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Ageing populations induce needs to adapt existing housing. With ageing, the number of frail old people, who require assistance in daily life, is also increased. Converting existing housing into assisted living enables them to remain in their community while receiving necessary support and care. The purpose is to investigate whether post-war mass housing is spatially appropriate for adaptation into group homes for older people.

The research material is attained from Finland. Spatial requirements for group homes are drawn from 130 units built or renovated during 2000–2015. Spatial characteristics of mass housing are mapped from 105 apartment buildings built in the 1970s. The latter are matched with the former by comparing the connectivity of layouts, sizes of units and the numbers and sizes of individual spaces.

Group homes typically utilize a linear layout, which can easily be created in apartment buildings. Individual spaces of a group home fit apartment buildings effortlessly. Whole group home units mostly prove to be spatially feasible but result in looser dimensioning than is typical in existing units. The mass housing stock can be considered a spatial reserve for adaptation into group homes.

This is the first study to employ a large-scale, multi-case spatial mapping approach to analyse the adaptability properties of mass housing into assisted living. The findings pertain primarily to the Finnish context, but a methodology is presented which can be applied to other countries and also to other spatial functions.

By observing facts of the “reversal of agglomeration” of Chinese enterprises during the period of rapid Internet development and using a new economic geography model combined with the data of the real estate sector, this paper deduces the influence of the “reshaping mechanisms” of the Internet on China's economic geography based on the “gravitation mechanism” of the Internet that affects the enterprises and the “amplification mechanism” of the Internet that amplifies the dispersion force of house prices.

In the empirical aspect, the dynamic spatial panel data model is used to test the micromechanisms of the impact of the Internet on enterprises' choice of location and the instrumental variable method is used to verify the macro effects of the Internet in reshaping economic geography.

It is found that in the era of the network economy, the Internet has become a source of regional competitive advantage and is extremely attractive to enterprises. The rapidly rising house price has greatly increased the congestion cost and has become the force behind the dispersion of enterprises. China's infrastructure miracle has closed the access gap which gives full play to network externalities and promotes the movement of enterprises from areas with high house prices to areas with low house prices.

The Internet is amplifying the dispersion force of congestion costs manifested as house prices and is reshaping China's economic geography. This paper further proposes policy suggestions such as taking the Internet economy as the new momentum of China's economic development and implementing the strategy of regional coordinated development.

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.

The world over is becoming urbanized, and people are migrating to cities in large numbers in search of opportunities. The increased urbanization has posed challenges such as congestion, rising crime, and growing urban poverty. The governments respond by providing amenities such as schools, hospitals, and housing to meet to increase in demand for these facilities. However, there is a need for the provision of facilities that meets the expectations of the people, particularly on the proximity of amenities and bundles of utility-bearing housing characteristics. In an attempt to address the challenge mentioned, the study estimated the hedonic characteristics influencing the willingness to accept and willingness to pay for housing facilities in the Eastern Cape Province, South Africa. Using a multiple linear regression model and artificial neural network, the study found out that properties with a bathroom, garage and large floor size have a higher value compared to properties without these facilities.When making decisions to acquire a property, buyers consider the availability of discounts and the prevailing property price. Overall, willingness to pay and accept decisions are mainly determined by location and the price at which homogeneous neighborhood properties were sold. Therefore, the study recommends that urban town planners and other housing authorities prioritize the construction of properties with larger floor areas, parking bays, and bathrooms using a cost-effective mechanism that makes the properties affordable to residents.

Faced with the challenges of rural population decline, combined with the widespread expansion of homesteads in rural areas, local Chinese governments hope to strictly control and minimize rural housing land. Accurately decomposing the process of rural housing expansion and revealing its driving factors will be helpful for land-use regulation by the government.

In this study, an unusually rich dataset of rural housing registration from Pudong New Area in Shanghai is employed. The study aimed to decompose the fragmented accumulation process and its expansion determinants on rural housing assets. The dataset covers all samples of rural households and housing plots at 72 surveyed villages in six towns.

Housing offers profitable capital and earning assets to villagers at the urban fringe, so they have a powerful incentive to build and expand more. The results of this analysis showed that the expansion of rural housing is largely due to the haphazard construction of auxiliary rooms by villagers, especially on plots of arable land that are adjacent to their houses that have been stealthily converted into auxiliary rooms and sheds. Low costs and weak penalties have led to an increase in rent-seeking expansions to rural houses. Houses with the smaller initial areas, families with more laborers and household heads, and the proximity of villages to downtown with convenient living services were the main driving factors for expanding houses. A concerted effort is needed to control the disorganized and unlicensed expansion of housing. This effort should include formulating areas for free use by villagers, high taxes on overused areas, serious penalties for unlicensed housing expansion and effective land-use planning.

An understanding of the expansion status and control measures related to rural houses in Shanghai provides an important reference that can help to guide the formulation of rural housing policies, and the sustainable development of cities worldwide. Of course, this study cannot generalize about housing distribution and expansion status worldwide based on the study area in China, because China's land tenure policies are unique. But land registry data exists that makes research like this feasible. There is a need to carefully examine the detailed housing distribution in each country before it can be decided on how best to address the disorderly increase in rural housing stock, and promote the reduction of rural residential expansion.

First, the process of rural housing expansion by using an unique dataset which covers ten thousands of samples is revealed. Second, the results have policy implications for reducing the amount of idle and inefficiently rural homestead. The focus is on rural housing growth and its driving factors in Shanghai, and the villagers' motivations for housing expansion are explored.

This study aims to explore the spatial externalities of apartment vacancy rates on housing rent by considering multiple vacancy durations.

This research uses smart meter data to measure unobservable vacant houses. This study made a significant contribution by applying building-level smart meter data to housing market analysis. It examined whether vacancy duration significantly affected apartment rent and whether the relationship between apartment rent and vacancy rate differed depending on the level of housing rent.

The primary finding indicates that there is a significant negative correlation between apartment rent and vacancy duration. Considering the spatial externalities of apartment vacancy rates, the apartment vacancy rates of surrounding buildings did not show any statistical significance. Moreover, quantile regression results indicate that although the bottom 10% of apartment rent levels showed a negative correlation with all vacancy durations, the top 10% showed no statistical significance related to vacancies.

This study measures the extent of spatial externalities that can differentiate taxation based on housing vacancies.

The findings indicate that landlords have asymmetric information about their buildings compared with the surrounding buildings, and the extent to which price adjusts for long-term vacancies differs depending on the level of apartment rent.

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.

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.