Housing Submarkets Research Articles

Do affluent neighbourhoods pay more for transit access? Exploring the capitalization of employment accessibility within different housing submarkets in Vancouver

Decades of research indicate that accessibility plays a fundamental role in the urban systems of cities by influencing land markets and household location choices. Accessibility is also often positioned as a policy tool in enhancing the well-being of disadvantaged population groups. Considered together, recent research into transportation equity underscores the need to critically investigate the distribution of accessibility with their affordability impacts. To better understand this dynamic, this research assesses variability in the relationship between employment access and house prices within different neighbourhood types in Metro Vancouver. We first calculate network accessibility to employment opportunities. Next, to integrate equity, we use sociodemographic indicators from census data to establish a typology of four neighbourhoods: Affluent, Stable Middle-class, At-Risk Middle-class and Economically-disadvantaged. Finally, using real estate data, we employ spatial econometric models to estimate differences in the capitalization of accessibility in residential property prices across these neighbourhood types. Results suggest that the value of transit access does differ by neighbourhood type in Metro Vancouver. While more affluent neighbourhoods exhibit the highest marginal willingness to pay for accessibility, predicted prices are highest in disadvantaged neighbourhoods due to their higher absolute levels of access. The research offers new insight into how property price effects vary according to different accessibility and sociodemographic contexts, and highlights important implications for both policy and future research.

Housing price determinants in Ecuador: a spatial hedonic analysis

Purpose This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities. Design/methodology/approach This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal. Findings The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation. Research limitations/implications The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation. Practical implications This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study. Originality/value This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid

The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales.

Intra-urban house prices in Madrid following the financial crisis: an exploration of spatial inequality

In this paper we explore the temporal dynamics of spatial inequality in housing prices for Madrid, the capital city of Spain. Spatial inequalities are a concerning feature of urban areas across the globe. It has been suggested that within cities housing prices are becoming more geographically unequal over time, particularly since the 2008 housing market crash. However, more evidence is needed at the intra-urban level to understand neighbourhood house price differences in large urban areas. Changes are analysed during a key period of the housing market bust (2010–2015) and boom (2016–2019), using data from a major housing listing portal. Fine grain space-time analysis of the distribution of housing prices supports an increase in spatial inequality and polarisation at the neighbourhood level. Two spatially differentiated housing sub-markets of high- and low-priced housing are identified. The persistence and growth of spatial house price inequality has important societal implications for the wealth gap and segregation of rich and poor in cities.

Defining the Existence of Housing Submarkets for Terraced Properties in Johor Bahru

Defining the Existence of Housing Submarkets for Terraced Properties in Johor Bahru

Fresh air in the city: the impact of air pollution on the pricing of real estate.

This study investigates the impact of ambient air pollution on housing prices in Warsaw, Poland, by examining spatial dependencies. The high concentration of particulate matter (PM10, PM2.5 and PM1) is expected to reduce real estate values. Using a hedonic model with approximately 15,000 observations and a spatial error model, we did not find evidence of this impact. Standard and premium housing submarkets differ in price determinants, but both are insensitive to environmental issues. This could be explained by the lack of comprehensive intra-urban historical information on air pollution, which limits investors' rationality and their ability to properly value real estate based on environmental issues. Additionally, measurement and aggregation issues, along with low pollution variability within the city, may contribute to the insignificance of this information in real estate sales prices. Our empirical research confirms a strong link between air pollution and weather conditions within the city, where low temperatures and low-speed southern winds worsen contamination levels, while high temperatures and westerly winds improve air quality. Furthermore, we find that incorporating pollution data using PM yearly mean concentration works better in modelling than the PCA-reduced air pollution index.

The Dynamics of Land Rents in Housing Submarkets: A Marxian Perspective

AbstractThis theoretical study delves into the dynamics of housing submarkets with the framework of Marxian land rents. It examines the relationship between residential spaces and the reproduction of labour power in terms of commuting and the differentiated quality of reproduction by submarket. This study shows that the differentiation process of venturing to create distinct spaces for surplus profit and the emulation process of copycatting spaces of higher rents consequently form multiple housing submarkets, each characterised by different land rents of monopoly, intra‐differential rent (intra‐DR) and inter‐differential rent (inter‐DR). The coexistence of these multiple layers of land rents, in turn, becomes a driving force propelling dynamic capital movements that seek to exploit rent gaps between the layers. To illustrate these concepts, the study reviews the case of high‐rise mixed‐use residential developments in Seoul, highlighting the dual dynamic processes through which land rents are pursued.

Konut Alt Piyasalarının Ankara Metropolitan Alanı Örneği Üzerinden Değerlendirilmesi

The housing market consists of heterogeneous products with different housing characteristics. Due to its heterogeneity, the housing market is better analyzed by dividing it into different homogeneous groups, especially submarkets with similar spatial, environmental and physical characteristics, rather than a single homogeneous market. This article discusses the housing submarkets by using the current housing supply in Ankara. In this context, it investigates whether the analysis for the city as a whole reflects a realistic housing price structure. Moreover, it seeks an answer the question of whether socio-economic submarkets based on average household income by neighborhood and spatial submarkets based on location by district are determinant for Ankara. Data were collected from one of Turkey's largest real estate websites on the dwelling units being advertised for sale in the nine central districts of Ankara between January and September 2021. Data were analyzed by hedonic regression model. Empirical results reveal that although a single market does not effectively reflect a realistic price structure, location by districts and average household income by neighborhood do affect the price of housing in each submarket. Each submarket has its own dynamics and the housing price structure in each submarket depends on different variables. For Ankara, spatial submarkets have more explanatory power than socio-economic submarkets.

서울 동남권과 경기 남부지역 신도시 아파트 가격변동 특성 비교 연구

Recognizing the importance of the housing market in the Seoul metropolitan area, this study conducted a time series study based on the theory of the housing submarket in the southern part of Gyeonggi-do apartment markets in the metropolitan area in terms of diversity in research. The main research results derived from the empirical analysis results are as follows. First, through the Grandeur causality test, the sale price of apartments in the southeastern part of Seoul was confirmed as the cause variable, and the sale price of apartments in Bundang, Pangyo, Gwanggyo, and Dongtan was confirmed as the result variable, and the causal relationship existed. Second, through the results of shock response analysis and variance decomposition analysis, it was found that apartment sales prices in Bundang, Pangyo, Gwanggyo, and Dongtan were greatly affected by the southeastern region of Seoul. Third, the results of impact response analysis and distributed decomposition analysis showed that the four regions of Bundang, Pangyo, Gwanggyo, and Dongtan had a mutual influence relationship. The research results showed how the housing market in the Seoul metropolitan area changed until 2022 after the long-term recession after the global financial crisis in late 2008, and the significance of the study was found in that it confirmed the relationship between regions in southern Gyeonggi Province.

Regional spillover of housing (un)affordability: an empirical study on the residential housing markets for first-time buyers in the U.K

ABSTRACT This study examines the lead–lag relationships between the levels of housing affordability of different regions of the U.K. By utilizing government data, a number of housing affordability indicators are constructed to explore whether spatial diffusion exists between different regional submarkets of first-time homebuyers over the period of 2000 – 2021. The results reveal that during periods of economic expansion, housing unaffordability tended to diffuse from regions of slower economic growth to regions of higher economic development. It is further evident that in the aftermath of the GFC, the London housing market Granger-caused other regional markets in terms of housing (un)affordability. Lastly, the U.K’.s decision to leave the EU in 2016 seems to have led to more divergence between the submarket regions, which in a Granger causal sense, have become less causally correlated in terms of pricing. We conjecture that the causal interactions between the different regional housing submarkets exist with the lead–lag relationships governed primarily by their underlying macroeconomic fundamentals.

Delineating Housing Submarkets Using Space–Time House Sales Data: Spatially Constrained Data-Driven Approaches

With the increasing availability of large volumes of space–time house data, delineating space–time housing submarkets is of interest to real estate agents, homebuyers, urban policymakers, and spatial researchers, among others. Appropriately delineated housing submarkets can help nurture submarket monitoring and housing policy developments. Although submarkets are often expected to represent areas with similar houses, neighborhoods, and amenities characteristics, delineating spatially contiguous areas with virtually no fragmented small areas remains challenging. Furthermore, housing submarkets can potentially change over time along with concomitant urban transformations, such as urban sprawl, gentrification, and infrastructure improvements, even in large metropolitan areas, which can complicate delineating submarkets with data for lengthy time periods. This study proposes a new method for integrating a random effects model with spatially constrained data-driven approaches in order to identify stable and reliable space–time housing submarkets, instead of their dynamic changes. This random effects model specification is expected to capture time-invariant spatial patterns, which can help identify stable submarkets over time. It highlights two spatially constrained data-driven approaches, ClustGeo and REDCAP, which perform equally well and produce similar space–time housing submarket structures. This proposed method is utilized for a case study of Franklin County, Ohio, using 19 years of space–time private house transaction data (2001–2019). A comparative analysis using a hedonic model demonstrates that the resulting submarkets generated by the proposed method perform better than popular alternative submarket creators in terms of model performances and house price predictions. Enhanced space–time housing delineation can furnish a way to better understand the sophisticated housing market structures, and to help enhance their modeling and housing policy. This paper contributes to the literature on space–time housing submarket delineations with enhanced approaches to effectively generate spatially constrained housing submarkets using data-driven methods.

Capitalisation of multiple ecosystems on property prices in Auckland, New Zealand

Ecosystems and their associated services have become essential in policymaking as they often have implications for the welfare of those who live nearby. Coupled with the shift toward integrating green infrastructure, ecosystem services, and nature-based solutions in urban and environmental planning, ecosystems may capitalise on property prices. But a potential phenomenon in applications of hedonic prices models is the offsetting between amenities and disamenities values, which may result in insignificant marginal effects. Some strategies to mitigate this phenomenon involve introducing a greater categorisation of open spaces and housing submarkets typology. Hence, this paper explores the capitalisation patterns of 14 terrestrial and aquatic ecosystems across the housing market segments of Auckland, New Zealand's largest city. We used a dataset of 274 thousand sales transactions between January 2011 and December 2019. Models are estimated via OLS and unconditional quantile regressions (UQR) to account for heterogeneity, outliers and heavy-tailed distributions. UQR produce more accurate estimates of the implicit prices and lead to more effective policy recommendations. We find multiple capitalisation patterns across the prices distribution and confirm offsetting effects when ecosystems are not controlled as separate variables. This paper informs land-use decisions involving the preservation or creation of natural spaces in residential areas and improves our understanding of the interaction between their economic valuation and regional diversity.

Squeezed in and pushed out: dual and contradictory displacements in Santa Ana, CA

This research examines housing insecurity and displacement within a gentrifying context. Through an interpretive analysis of four years of survey data produced through a community-based research (CBR) project on households in the Lacy neighborhood within the City of Santa Ana, California, we find that the neighborhood is simultaneously a site of eviction-based displacement and extreme overcrowding. The results complicate assumptions in the literature regarding the quantification of gentrification and suggest that in addition to direct spatial dislocation and the outward movement of households, highly localized micro-gentrification and regional exclusion may together produce forms of extreme spatial concentration within neighborhoods that make estimating gentrification-induced displacement difficult. Ultimately, by drawing attention to the combination of contrary displacement forms and observable housing deprivation, we argue that the special conditions that have emerged in the Lacy neighborhood are representative of a housing submarket that combines exclusion and insecurity with unequal exchange for renters.

Understanding active transportation accessibility's impacts on polycentric and monocentric cities' housing price

Active transportation (AT) accessibility, specifically walking and cycling accessibility, has a significant impact on housing prices and equity. However, the spatial variation of the impacts of both walking and cycling accessibility and the influence of urban structure on housing submarkets are often overlooked in existing studies. This research aims to fill this gap by investigating the impacts of eight types of AT accessibility, inherent and locational attributes on housing prices in polycentric and monocentric cities. Geographically weighted regression models were estimated using housing price data from 3496 communities in Shanghai (a monocentric city) and 1100 communities in Wuhan (a polycentric city), China. The results illustrate the spatially varying impacts of AT accessibility on housing prices and highlight the existence of housing submarkets within cities due to varying factors such as urban structure, job-housing imbalance, consumer demand, public and private investment, and residential self-selection process. These findings provide valuable insights for investing in residential properties and designing policies and projects to improve AT accessibility in a way that promotes equity.

Unintended Consequences of Housing Policies: Evidence from South Korea

South Korea has crafted a series of policy measures to regulate or stimulate housing markets. One interesting feature is that policy makers and market observers have paid enormous attention to one particular housing market, the Greater Gangnam Area in Seoul. The area is an upscale and self-sufficient urban neighborhood with high-priced residential properties; the nation’s housing policies have been directed toward tempering housing price appreciation there. This leads to the following research question: whether the housing policy tools achieved the intended goals or not. This study examined the differential impacts of government-initiated policy measures on housing submarkets in the primary real estate market. Quasi-experimental econometric evaluations, which are AITS-DID methods, revealed that recent policy measures did not achieve the intended goal of housing price stabilization. Rather, those policy instruments brought about unintended consequences. The Housing Welfare Roadmap measure was intended to cool down the Gangnam housing market, but it in fact increased the Gangnam housing prices by 5.69 percent points in comparison to the non-Gangnam area. In order to tackle housing market imbalances, the government should devise long-term urban and regional planning strategies to create self-sustaining communities suitable for various population groups so that they can compete with existing strong real estate markets.

The effects of jobs, amenities, and locations on housing submarkets in Xiamen City, China

Numerous studies have found that jobs, amenities, and location influence housing prices in urban areas. However, there is still a lack of in-depth understanding of the impacts of these factors on various housing submarkets within a city. With the case study of Xiamen, this paper investigates the impacts of jobs, amenities, and location on four housing submarkets, classified by owner-occupied and rental housing, in inner and outer districts. The hedonic model, Chow test, and Tiao-Goldberger test are applied to analyze differentiation in the determinants of housing prices between four submarkets. The results show that all submarkets are influenced by blue-collar jobs (which have negative effects) and seascape (which has a positive impact). Besides, differentiated after submarkets show that school districts and public transportation have a greater influence on owner-occupied markets than on rental markets. A heterogeneity exists between inner-district and outer-district markets. For instance, bus rapid transit (BRT) has a positive effect on housing and rental prices in the outer districts but not in the inner districts. These differences are mainly caused by the disparities of spatial quality, economic development, and public facilities and amenities. The findings have profound implications for decision-making and planning practices.

Lead-Lag Relationship between the Price-to-Rent Ratio and the Macroeconomy: An Empirical Study of the Residential Market of Hong Kong

The price-to-rent (PtR) ratio is one of the most commonly used indicators to assess housing market conditions by policy makers and real estate practitioners. It is often employed as an economic barometer to detect whether a housing bubble exists and determine whether the property market has become unaffordable relative to historical trends. Despite a plethora of research studies on the PtR ratio in the housing literature, relatively little is known about its long-term dynamics with macroeconomic and financial determinants. By utilising time series data on the Hong Kong residential property market, this study examines the cointegration and causal relationships between a wide spectrum of macroeconomic indicators and the PtR ratios of housing segments of different tiers which comprise different socioeconomic groups of homebuyers and investors. The results point towards market compartmentalisation, in the sense that the PtR ratios of the housing submarkets respond to changes in macroeconomic fundamentals in a differential manner. For instance, the PtR ratios of housing segments with a greater proportion of owner-occupiers are statistically less y correlated with investment-related macroeconomic attributes, such as foreign direct investment and equity market performance. On the other hand, the pricing of large-sized housing units in prime locations, generally favoured by investors from mainland China, are found to be Granger-caused by the exchange rate of the Chinese Yuan to the Hong Kong dollar.

Classification of Housing Submarkets considering Human Preference: A Case Study in Shenyang, China

Housing submarket is affected by the quality of public facilities in neighborhood. In this article, a three-step framework is proposed to classify the housing market in Shenyang city, China. The saliences of point of interest, POI, are used to quantify the degree of activities using social media data and merged into the model of density-field hotspot detector to extract high-quality of urban facilities, aiming at exploring the influences on the housing price. The multiscale geographically weighted regression model, MGWR, is applied to investigate relations between the housing price and attribute variables geographically and to identify multiscale impacts on the housing price with flexible bandwidths. The spatial “k”luster analysis by the tree edge removal method, SKATER, is applied to delimit urban housing submarkets based on the MGWR betas, where ten groups of the housing submarkets are recognized. The proposed classification method captures multiscale spatial heterogeneity of the housing price and predominant influencing factors for each housing submarket in Shenyang, China. It also offers suggestions for urban planners to restrain the unbalanced development of the housing market.

Identifying Housing Submarkets in Johor Bahru and Kulai, Malaysia: A Data-Driven Method

Abstract Generally, housing within the same area tends to share similar structural and locational characteristics as well as being characterized by the same homebuyers’ preferences. This information provides insight on the existence of housing submarkets. Thus, this study aims to identify the existence of housing submarkets in Johor Bahru and Kulai, Malaysia through principal component analysis and cluster analysis. A total of 29 housing attributes were included in the study. The results from the principal component analysis have successfully identified four principal components that represent the basic facility, main infrastructure, less common facility and housing quality characteristics. On the other hand, cluster analysis managed to identify the existence of four housing submarkets in Johor Bahru and Kulai, where three submarkets showed the characteristics of housing quality, whereas one submarket appeared to exhibit the characteristic of main infrastructure. The findings of this study provide valuable information on the existence of four housing submarkets in Johor Bahru and Kulai areas, which will benefit the city governments, real estate developers, mortgage lenders, non-profit organizations, as well as individual homeowners in making critical choices and developing market-appropriate strategies.

Multilevel approach to the analysis of housing submarkets

ABSTRACT 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 subdivision 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 2175 transactions in the Istanbul housing market of Turkey, 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 subdivision 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.