrification in different residential areas in a case study of Stockholm, Sweden. Gentrification will be measured in two dimensions, namely, income and population. In step two, this measure is included in a traditional hedonic pricing model where the intention is to explain future housing prices.

The results indicate that the parameter estimate is statistically significant, suggesting that gentrification contributes to higher housing values in gentrified areas and near gentrified neighbourhoods. This latter possible spillover effect of house prices due to gentrification by income and population was similar in both the hedonic price and treatment effect models. According to the hedonic price model, proximity to the gentrified area increases housing value by around 6%–8%. The spillover effect on price distribution seems to be consistent and stable in gentrified areas.

A few studies estimate the effect of gentrification on property values. Those studies focussed on analysing the impacts of gentrification in higher rents and increasing house prices within the gentrifying areas, not gentrification on property prices in neighbouring areas. Hence, one of the paper’s contributions is to bridge the gap in previous studies by measuring gentrification’s impact on neighbouring housing prices.

This paper aims to examine tenure choice in the Swedish housing market with explicit consideration of households’ credit constraints in combination with age and ethnic background.

Observations of some 940,000 households were used to analyse the Stockholm housing market in 2008, prior to the implementation of the mortgage cap. The tenure choice models were estimated using a two-stage instrument variable (IV) logit and probit model with ownership or renting as outcome.

The results suggest, as expected, that being financially restricted is negatively related to owning. In particular, financial restriction is more binding for young households and households with a foreign background than for other types of households. These two sub-groups are also known to have difficulties establishing themselves in the rental housing market, and are therefore specifically vulnerable to further financial constraints such as borrowing restrictions or amortization requirements.

The government in Sweden has become concerned with the rapid growth in household indebtedness. As a response, a 0.85 loan-to-value ratio mortgage cap was introduced in 2010. However, critics are concerned with the effects this may have on the possibility for certain households to purchase a dwelling.

The purpose of this paper is to analyze the relationship between housing markets and new firm formation in six different industries in all 284 municipalities in Sweden.

The authors have used data from Statistics Sweden and The Swedish Agency for Economic and Regional Growth to develop a model to analyze the relationship between house prices and industry-specific new firm formation, with the interaction effect of financial infrastructure.

In the data, stable high house prices have no effect on entrepreneurship. However, a market with rising house prices has a positive effect on new firm formation, in retail, construction, business-to-business services and miscellaneous sectors, but produced no effect in either mining, agriculture and fishing or in manufacturing. The interaction between rising house prices and financial infrastructure does not change the positive effect on retail, business-to-business services and miscellaneous sectors, but within the construction industry, the positive effect on new firm formation disappears. In manufacturing, the authors observe the opposite – a positive effect, instead of no effect previously.

The contribution of this study is to provide evidence of how house prices are associated with entrepreneurship in different industries, as well as analyzing how the interaction between house prices and financial infrastructure is associated with entrepreneurship. By separating observations in time, endogeneity is controlled and a causal relationship where higher house prices is postulated, which leads to an increase in entrepreneurial activity in different industries. By using a spatial Durbin model, the authors control for spatial dependency.