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Investors often utilize brokers to assist them in property acquisitions. These brokers are compensated through a cooperative commission, or bonus, that is publicized on the listing service. The purpose of this paper is to determine the relationship between advertised compensation packages and selling price, time-on-market and listing characteristics.

To examine variables likely to influence earnings of the buyers' broker, this study utilizes multiple and logistic regressions. Given the range of prices found in the 196,276 listings, the data was sorted on listing price and then split into ten, approximately equal, deciles.

The explanatory power of models with cooperative commission as the dependent variable was highest in the lowest deciles with type of financing, size and distressed status being highly significant. When comparing list- to selling price the average was 96.1%. As cooperative commission increased, the higher priced parcels sold at a higher price relative to list price. This potentially justifies higher cooperative commissions or exemplifies the principal-agent problem where effort is based on potential earnings. Fixed bonuses were used predominately for parcels under $62,234, likely to provide a minimum earnings amount. However, surrounding the median, it seems they may differentiate a property.

This research provides insight for practitioners on the impact of different variables, including cooperative commissions, on sale price and time-on-market. For example, cooperative commission increased for properties in the outer deciles implying that agents may be compensating for suspected difficulty. Additionally, the seasonality findings imply that agents can determine when to list and when to provide a fixed bonus to solicit attention. Results also suggest that practitioners will find it beneficial to market at an appropriate price rather than list high to create negotiating room.

This paper follows only one paper that covered a similar topic. However, this paper uses twenty years of multi-unit property listings from a major US city from 1996 to 2015. The focus on multi-unit properties is an effort to focus on a more sophisticated group of buyers that may be more experienced and make decisions more rationally.

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

This paper aims to provide a comprehensive analysis of the transformative impact of Artificial Intelligence (AI) and Large Language Models (LLMs), such as GPT-4, on the real estate industry. It explores how these technologies are reshaping various aspects of the sector, from market analysis and valuation to customer interactions and evaluates the balance between technological efficiency and the preservation of human elements in business.

The study is based on an analysis of the strengths and weaknesses of AI as a technology in applications for real estate. It uses this framework to assess the potential of this technology in different use cases. This is supplemented by an emerging literature on the topic, practical insights and industry expert opinions to provide a balanced perspective on the subject.

The paper reveals that AI and LLMs offer significant benefits in real estate, including enhanced data-driven decision-making, predictive analytics and operational efficiency. However, it also uncovers critical challenges, such as potential biases in AI algorithms and the risk of depersonalising customer interactions.

The paper advocates for a balanced approach to adopting AI, emphasising the importance of understanding its strengths and limitations while ensuring ethical usage in the diverse and complex landscape of real estate.

This work stands out for its balanced examination of both the advantages and limitations of AI in real estate. It introduces the novel concept of the “jagged technological frontier” in real estate, providing a unique framework for understanding the interplay between AI and human expertise in the industry.

Purchasing real estate is one of the most important and complex decisions in a life of an individual, which should take numerous factors into account. The purpose of this research is to identify which behavioral factors significantly affect the intention to buy real estate. Since the real estate market is continuously changing, along with other economic and life conditions, it is expected that different generations have different characteristics which affect their behavior; therefore, it is important to analyze generational influence on buyers' behavior.

A survey analysis was conducted on a sample of 434 respondents in Croatia. Partial least squares structural equation modeling was used to obtain the results. The moderating effect of generational affiliation was observed.

Overconfidence significantly affects intention to buy real estate, but it doesn't affect the level of importance individuals give to financial factors. On the other hand, herding significantly affects the level of importance given to financial factors, whereas it does not directly affect buying intention. A significant moderating effect of generational affiliation was found for the impact of overconfidence on financial factors, suggesting a negative effect for younger generations and a positive effect for older generations.

This research proposes a novel unique model with both behavioral and financial factors as predictors of the intention to buy real estate, together with generational differences in buyers' behavior. Understanding normal human behavior is crucial to determine how buyers' decisions and intentions change under the influence of certain biases or characteristics such as generational affiliation.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

The purpose of this paper is to examine the effect of visual technology on the price discovery process in listings of residential properties in Singapore from 2015 to 2018.

The authors empirically model the effects of 360 virtual tours and drone video on four dimensions in price discovery – buyers’ arrival rate, sale probability, transaction prices and time-on-market – using a comprehensive data set for the residential properties in Singapore.

The analysis shows that the availability of virtual tours or drone video in a listing increases the arrival rate from potential buyers, the probability of a successful sale and the selling price. These findings are consistent with the hypothesis that technologically enhanced tools improve the quality of information and the marketability of property. However, listings with virtual tours tend to be associated with longer marketing time, which is consistent with the prediction of the information overload hypothesis.

This paper extends the housing and price discovery literature by examining how technologically enabled new information affects property transactions.

To the best of the authors’ knowledge, this is the first paper to consider the impact of drone video on property market outcome.

This study illustrates the economic and financial dynamics of the sector, analysing the evolution of the main ratios of profitability and financial structure of 1,559 Italian real estate companies divided into the three macro-regions: North, Centre and South, in the period 2011–2020. In this way, it is also possible to verify the responsiveness to the 2020 pandemic crisis.

The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance, supplemented by the Tukey–Kramer test, to identify significant differences between the three Italian macro-regions.

The study shows the increase in profitability after the 2008 crisis, despite its reverberation in the years 2012–2013. The financial structure of companies improved almost everywhere. The pandemic had modest effects on performance.

In the future, other indices should be considered to gain a more comprehensive view. This is a quantitative study based on financial statements data that neglects other important economic and social factors.

Public policies could use this study for better interventions to support the sector. In addition, internal management can compare their company's performance with the industry average to identify possible improvements.

The research analyses an economic field that employs a large number of people, especially when considering the construction and real estate services covered by this analysis.

The study contributes to the literature by providing a quantitative analysis of industry dynamics, with comparative information that can be deduced from financial statements over the years.

High-density development requires large land parcels, but fragmented land ownership can impede redevelopment. While earlier compact city development in Sydney occurred on large-scale brownfield sites, redeveloping and re-amalgamating older strata-titled properties is now integral to further densification. The purpose of this study is to examine collective sales activity in one Sydney suburb where multiple strata-titled redevelopments and re-amalgamations have been attempted. The authors explore how owners navigate the process of selling collectively, focusing on their experience of legislation introduced to facilitate this process, the Strata Schemes Development Act 2015 [New South Wales (NSW)].

By reviewing sales listings, development applications and media coverage, and interviewing owners, lawyers and estate agents, the authors map out collective sale activity in a case study area in Sydney’s northwest.

Strata collective sales are slow and difficult to complete, even when planning and market drivers align. Owners find the Strata Scheme Development Act 2015 (NSW) difficult to navigate and it has not prevented strategic blocking attempts by competing developers. The long timelines required to organise collective sales can result in failure if the market shifts in the interim. Nonetheless, owners remain interested in selling collectively.

This case study is important for understanding the barriers to redevelopment to achieve a more compact city. It highlights lessons for other jurisdictions considering similar legislative changes. It also suggests that legislative change alone is insufficient to resolve the planning challenges created by hyper-fragmentation of land through strata-title development.

To investigate whether additional information of the permanent news flow, especially reporting intensity, can help to increase transparency in housing markets, this study aims to examine the relationship between news coverage or news sentiment and residential real estate prices in Germany at a regional level.

Using methods in the field of natural language processing, in particular word embeddings and dictionary-based sentiment analyses, the authors derive five different sentiment measures from almost 320,000 news articles of two professional German real estate news providers. These sentiment indicators are used as covariates in a first difference fixed effects regression to investigate the relationship between news coverage or news sentiment and residential real estate prices.

The empirical results suggest that the ascertained news-based indicators have a significant positive relationship with residential real estate prices. It appears that the combination of news coverage and news sentiment proves to be a reliable indicator. Furthermore, the extracted sentiment measures lead residential real estate prices up to two quarters. Finally, the explanatory power increases when regressing on prices for condominiums compared with houses, implying that the indicators may rather reflect investor sentiment.

To the best of the authors’ knowledge, this is the first paper to extract both the news coverage and news sentiment from real estate-related news for regional German housing markets. The approach presented in this study to quantify additional qualitative data from texts is replicable and can be applied to many further research areas on real estate topics.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.