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Multi-level Impacts of Urban Amenities on Housing Price in Tehran, Iran
Abstract
This study uses a multilevel hedonic model to examine information from official sources including the population census, the land use map, and the field survey to draw conclusions about the 32,108 Tehran residences that make up the statistical sample. These results show that other factors besides proximity to urban facilities are important in determining the value of a home in Tehran, too. The results show that proximity to education, culture, entertainment, tourism, administration, and social service centres has a positive effect on housing prices, while proximity to some urban amenities, such as medical centers, commercial strips, and parks, has a negative effect on housing prices. This gap can be explained in part by the externalities of these facilities, including the creation of through traffic, congestion and reduced security levels, which discourage home-buyers from getting too close to these amenities. Furthermore, the study found that the effect of proximity differs from that of service provision at the neighborhood level. For example, while the presence of a medical/health center at neighbourhood level was found to be a positive determinant of house prices, its proximity had a negative effect on house prices. In fact, home-buyers prefer to purchase properties in neighbourhoods that offer such amenities but are less likely to purchase properties in areas adjacent to such amenities. The higher the ratio of non-residential to residential uses, the higher the price of housing in those areas. In fact, this shows the advantage of neighborhoods with a variety of uses more than single-use neighborhoods. The data also demonstrate that the variances across neighbourhoods in Tehran account for a significant amount of the housing price variation, with 11% and 48% of the total variance attributed to local and regional levels of analysis, respectively. This highlights the nested and hierarchical nature of housing price data and the use of multilevel modelling for estimating home prices in Tehran. The results presented in this work may have theoretical and practical uses for scholars, insurance firms, banks, real estate developers, and others in the field of property economics.
Keywords: Urban Amenities; Hedonic; Multi-level Modeling; Tehran.
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There is a relationship between spectral indices, urban morphology, and spatial patterns with land surface temperature (LST). In this paper, LST and spectral indices in Tehran have been calculated using Landsat satellite images from 2000 to 2019. LST was improved and the results were evaluated using synoptic stations in Tehran. Pearson correlation coefficient and mean root error are used to evaluate the accuracy of temperature difference. The spatial variables affecting LST are examined including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Digital Elevation Model (DEM), Building Density (BD), and Road Density (RD). The rise in the temperature of the land surface temperature, which causes the formation of heat islands, is continuously increasing over time and can be seen in some parts of the city. The findings indicate that LST relative accuracy was 0.98 and the root-mean-square error is 2.65 °C. The distance pattern based on the Pearson correlation test showed an effective relationship between LST and the examined indicators. Spots prone to heat islands were identified. Each of the heat island areas of Tehran was studied according to population centers (dense areas using residential, commercial, and industrial lands). The built-up and barren areas index had the highest correlation with surface temperature in most areas. The results showed that regions 9, 13, 18, 21, and especially region 22, which has the most barren land, have the highest surface temperature compared to the surrounding areas. The findings of this study can be used in future urban planning and policy-making.
Many communities and territories in developing countries experience significant gaps in access to and use of information and communication technology (ICT), which is viewed as a major impediment to socioeconomic and health-related vulnerabilities. Geographic considerations, on the other hand, as well as variations in motivation for ICT usage and technology adoption within and across nations, as well as between cultures, have exacerbated the digital divide. This paper investigates disparities in access to and utilization of ICT in 31 Iranian provinces from 2011 to 2020. The research discovers evidence that family income and the number of R&D centers affected the formation of the digital divide, albeit the degree of the split has fluctuated through time and space. This study discovered that digital inequality is associated with social exclusion throughout Iranian regions, and it went on to try to better understand the underlying issues and potential solutions. In this regard, some policy avenues are suggested for government action, particularly for marginalized socioeconomic groups, such as the provision of infrastructure, training and skill augmentation, and the easing of digital services supplied by the government or private sector.
Disparity in spatial accessibility is strongly associated with growing inequalities among urban communities. Since improving levels of accessibility for certain communities can provide them with upward social mobility and address social exclusion and inequalities in cities, it is important to understand the nature and distribution of spatial accessibility among urban communities. To support decision-makers in achieving inclusion and fairness in policy interventions in cities, we present an open and data-driven framework to understand the spatial nature of accessibility to infrastructure among the different demographics. We find that accessibility to a wide range of infrastructure in any city (54 cities) converges to a Zipf’s law, suggesting that inequalities also appear proportional to growth processes in these cities. Then, assessing spatial inequalities among the socioeconomically clustered urban profiles for 10 of those cities, we find urban communities are distinctly segregated along social and spatial lines. We find low accessibility scores for populations who have a larger share of minorities, earn less and have a relatively lower number of individuals with a university degree. These findings suggest that the reproducible framework we propose may be instrumental in understanding processes leading to spatial inequalities and in supporting cities to devise targeted measures for addressing inequalities for certain underprivileged communities.
This paper is based on reviewing the literature in the past 10 years on the drivers of land use and land cover change (LULCC) in urban areas. It combines quantitative and qualitative keyword analysis of papers drawn out from the Scopus database. The analysis is primarily based on the number of mentions of keywords in the titles and abstracts of the papers, in addition to the number of keywords appearing in the papers. On the basis of content analysis, a three-level structural categorization of the driving factors was developed. These are presented in a schematic diagram, where the contextual factors are shown as influencing economic and financial factors and policy and regulation, which in turn influences transportation investments and availability, and industrial and residential location choices. Transportation availability was seen as the most frequent factor identified in the literature. This research contends that LULCC is mostly determined by interactions among these four themes in a three-level structure, and on this basis, a model is presented that illustrates LULCC drivers based on local circumstances across the globe.
The goal of this study was to find out how suitable the existing design of urban forms is for adopting transit-oriented development (TOD) basic ideas. Within a major metropolis and a medium-sized city, three varieties of Iranian urban fabric (historic, transitional, and modern) around transit stations were selected using the case study research technique. Then, for two sizes of station areas (macro) and street scales (micro), several TOD design dimensions were evaluated. The results of the comparative research indicated that Iranian cities offer greater chances for TOD design in inner urban areas (including historical and transitional urban forms), whereas microscale characteristics are less reliant on the kind of urban form.
Education Elites (i.e, higher-educated people) with new ideas and technologies are widely acknowledged to be a source of innovation and the engine of regional economic growth. A city that can attract higher-educated people will ultimately become the winner of the future. This paper examines whether city amenities attract higher-educated people. Using three different datasets from China, we find that people with college degree or above are much more likely to choose the best amenity cites, which in fact results in 68.4% increase in the concentration of these higher-educated people. In addition, we find that people with master degree or above education can enjoy 45.61 percent income premium in the best amenity cities, and people with four-year college education can also enjoy 30.68 percent income premium. Furthermore, we find that these higher-educated people have much higher satisfaction on income and job promotion opportunities. However, with prices soaring in the best amenity cities, housing becomes increasingly unaffordable. In fact, we find strong evidence that the attractiveness of the best amenity cities has declined over time. This finding suggests that policies that curb the rapid increase in housing prices in the best cities are necessary for local governments in order for their cities continuing to be the winner of the future.
The ‘15-minute city’ concept is emerging as a potent urban regeneration model in post-pandemic cities, offering new vantage points on liveability and urban health. While the concept is primarily geared towards rethinking urban morphologies, it can be furthered via the adoption of Smart Cities network technologies to provide tailored pathways to respond to contextualised challenges through the advent of data mining and processing to better inform urban decision-making processes. We argue that the ‘15-minute city’ concept can value-add from Smart City network technologies in particular through Digital Twins, Internet of Things (IoT), and 6G. The data gathered by these technologies, and processed via Machine Learning techniques, can unveil new patterns to understand the characteristics of urban fabrics. Collectively, those dimensions, unpacked to support the ‘15-minute city’ concept, can provide new opportunities to redefine agendas to better respond to economic and societal needs as well as align more closely with environmental commitments, including the United Nations’ Sustainable Development Goal 11 and the New Urban Agenda. This perspective paper presents new sets of opportunities for cities arguing that these new connectivities should be explored now so that appropriate protocols can be devised and so that urban agendas can be recalibrated to prepare for upcoming technology advances, opening new pathways for urban regeneration and resilience crafting.
Cities are home to a vast array of amenities, from local barbers to science museums and shopping malls. But these are inequality distributed across urban space. Using Google Places data combined with trip-based mobility data for Bogot\'a, Colombia, we shed light on the impact of neighbourhood amenities on urban mobility patterns. Deriving a new accessibility metric that explicitly takes into account spatial range, we find that a higher density of local amenities is associated a higher likelihood of walking as well as shorter bus and car trips. Digging deeper, we use a sample stratification framework to show that socioeconomic status (SES) modulates these effects. Amenities within about a 1km radius are strongly associated with a higher propensity to walk and lower driving time only for only the wealthiest group. In contrast, a higher density of amenities is associated with shorter bus trips for low and middle SES residents. As cities globally aim to boost public transport and green travel, these findings enable us to better understand how commercial structure shapes urban mobility in highly income-segregated settings.
Cities accessible in 15 minutes represent a new possibility for reorganizing the urban system (times, spaces and activities) to try to respond to many current challenges, including ageing populations, energy saving and, more recently, Covid-19. A renewed concept of urban accessibility, together with the redefinition of public spaces and "soft" ways of moving (pedestrian and cycle) to reach neighbourhood services, represent a starting point not only from which to face new challenges but also to rediscover the sense of community, especially at the neighbourhood scale. The 15-minute city draws its origins from the concept of the "neighbourhood unit", developed in 1923 in a competition for the city of Chicago, to define compact residential neighbourhoods where the proximity between services and homes contributed to set out the identity character of that part of the city and to create "the sense of belonging" of a community to a place. The events of the last year strongly re-propose experimentation with this approach, also following the proposal by the mayor of Paris, Anne Hidalgo, aimed at giving a new face to Paris starting from the creation of extensive pedestrian green areas on the large boulevards that are now crossed by the cars and, therefore, to allow the inhabitants to reach, by walking, essential urban services within the 15-minute threshold. Other cities, such as New York and Milan, have also begun to work to make their neighbourhoods, especially peripheral ones, accessible in 15 minutes on foot or by bicycle, without neglecting the goal of transforming these neighbourhoods into attractive places for social gathering for local communities. In this perspective, the research work aims at identifying the urban characteristics that define a 15-minute city, starting from the cities that are launching this experimentation. Among the urban characteristics, the geomorphological, physical (concerning both the spaces and the paths, such as the geometry of the pedestrian and cycle networks), functional (distribution and location of services), socioeconomic (of the population) and settlement ones are taken into consideration. According to the weights of the variables identified as significant, the work defines different areas accessible in 15 minutes based on users' willingness to walk and the geomorphological, physical, settlement and functional characteristics identified in each urban area. The work is carried out in selected districts of the city of Naples which, due to their demographic, morphological and settlement characteristics, make them a significant area of experimentation. Abstract Cities accessible in 15 minutes represent a new possibility for reorganizing the urban system (times, spaces and activities) to try to respond to many current challenges, including ageing populations, energy saving and, more recently, Covid-19. A renewed concept of urban accessibility, together with the redefinition of public spaces and "soft" ways of moving (pedestrian and cycle) to reach neighbourhood services, represent a starting point not only from which to face new challenges but also to rediscover the sense of community, especially at the neighbourhood scale. The 15-minute city draws its origins from the concept of the "neighbourhood unit", developed in 1923 in a competition for the city of Chicago, to define compact residential neighbourhoods where the proximity between services and homes contributed to set out the identity character of that part of the city and to create "the sense of belonging" of a community to a place. The events of the last year strongly re-propose experimentation with this approach, also following the proposal by the mayor of Paris, Anne Hidalgo, aimed at giving a new face to Paris starting from the creation of extensive pedestrian green areas on the large boulevards that are now crossed by the cars and, therefore, to allow the inhabitants to reach, by walking, essential urban services within the 15-minute threshold. Other cities, such as New York and Milan, have also begun to work to make their neighbourhoods, especially peripheral ones, accessible in 15 minutes on foot or by bicycle, without neglecting the goal of transforming these neighbourhoods into attractive places for social gathering for local communities. In this perspective, the research work aims at identifying the urban characteristics that define a 15-minute city, starting from the cities that are launching this experimentation. Among the urban characteristics, the geomorphological, physical (concerning both the spaces and the paths, such as the geometry of the pedestrian and cycle networks), functional (distribution and location of services), socioeconomic (of the population) and settlement ones are taken into consideration. According to the weights of the variables identified as significant, the work defines different areas accessible in 15 minutes based on users' willingness to walk and the geomorphological, physical, settlement and functional characteristics identified in each urban area. The work is carried out in selected districts of the city of Naples which, due to their demographic, morphological and settlement characteristics, make them a significant area of experimentation.
Real estate is potentially contributing to the economic growth. It has a strong correlation between property owners and beneficiaries. The accurate forecast of future property prices is particularly important. Therefore, real estate prices are reflecting the economic level of counties, and their price ranges are of great interest to both buyers and sellers. Developing a land‐price forecast model could significantly assist in predicting future land prices and setting real estate regulations. In contrast, machine learning (ML) algorithms have demonstrated a great potential to perform predictions. Motivated by these assumptions, we develop in this article a set of ML algorithms to build models capable to increase the effectiveness of land price estimation. The ML algorithms adopted in this work include the decision tree, random forest (RF), and linear regression. We collected data from 5946 lands localized in the northern area of Riyadh, KSA. This data has been collected using GeoTech's DAAL website. The performance of the developed models has been assessed based on state‐of‐art performance metrics including mean absolute error, mean squared error, and median squared error. The experiments show that the RF based model outperforms the remaining models.
This paper uses the classical and easy-to-understand Vector Auto-regression Model Method to study the impact of economic and monetary policy variables on the residential property market in Macau. By using sample monthly data from Jan. 2005 to May 2021, the empirical results show the dynamic relationship among the residential property value, key economic and monetary variables. Furthermore, adopting the Granger causality test, generalized impulse response function, variance decomposition and co-integration test of the corresponding VAR specification, the tables and graphs clearly explain the residential housing value has a positive relationship with the gambling gross income; it has a negative relationship with the unemployment rate; it has a positive relationship with the money supply; it has a negative relationship with the consumer price index. Finally, this paper gives five policy actions to help government stabilize the residential property market in Macau and make it healthy develop.
A growing research literature is focusing on how effects from increased accessibility may differ according to socio-economic aspects of neighbourhoods and households. This has raised concerns of whether areas with high accessibility may became unaffordable for low-income households, who, at the same time, may depend on public transport accessibility more than average households. We use difference-in-difference estimates of hedonic price models to study the distribution of effects around the new stations provided by a railway tunnel in Malmö, Sweden. The study finds that the lower-price segments are the most affected by the improved accessibility, which becomes clearer when different control groups are used for different market segments. Accessibility is therefore interpreted as highly important for households in the lower-price segments. From a housing accessibility perspective, this is described as a double-edged sword. On the one hand, the owners of the apartments in these neighbourhoods, who benefit from the price increase, are typically low-income households. On the other hand, low-income households who are not owners may find the area prohibitively expensive due to price increases. Tenure of the catchment area is thus an important factor in understanding the distributional effects of accessibility. Integrating transport and housing-related planning, and specifically addressing the issue of affordability in early assessments, are therefore recommended.
The socio-economic impacts on cities during the COVID-19 pandemic have been brutal, leading to increasing inequalities and record numbers of unemployment around the world. While cities endure lockdowns in order to ensure decent levels of health, the challenges linked to the unfolding of the pandemic have led to the need for a radical re-think of the city, leading to the re-emergence of a concept, initially proposed in 2016 by Carlos Moreno: the “15-Minute City”. The concept, offering a novel perspective of “chrono-urbanism”, adds to existing thematic of Smart Cities and the rhetoric of building more humane urban fabrics, outlined by Christopher Alexander, and that of building safer, more resilient, sustainable and inclusive cities, as depicted in the Sustainable Development Goal 11 of the United Nations. With the concept gaining ground in popular media and its subsequent adoption at policy level in a number of cities of varying scale and geographies, the present paper sets forth to introduce the concept, its origins, intent and future directions.
The issue of property evaluation and appraisal has been of high interest for private and public agents involved in the housing industry for the purposes of trade, insurance and tax. This paper aims to investigate how different factors related to the location of a property affect its price over time. The predictive models applied in this research are driven by real estate transactions data of Tehran Metropolitan Area, captured from open data available to the public. The parameters of the functions that describe the behavior of the housing market are estimated through applying different types of statistical models, including ordinary least squares (OLS), geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR). This suite of models has been run in order to compare their efficiency and accuracy in predicting the variations in housing price. The GTWR model showed significantly better performance than OLS and GWR models, as the goodness of fit index (adjusted R²) improved by 22 percent. Therefore, spatio-temporal non-stationary modelling is significant in the explanation of the variations in housing value and the GTWR coefficients were found more reliable. Three internal factors (size of building; building age; building quality), and eight external factors (topography; land-use mix; population density; distance to city center; distance to subway station; distance to regional parks; distance to highway; distance to airport) influence the property price, either positively or negatively. Moreover, using significant variables that extracted from regression models, the optimum number of housing value clusters is generated using the spatial ‘k’luster analysis by tree edge removal (SKATER) method. Five clusters of housing patterns were recognized. The policy implication of this paper is grouping of Metropolitan Tehran housing value data into five clusters with different characteristics. The varying factors influencing housing value in each cluster are different, making this data analysis technique useful for policy-makers in the housing sector.
Hedonic house price models are frequently used to improve our understanding of local housing markets. In recent years, rich registers containing details about home‐qualities and neighbourhood characteristics have successfully been coupled with spatial qualities such as job‐accessibility or distances to transport. Additional data sources provided by Open data communities, NGOs, data created by governmental agencies on regional national and international level has the potential of being very useful for analysing housing prices. However, the recent methodological advances in GIS and spatial analysis have not been extensively applied. We expand the hedonic price modelling toolbox with geo‐coded free data on environmental amenities. We specifically include local measures describing the view‐shed, and more varied specifications of access or dominance of green and blue amenities, in addition to urban public‐type service and sport facilities. The GIS‐derived data is used to study how the variables should be specified and to study their ability to improve even well specified hedonic price models. To our knowledge, this paper is the first to combine all the listed environmental properties in a hedonic model, and at the same time controlling for a large number of other important local neighbourhood characteristics.
This research investigation falls in the very few studies that seek the association between city development and sustainable urban planning. This study emphasized to examine and explore effects of urban village disamenity on Nanjing suburban and posh area property valuation. Globally, slums exist in almost every country; however, researchers focused less on slums' crumbled infrastructure, crimes, and other social problems; however, few kinds of research studies covered the adverse impact on the property value. This paper plans to seek the effects of rural area migrants on Nanjing City in response to the late 1970 economic reform implementation as they aimed to extract benefits of reforms offering enhanced economic opportunities, higher rate of income and employment, and comprehensive social benefits. This study examined the negativity of urban villages on the surrounding area valuation and its impact on residential properties. The results of this study specified an adverse effect of urban villages on the value of nearby apartments; however, residential units closer to these villages experienced the worst impact compared to the residential apartments located far away. The findings revealed that urban villages' neighborhood negatively influences home selling value when compared with other houses located far away. This study is helpful for government officials and policymakers to resolve urban villages' issues.
The US housing price run-up and its subsequent collapse are considered by many to be central elements in causing the global financial crisis. This paper aims to estimate housing bubbles in 140 US cities by identifying the origination and termination of the bubble, during the period from 1989 to 2017. The study introduced the generalized supremum augmented Dickey-Fuller (GSADF) test, an innovative and powerful bubble detection method, to evaluate the regional US housing bubbles. Estimation results show several important findings. (1) Forty-five out of the 140 studied cities had existing housing bubbles. Phoenix-Mesa-Scottsdale had 8 bubbles, which was the largest quantity. Nine cities had one bubble, which are Northern New Jersey–Long Island, Richmond, Honolulu, Sarasota-Bradenton-Venice, Boise City–Nampa, Pensacola–Ferry Pass–Brent, Ocala, Atlantic City, and Barnstable Town. The bubble duration of Northern New Jersey-Long was the longest, ranging from 1999 Q4 to 2007 Q4. (2) The time period of bubbles can be divided into three stages based on its beginning time and duration. There were 48 bubbles at the first stage from 1992 to 2002, 85 at the second stage from 2003 to 2008, and 12 at the third stage from 2009 to 2017. The housing bubbles were mainly concentrated in the second stage. (3) Significant regional disparity can be observed in the bubbles. The 45 cities where there were bubbles were divided into three groups according to the population. Urban population is positively correlated with the average duration of the bubble. The lessons drawn from the US contribute to an in-depth understanding of the market dynamics and evolution patterns of housing bubbles.
Accessibility to transit facilities is perceived to affect property prices. However, accessibility by transit has rarely elicited adequate scholarly attention in property price analyses. Additionally, previous studies on how transit accessibility affects property prices mainly focused on rail and bus rapid transit systems, while conventional bus transit, which is very popular in many contexts, has seldom been investigated. Moreover, whether there is spatial heterogeneity in the price (or capitalization) effects of conventional bus accessibility remains to be explored. To fill these gaps, this study aims to investigate the role of accessibility to and by bus in determining housing prices in a bus-dependent city where urban transit service is offered mainly by a bus system rather than other transit systems. Using a database of 4966 condominium units in Xiamen, China, this study develops a battery of spatial econometric models to estimate global and local relationships between to-bus and by-bus accessibility and housing prices. The findings are as below: (1) to-bus accessibility (measured by the number of nearby bus stops) is positively associated with nearby housing prices; (2) by-bus accessibility (measured by travel time to city centers by bus and bus frequency) significantly affects nearby housing prices; (3) spatial heterogeneity exists in the price effects of bus accessibility; and (4) bus frequency exerts a larger price effect in the peripheral area than in the central. Finally, practical and policy implications are discussed.
Urban security is of great importance, especially in public spaces like parks, which are considered a place for relaxation, comfort, and recreation. Various crimes occur in such places due to problems such as lack of supervision, large numbers of people, and reduced lighting, which increase the number of defenseless and private places. To analyze the security status of public parks, this study employed geographic information systems (GIS)‐based spatial analysis of crimes in the Tehran metropolis, the capital of Iran. The study includes twenty-four crimes occurring in forty-one parks in Tehran in 2015. Required information was collected by contacting the police department and the weight of each index was determined using an analytical network process. To rank the parks, the PROMETHEE model was applied and graphical statistical methods were used in a GIS environment to detect and display the spatial pattern of crimes. The results of the analysis show that crime is low in central, northern, and southern parts of the city, whereas it is high in the western and eastern parts of Tehran. In addition, there is a significant positive relationship between two variables such as area and park ranks in terms of security. It is essential to detect the place conditions resulting in crime opportunities or simplify it by relying on geographic science principles. Results of this research are of great importance for several organizations in Tehran, such as the Municipality of Tehran, police department, justice system, and other relevant organizations to increase safety in parks and for the development of GIScience in the context of crime analysis.
The study presents a political economic analysis of spatial inequality in Tehran focusing on four sectors of social reproduction, namely, housing, healthcare, education, and transportation. The study argues that spatial inequality is rooted in the peculiarities of Iranian capitalism. Struggling with low productivity, the manufacturing sector needs wages to remain low and unemployment to remain high in order to maintain profits. Bringing urban amenities and resources into the market is the second strategy, a process facilitated by the state. The study also discusses the role of the luxury market in neutralising the anticipated negative feedback mechanism of low effective demand.
The current study argues that the capitalisation effect of urban public facilities on housing will be considerable when the accessibility or availability of facilities has a serious stake in the location or property rights of houses. The supply level and supply quantity of urban public facilities determine whether there is a significant difference in the accessibility or availability of facilities amongst neighbourhoods, and subsequently determines whether the capitalisation effect of facilities on surrounding houses is considerable, which ultimately affects the spatial inequality in housing prices (i.e. spatial dispersion of housing prices). However, previous studies have rarely considered the fact that the supply and demand of urban public facilities vary with the type of facilities. Thus, according to the law of diminishing marginal utility, the current study proposes a theoretical framework for the impact of the allocation of urban public facilities at different supply levels on the spatial inequity in housing prices and verifies this through a case study. Results indicate that the difference in urban public facility allocation caused by the unequal supply quantity or unbalanced spatial distribution has a notable impact on the spatial inequality in housing prices. There are three states of allocation of urban public facilities available according to different supply levels, namely, disequilibrium, quantitative equilibrium and spatial equilibrium: (I) Scarce and high-quality public resources that may always be in the disequilibrium state create a substantial capitalisation effect on nearby housing, and their presence will aggravate spatial inequality in housing prices; (II) Public facilities that can only reach the quantitative equilibrium state have a considerable capitalisation effect on nearby housing, and their supply densities have a positive impact on the spatial inequality in housing prices; (III) Public facilities in the spatial equilibrium state have a negligible capitalisation effect on nearby housing, and their supply densities have a negative impact on the spatial inequality in housing prices. Therefore, it is reasonable to argue that urban public facilities at different supply levels have a diversified impact on the housing market. This study can contribute to having a comprehensive and in-depth understanding of the diversified impact of urban public facilities on the housing market.
Numerous studies have examined the determinants of housing prices in Chinese cities. However, most of them ignored the hierarchical structural characteristics of housing prices because of the use of ordinary least squares and hedonic pricing to model the housing prices for a single city. Therefore, this study explores the multi-level determinants of housing prices and their interactions at different levels. To this end, it proposes a three-level hierarchical linear model (HLM) using observations from 146,099 communities nested in 1120 counties of 31 provinces in China as a case study. The results of the hierarchical linear regression indicate significant variances in average housing prices. This finding suggests HLM to be appropriate when dealing with housing prices inherently nested at multiple geographic levels. Overall, housing values in China are not only determined by accessibility factors but are also driven by multi-level socioeconomic aspects. Among the selected variables, high-speed railway shows a significant positive effect, while ordinary railway shows a significant negative effect on housing values at the community level. At the county level, rural–urban migration and per capita living space have significant positive impacts on housing value. At the province level, the relationship between rural–urban migration and housing prices depends on economic development and urban employment. Similarly, the average wages of urban employment influence the relationship between per capita living space and housing prices. These results suggest that contextual effects exist between the determinants of housing prices at county and province levels.
Recently, the improvement of job accessibility and the encouragement of mixed land use have been gaining popularity in the planning field. However, little is known about whether these two factors are able to meet housing consumers’ needs. This study aims to analyze how job accessibility and mixed land use satisfy housing consumers’ needs. Particularly, this study investigates housing consumers’ willingness to pay for these two features by using housing prices and rents in the Chicago metropolitan area. In order to deal with endogeneity between land use and housing prices and spatial autocorrelation between housing prices, spatial econometric models are used with instrumental variables. Interestingly, our findings show that an increase in job accessibility leads to an increase in housing prices, whereas it is not related to rents. We also found that mixed land use decreases housing prices, but increases rents.
The premium effect of public service facilities on the housing market is a critical determinant of housing prices, leading to the competition of different social groups in the housing market and fueling spatial inequality. Taking Xi’an, China as a case in point, this study uses Geographic Information System (GIS) to describe the spatial distribution pattern of housing prices and urban public service facilities. Using the mixed geographically weighted regression model (MGWR) and the geographical detector model (GD), this study reveals the spatial effects of these facilities’ accessibility on housing prices. The results show that commercial and leisure facilities are spatially stationary, whereas a non-stationary effect is observed among those providing educational, medical, cultural, sport, and financial services. From the urban spatial resource allocation perspective, facilities meeting people’s basic needs, such as medical care and education, constitute the basic elements of housing price differentiation. When any two of these interact, a bivariate-enhanced effect emerges. The decisive interactive elements of housing price differentiation involve the facilities meeting people’s higher-level needs, such as leisure, culture, sports, and finance. When these interactive elements interact with other facilities, a non-linear enhancement effect is induced. This research is of practical value for improving people’s living quality, optimizing the spatial distribution of public service facilities, and eliminating urban spatial inequality.
Access to housing in a society is a widely held goal that its achievement could be challenged by disregarding the social status of housing and the lack of systematic approach in housing policy-making and planning processes. This would result in spatial (i.e., multidimensional, including economic, social, physical, dimensions) inequality. Tehran, the capital of Iran that has densely contained various social, economic and political activities, is a complex urban system, which its complexity makes it more vulnerable when confronting deficiencies in policy-making, especially in regards to its having an incomplete and unsystematic planning approach. Due to the intense, accelerated, unplanned and migratory growth of Tehran and its surrounding environment, the severe inequality is more evident compared to the other cities of Iran.
Most existing studies empirically investigated the impact of rail transit on housing prices using the traditional hedonic price model, which is based on ordinary least squares (OLS). This method can estimate only the average implicit prices of housing characteristics and may ignore possible heterogeneous impacts of rail transit at different housing price levels. As a useful supplement to OLS regression, quantile regression can measure how implicit prices of explanatory variables vary across different price levels, thereby providing a comprehensive picture of the relationship between housing characteristics and prices. By using Hangzhou, China as an example, this study adopts the traditional hedonic price model and quantile regression model to investigate the capitalization effect of a new subway line on housing prices. Empirical results suggest the significant impacts of accessibility to subway stations. The average housing price within 2 km of the station is 2.1% to 6.1% higher than those outside. We also find that the impacts of the subway differ significantly across the distribution of housing prices, wherein the absolute value of estimated coefficients increased from 0.023 for the 15th quantile to 0.086 for the 95th quantile. The subway opening strengthens the capitalization effect of traffic accessibility. The absolute value of price elasticity increases from 0.044 to 0.053, and the range of influence is expanded from 1500 m to 2000 m.
This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan (RMB)/m 2 , and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.
The purpose of this study is to quantify the marginal willingness to pay (MWTP) for clean air in China. We provide the first estimate of MWTP for clean air by implementing a hedonic method using housing price and air quality data from Shanghai. Our estimates imply that air pollution has a significant and negative impact on housing price. We also find that the willingness to pay for better air quality varies significantly across different income groups. This paper helps to deepen our understanding of the economic impacts of air pollution in emerging Asian metropolises where residents are suffering from the most severe respiratory health problems.
Urban sprawl is one of the main important phenomena threatening the spatial structure of cities and affecting the overall quality of them. This paper studied the urban growth pattern and sprawling of Kazerun, a medium-sized city in the south of Iran. Using the population and land use data available from secondary formal sources, the growth pattern was compared to national statistics. Furthermore, two well-known landscape metrics included Holdern and Shannon indices were applied to measure the extent of urban sprawl. As a qualitative research methodology, some experts (n = 30) working in governmental agencies were asked to explain the causes and effects of urban sprawl in the case study area. The results confirmed that several factors including the former masterplans affected the urban development pattern of the area. Moreover, a number of physical, environmental, economic and social impacts were discovered as the consequence of current fragmented growth pattern. Finally, a set of applicable policies are recommended to overcome sprawl and to achieve a more balanced development.
Two advanced modelling approaches, Multi-Level Models and Artificial Neural Networks are employed to model house prices. These approaches and the standard Hedonic Price Model are compared in terms of predictive accuracy, capability to capture location information, and their explanatory power. These models are applied to 2001-2013 house prices in the Greater Bristol area, using secondary data from the Land Registry, the Population Census and Neighbourhood Statistics so that these models could be applied nationally. The results indicate that MLM offers good predictive accuracy with high explanatory power, especially if neighbourhood effects are explored at multiple spatial scales.
Conventional housing price prediction methods rarely consider the spatiotemporal non-stationary problem in a large data volumes. In this study, four machine learning (ML) models are used to explore the impacts of various features – i.e., property attributes and neighborhood quality - on housing price variations at different geographical scales. Using a 32-year (1984–2016) housing price dataset of Metropolitan Adelaide, Australia, this research relies on 428,000 sale transaction records and 38 explanatory variables. It is shown that non-linear tree-based models, such as Decision Tree, have perform better than linear models. In addition, ensemble machine learning techniques, such as Gradient-Boosting and Random Forest, are better at predicting future housing prices. A spatiotemporal lag (ST-lag) variable was added to improve the prediction accuracy of the models. The study demonstrates that ST-lag (or similar spatio-temporal indicator) can be a useful moderator of spatio-temporal effects in ML applications. This paper will serve as a catalyst for future research into the dynamics of the Australian property market, utilizing the benefits of cutting-edge technologies to develop models for business and property valuation at various geographical levels.
In the mass appraisal of properties, Machine Learning (ML) algorithms have produced effective and promising results. Analysts use various algorithms to train their models with limited data and make appraisals on large data sets. However, research into which value determinants the models take into account when appraising values is insufficient. This research looks at how eXplainable Artificial Intelligence (XAI) methods can be integrated with mass real estate appraisal studies. Experimental studies were carried out on a data set containing 1002 samples and 43 independent variables. Tree-based ML regressors, namely Random Forest, XGBoost, LightGBM, and Gradient Boosting, were used for training the predictive models. The performance of these regressors was compared with that of classical multiple regression analysis. The Permutation Feature Importance (PFI) technique was used for the selection of the variables that contributed the most to the training of the models. Models retrained with selected variables were locally interpreted using the SHapley Additive eXplanations (SHAP) method. In this way, it was possible to observe the value determinants that contribute to the price estimation of each real estate sample. This study demonstrates that XAI approaches should be integrated into mass real estate valuation systems specifically, and into urban and housing research more generally, helping analysts and scholars to explain their models more transparently. The outcomes of this study can be a harbinger for analysts and scholars who wish to explain their models more transparently. Last but not least, this study advocates the use of tree-based ML algorithms since they not only allow us to implement XAI approaches but also outperform the stand-alone ML regressors.
Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local through global scales. Although many definitions and classifications have been proposed to better understand the causes and evolution of LST, not enough attention has been paid to their relationship to factors such as socioeconomic characteristics and spatial configuration of cities and the fact that city-level temperatures may not exist. Aiming to elucidate the effects of these factors and their relation to surface temperature dynamics, this study explored the relationship between land surface temperature (LST) variations and (using the Local Climate Zone [LCZ] method) space syntax (using Open Street Map data), and other physical and non-physical (e.g., socioeconomic) factors (and their combined effects through the Analytic Hierarchy Process (AHP) method). In the next step, a map of socio-spatial segregation, comprising four urban zones, was created. The lowest and highest mean LST figures were related to "water body" and "bare soil or sand" LCZs. Among the four segregated zones in this study, the lowest mean LST was recorded in the integrated areas. The highest mean LST was observed in the absolute (involuntary) segregation zone. The results suggested that integrated areas have a lower standard deviation than those of other zones, indicating a more consistent or stable cooling effect. In addition, the varied composition of building heights could help to mitigate the urban heat island. Finally, some considerations were presented for local climate improvement in the urban spatial structure.
There is a vast literature that seeks to define and identify spatial submarkets in metropolitan housing systems. These tend to use one of three methods to delineate submarkets: a priori geographies, ad-hoc sub-division and data-driven approaches to grouping units. Recently, analysts have increasingly used multilevel modelling strategies to analyse spatial segmentation in the housing market. Despite the increasing prevalence of multilevel approaches, there is no existing systematic analysis of which of these three main approaches to submarket definition has the greatest effectiveness when employed in a multilevel modelling framework. This paper addresses the gap in the literature by comparing the utility of these main approaches to submarket definition. It develops and evaluates three separate, distinct multilevel models of submarkets to a data set comprising of 2175 transactions in the Istanbul housing market, an emergent market context. The results show that multilevel models with a priori submarket dummy variable can predict price more accurately than the models with ad-hoc sub-division or data-driven stratified submarkets. Similarly, test results indicate that multilevel models with neighbourhood submarket dummy variables (a priori) perform better than other models. These test results show that granular definition of submarkets tend to perform better in terms of predictive accuracy than less spatially granular models. The paper also suggests that real estate agents’ views of submarket structures might be particularly useful as inputs into micro modelling processes in contexts where datasets are thin.
By raising road transportation costs, an increase in gasoline prices should be expected to reduce housing demand in locations further from the central business district (CBD) relative to inner-city locations. This study uses a monthly real estate area-level dataset for 19 large cities in China over 2010–2018 to investigate the impact of gasoline prices on intra-city spatial differentials in housing prices. The findings suggest that higher gasoline prices on average lead to a relative decline in housing prices in outer suburbs, with a 1% increase in gasoline prices on average leading to a 0.004% relative reduction in home values for every additional kilometer from the CBD. The effect is larger in cities that have higher automobile ownership rates and that are less densely populated. The results are consistent with a conclusion that the rise of electric vehicles, autonomous vehicles, and working from home is likely to contribute to a lowering of geographical price differentials within Chinese cities over time.
Despite the implied centrality of students to studentification research, much of the current debate on universities and urban change often ignores the voices of students. Common presumptions about students attending universities take for granted middle-class trajectories that do not account for their variegated identities and experiences. This paper examines university students' housing experiences in Toronto, a city that has experienced a rapid financialization and commoditization of housing markets. Using focus group data, we examine the housing landscape of students from their perspective, including their housing preferences, strategies to access affordable accommodations, and the effects of housing and economic stress on their wellbeing. Our findings indicate that the lack of adequate, affordable, and accessible housing for students has forced them to tacitly learn to be “resourceful” and “creative” in the task of securing housing. We argue that variations in student housing experiences act as a “hidden curriculum” that normalizes socio-economic burdens. This hidden curriculum, embedded in inadequate housing experiences, has forced students to hustle to survive, further marginalizing students and reproducing social hierarchies and class, gender, racialized, or ageist divides. We conclude with recommendations, some suggested by students themselves, to improve the student housing situation in cities with expensive housing markets.
Between 2003 and 2006, the Federal Reserve raised rates by 4.25%. Yet it was precisely during this period that the housing boom accelerated, fueled by rapid growth in mortgage lending. There is deep disagreement about how, or even if, monetary policy impacted the boom. Using differences in exposure to the deposits channel of monetary policy, we show that Fed tightening induced a large reduction in banks’ deposit funding, which led banks to contract portfolio mortgage lending by 32%. However, this contraction was largely offset by substitution to privately-securitized (PLS) mortgages, led by nonbank originators. Fed tightening thus induced a shift in mortgage lending away from stable, insured deposit funding toward run-prone and fragile capital markets funding with little impact on overall lending. We find similar results during the most recent tightening cycle over 2014–2017 when PLS lending reemerged.
Sustainable housing has been an overarching goal globally, and Ghana is no exception vis-à-vis the country's energy and housing affordability challenges particularly in cities. Yet, its implementation has been plagued by barriers. This study assesses the influence of barriers on sustainable housing in Ghana. Forty-seven valid responses were obtained from a questionnaire survey. Confirmatory factor analysis validated three main groupings of barriers: "cost-related," "incentive-related," and "retrofit-related." Partial least square structural equation modeling of the responses showed that incentive-related barriers have medium effect size (0.192) on sustainable housing while retrofit-related barriers have high effect size (0.430) on sustainable housing. Furthermore, incentive-related barriers have a significant impact on retrofit-related barriers. Cost-related barriers only had a significant impact on incentive-related barriers. Accordingly, cost-related barriers are secondary barriers to sustainable housing. The findings apprise policymakers of barriers that require efficient resource allocation for sustainable housing for sustainable built environment.
The deployment of faster household Internet speeds enables new opportunities for entertainment, social interaction, and personal development, and many consider such access an essential component of everyday life. However, rural residents face lower availability, slower speeds and limited provider options, putting them at a disadvantage when compared to their urban counterparts. Connected rural households, especially those with higher speeds, may experience a premium on their home value. Data from the National Broadband Map, the Federal Communications Commission, and over 2,700 housing transactions from June 2011 to June 2017 are used to examine the impact of broadband availability on housing values in two rural Oklahoma counties via a hedonic price model. The results find no support for the existence of a broadband premium, and stress that differences across counties are crucial in assessing rural housing prices.
Rail transit is becoming significant to urban growth and sustainability in China since China is investing more than 2 trillion yuan in urban rail transit during the 13th Five-Year Plan (2016–2020). Transit-oriented development (TOD) emphasizes using built-environment factors such as mixed-use, density, and walkability, to maximize the benefits of improving transit accessibility. Although studies illustrate how transit-oriented built-environments amplify the rail transit impact on housing prices within the American context, little attention has been paid to Chinese cities. Using a case study of Wuhan City in China, this study shows that a higher transit proximity premium of housing prices is more likely to occur in the place with more mixed land uses. Policy implications on TOD planning and land value capture near transit station areas are derived.
This paper attempts to explore the spillover effects of ecological lands, including forest, grassland, wetland, and cultivated land, on housing prices. To this end, we test hypotheses from a spatial multilevel hedonic model in Wuhan, China. We find that forest size and wetland size has a linear positive spillover effect on urban housing prices, and a moderate grassland area and distance from wetland generates positive spillover effect on urban housing prices, while too much or poor grassland area and distance from wetland may not. Also, only cultivated land very proximity to urban residential areas may raise the housing prices, most of the cultivated land in our case may reduce the housing prices. This article contributes to the literature by integrating different ecological lands into the hedonic analysis based on spatial multilevel models and deepens the relationship between the accessibility and visibility of ecological lands and housing prices. This result implies that demand for the forest, grassland, and wetlands can be well reflected in the housing market, while demand for cultivated land is less reflected in the housing market. Our findings urge policymakers to increase the effective supply of ecological lands through urban development planning and maintain the continuous supply of existing ecological lands by implementing market, differential ecological protection mechanisms
Housing values are heavily influenced by urban facilities. However, the amenity effects of urban facilities have not been fully assessed, and their spatial heterogeneity has largely been neglected. Taking Nanjing as an example, this study adopts a synthetic approach that considers scarcity, accessibility, and submarkets, and highlights the influence of the idiosyncratic characteristics of urban facilities and heterogeneous urban spaces on housing prices. We divide urban facilities into two categories based on scarcity: irreplaceable and replaceable facilities. We find that the influence of urban facilities differs among different categories and submarkets, and the influence of irreplaceable facilities is highly dependent on accessibility in all submarkets, while that of replaceable facilities relies on both the accessibility and scarcity of those facilities. This study contributes to our understanding of the importance of and differences among the effects of various public facilities on property values in the intra-urban environment.
Educational facilities are valued highly by citizens and play an important part in improving housing prices. Most existing studies applied the hedonic price model to obtain the average effects of educational facilities on housing prices. However, these effects may be heterogeneous in different conditional quantiles of housing prices. Aside from the average effects, scholars are also interested in the different behaviors of high-, medium-, and low-priced homebuyers, behaviors that cannot be simply explained by ordinary least-squares estimation without sample truncation. To fill this gap, this study utilizes quantile and spatial quantile regression models to obtain a complete description of the entire distribution. On the basis of 2015 housing transaction data in Hangzhou, substantial differences are identified in the effects of educational facilities on housing price across the conditional distribution. In particular, primary and secondary schools considerably improve housing prices over the entire conditional distribution, whereas kindergartens are only valued by homebuyers of lower-and higher-priced housing. Furthermore, only households of high-priced housing are willing to pay a premium for the accessibility of senior high schools and universities. The effect of spatial dependence on higher-priced housing is more pronounced than that on lower-priced housing, and the consideration of spatial attributes improves the explanatory ability. Empirical results indicate that the average effects of educational facilities on housing prices obtained by existing studies that used the traditional hedonic price model is far from adequate in shedding light on this real-world phenomenon. In particular, the quantile effect should be further revealed to obtain an in-depth understanding.
Skyrocketing housing prices in China's megacities have generated broad concerns. By integrating open data from Lianjia.com, Dianping.com, Mobike.com, and Baidu Map POI, we analyze spatial patterns of apartment prices and their association with local attributes in Shanghai. We find that Shanghai's residential market still has a monocentric structure because of the centralized distribution of public transport facilities and amenities. Hedonic models further confirm that structural attributes, accessibility, as well as public and private service amenities significantly shape the real estate market. These factors also are differentiated so as to form a pattern of concentric rings. In the inner-city and expanded inner-city areas, public service amenities such as parks, schools, hospitals, and banks, as well as private service amenities such as entertainment, shopping, and residential service facilities, boost housing prices. In the suburbs, better access to bike sharing, bus stops, and metro stations are the top preferences for apartment buyers. Our study also indicates that the Chinese government needs to make public and private services more accessible, not only spatially in urban peripheries and villages, but also institutionally to lower income families who cannot afford apartments in expensive neighborhoods.
While transportation infrastructure can increase housing price by improving accessibility to opportunities, it generates environmental health risks, such as noise and air pollution, which may have negative effects on housing price. However, the combined effects of accessibility and environmental health risk on housing price have not been well examined in the literature, especially in the auto-oriented urban context of the United States. In this study, we use assessed housing value data and the hedonic model to examine the single-family housing market's reaction to accessibility and environment health risks in Salt Lake County, a growing metropolitan area in Utah experiencing significant air pollution. Three regression models are employed with the consideration of spatial effects: ordinary least squares (OLS), spatial lag regression (SLR), and hierarchical linear modeling (HLM, or multilevel modeling/MLM). By controlling for the influences of structural attributes and socioeconomic conditions, we find that the negative impacts (traffic noise and air pollution) of transportation systems on single-family housing prices are greater than the positive impact (accessibility). Single-family residents in Salt Lake County are willing to pay more to reduce environmental health risks than to get better accessibility. These findings are different from what have been found in some dense and compact urban areas in the literature. These findings suggest that people's willingness to pay for minimizing environmental health risks varies across different urban contexts.
Attaining a mixture and diversity of land use within walkable neighborhoods is an essential principle within contemporary urban planning and design. Empirical studies by New Urbanists argue that mixed land use, neo-traditional, and walkable neighborhoods yield socioeconomic benefits and generate a substantial premium in residential property prices. However, few studies apply reliable metrics to capture the connections among urban form, the spatial distribution of land use, and travel behavior and then value their combined effects on housing prices. To bridge this gap, this study calculates the metrics of spatial accessibility and centrality, combining street nodes, networks, and built density by land use type within walkable neighborhoods. We investigate empirically the extent to which residents value spatial accessibility and centrality to residential, commercial, office, and industrial space regarding housing prices in Seoul, South Korea in 2010. The multilevel hedonic price models used suggest that residents highly value urban settings that access larger volumes of commercial and residential buildings in densely spaced areas along dense street networks. However, homeowners respond negatively to higher access to industrial property and weakly to office space. This analysis identifies the value of spatial access to heterogeneous land-use density in housing prices and provides policy implications for land use, transportation, and urban design.
Accessibility to parks could be an important determinant of housing prices. This article applies the gravity model to calculate accessibility based on park classification in Shenzhen, China. Unlike most traditional studies that use the ratio method and nearest distance (including straight-line distance and network distance) to measure accessibility to given facilities, in this study, we use gravity-based accessibility by park type. Then, we explore the relationships between accessibility to parks and housing prices using a hedonic price model. In addition, we apply a geographical detector method to assess the association between housing price and related factors. The results indicate the following conclusions: (1) compared to traditional methods, the gravity model provides a more effective and objective measure of accessibility to parks because it considers distance decay effects, supply, and demand; (2) it is necessary and important to investigate the effects of the accessibility to different park types on housing prices; and (3) geographical detector models can efficiently detect correlations and interactions among housing prices and related factors.