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The price-to-rent ratio is often regarded as an important indicator for measuring housing market imbalance and inefficiency. A central question is the extent to which house prices and rents form part of the same market and thus whether they respond similarly to parallel stimulus. If they are close proxies dynamically, then this provides valuable market intelligence, particularly where causal relationships are evident. Therefore, this paper aims to examine the relationship between market and rental pricing to uncover the price switching dynamics of residential real estate property types and whether the deviation between market rents and prices are integrated over both the long- and short-term.

This paper uses cointegration, Wald exogeneity tests and Granger causality models to determine the existence, if any, of cointegration and lead-lag relationships between prices and rents within the Belfast property market, as well as the price-to-rent ratios amongst its five main property sub-markets over the time period M4, 2014 to M12 2018.

The findings provide some novel insights in relation to the pricing dynamics within Belfast. Housing and rental prices are cointegrated suggesting that they tend to move in tandem in the long run. It is further evident that in the short-run, the price series Granger-causes that of rents inferring that sales price information unidirectionally diffuse to the rental market. Further, the findings on price-to-rent ratios reveal that the detached sector appears to Granger-cause those of other property types except apartments in both the short- and long-term, suggesting possible spill-over of pricing signals from the top-end to the lower strata of the market.

The importance of understanding the relationship between house prices and rental market performance has gathered momentum. Although the house price-rent ratio is widely used as an indicator of over and undervaluation in the housing market, surprisingly little is known about the theoretical relationship between the price-rent ratio across property types and their respective inter-relationships.

Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity.

Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria.

The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error.

Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

The purpose of this research is to examine the nature of housing market affordability. Although the problem of housing affordability has been widely discussed, the theoretical underpinnings of the concept have received less attention. It has become increasingly evident that more holistic insights and integrated approaches are needed to provide a platform to define affordability to influence research and policy discourse.

Given the increasing importance of affordability within housing policy reform, this paper seeks to “unearth” the most important prognosticators of affordability. The paper uses principal component analysis to determine how affordability, as a key policy tool, should be analysed. In addition, co integration techniques, Granger causality and impulse response analysis are applied to test the movement and shocks of the key affordability indicators and the two common affordability metrics.

The principal conclusions stemming from this paper demonstrate that affordability is a multifaceted policy concept influenced by financial access (purchase) costs and the repayment costs of housing services which are correlated and interchangeable but significantly were found not to be co integrated.

Understanding the nature of housing market affordability remains problem for policy-makers. This paper adds to the debate and empirical understanding of the cyclic nature of affordability and how it is defined. It shows that there are intricate causal short-term relationships between the key affordability indicators. This is problematic for contemporary housing policy and the key directions in which policy must turn.

Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is problematic to understand the nature of the relationship between house prices and the accessibility to services and prominent neighbourhood landmarks and amenities. Therefore, this paper aims to quantify and measure the (dis)amenity effects on house pricing levels within particular geographic housing sub-markets.

Most hedonic models are estimated using regression techniques which produce one coefficient for the entirety of the pricing distribution, culminating in a single marginal implicit price. This paper uses a quantile regression (QR) approach that provides a “more complete” depiction of the marginal impacts for different quantiles of the price distribution using sales data obtained from 3,780 house sales transactions within the Belfast Housing market over 2014.

The findings emerging from this research demonstrate that housing and market characteristics are valued differently across the quantile values and that conditional quantiles are asymmetrical. Pertinently, the findings demonstrate that ordinary least squares (OLS) coefficient estimates have a tendency to over or under specify the marginal mean conditional pricing effects because of their inability to adequately capture and comprehend the complex spatial relationships which exist across the pricing distribution.

Numerous studies have used OLS regression to measure the impact of key housing market externalities on house prices, providing a single estimate. This paper uses a QR approach to examine the impact of local amenities on house prices across the house price distribution.

Information transparency is crucially important in price setting in real estate, particularly when information asymmetry is concerned. This paper aims to examine how a change in government policy in relation to information disclosure and transparency impacts residential real estate price discovery. Specially, this paper investigates how real estate traders determined asking prices in the context of the Scottish housing market before and after the implementation of the Home Report, which aimed to prevent artificially low asking prices.

This paper uses spatial lag hedonic pricing models to empirically observe how residential asking prices are determined by property sellers in response to a change in government policy that is designed to enhance market transparency. It uses over 79,000 transaction data of the Aberdeen residential market for the period of Q2 1998 to Q2 2013 to test the models.

The empirical findings provide some novel insights in relation to the price determination within the residential market in Scotland. The spatial lag models suggest that spatial autocorrelation in property prices has increased since the Home Report came into effect, indicating that property sellers have become more prone to infer asking prices based on prior sales of dwellings in close vicinity. The once-common practice of setting artificially low asking prices seems to have dwindled to a certain extent statistically.

The importance of understanding the relationship between information transparency and property price determination has gathered momentum over the past decade. Although spatial hedonic techniques have been extensively used to study the impact of various property- and neighbourhood-specific attributes on residential real estate market in general, surprisingly little is known about the empirical relationship between spatial autocorrelation in real estate prices and information transparency.

Real estate valuation studies gained popularity with the availability of large-scale property transaction data in the latter part of the twentieth century. Hedonic price modeling (HPM) was the most popular method in the initial years until it was taken over by advanced modeling methods in the twenty-first century. Even though there exist a few literature reviews on this topic, no comprehensive bibliometric analysis is conducted in this area. In view of gaining a better understanding of the dynamics of property valuation studies, this paper aims to conduct a bibliometric analysis.

A comprehensive search in the Scopus database, followed by detailed screening resulted in 1,400 articles. The identified research articles spanning over five decades (1964–2019) are analyzed using the open-source R package “bibliometrix.”

The study found the USA to be the most productive country in various aspects, such as number of publications, number of authors and publication hotspots. The findings also demonstrate assessments on the publication trends, journals, citations, keywords, co-citation and collaboration networks. It was observed that there exists an upsurge in the number of publications after the year 2000 owing to improved data availability and better modeling techniques.

This study is significant in understanding the major research areas and modeling techniques used in property valuation. Future studies can incorporate multiple database sources and include more articles.

The current study is one of the first bibliometric studies on property valuation. Previous studies have not explored the possibilities of geographic information system in bibliometric research. Spatial mapping and analysis of publications provide a geographical perspective of valuation research.

Buildings contribute significantly to CO₂ production. They are also subject to considerable taxation based on value. Analysis shows that while similar attributes contribute to both value and CO₂ production, there is only a loose relationship between the two. If we wish to use taxation to affect policy change (drive energy efficiency behaviour), we are unlikely to achieve this using only the current tax base (value), or by increasing the tax take off this current tax base (unlike extra taxation of cigarettes to discourage smoking, for example). Taxation of buildings on the basis of energy efficiency is hampered by the lack of current evidence of performance. This paper aims to model the now-obligatory (at sale or letting) energy performance certificate (EPC) data to derive an acceptable appraisal model (marked to market, being the EPC scores) and deploys this to the entire population of properties. This provides an alternative tax base with which to model the effects of a tax base switch to energy efficiency and to understand the tax incidence effects of such a policy.

The research uses a multiplicative hedonic approach to model energy efficiency utilising EPC holding properties in a UK jurisdiction [Northern Ireland (NI)] as the sample. This model is then used to estimate discrete energy assessments for each property in the wider population, using attributes held in the domestic rating (property tax) database for NI (700,000+ properties). This produces a robust estimate of the EPC for every property in its current condition and its cost-effective improved condition. This energy assessment based tax base is further used to estimate a new millage rate and property tax bill (green property tax) which is compared against the existing property tax based on value to allow tax incidence changes to be analysed.

The findings show that such a policy would significantly redistribute the tax burden and would have a variety of expected and some unexpected effects. The results indicate that while assessing the energy performance of houses can be a complex process involving many parameters, much of the explanatory power can be achieved via a relatively small number of input variables, often already held by property tax jurisdictions. This offers the opportunity for useful housing stock modelling – such as the savings possible from power switching. The research also identifies that whilst urban areas display the expected “heat island” effect in terms of energy consumption, urban properties are on average more efficient than suburban/rural properties. This facilitates spatial targeting of policy messages and initiatives.

Analogous with other studies, data deficiencies introduce the risk of omitted variable bias. Modelling of the energy efficiency in the sample is limited to property attributes that are available for the wider population of properties. While this limits the modelling exercise, it is a perennial issue facing mass appraisal worldwide (where knowledge of the transacted sample attributes generally exceeds knowledge of the unsold properties). That said, the research demonstrates the benefits of sharing data and improving knowledge of the housing stock, as taxation databases would be stronger, augmented with EPC-derived property attributes for example.

The EPC lead in time for wide residential coverage is likely to be considerable. The paper contributes to emerging literature and policy debate surrounding the effect, performance measurement and implementation of energy efficiency certification, through a greater understanding of the sectorial and geographical dispersion of energy efficiency. It provides high level research to help guide policy and decision-making, identifying key locales where there is more of a physical problem and locations where there is more to gain in terms of targeting energy improvement and/or encouraging behavioural change. The paper also allows a glimpse of the implications of a change towards a taxation regime based on energy efficiency, which contributes to the debate surrounding the “greening” of property based taxes.

The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.

The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.

Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.

The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.

This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.

The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.

The purpose of the paper is to examine the role of securitised real estate within the confines of a multi‐asset investment portfolio and to identify if indeed securitised real estate can afford investors the desired investment benefits of direct property investment whilst mitigating many of the recognised barriers and risks.

The paper employs a suite of analytical techniques; lead‐lag correlations are utilised to examine market dynamics between listed and direct real estate markets across jurisdictions. Grainger causality and co‐integration techniques are applied to examine the nature and extent of relationships between investment markets with optimal portfolio analysis utilised to explore the role of securitised real estate and the optimum weighting allocation within the confines of a multi‐asset investment portfolio.

The findings demonstrated the unresponsive nature of direct real estate markets relative to listed real estate markets – in some jurisdictions the extent of lag can be as much as 12 months. Whilst the research did not identify a Grainger causality relationship between listed and direct property markets across the jurisdictions, co‐integration analysis does infer trend reverting price behaviour in the long run (ten years) between direct and listed real estate markets. Optimal portfolio analysis serves to demonstrate the crucial role of real estate within a multi‐asset portfolio in terms of both mitigating risk and enhancing performance over the ten‐year time series. Indeed, the optimal portfolio analysis highlights the compatibility and complementarity of listed and direct real estate within a multi‐asset investment portfolio.

The question if securitised real estate is a viable proxy for direct property investment is as inconclusive as it is enduring. In contrast to the large embodiment of previous work, this paper adopts an international market perspective depicting the global nature of securitised real estate investment markets whilst also reflecting on the extent of co‐integration between asset classes and across jurisdictions during a period of extreme financial and economic distress.

THE first surprise when travelling by air from Fiji to Western Samoa is that one leaves Fiji on Saturday and arrives in Western Samoa on Friday. This is because of crossing the International Date Line en route and a story is told in Fiji of a man who owned a shop through which the date line ran. Sunday trading is frowned on in Fiji but our friend got round this by opening only half of his shop on Sunday—the half on the Saturday or Monday side of the date line. It is therefore not surprising that the Fiji Times describes itself as “the first newspaper in the world every day”.