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The real estate market is highly intermediated, with 90% of buyers and sellers hiring an agent. However, low barriers to entry and fixed commission rates result in large market share for inexperienced intermediaries. Using micro-level data on 8.5 million listings and a novel research design, we show that house listings by inexperienced agents have a lower probability of selling, and this effect is strongest during the housing bust. We estimate that 3.7% more listings would have been sold in a flexible commission equilibrium. Eighty percent of this improvement comes from competition and the remainder from commission variation across experience. [ABSTRACT FROM AUTHOR]

The AHURI Final Report delves into strategies for incentivizing small-scale investors to offer affordable private rental housing. It examines the motivations and obstacles faced by these investors, the impact of regulations and taxes on their decisions, and policy options to encourage their involvement. The report highlights headleasing as a method to increase affordable housing stock and stresses the importance of positive cash flow, make-good provisions, and effective communication to attract small-scale investors. Survey results indicate that investors are more likely to participate in affordable rental housing schemes that offer rental guarantees and make-good provisions, with a significant portion willing to engage in schemes similar to NRAS or headleasing. The report also analyzes the distribution of rental properties owned by small-scale investors in Australia, focusing on factors such as the number of properties owned, age of property owners, and income levels. Additionally, the text includes a range of research reports and articles exploring various aspects of housing and housing assistance in Australia, Britain, and Flanders, offering valuable insights into housing markets and policies in different regions. [Extracted from the article]

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R2 score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R2 and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector. [ABSTRACT FROM AUTHOR]

The given text provides a summary of the AHURI Final Report titled "Measuring housing affordability: Scoping the real cost of housing." It explores the relationship between housing, energy, and transportation in relation to affordability and discusses various policies and programs across different jurisdictions in Australia. The report highlights the need for a more comprehensive approach to assessing housing affordability, considering factors such as housing standards, energy efficiency, spatial characteristics, and transportation accessibility. It also emphasizes the need to address energy hardship and transportation disadvantage in relation to housing affordability. [Extracted from the article]

This study examines the interplay between Airbnb and gentrification in Thessaloniki and Greece, focusing on their economic and social impacts on urban neighborhoods. Utilizing data from 110 online publications and qualitative insights from ten semi-structured interviews with real estate agents, Airbnb stakeholders, residents, and experts, the research provides a nuanced view of these dynamics. The findings suggest that Airbnb influences housing markets by driving up rental and home prices, potentially exacerbating housing scarcity and displacing vulnerable populations in gentrifying areas. While this aligns with the existing literature, the results remain tentative due to the complexities involved. The trend toward corporate-hosted short-term rentals appears to shift Airbnb away from its original community-focused model, though this shift is still evolving. The COVID-19 pandemic introduced changes, such as a move from short-term to long-term rentals and the conversion of commercial spaces to residential use, impacting neighborhood dynamics. However, these effects may be temporary and do not fully address broader housing issues. While an oversupply of Airbnb accommodations might stabilize rental prices to some extent, its impact on the overall housing crisis remains uncertain. Future research should investigate the long-term effects on housing affordability and social equity, considering the limitations of current findings. [ABSTRACT FROM AUTHOR]

This study highlights the critical function that digital real estate platforms, like Zillow, serve in facilitating effective property transactions. They do this by transmitting vital property information from sellers to buyers, thereby enriching the value of offline deals. Our findings indicate that Zillow, as a source of information, is incredibly valuable for properties that deviate significantly from their neighborhood's average value, either above or below. It's particularly useful in conveying experiential details through images and textual descriptions. For potential buyers, Zillow is a trustworthy source of property information for estimating property value, especially when alternative sources of information are limited. This study underscores the necessity for sellers and their agents to effectively represent property information online, considering its significant impact on sale prices. This is especially true for unique properties and properties with notable experiential elements. Furthermore, our study suggests that real estate professionals need to modify their business practices to take full advantage of digital platforms and provide superior services to their clients. Finally, digital real estate platforms can use these insights to enhance their platform design by focusing on the collection and display of significant information, ultimately increasing the value provided to both buyers and sellers of properties. Digital platforms facilitate the flow of information and the execution of transactions. This study investigates the impact of signals from platform-provided online information regarding search and experience attributes of products on the prices of their offline transactions. We situate our theorizing and empirical work in the context of digital real estate platforms. Our results suggest that online information pertaining to properties' experience attributes has a significant influence on the prices of offline property transactions. The amount of online information relating to experience attributes—specifically, length of textual property description and the number of photos—positively influences the sale price of a property. In contrast, the amount of online property information related to search attributes—specifically, facts and features—has no significant influence on the property's sale price. In addition, online property information on experience attributes has a significant impact on the sale price of uncommon properties (those valued significantly above or below their neighborhood averages), whereas its impact on the price of common properties (those valued close to their neighborhood averages) is insignificant. The findings are robust to various model specifications and across property transactions in different years, seasons, and geographical regions. They are also neither subject to confounding effect of real estate agents' service quality nor driven by unobserved property heterogeneities. The findings shed light on how signals from online property information are used by home buyers and sellers for different types of properties. The insights have implications for how real estate professionals can better utilize digital platforms to convey signals regarding properties and facilitate property transactions and for how the platforms can be designed to support the exchange of information that provides signals on the quality of offline goods that are highly risky and experiential. History: Rajiv Kohli, Senior Editor; Huigang Liang, Associate Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2020.0658. [ABSTRACT FROM AUTHOR]

The article reflects on the outgoing president's year in office and offers reflections on their journey as the 156th President of the Mississippi State Medical Association (MSMA). Topics include the discovery of a previous MSMA president from Pontotoc, a personal research into Dr. C. D. Mitchell's life and career, and a shared goal of increasing MSMA membership, which mirrors the historical focus of past leadership.

The use of online listing portals helps improve search efficiency in real estate markets. Unlike the traditional channel, buyers could search more properties and find ones with characteristics that were closer to their preferences via the online channel. Using the merged data sets containing online listings and transactions in the private residential property markets in Singapore, we found that real estate agents sell houses for an average premium of 4.21% using online listings relative to other agents using the traditional listing channel. The models use the propensity score matching approach to remove compositional differences in the samples and include property and spatial attributes in the controls, as well as the fixed effects on the agency, planning sector, and month‐year. Using only the matched online listing‐transaction samples, we found that exclusive listings, using more words and numeric in listing content, making listing descriptions easy to read, and selecting multiple listing portals are online listing strategies that enable real estate agents to sell houses faster and above the asking prices. [ABSTRACT FROM AUTHOR]

This paper examines the principle-agency problem between landlords and real estate agents using novel data on rental contracts. Real estate agents are found to obtain higher contract rents by approximately 1% more for themselves (and family members) than for other landlords, which is economically small. The results suggest that the principle-agency program with real estate agents is less of a concern in the rental market than the ownership market. The reason potentially relates to the commission structure, the relatively low effort associated with finding a tenant, the landlord's ability to evaluate an agent's performance, and reputation concerns from repeated interactions. [ABSTRACT FROM AUTHOR]

The AHURI Final Report titled "Getting off the waiting list? Managing housing assistance provision in an era of intensifying social housing shortage" examines the effectiveness of housing assistance in Australia, particularly in relation to the social housing waiting list. The report highlights the growing emphasis on private rental assistance (PRA) as a means to alleviate pressure on waiting lists and provide temporary housing. However, the report also reveals that many applicants find the application process complex and communication inadequate. The report suggests policy development options to improve the housing assistance system, including tailored delivery of housing services and partnerships with private housing providers. [Extracted from the article]

Purpose: Due to the relatively low level of exploration of the topic of ESG in social media marketing communications, the authors of this text decided to diversify the purpose of the article: 1. to explore the topic of ESG activities in warehouse logistics and its communication in SM; 2. to carry out and present the results of quantitative research involving statistical analysis of the results of social media marketing publications on ESG. Design/methodology/approach: The following research question was posed by analogy: P1. Which area of ESG activity is most attractive to customers from the perspective of engagement on LinkedIn? The research hypothesis was then stated: H1. There is a statistically significant relationship between the amount of storage space and the marketing performance of publications on LinkedIn and is positive in nature. To achieve the stated objectives and verify the hypotheses, a two-stage research process was designed. It began with a literature analysis and synthesis and was followed by a quantitative study of marketing performance. Among other things, the first stage of the research defined the key areas of ESG activity in the real estate market. This division became the basis for the second, quantitative stage of the research. The study consisted of an analysis of online data from November 2022 - July 2023 compiled from the results of 140 posts published on the social media platform LinkedIn. This is because the pilot study, as well as the analysis of the literature, showed that this is the platform most often chosen to communicate ESG activities. Statistical analysis included: a comparison of client engagement scores in publications on key ESG areas in the real estate market, and a correlation analysis of warehouse space and social media marketing scores. Findings: It was observed that in most cases, communication of ESG-related activities formed the majority of a company's total communication on its LinkedIn profile. It proves that the most frequently communicated ESG area on LinkedIn is environmental aspects. However, they are associated with the lowest average engagement. Subsequently, Pearson's correlation coefficient has been used to examine the severity of the relation between the different variables. Once the analysis is executed, it may be concluded that there is a statistical significant correlation between the size of the warehouse and the outcome of publications communicating ESG on LinkedIn. A stronger correlation occurred in the analysis of engagement scores. Practical implications: It was found that there is a very high correlation between the size of the warehouse and consequently the ranking of developers in Europe and the results of publications communicating ESG on LinkedIn. Based on the results, it can be recommended that further research should be pursued in the direction of engaging customers in ESG marketing communications. Social implications: The presented article focused on exploring the topic of ESG activities in warehouse logistics and its communication in social media. The construction sector contributes significantly to greenhouse gas emissions. The sector therefore faces a number of challenges that it is socially expected to solve. Following in the footsteps of researchers, an increasing number of institutions at both the European and national levels are introducing laws and regulations to accelerate this process. EU directives, national laws and guidelines from independent institutes are aimed at forcing the industrial sector to respond and implement solutions. The ESG revolution is gaining momentum because of this. We are currently in a transitional phase, and there is still a lot of work to be done, but change is already inevitable. ESG activities that are being implemented by companies are being communicated in parallel on social media. Originality/value: The results of the research evaluate the correlation between warehouse size and marketing performance, show areas of ESG, which are the most frequently communicated and the most attractive to customers from the perspective of engagement on LinkedIn. It can be a basis for future studies on the implementation of ESG activities in warehouses and can serve practitioners in selecting projects of interest and worth deploying. [ABSTRACT FROM AUTHOR]

Property prices have surged recently, mainly due to limited real estate supply amid high demand supported by a large monetary base. This has driven up rental prices, especially in major cities compared to their peripheries. This manuscript examines rental prices in Prague, Brno, Ostrava, Pilsen, and České Budějovice from March to October 2023. It uses distribution functions, correlation coefficients, contour heat maps, and parameterized regression to define a probability price interval for office properties, identify a positive correlation between area and rental prices, and develop an algorithm for setting appropriate rental prices. The findings are useful for practitioners, real estate agents, and government institutions, though they are closely linked to macroeconomic variables. Future potential options are discussed. [ABSTRACT FROM AUTHOR]

This article explores the integration of real estate studies into planning scholarship, highlighting the challenges and opportunities that arise from this intersection. It emphasizes the need for a deeper understanding of the interrelationships between planning and real estate dynamics, and the importance of incorporating qualitative insights from urban planning in real estate research. The text also discusses the challenges of redeveloping abandoned public-owned land through public-private partnerships, the need for a community-oriented approach, and the lack of exploration in planning literature regarding specific stakeholders and investors involved in these operations. Additionally, the article addresses the integration of market knowledge in planning, particularly in the context of post-pandemic workplace ecosystems, and proposes a three-step approach to overcome existing research limitations. It argues for a hybrid approach that combines qualitative and quantitative methods, interdisciplinary collaboration, and the breaking down of dichotomies between state and market to produce comprehensive research in this field. [Extracted from the article]

In the housing market, the prices sellers would be willing to accept to sell their properties exceed the prices buyers would be willing to pay for them. Behavioural economics postulates that this discrepancy is due to the endowment effect, which is a cognitive bias that causes the overvaluation of the goods we own. However, the existing literature lacks theoretical considerations and empirical evidence on the impact of the use of real estate agents by parties to transactions on the endowment effect in the housing market. Therefore, this study aims to assess the influence of real estate brokerage on the endowment effect using the example of the Polish residential market. To achieve the purpose of this study, a lab-in-the-field experiment was conducted with 248 respondents divided into sellers and buyers. The results indicate that the participation of real estate agents during transactions does not lead to the weakening or elimination of the endowment effect but, in some cases, to its intensification, which is due to the framing by buyers and sellers of the commission charged by agents as a loss requiring compensation. This research confirms the inefficiency of commissionbased real estate brokerage services and points to possible corrective actions for legislators. [ABSTRACT FROM AUTHOR]

Purpose: A perusal of extant literature suggests asymmetric devotions in empirical research that overlooks unique social and industrial dynamics of married working women in highly competitive male-dominated industries. Drawing on the social role theory, the study addressed this asymmetry by proposing a multivariate regression model that examined performance drivers of married female real estate brokers, as mediated by organizational, cognitive and social influences. Design/methodology/approach: The study employed a descriptive design. A structured electronic questionnaire was used to collect data from a sample of 256 married female brokers who are active on online messaging platforms. Snowball sampling was used to reach the research participants. Collected data were subjected to multivariate regression analysis. Findings: The results infer that social influences were the most significant drivers of married female brokers in the real estate industry (r = 0.932, p < 0.05, F = 556.581). Significant interventions of diversity management, work-life balance, family support and access to economic opportunities were found. Practical implications: In acknowledging the societal stereotype and culturally imposed burdens married women face in emerging economies, the study advanced theoretical, practical and policy initiatives for a more inclusive, supportive and learning-oriented work environment. Originality/value: The novelty of the paper lies in its exposition of the unregulated real estate brokerage market in an emerging economy and the dynamic organizational, cognitive and societal influences of married female brokers. [ABSTRACT FROM AUTHOR]

This document discusses the application of the United Brands test for excessive pricing in the digital sector, specifically in Estonia. The author notes that there have been few excessive pricing investigations in the digital sector, likely due to the controversial nature of pricing investigations and the absence of clear rules for assessing fairness. The author argues that the digital sector, characterized by dynamic competition and innovation, does not typically warrant price regulation or intervention. The document also explores market definition and dominance in the digital sector, as well as elements for excessive pricing in this sector. The author concludes by questioning the need for excessive pricing investigations and suggesting a focus on ensuring competition and value for customers. [Extracted from the article]

Property technology has ushered in new possibilities for the real estate industry, including the use of virtual reality (VR) technologies by real estate agents when marketing properties. Previous studies have identified factors in determining purchase intents within a virtual setting, but little is known about how such technologies affect homebuyers' purchase decisions. In this study, a family-home purchase decision-making model is used to conceptualise how VR can affect buyers' involvement in the property purchasing process. Using transaction data from Wuhan City, China, the study found that a 1% increase in the number of followers visiting an online property portal resulted in a 21% increase in physical home visits. The study also indicates that VR tours shorten the marketing time of property by 6.4% and narrow the bid-ask spread by 2%, ceteris paribus. These findings suggest that emerging property technologies such as VR can enhance the purchase decision-making process and reshape the role of the real estate agent. [ABSTRACT FROM AUTHOR]

This article explores the themes of walls, paths, and gardens in various contexts, including the author's personal experience in Dublin during the pandemic. It discusses the tension between identity rooted in place and identity rooted in movement, drawing on religious texts as examples. The article also highlights the impact of walls in terms of control, privacy, and colonialism, and mentions the presence of walls and paths in Ireland's history, particularly in relation to pilgrimage routes and ancient stone fortresses. The text discusses the themes of land, walls, paths, and gardens in relation to historical and cultural contexts, including the impact of colonization on Indigenous peoples in North America and Ireland. It also touches on the significance of pilgrimage, the decline of the Irish language, and the presence of graves and cemeteries as sites of remembrance and reflection. The author's experience in Dublin, visiting the Glasnevin Cemetery and the Gravediggers' Pub, is described, along with their observations of the city's history and interactions between locals and mourners. The text concludes by emphasizing the significance of land and its connection to identity, while acknowledging the historical misdealings of colonialist societies. [Extracted from the article]

This paper shows that the entry of a for-sale-by-owner platform reduces the intermediation pricing. Intermediary agents play a crucial role in connecting sellers and buyers across various markets. However, the emergence of digital platforms has provided more affordable channels for direct transactions, challenging the dominance of agents. In this study, we examine the impact of a for-sale-by-owner (FSBO) platform on real estate agents' listing prices. By using a natural experiment in China, we find that the introduction of the FSBO platform leads to a 2.8% decrease in listing prices, a percentage comparable to the commission rate typically charged by agents. The cost advantage provided by the FSBO platform appears to be the primary driver behind this price drop rather than selection bias in listings. Furthermore, the impact of reduced listing prices extends to the final sales price of the properties. These findings carry important policy implications for competition regulation of intermediaries. History: Catherine Tucker served as the senior editor. Funding: This work was supported by the startup grant at the Hong Kong Polytechnic University [1-BE6Y]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mksc.2022.0245. [ABSTRACT FROM AUTHOR]

As China's urbanization process continues to accelerate and the price of traditional commercial property continues to rise, a number of small property rights houses without construction permits and approvals have appeared on rural land. Although small property rights housing does not have the validity of property rights or the legal attributes of ordinary commercial housing, and the Chinese government has repeatedly introduced corresponding laws and regulations to regulate its purchase and sale, small property right housing is still purchased by many consumers because of its price advantage, and the number of disputes arising from its purchase and sale is on the rise every year. In addition, the phenomenon of developers building on rural land in violation of the law and real estate agents guaranteeing property without authorization has led to a waste of judicial resources and the infringement of property rights. This paper analyzes the actual situation and the main problems surrounding the governance of small property rights housing and constructs a quadrilateral evolutionary game model with the government, consumers, developers, and real estate agents as the main players. By analyzing various equilibrium points corresponding to different stages of small property rights housing governance, a simulation analysis is conducted using Matlab2016a software to examine the strategic choices of each stakeholder. By adjusting and simulating various parameters, this study investigates the key factors influencing the governance of small property rights housing. This study revealed the following points: (1) at different stages in the governance of small property rights housing, the choice of a strategy by each subject is affected by the expected benefits and costs; (2) the relevant government fines will regulate the strategic choices of developers and real estate agents; (3) consumers' purchasing tendency is affected by the price of small property rights housing and the risk estimation; (4) the governance of small property rights housing needs a long-term standardization of practice, and the government not only needs to improve the reward and punishment mechanism but also needs to provide positive guidance to the consumers. Through numerical simulation, we explore the impact of the main parameters of the current small property rights housing governance process on the strategic selections made by the game players, which is of great significance for the current policy and future governance of small property rights housing. This paper contributes additional insights to the existing body of theoretical literature through quantitative analysis. Nonetheless, there is a need for the further refinement of the parameter settings used in the study. Additionally, while the simulation analysis provides valuable perspectives, it is somewhat subjective and possesses certain limitations. [ABSTRACT FROM AUTHOR]

Introduction: Predicting property prices is a crucial task in the real estate market, and machine learning algorithms offer valuable tools for accurate predictions. In this study, we introduce a comprehensive comparison of eight well-known machine learning algorithms, namely, ensemble empirical mode decomposition (EEMD)-stochastic (S) + deterministic (D)-support vector machine (EEMD- SD-SVM), support vector machine (SVM), gradient boosting, random forest, K-nearest neighbors (KNN), linear regression, artificial neural networks (ANN), and decision trees. The focus is on predicting property prices in Dubai, with the primary objective of assessing the predictive performance of these algorithms within this specific market context. Methods: The evaluation is based on four key performance metrics: R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). These metrics provide insights into prediction errors, accuracy in percentage terms, and the proportion of variance in property prices explained by independent variables. The study compares the strengths and limitations of each algorithm for predicting property prices in Dubai, highlighting scenarios where certain algorithms excel based on the nature of decision boundaries, handling complex data, capturing localized patterns, and offering interpretability. Results: Findings from the comparative analysis shed light on the performance of each algorithm in predicting property prices in Dubai. EEMD-SD-SVM and SVM excel in scenarios requiring precise decision boundaries, while gradient boosting and random forests demonstrate robust performance with complex and noisy property price data. KNN captures localized patterns effectively, linear regression is suitable for straightforward regression tasks, ANN excels with extensive datasets, and decision trees offer interpretability in understanding factors influencing property prices. Discussion: The study emphasizes the significance of model tuning, feature selection, and data pre-processing to enhance predictive power. Additionally, practical aspects such as computational efficiency, model interpretability, and scalability in real-world applications are discussed. The comparative analysis provides valuable guidance for stakeholders, including real estate professionals, data scientists, and stakeholders interested in selecting the most suitable machine learning algorithm for predicting property prices in Dubai, with a focus on the essential evaluation metrics of MSE, RMSE, MAPE, and R2. This study offers insights into the applicability and performance of different machine learning algorithms for predicting property prices in Dubai. Stakeholders such as real estate agents, buyers, sellers, or investors can leverage these insights to make informed decisions in the Dubai real estate market. [ABSTRACT FROM AUTHOR]

The article discusses changes in the private rental sector (PRS) in Australia, highlighting a significant long-term shift in the distribution of household incomes. Topics include the shortage of affordable private rental supply for lower-income households, with a national shortage of 348,000 dwellings in 2021 and 82% of Q1 PRS households facing unaffordable rents.

Evaluations of Model Cities have focused on whether federal officials and local planners lived up to the programme's rhetoric of citizen empowerment. However, as this case study of St. Louis will demonstrate, more attention is needed to the ways that the local real estate sector influenced and frustrated government officials and community activists alike. Policies of disinvestment in mid-twentieth century cities produced a small but powerful group of landlords and real estate brokers in St. Louis, whose near-monopoly control of the housing market hindered grassroots rehabilitation efforts. In 1967, a Black-led community group known as Jeff-Vander-Lou, Inc. (JVL), found that the 'slumlords' who dominated their neighbourhood were too powerful to be compelled by municipal tools such as code enforcement. Realizing they could not defeat the landlords, they reluctantly joined them, and crafted a Model Cities demonstration programme that they hoped could improve housing conditions and attenuate the strangle-hold of landlords on their neighbourhood. As this article will detail, this momentary alliance allowed landlords to influence the St. Louis housing policy for Model Cities and beyond. [ABSTRACT FROM AUTHOR]

We provide first evidence of the economic factors that motivate individuals to obtain a real estate license to potentially pursue a career in real estate sales. We focus on four key housing indicators: median house price, median days on the market, housing affordability, and housing sales performance. Our results indicate that house price is the main driving force for individuals to take the California real estate salesperson license exam as well as for those real estate salespersons wanting to upgrade to the broker license. [ABSTRACT FROM AUTHOR]

Case studies have illuminated that U.S. real estate agents, as key housing market gatekeepers, continue to maintain racial residential stratification well into the twenty-first century. We use novel survey data gathered from real estate agents across the United States to descriptively explore agents' ideas about clients of color in the housing market, as well as their practices, such as conducting business through social networks. Our findings provide evidence of the subtle and more overt ways that these ideas and practices that, when taken together, constitute what we call racialized real estate agency and contribute to ongoing racial segregation. We issue a call for future research to continue examining the ways agents' and other gatekeepers' ideas and practices contribute to or mitigate stratifying processes and describe the utility of such research for policy. [ABSTRACT FROM AUTHOR]

This article unearths a meticulous system of residential segregation operative in Grosse Pointe, Michigan—a wealthy suburb of Detroit—from 1945 to 1960. Potential homebuyers were ranked based upon their ethnic descent, nativity, accent, manner of dress, and "swarthiness" of skin, among other characteristics; as such, the Point System stringently measured and indeed vested real estate brokers with the power to construct suburban whiteness. After analyzing what I call the "work of white supremacy" that undergirded the Point System, this article then tracks its demise after well-publicized state and federal hearings. This article then details the community's response to the first "move-ins" of black families in 1964, as well as longer-running liberal integration efforts spearheaded by local activists through the decade's end. Ultimately, the Point System adds to our understanding of production of race and suburban exclusion in the midcentury United States. [ABSTRACT FROM AUTHOR]