The Evolution of Real Estate Advertisement Language in Racially Changing Neighborhoods

One's native language is normally a marker of national identity. This is particularly true of China, which many regard as a relatively linguistically homogeneous nation. The huge impact of the spread of English on the local culture of China alongside a buoyant wave of global capitalism raises interesting questions such as the following: (i) Does the spread of English challenge or undermine the sense of China's national identity? (ii) By drawing upon English as a new linguistic and cultural resource, is China now redefining its own culture? (iii) What strategies are observable in the use of English intranationally in contemporary China? To answer these questions, this study examines the use of English in China's real estate advertising. The relatively new discourse of real estate advertisements in mainland China has been attributed to the process of increasing urbanization which has accelerated since 2000. In addition, as one of the most fundamental symbols of a nation, land is closely associated with national identity, which suggests that real estate transformed from land can be taken as a source discourse for an investigation of national identity (Smith, 1991; First and Avraham, 2007). By focusing on the use of English in China's real estate advertising and its possible association with the national identity of mainland China, this study discusses the strategic use of English as a linguistic and cultural resource in identity construction.

This research attempts to find out similarities and differences of values through comparison of pragmatic identities constructed in china real estate advertising and that in the US real estate advertising. The study selected 50 Chinese and 50 American electrical real estate advertising.to analyze pragmatic identities constructed in these advertisements and concluded 16 kinds of identities. Both American and Chinese advertisers tend to presuppose consumers’ identity to advertise real estate although explicit linguistic resources regarding identity such as champion are also used to construct identity. Advertisers tend to appeal and provoke consumers’ purchase desire so that they construct different identities according to Maslow’s hierarchy of needs especially belonging (love) and self-realization needs. As regard to differences of identities embodied in Chinese and American real estate advertising, Hofstede’s cultural dimensions are adopted to explain.

Browsing unstructured Web texts using Formal Concept Analysis (FCA) confronts two problems. Firstly, online Web data is sometimes unstructured and any FCA system must include additional mechanisms to discover the structure of input sources. Secondly, many online collections are large and dynamic, so a Web robot must be used to automatically extract data when it is required. These issues are addressed in this chapter, which reports a case study involving the construction of a Web-based FCA system used for browsing classified advertisements for real estate properties1. Real estate advertisements were chosen because they represent a typical semi-structured information source accessible on the Web. Furthermore, data is relevant only for a short period of time. Moreover, the analysis of real estate data is a classic example used in introductory courses on FCA. However, unlike the classic FCA real estate example, whose input is a structured relational database, we mine Web-based texts for their implicit structure. The issues encountered when mining these texts, and their subsequent presentation to the FCA system, are examined in this chapter. Our method uses a handcrafted parser for extracting structured information from the real estate advertisements, which are then browsed via a Web-based front-end employing rudimentary FCA system features. The user is able to quickly determine the trade-offs between different attributes of real estate properties and to alter the constraints of the search in order to locate good candidate properties. Interaction with the system is characterized as a mixed initiative process in which the user guides the computer in the satisfaction of constraints. These constraints are not specified apriori, but rather drawn from the data exploration process. Further, the chapter shows how the Conceptual Email Manager, a prototype FCA text information retrieval tool, can be adapted to the problem.

We examine the mediating role of real estate agency in the residential housing market with reference to intraurban place meaning. Our focus is on real estate advertising because it is the central mediating technique used by sales consultants to incorporate and extend the market activities of a range of actors whose interests and needs are intertwined in the interpretation and representation of people's houses and homes, and those parts of the city in which they are located. We therefore discuss the process of making and deploying such advertising and the ways real estate sales consultants help continually to reinvigorate the meaning of suburbs and other urban localities.

The authors ‘field experiment indicates that including African-Americans in real estate advertisements produces a positive effect for (1) African-American readers in terms of liking the models pictured in the photographs and (2) African-American high ethnic identifiers in terms of identifying with the models pictured in the photographs. However, based on responses to the dependent measures of behavioral purchase intentions and attitude toward the advertising campaign, message, and product, the results do not support the hypothesis that racially exclusive advertising sends a racially exclusive message. A follow-up content analysis of real estate newspaper advertising suggests that cities with higher percentages of ethnic minorities are more responsive to including more ethnic minorities in real estate advertisements. However, the results do not show evidence of a chilling effect, that is, a reduction in the use of real estate advertisements with models.

In choosing the level of quality to purchase, the buyer of a differentiated product also chooses a point on the marginal price schedule for that product. Hence, in general, the demand functions for product characteristics cannot be consistently estimated by ordinary least squares. Market equilibrium results in a matching of characteristics of demanders and suppliers. This matching restricts the use of buyer and seller characteristics as instruments when estimating demand and supply functions for product characteristics. The paper develops these issues. A stochastic structure for hedonic equilibrium models is then proposed, identification results are presented, and estimation procedures are outlined.

Alarge body of research documents the difficulty congregations have in creating and sustaining racially diverse memberships. However, little scholarship explores the overlapping consequences of racial change in congregations and neighborhoods over time. Since the number of all-white neighborhoods has fallen sharply in recent decades, we ask in this study: what are the consequences of racial change in congregations and neighborhoods on congregational attendance? We employ longitudinal data from over 20,000 United Methodist congregations between 1990 and 2010 paired with census tract data for the same time period. We use growth curve models to test three hypotheses derived from Organizational Ecology Theory. While Methodist churches have decreasing attendance, we find that racial diversity inside a church is associated with higher average attendance by year and across years. Outside a church, percent white in the neighborhood positively predicts attendance, at least in the short term. Both white and nonwhite Methodist churches have higher attendance when located in white neighborhoods; white churches in nonwhite neighborhoods fare the worst. Our conclusion discusses these patterns and highlights the complexities of accommodating racial differences in congregations amidst ongoing demographic changes outside their doors.

Mapping of Real Estate Prices Using Data Mining Techniques

The valuation of real estate is a fundamental process for the proper functioning of the market and the formulation of public policies. The mother of the traditional methodologies is the comparative method, which is based on data that is often incomplete or unreliable, especially in real estate markets with limited transparency. In contrast, online advertisements offer a wealth of unstructured information, which requires advanced analysis techniques. Even if they reflect price demands rather than real estate’s final market values, they provide a wealth of data that is particularly useful for market transparency. Real estate advertisements are also enriched with photographs and videos of the property. This paper proposes a methodological framework that integrates artificial intelligence techniques (natural language processing and computer vision) to extract structured features from text and photographs of advertisements. The resulting dataset feeds the comparative method of property valuation, applied to thousands of properties. Geographic analysis and neighborhood characteristics of properties enrich the proposed algorithm, covering all the available geodata that affect real estate values. The empirical analysis of apartments in the city of Thessaloniki demonstrates significant improvements in valuation accuracy and the completeness of the characteristics. At the same time, the prediction of the energy class of properties, among other traits that the proposed methodology can accurately calculate, further enriches the valuation process. The work highlights the potential integration of artificial intelligence into modern valuation practice, offering a transparent, scalable, and auditable tool for professionals and policymakers.

Changes in the spatial pattern of low birth weight in a southern California county: the role of individual and neighborhood level factors

Purpose– The main purpose of this paper is to explore the listing behaviours of agents and sellers. In particular, the paper analyzes listing prices and the predicting power of the house features described in advertisements, to improve their use in real estate valuations. In Italy, selling prices are not public information and therefore listing prices play a key role for market analyses and are used by real estate companies and appraisers for estimating house values.Design/methodology/approach– A traditional hedonic model was used to measure the overall contribution to listing price of the characteristics described in advertisements. The analysis was performed both on houses put on the market by agents and on houses put on the market by sellers. Listing price distributions and their deviation from normality were analyzed. Furthermore, a hedonic analysis was performed, which consisted of two steps. First, the coefficient of determination for any characteristic was computed. Second, the overall contribution to the listing price of the characteristics described in advertisements was measured.Findings– The analysis shows the presence of factors which affect listing prices and which are not revealed to buyers in real estate advertisements. On the other hand, the presence of characteristics that do not affect the listing price but are described in advertisements was also found. Furthermore, agents and sellers showed different behaviours. While the marginal contributions of each characteristic estimated on a sample of houses put on the market by agents were significant, the analysis reveals that listing prices of houses put on the market by sellers are not explained by the house features.Originality/value– To the best of the authors’ knowledge, this is the first study to propose a hedonic approach to exploring the major determinants of listing prices of houses on sale on the Italian market. The listing behaviour of agents and sellers and the predicting power of the observable characteristics could address the use of listing prices in real estate valuations. At the same time, the potential presence of unobservable factors that affect the listing price could be a source of bias in estimating the value of houses.

Real estate is an enormous and essential field in many countries. Taking advantage of helpful information from real estate advertisement posts can help better understand the market condition and explore other vital insights, especially for the Vietnamese market. It is worth noting that in the representative information of real estate, the address or the location is required information. However, there are different ways to write down the address information in Vietnam. For this reason, detecting the relevant text representing the address information from real estate advertisement posts becomes an essential and challenging task. This paper investigates the address detecting and parsing task for the Vietnamese language. First, we create a dataset of real estate advertisements having 16 different attributes (entities) of each real estate and assign the correct label for each entity detected during the data annotation process. Then, we propose a practical approach for detecting locations of possible addresses inside one specific real estate advertisement post and then extract the localized address text into four different levels of the address information: City/Province, District/Town, Ward, and Street. The experiment results indicate that the ${\mathrm {PhoBERT}}_{bas\mathrm{e}}$ model achieves the best performance with an F1-score of 0.8195. Finally, we compare our proposed method with other approaches and achieve the highest accuracy results for all levels as follows: City/Province (0.952), District/Town (0.9482), Ward (0.9225), Street (0.8994), and the combined accuracy of correctly detecting all four levels is 0.8367.

The COVID-19 pandemic has affected a number of areas including the real estate market. The crisis occurred both on the demand and on the supply side, but also, for example, in the field of real estate valuation. Such crises are quite common on the real estate market with cyclically recurring situations. Therefore, it is necessary to pay attention to this issue and to be able to recognize the first symptoms of the problems. The aim of the presented article is to report on the development of the real estate market in the Czech Republic in the time period 2019-2021, focusing on the effects of the COVID-19 pandemic, if these had an impact on the market at all. Data from real estate advertising is collected in a period of one month; beginning April 2020, the data is collected in the period of 15 days. The data is concerned mainly with the offer prices of flats intended for rent or sale and represents all municipalities in the Czech Republic. The data is purged from duplicate advertisements, unreliable advertisements and advertisements for which the price data has not been filled in. The authors evaluate the real estate market using their own SW tool EVAL while the research refutes the premises about the significant impacts of the COVID-19 pandemic on the real estate market in the Czech Republic, which corresponds with the opinions of other authors. The research shows that the potential impacts are more noticeable especially in the short-term rental sector. The real estate market in the Czech Republic has been in imbalance for a long time as it is affected by many opposing factors pushing the supply and demand for real estate up and down at the same time. The situation regarding COVID-19 intensified this fact even more and caused a confusing and, in the future, difficult situation to predict.

The article defines the essence of digital marketing and its tools used to analyze the behavior of investors in the real estate market is defined. Digital marketing strategies are substantiated, in particular, social media marketing, e-mail marketing, paid and targeted advertising. Attention is focused on advertising targeting as a tool for achieving sales efficiency in the real estate market in modern conditions. In real estate advertising, targeting involves reaching demographic groups of consumers on certain grounds (age, marital status, social status, income level) in order to achieve success in promoting sales objects. Targeting allows potential buyers to be identified by needs and interests, geographic data, and social status, which allows developers to provide complete information about the objects for sale with the possibility of attracting potential buyers as real estate investors. Using the example of a private developer, Intergal-Bud, the article proposes to evaluate the effectiveness of targeted advertising in the real estate market by calculating the cost per click, determining the cost per lead, calculating the conversion rate, determining the CPA coefficient, calculating the ROMI indicator, and calculating the ROI coefficient. The results of the effectiveness of targeted advertising of a private developer are determined, which include: an increase in the number of potential customers and sales, an increase in conversion, price lead with optimization of the advertising budget. It is proved that targeted advertising is an effective marketing strategy in the context of digitalization, which contributes to improving the efficiency of real estate business and the company's financial performance.

This article takes a cultural economy approach to the analysis of housing markets as spaces in which class cultures are performed. The design and marketing of real estate projects are understood as the outcome of the interplay of different narratives, practices and materials involving cultural and economic calculations. I explore one particular type of cultural knowledge used during the production and sale of houses: the meanings of class and social mobility. I argue that housing markets and housing production involve the interrelation of several cultural calculations on class and social mobility. In other words, meanings of class and social mobility are instrumentally produced and used in the design, production and marketing of real estate. I describe how cultural calculations about class are inscribed into house location, house design and real estate advertising and marketing devices. Indeed, it is argued that in designing ‘real estate’ projects, agents and executives work as ‘sociologists at large’: they create and perform new meanings of class and social mobility.