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House price-to-rent (P-t-R) ratios are among the most widely used measures of housing market conditions. Given the theoretical and apparent bidirectional, causal relationships and imbalances between the housing market, broader economy and financial market determinants, it is important to understand the relationship between macro- and micro-economic characteristics in relation to the P-t-R ratio to enhance the understanding of housing market dynamics. This paper studies the joint dynamics and persistence of house prices and rents and examines the temporal interactions of the P-t-R ratio and economic and financial determinants.

The authors examine the lead–lag relationships between the P-t-R ratios and a spectrum of macroeconomic variables using cointegration and causality methods, initially at the aggregate position and also across housing types within the Northern Ireland housing market to establish whether there are subtle differences in how various housing segments react to changes in economic activity and market fundamentals.

The findings reveal price switching dynamics and some very distinct long- and short-run relationships between macroeconomic and financial indicators and the P-t-R ratios across the various housing segments. The results exhibit monetary supply, foreign exchange markets and the stock market to be important drivers of the P-t-R ratio, with P-t-R movements seemingly tied, or are in tandem, with the overall economy, particularly with the construction sector.

The study shows that the P-t-R ratio can be used as an early measure for establishing the effects of macroprudential policy changes and how these may manifest across housing tiers and quality, which can further act as a signal for preventing or at least mitigating future irrational price cyclicity. These insights serve to inform housing and economic policy and macroprudential policy design, principally within lending policy and the effect of regulatory interventions and further enhance the understanding of the P-t-R ratio on housing market structure and dynamics.

This study is the first in the housing literature that examines the causal relationships between the P-t-R ratio and macroeconomic activity at the sub-market level. It investigates whether and how money supply, inflation, foreign exchange markets, general economic productivity and other important macroeconomic factors interact with the pricing of different property types over time.

Role-taking, perspective taking, and empathy have developed through parallel literatures in sociology and psychology. All three concepts address the ways that people attune the self to others’ thoughts and feelings. Despite conceptual and operational overlap, researchers have yet to synthesize existing research across the three concepts. We undertake the task of theoretical synthesis, constructing a model in which role-taking emerges as a multidimensional process that includes perspective taking and empathy as component parts.

We review the literatures on role-taking, perspective taking, and empathy across disciplines. Focusing on definitions, measures, and interventions, we discern how the concepts overlap, how they are distinct, and how they work together in theoretically meaningful ways.

The review identifies two key axes on which each concept varies: the relative roles of affect and cognition, and the relative emphasis on self and structure. The review highlights the cognitive nature of perspective taking, the affective nature of empathy, and the structural nature of role-taking. In a move toward theoretical synthesis, we propose a definition that centers role-taking as a sociological construct, with perspective taking and empathy representing cognition and affect, respectively.

Role-taking is an important part of selfhood and community social life. It is a skill that varies in patterned ways, including along lines of status and power. Theoretical synthesis clarifies the process of role-taking and fosters the construction of effective interventions aimed at equalizing role-taking in interpersonal interaction.

Role-taking is a basic social process underpinning much of the structural social psychology paradigm – a paradigm built on empirical studies of human interaction. Yet today, our social worlds are occupied by bots, voice assistants, decision aids, and other machinic entities collectively referred to as artificial intelligence (AI). The integration of AI into daily life presents both challenges and opportunities for social psychologists. Through a vignette study, the authors investigate role-taking and gender in human-AI relations.

Participants read a first-person narrative attributed to either a human or AI, with varied gender presentation based on a feminine or masculine first name. Participants then infer the narrator's thoughts and feelings and report on their own emotions, producing indicators of cognitive and affective role-taking. The authors supplement results with qualitative analysis from two open-ended survey questions.

Participants score higher on role-taking measures when the narrator is human versus AI. However, gender dynamics differ between human and AI conditions. When the text is attributed to a human, masculinized narrators elicit stronger role-taking responses than their feminized counterparts, and women participants score higher on role-taking measures than men. This aligns with prior research on gender, status, and role-taking variation. When the text is attributed to an AI, results deviate from established findings and in some cases, reverse.

This first study of human-AI role-taking tests the scope of key theoretical tenets and sets a foundation for addressing group processes in a newly emergent form.

The role of energy efficiency and particularly energy performance certificates (EPCs) has emerged as a topical and important aspect of real estate markets. Various studies have been carried out investigating the perceived capitalisation effects of energy efficiency on property prices. There, however, remains divergence of opinion whether the capitalisation effect is truly in existence with extant research showing differing magnitudes of effects, if any. To date, no study (that the authors are aware of) has investigated the nature of the transition between EPC bands and price effects. The purpose of this study is to add to the research of the energy efficiency of housing to examine the nature of the likelihood of property characteristics being associated with higher EPC scores and value.

This research undertakes a suite of methodological tests to investigate the more latent relationships between EPC bands and pricing behaviour using 3,797 achieved sales prices within the Belfast housing market. Binary logit regression models are specified in conjunction with a Polytomous Universal Model to examine the likelihood of EPC bands falling within a particular property type and the likelihood of any pricing effects.

The findings show the differing property types to comprise very distinct and complex relationships in terms of price and EPC banding. The binary logit model estimations for both terrace properties and apartments reveal an increased likelihood to obtain higher EPC scores, with the semi-detached sector displaying a “mixed effect” with detached property revealing decreased probability of having superior energy performance and decreased likelihood of having poorer energy performance. The ordinal model estimations indicate that sales price comprises no relationship with energy performance, inferring that there is no increased probability of an increase in sales price with higher EPC rating.

This research offers new insights and focus on achieving a better understanding of the nexus between energy performance and property characteristics using alternative modelling approaches. This provides more exploratory insights into the complex relationships and offers awareness for policy discourse in terms of targeting properties which will tend to be poorer in energy efficiency.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.

This paper aims to examine the association of accruals and disaggregated pension components with future cash flows and also to investigate whether investors distinguish between pension information that is recognized (SFAS 158) versus disclosed (SFAS 132).

Regression analysis is used with a proxy for expected future cash flows as the dependent variable, and the components of pension disclosures as well as controls for the 2008-2009 financial crisis as the independent variables.

The results reveal that incorporating disaggregated pension components increases the ability to predict future cash flows, and that investors attach different pricing multiples to the various components in the models. The authors also find that during the 2008-2009 financial crisis, the signs of the coefficients on these components changed. Finally, the results indicate that investors assign more significance to pension accounting information that is recognized, as opposed to disclosed, and that disclosure affects the allocation of pension assets.

The authors provide empirical support for the conjecture posited by Amir and Benartzi (1998) that the prediction of future cash flows will be enhanced by the incorporation of the components of pension assets and liabilities. Importantly from a standard setting perspective, the authors also find evidence that investors assign more significance to pension accounting information that is recognized in the financial statements than to pension information that is disclosed.

A number of studies have investigated the relationship between energy performance certificates (EPCs) and house prices. A majority of studies have tended to model energy performance pricing effects within a traditional hedonic conditional mean estimate model. There has been limited analysis that has accounted for the relationship between EPCs and the effects across the pricing distribution. Moreover, there has been limited research examining the “standard cost improvements EPC score”, or “potential score”. Therefore, this paper aims to quantify and measure the dynamic effects of EPCs on house prices across the price spectrum and account for standardised cost-effective retrofit improvements.

Existing EPC studies produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price effect. The approach within this study applies a quantile regression approach to empirically estimate how quantiles of house prices respond differently to unitary changes in the proximal effects of EPCs and structural property characteristics across the conditional distribution of house prices. Using a data set of 1,476 achieved transaction prices, the quantile regression models apply both assessed EPC score and bands and further examine the potential EPC rating for improved energy performance based on an average energy cost improvement.

The findings show that EPCs are valued differently across the quantiles and that conditional quantiles are asymmetrical. Only property prices in the upper quantiles of the price distribution show significant capitalisation effects with energy performance, and only properties with higher EPC scores display positive significant effects at the higher end of the price distribution. There are also brown discount effects evident for lower-rated properties within F- and G-rated EPC properties at the higher end of the pricing distribution. Moreover, the potential energy efficiency rating (score) also shows increased effects with sales prices and appears to minimise any brown discount effects. The findings imply that energy performance is a complex feature that is not easily “averaged” for valuation effect purposes.

While numerous studies have investigated the pricing effects of EPCs, they have tended to provide a single estimate to determine the relationship with price. This paper extends the traditional analytical insights beyond the conditional mean estimate by examining the quantiles of the relationship between EPCs and house prices to enhance the understanding of this esoteric and complex issue. In addition, this research applies the assessed energy efficiency potential to establish whether effective cost improvements enhance the relationship with sales price and capitalisation effects.