Real Estate Agents Research Articles

Accommodation for profit, not for refugees: Racial capitalism and the logics of Berlin’s refugee accommodation market

In response to the difficulties refugees face in finding housing, Berlin’s government has developed new housing-like shelters that offer longer-term accommodation. Drawing on literature concerning racial capitalism and urban migration governance, I explain how these shelters represent a multilayered business opportunity for revenue extraction, resulting in the ongoing displacement, spatial fixing, and continued racialization of refugees. Notably, I reveal the prominent roles of Berlin’s government, city-owned housing, and public real estate agencies. They use the construction of new refugee shelters in an entrepreneurial way in order to revitalize their own fiscal budgets, as well as to put urban land into production. This allows them to develop and then turn refugee shelters into substandard, racialized, and highly profitable forms of new urban housing for refugees and other racialized and low-income populations. Expounding the ways the building of accommodation as substandard urban housing leads to race-based pursuit of profit, I argue that refugee housing serves as an urban migration fix and is developed within the logic of racial capitalism. This article contributes to attempts to use racial capitalism as a framework to scrutinize urban and migration processes, helping us comprehend the distinctly racial logics of urban and migration governance, and housing precarity.

Real-estate agent commission structure and sales performance

Real-estate agent commission structure and sales performance

Real Estate Price Prediction Using Machine Learning

Abstract: The abstract of the real estate price prediction project for properties in Bengaluru involves using machine learning algorithms to develop a predictive model that can estimate the prices of properties in the city based on various features such as location, size, amenities, and so on. The dataset used for this project contains information on thousands of properties in different parts of Bengaluru, including their sale prices, size, location, number of bedrooms, bathrooms, and other key features. To build the predictive model, several machine learning algorithms such as linear regression, decision tree, random forest, and XGBoost are used to train the model on the given dataset. The performance of each model is evaluated using metrics such as mean absolute error, mean squared error, and R squared score, and the best performing model is selected as the final predictive model. Once the model is trained and tested, it can be used to predict the prices of new properties in Bengaluru based on their features. The model can also be used to identify the most important features that affect the price of properties in the city, which can be useful for real estate agents, property developers, and investors looking to buy or sell properties in Bengaluru.

ORGANIZATIONAL AND ECONOMIC TOOLS OF STATE POLICY FOR THE DEVELOPMENT OF THE HOUSING SECTOR

The study focuses on the importance and problems of the development of the housing stock in Ukraine, which was actualized at the stage of the post-war reconstruction of the national economy and the need to preserve the social infrastructure in Ukraine. The purpose of the study is to substantiate the organizational and economic tools of the state policy of stimulating the development of the housing construction sector. It is shown that from the point of view of state regulation, it is important to ensure the formation of a full-fledged sector of housing construction, formed from construction enterprises and elements of functional and auxiliary infrastructure (subjects of market infrastructure; real estate agencies; development structures; design bureaus; consulting, design, financial -credit institutions; investment and financial funds; manufacturers and suppliers of materials, fixed assets; certification centers and laboratories; companies servicing construction facilities, etc.). It has been proven that in order to properly stimulate the development of the housing construction sector in Ukraine, a complex of organizational and economic tools should be implemented, when the implementation of organizational tools is designed to form a high-quality management system of housing construction development processes – from central and local authorities to direct investment and construction projects. The toolkit of the economic block should focus on the formation of appropriate incentives for business entities, buyers, market and branch infrastructure entities regarding investments in housing construction, development of this industry / sector of the national economy in the direction of improving the country’s social infrastructure. It has been established that the purpose of the state program in this area is to determine the implementation of the principles, mechanisms and means of implementing state policy in the field of development of the housing construction sector in Ukraine. The expected results of the implementation of state regulatory instruments are defined as: creation, development and ensuring the effective operation of the system of entities that make up the housing construction sector.

A Luxury Tourist Destination in Housing for Tourist Purposes: A Study of the New Airbnb Luxe Platform in the Case of Marbella

Marbella has a wide range of luxury property rentals, which are the subject of study within the framework of the collaborative economy, where the Airbnb platform stands out as the most used channel. This research focuses on the analysis of the total number of luxury villas offered in Marbella by Airbnb Luxe and Luxury Retreats. The methodology was carried out in four phases: the first phase involved a review of the literature on tourism and luxury and a study of Marbella as a luxury destination. The second phase focused on the analysis of the types of accommodation that Marbella offers. The third phase focused on the collection of data from the analyzed platform: Airbnb Luxe and Luxury Retreats. Finally, qualitative research consisting of in-depth interviews with owners and real estate agents focused on the luxury sector in Marbella was carried out. The results obtained yield relevant conclusions: Marbella is one of the main luxury holiday destinations in Spain, and the most demanded type of luxe accommodation in Marbella is large luxury villas. The term ‘luxury’ can be subjective, referring to such things as the visible and non-visible aspects that determine the category of a luxury property, hence the need to carry out a study on the luxury segment to establish a precise definition.

Towards Regulating Real Estate Agency Practice in Tanzania: A need for Subtle Approach?

Towards Regulating Real Estate Agency Practice in Tanzania: A need for Subtle Approach?

Restricted access: Real estate agent response to client race, ethnicity, gender, and side of market

AbstractWe design and implement a correspondence experiment to test for differences in real estate agent response across client race, ethnicity, gender, and side of market. Real estate agents are more likely to respond to White clients than Black or Hispanic clients—Whites have a 32.18% higher response rate than Hispanics and an 8.48% higher response rate than Black clients. We also find that real estate agents are more responsive to sellers (23.81% premium over buyers) and female clients (13.57% premium over males).

Dream House Price Predictor

Abstract: The dream house price predictor project aims to build a machine learning model that can predict the selling price of a house based on various features such as location, number of bedrooms, square footage, and other relevant factors. The model will be trained on a dataset of historical housing prices and features, and will use regression techniques to make predictions on new, unseen data. The project will also explore the impact of different features on house prices and provide insights into which factors are the most important in determining the value of a property. The goal of the project is to provide a tool for homebuyers, sellers, and real estate professionals to better understand the market and make informed decisions. The Dream House Price Predictor project is aimed at predicting the prices of residential properties based on various features such as location, size, number of bedrooms, and other amenities. The project uses a dataset of real estate transactions and employs machine learning algorithms to build a predictive model. The model is trained on the historical data and tested on a validation set to ensure its accuracy. The results of the project can be used by real estate agents, property buyers, and sellers to make informed decisions about pricing and investment opportunities. This project demonstrates the potential of machine learning to assist in the real estate market and provides a valuable tool for predicting property prices.

Review of “Liberty Road: Black Middle-Class Suburbs and the Battle Between Civil Rights and Neoliberalism”

Liberty Road: Black Middle-Class Suburbs and the Battle Between Civil Rights and Neoliberalism is an extraordinarily well researched and highly readable historical and ethnographic book that situates the suburbs as central sites for development of racial and political identities in the United States. Drawing on ethnographic observations, 50 interviews and archival research, Gregory Smithsimon argues that the landscape of Black suburbanization was produced by dualling forces of everyday activism for greater housing opportunities and everyday neoliberalism seeking to tear down the advances of the Civil Rights movement. In doing so, he centers Black agency in the process of expanding housing choices for African Americans and acknowledges how structural racism continues to shape those choices as well, even for more economically advantaged African Americans. In the first chapter, Beyond Blockbusting, Smithsimon uses archival research to show how the image of the blockbuster was created in the popular press and reproduced in academic literature with little basis in actual events. The blockbuster narrative that profit hungry real estate agents scared white residents in transitioning neighborhoods into selling their homes at a loss, which they then sold to unwitting Black families at a huge markup took the blame off of white residents who left and ignored the agency of Black homeseekers and Black integration activists. Instead, Smithsimon develops a fuller account of how racial transition actually happens, particularly in the suburbs, through a conscious effort on the part of multi racial constellation activists who included real estate agents, mortgage lenders, and political actors who sought integration for varied reasons and ultimately did manage to secure increased and improved housing options for Black households.

Cypriot Diaspora Direct Investment Patterns in times of Financial Crisis

This qualitative study aims to study the phenomenon of diaspora direct investments in the Republic of Cyprus during the period 2013–2021, which succeeded the 2013 Financial Crisis. The unit of analysis has been real estate agencies established on the island, as real estate consists the major sector where diaspora investment takes place. A multiple case study, including in-depth interviews, and an inductive analytical approach were adopted. Data has been triangulated leading to results which contribute theoretically by asserting that diaspora direct investments are predominantly emotionally driven, and not only socio-economically influenced, as in the case of foreign direct investments.

Perspectives of Southwest Florida Homeowners and Real Estate Agents before Hurricane Ian

In September 2022, Hurricane Ian made landfall in southwest Florida, causing an estimated $67 billion in damage and the loss of almost 150 lives. Before this date, demand and house prices in this area were rising faster than anywhere else in the country. What did homeowners in southwest Florida believe about flood risk to their own homes, and what did real estate agents believe about the role of flood risk in the residential housing market? The survey research summarized in this article shows that not only did residents feel that they were not particularly at risk from flooding, but also that damaging floods would not affect future home values in their neighborhoods or for their own homes. We found that political party affiliation was strongly correlated with the direction and strength of these beliefs. Real estate agents also reported strong demand for low-elevation coastal housing, noting that coastal lifestyle outweighed the prospect of flooding in residential decision-making. They also observed that the detailed maps of flood risk that are now available at realtor.com for anyone browsing for houses for sale had no effect on homebuyers, and that most of their customers were generally unconcerned with flood risk.

사적자치와 공법적 제어의 조화

In principle, everyone is guaranteed freedom of contract. In principle, the contents of a real estate sales contract can also be freely determined by agreement between the seller and the buyer without state control. However, even if the contract is based on the civil law to which private autonomy is applied, there are some cases which could be also a subject to public law. For example, in accordance with ACT on report on real estate transaction (ARRET), the sales price of a real estate sales contract must be reported to the authority within 30 days from the date of conclusion of the sales contract. This is a kind of reporting obligation. Recently, it is common to conclude a provisional contract before concluding a real estate sales contract in Korea. Regarding this situation, namely provisional contract, there is a controversial case, in which a licensed real estate agent was fined for violating the reporting obligation. This is understood to be due to differences in perception of how to understand provisional contracts. In the controversial case, the authority judged the starting point of transaction in a report as the date of deposit of the provisional contract, not the date of conclusion of the main contract. The ARRET does not define the date of conclusion of a sales contract or provides a guideline for interpretation. The date of conclusion of the contract means the date on which the contract is actually concluded, not the date of written contract or the date on which the contract is drafted. Based on this date, the report must be made within the period. Regarding the date, there are several problems in the disposition of fine by the authority. First, fine for negligence are imposed for delay of reporting obligation, and the amount of fine actually imposed is due to false reports. Second, various contracts called provisional contracts cannot be identified as the same contract as the main contract. Third, there are cases in which reporting is impossible at the time an agreement on the sales contract is established. Imposing a fine for negligence is a kind of an intrusive administrative disposition that imposes certain obligations. Therefore, the legal basis for disposition must be clear, and extended or inferential interpretations are not permitted. Furthermore, the authority should judge more carefully whether the fundamental rights of the constitution are not violated or whether the legal basis for the disposition is clear when taking an intrusive administrative disposition against a private person.

Pay for elite private schools or pay for higher housing prices? Evidence from an exogenous policy shock

This study provides the first direct empirical evidence on whether private school lottery enrollment program affects school district house prices and residential sorting behaviors. Although the new lottery enrollment policy for private schools in China aims to promote equal and affordable education, it also introduces uncertainty. We assessed the policy impact using the panel data of residential housing transactions from the largest real estate agent in Chengdu during 2018–2020 and controlling for grid-level demographic and economic variables from mobile Internet data. We found that the lottery-based private school enrollment uncertainty leads to a substantial rise in the value of the key and elite school district house (SDH) prices. The house price premiums of the key SDH and elite SDH increased by 2.8% and 7.8% in the post-policy period, respectively. We also observed a significant intensification of residential sorting behavior within the elite public school districts afterwards. These results suggest that parents pay higher house prices when access to quality private schools becomes uncertain.

The Effect of the Broker’s Competence on Recontract Intention in Real Estate Brokerage Services

This study examines the effect of brokerage competencies on re-contract intention and customer satisfaction with the elements of ability, knowledge, and attitude as well as understanding the mediation of customer satisfaction on competencies and re-contract intention. The study surveyed a questionnaire with customers in Tan Thoi Dai agent which was a total of 200 samples. The study applied SPSS 20.0 and AMOS 24 to complete all steps of analyzing data in SEM and provide accurate results. Based on the results, the competencies of a broker positively impact the satisfaction of service users through the element of ability, attitude, and knowledge. Moreover, these indicators (ability and attitude) play a significant impact on recontract intention in the direct aspect, in contrast, Knowledge has an insignificant role. Lastly, the level of satisfaction is proved to play a mediating role in the relationship between all the elements of the broker’s capacity and recontract intention due to reducing the coefficient. Interestingly, Customer satisfaction has a full mediation in the correlation of knowledge and recontract intention, while it keeps a partial one in the case of ability and attitude. This study has contributed to the area of developing the efficiency of real estate agents, brokers, and managers. It is necessary to reinforce the competitiveness of real estate agents by raising the rate of revisit intention of old customers. Therefore, to enhance the recontract intention, it is not only paying attention to training for brokers with essential capacities but also the increasing level of satisfaction.

Disruptive Innovation and Real Estate Agency: The Disruptee Strikes Back

Disruptive Innovation and Real Estate Agency: The Disruptee Strikes Back

The impacts of pandemic on urban housing prices: evidence from the outbreak of COVID-19 in Beijing, 2020

PurposeThe COVID-19 pandemic has significantly raised economic risk and uncertainty worldwide. How does COVID-19 affect urban housing markets? Is there any difference when different areas encounter COVID-19? This study aims to investigate the impacts of the pandemic on housing prices by using Beijing’s housing markets data in 2020.Design/methodology/approachThe authors use transaction-level data from April to September in 2020 to conduct a hedonic price analysis of the housing markets in Beijing. The data included 70,843 transactions scraped from a real estate agent’s website. The authors use the difference-in-differences approach to evaluate the impacts of the COVID-19 outbreak from the Beijing Xinfadi market (the largest and most important food wholesale market in Beijing) in 2020.FindingsThis outbreak of COVID-19 caused a 6.3% drop in housing prices in Beijing from April to September in 2020. However, the impacts of COVID-19 on housing prices in different urban neighbourhoods were spatially heterogeneous. Housing prices in neighbourhoods with industries that rely on face-to-face communication were more affected by the pandemic, while those that can work remotely were less affected.Originality/valueBy investigating the impacts of COVID-19 on housing prices in Beijing, this study illustrates that urban housing prices would be impacted by the pandemic, at least in the short term. While the rise and fall of housing prices were found spatially heterogeneous in Beijing, it suggests that urban neighbourhoods with specific socioeconomic characteristics and geographic locations would unfold different resilience when encountering pandemic. By using data scraping and rigorous statistical tools, the study is probably one of the first ones examining the consequences of COVID-19 in intra-urban housing markets.

LEGAL REGULATION OF PROPTECH IN THE USA

The purpose of the research. This article summarizes and explores the main provisions of US law related to PropTech. Taking into account the opportunities for further development of PropTech in the context of digitalization, it is important to study the issues of its legal regulation in the United States, a country recognized as a world leader in this field. PropTech is a cluster of innovative technologies designed for the real estate market, which includes many segments, requires proper legislative regulation and institutionalization. Results. Despite all the difficulties of formation, development and regulation, it is predicted that the PropTech market in the USA will have great prospects. It is of interest to predict that the entire US real estate market may eventually switch to a remote method of searching and renting/purchasing real estate, effectively displacing real estate agencies: they will be completely replaced by innovative technologies and online services that simplify the process of real estate transactions at all stages. If earlier the buyer, together with the agent, went to the site, and then had long negotiations with the seller, now the entire purchase process often takes place remotely. At the same time, the potential problem of fixing the expression of will by electronic or other similar technical means should be mentioned. In the very near future, at a breakthrough technological stage, mass adoption in the field of PropTech, including ConTech (design and construction of real estate) of the above-mentioned key elements of Industry 4.0, such as: AI and machine learning, augmented and virtual reality, drones and lidars, is clearly to be expected, 3D printing and robotization, etc.

House Price Prediction Model Using Random Forest in Surabaya City

A home is one of many fundamental human needs. Therefore, it is essential to arrange so that each family has a separate dwelling. Several prediction algorithms are presented in this study to forecast future property values. By interviewing real estate agents, combining many interviews with multiple agents engaged in the purchasing and selling of homes. Consequently, this study investigates Surabaya Real estate price forecasting models employing Random Forest machine learning algorithms and adopting seventeen regularly used characteristics from real estate agents, which are the most influential factor in determining house prices. The final model may assist in determining the appropriate price for the house. Several research trials have been conducted to achieve a high predictive value; however, the highest predictive value was achieved by using 80% of the data set for training and 20% of the data set for testing to provide output values with an 88% accuracy rate.

DIY可能賃貸住宅の特性とそれを取り扱う不動産業者の意向

This paper aims to find issue to increase the number of rental housing units that allow DIY, the characteristics of rental housing units that allow DIY and the intentions of real estate agents who handle these units were identified. In order to promote the spread of DIY-ready rental housing, it is necessary to establish a method for disclosing information about DIY at recruiting residents, establish the concept of Right to Request Purchase of Interior Decorations and Fixtures and the right to demand reimbursement of beneficial costs, create and present guidelines, develop a system for mediation and arbitration, and provide consumer education.

Housing Prices and the Characteristics of Nearby Green Space: Does Landscape Pattern Index Matter? Evidence from Metropolitan Area

This study aimed to examine the association between housing prices and green space characteristics with a special focus on exploring the effects of the shape pattern index. The research was based on a hedonic price model across two main distance buffers from residential properties to urban green spaces. Green spaces were characterized by size and shape measured by a landscape shape index (LSI). This study was based on 16,222 housing transaction data obtained from the website of real estate agencies during December 2019 in the Metropolitan Area of Beijing. Linear regression and semi-log regression analysis were used to examine the associations between independent housing and neighborhood characteristic variables and housing prices. The results suggested that a one-unit increase in the natural logarithm of the landscape shape index (LSI) can increase housing prices by 4% (5543 CNY ≈ 826 USD). Such marginal effects were more pronounced for residences located close to urban green spaces and tended to decay as the distance from residences to green spaces increased. Additional analysis captured the marginal effects of the natural logarithm of the landscape shape index (LSI > 1.3) on achieving the maximum monetary evaluation of the property. The findings of this study suggest that the effects of specific green space characteristics on housing prices should be taken into account in landscape and urban design.