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1 – 10 of 282Syden Mishi and Robert Mwanyepedza
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…
Abstract
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.
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Fredrik Brunes, Cecilia Hermansson, Han-Suck Song and Mats Wilhelmsson
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…
Abstract
Purpose
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).
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Berna Keskin, Richard Dunning and Craig Watkins
This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.
Abstract
Purpose
This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.
Design/methodology/approach
The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.
Findings
The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.
Research limitations/implications
The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.
Practical implications
The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.
Social implications
The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.
Originality/value
The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.
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Mats Wilhelmsson, Mohammad Ismail and Abukar Warsame
This study aims to measure the occurrence of gentrification and to relate gentrification with housing values.
Abstract
Purpose
This study aims to measure the occurrence of gentrification and to relate gentrification with housing values.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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André Kallåk Anundsen, Christian Bjørland and Marius Hagen
Commonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this…
Abstract
Purpose
Commonly used rent indices are based on average developments or expert opinions. Such indices often suffer from compositional biases or low data coverage. The purpose of this paper is to overcome these challenges using the authors' approach.
Design/methodology/approach
The authors construct a quality-adjusted rent index for the office market in Oslo using detailed data from 14,171 rental contracts.
Findings
The authors show that compositional biases can have a large impact on rental price developments. By adding building-fixed effects to a standard hedonic regression model, the authors show that the explanatory power increases considerably. Furthermore, indices excluding location-specific information, or which include less granular location controls than at the building level, portray quite a different picture of rent developments than indices that do take this into account. The authors also exploit information on contract signature date and find that a more timely detection of turning points can be achieved by using the signature date instead of the more typically used start date of the lease.
Research limitations/implications
The study is confined to Norwegian data, and an avenue for future research would be to explore if similar results are obtained for other countries. A weakness with the paper is that authors' do not observe quality changes over time, such as renovation. Controlling for time-varying and unit-specific attributes in hedonic models for the commercial real estate (CRE) market would be useful to purge indices further for compositional effects and unobserved heterogeneity. While the authors do control for building-fixed effects, there are additional variations within a building (floor, view, sunlight, etc.) that the authors do not capture. Studies that could control for this would certainly be welcome, both in order to estimate the value of such amenities and to see how it affects estimated rent developments. Another promising avenue for future research is to link data on rental contracts in the CRE market with firm-specific information in order to explore how firm profitability and liquidity may affect rental contracts.
Practical implications
The authors show that the hedonic index yields a sharper fall in rents after the global financial crisis and more muted developments in the period between 2013 and 2015 than the average rent index. The results show that rents have followed their estimated equilibrium closely and have re-adjusted quickly in periods of deviation. From a financial stability perspective, the risk of a sharp fall in rents is reduced because rents often are in line with their fundamentals.
Social implications
The authors find that a more timely detection of turning points can be achieved by using information on the signature date. This is an important finding. The financial system is heavily exposed toward CRE, and timely detection of turning points is critical for policymakers.
Originality/value
The financial system is heavily exposed toward the commercial real estate market and timely detection of turning points is of major importance to policymakers. Finally, the authors use our quality-adjusted rent index as the dependent variable in an error correction model. The authors find that employment and stock of offices are important explanatory variables. Moreover, the results show that rents have followed their estimated equilibrium path.
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Miroslav Despotovic, David Koch, Eric Stumpe, Wolfgang A. Brunauer and Matthias Zeppelzauer
In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation…
Abstract
Purpose
In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.
Design/methodology/approach
The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.
Findings
The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.
Originality/value
To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.
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Peter Karpestam and Peter Palm
The authors investigate how prices of condominiums are affected by the size of the tenant-owner associations that they belong to.
Abstract
Purpose
The authors investigate how prices of condominiums are affected by the size of the tenant-owner associations that they belong to.
Design/methodology/approach
The authors use data of sold apartments in the Swedish municipality Malmö 2013–2018 and estimate hedonic price regressions. The authors also perform semi-structured interviews with three senior professionals in real estate companies.
Findings
The authors find significantly negative relationships between the prices of condominiums and the size of tenant-owner associations. Also, regression results indicate that associations should be no smaller than 6–10 apartments. The interviews support that associations should not be too small or too big. The lower and upper limit was suggested by the respondents to 40–50 and 80–150 apartments, respectively. In these ranges, economies of scale can be achieved, and residents will not lose the sense of community and responsibility.
Research limitations/implications
The authors do not prove causality. Smaller associations may have relatively exclusive common amenities, about which we lack data. The same relationships may not exist in different market conditions.
Originality/value
The authors are not aware of previous studies with the same research question. The size of tenant-owner associations may affect the price through different channels. First, several of the banks in Sweden do not always grant mortgages for condominiums that belong to small associations. Second, larger associations may have better economies of scale and more efficient property management. Third, homeowners may prefer smaller tenant-owned associations, because they may feel less anonymous and provide more influence on common amenities.
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In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property…
Abstract
Purpose
In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.
Design/methodology/approach
All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.
Findings
Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.
Practical implications
AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.
Originality/value
According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.
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Mats Wilhelmsson, Vania Ceccato and Manne Gerell
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…
Abstract
Purpose
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.
Design/methodology/approach
Using the regression discontinuity design method, the authors can estimate the short-term effects of shootings.
Findings
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.
Originality/value
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.
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Mohammad Ismail, Abukar Warsame and Mats Wilhelmsson
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Social implications
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.
Originality/value
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.
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