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In the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.

All tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.

Machine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.

AVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.

According to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.

The aims of this study were to evaluate fitness levels in a cohort of police trainees and compare these results to other police trainees and the general population.

Retrospective data for 274 male and 152 female police trainees were supplied. Measures included height, body mass and physical appraisal test (PAT; 2.4 km run, vertical jump, push-ups and grip strength) results, assessed twice, prior to commencement of training, separated by several months. Wilcoxon signed rank tests were used to analyze non-parametric initial and final PAT scores and Mann–Whiney U tests were used to determine variance between groups.

Male trainees were significantly quicker in the run (−12%, p < 0.001), completed more push-ups (+74%, p < 0.001) with greater grip strength (+52% left and +50% right, p < 0.001) when compared to female trainees. Following the second PAT assessment, the significant differences between male and female trainees remained (p < 0.001). Only female trainee 2.4 km run times improved significantly between initial and final PAT (−4%, p = 0.002).

When compared to the general population from which they were drawn and to other law enforcement trainees, the police trainees in this study were quicker, more powerful and stronger. While there was no loss of fitness between initial and final PAT performance, a conditioning program, spanning the periods between initial and final PAT may be of benefit to increase fitness prior to training commencement especially for female trainees who were generally less fit than, yet must complete the same training as, male trainees.

This study aims to identify clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level. In addition, this work is intended to detect the socio-economic factors driving the cluster formation.

To group the studied housing markets into homogeneous clusters, this analysis uses a proprietary algorithm based on taxonomic and k-means++ methods. In turn, the generalised ordered logit (gologit) model was used to explore factors influencing the cluster formation.

The results obtained revealed that Polish county housing markets can be classified into three or four homogeneous clusters in terms of the size and quality of the housing stock and price level. Furthermore, the results of the estimation of the gologit models indicated that population density, number of business entities and the level of crime mainly determine the membership of a given housing market in a given cluster.

In contrast to previous studies, this is the first to examine the existence of homogeneous clusters amongst the county housing markets in Poland, taking into account the criteria of size and quality of the housing stock, as well as price level simultaneously. Moreover, this work is the first to identify the driving forces behind the formation of clusters amongst the surveyed housing markets.

This paper aims to explore the impact of a recent earthquake activity on house prices and their spatial distribution in the Istanbul housing market.

The paper uses a multi-level approach within an event study framework to model changes in the pattern of house prices in Istanbul. The model allows the isolation of the effects of earthquake risk and explores the differential impact in different submarkets in two study periods – one before (2007) and one after (2012) recent earthquake activity in the Van region, which although in Eastern Turkey served to alter the perceptions of risk through the wider geographic region.

The analysis shows that there are variations in the size of price discounts in submarkets resulting from the differential influence of a recent earthquake activity on perceived risk of damage. The model results show that the spatial impacts of these changes are not transmitted evenly across the study area. Rather it is clear that submarkets at the cheaper end of the market have proportionately larger negative impacts on real estate values.

The robustness of the models would be enhanced by the addition of further spatial levels and larger data sets.

The methods introduced in this study can be used by real estate agents, valuers and insurance companies to help them more accurately assess the likely impacts of changes in the perceived risk of earthquake activity (or other environmental events such as flooding) on the formation of house prices in different market segments.

The application of these methods is intended to inform a fairer approach to setting insurance premiums and a better basis for determining policy interventions and public investment designed to mitigate potential earthquake risk.

The paper represents an attempt to develop a novel extension of the standard use of hedonic models in event studies to investigate the impact of natural disasters on real estate values. The value of the approach is that it is able to better capture the granularity of the spatial effects of environmental events than the standard approach.

The assessment of the Real Estate (RE) prices depends on multiple factors that traditional evaluation methods often struggle to fully understand. Housing prices, in particular, are the foundations for a better knowledge of the Built Environment and its characteristics. Recently, Machine Learning (ML) techniques, which are a subset of Artificial Intelligence, are gaining momentum in solving complex, non-linear problems like house price forecasting. Hence, this study deployed three popular ML techniques to predict dwelling prices in two cities in Italy.

An extensive dataset about house prices is collected through API protocol in two cities in North Italy, namely Brescia and Varese. This data is used to train and test three most popular ML models, i.e. ElasticNet, XGBoost and Artificial Neural Network, in order to predict house prices with six different features.

The models' performance was evaluated using the Mean Absolute Error (MAE) score. The results showed that the artificial neural network performed better than the others in predicting house prices, with a MAE 5% lower than the second-best model (which was the XGBoost).

All the models had an accuracy drop in forecasting the most expensive cases, probably due to a lack of data.

The accessibility and easiness of the proposed model will allow future users to predict house prices with different datasets. Alternatively, further research may implement a different model using neural networks, knowing that they work better for this kind of task.

To date, this is the first comparison of the three most popular ML models that are usually employed when predicting house prices.

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

This paper aims to investigate cross-gender effects of gender stereotypes in advertising. More specifically, it proposes that the negative effects found in studies of women’s reactions to stereotyped female portrayals should hold across gender portrayal and target audience gender.

In two experimental studies, the effects of stereotyped portrayals (vs non-stereotyped portrayals) across gender are compared.

The results show that advertising portrayals of women and men have a presumed negative influence on others, leading to higher levels of ad reactance, which has a negative impact on brand-related effects across model and participant gender, and for gender stereotypes in terms of physical characteristics and roles.

Whereas previous studies have focused on reactions of women to female stereotypes, the current paper suggests that women and men alike react negatively to stereotyped portrayals of other genders.

The results indicate that marketers can benefit from adapting a more mindful approach to the portrayals of gender used in advertising.

The addition of a cross-gender perspective to the literature on gender stereotypes in advertising is a key contribution to this literature.

To cope with the COVID-19 pandemic, contact tracing mobile apps (CTMAs) have been developed to trace contact among infected individuals and alert people at risk of infection. To disrupt virus transmission until the majority of the population has been vaccinated, achieving the herd immunity threshold, CTMA continuance usage is essential in managing the COVID-19 pandemic. This study seeks to examine what motivates individuals to continue using CTMAs.

Following the coping theory, this study proposes a research model to examine CTMA continuance usage, conceptualizing opportunity appraisals (perceived usefulness and perceived distress relief), threat appraisals (privacy concerns) and secondary appraisals (perceived response efficacy) as the predictors of individuals' CTMA continuance usage during the pandemic. In the United States, an online survey was administered to 551 respondents.

The results revealed that perceived usefulness and response efficacy motivate CTMA continuance usage, while privacy concerns do not.

This study enriches the understanding of CTMA continuance usage during a public health crisis, and it offers practical recommendations for authorities.

People who experience mental illness often demonstrate limited help-seeking behaviours. There is evidence to suggest that media content can influence negative attitudes towards mental illness; less is known about how media impacts help-seeking behaviours. The purpose of this study is to identify if media plays a role in people’s decisions to seek help for their mental health.

The databases Academic Search Complete, CINAHL Plus with Full Text, MEDLINE, APA PsycArticles, APA PsycInfo, Social Sciences Full Text [H.W. Wilson] and Soc Index were systemically searched for papers in the English language that investigated the link between media and help-seeking for mental illness.

Sixteen studies met eligibility criteria. There was some evidence to suggest that various forms of media – including video and online resources – can positively influence help-seeking for mental health. Print media had some limited effect on help-seeking behaviours but was weaker in comparison to other forms of media. There was no evidence to suggest that media discourages people from seeking help.

This review identified that, given the heterogeneity of the included papers, and the limited evidence available, there is a need for more focused research to determine how media impacts mental health-related help-seeking behaviours.