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The London office market is a major destination of international real estate capital and arguably the epicentre of international real estate investment over the past decade. However, the increase in global uncertainties in recent years due to socio-economic and political trends highlights the need for more insights into the behaviour of international real estate capital flows. The purpose of this study is to evaluate the influence of the global and domestic environment on international real estate investment activities within the London office market over the period 2007–2017.

This study adopts an auto-regressive distributed lag approach using the real capital analytics (RCA) international real estate investment data. The RCA data analyses quarterly cross-border investment transactions within the central London office market for the period 2007–2017.

The study provides insights on the critical differences in the influence of the domestic and global environment on cross-border investment activities in this office market, specifically highlighting the significance of the influence of the global environment in the long run. In the short run, the influence of factors reflective of both the domestic and international environment are important indicating that international capital flows into the London office market is contextualised by the interaction of different factors.

The authors provide a holistic study of the influence of both the domestic and international environment on cross-border investment activities in the London office market, providing more insights on the behaviour of global real estate capital flows.

This paper aims to decompose the value effects of green retrofits on commercial real estate. The paper disentangles various sources of value capture mechanisms that can be attained through green retrofit actions and profiles the extent to which green retrofit solutions can be effectively capitalised using transaction evidence from the Munich housing market. The insights offered can help real estate owners and investors during their ex ante analysis of future energetic retrofit investments.

The authors offer their reader both a conceptual framework and the results from an empirical analysis to identify the value effects of retrofits and the associating gains in energy efficiency. The conceptual framework theorises the different value components that a deep retrofit has to offer. The regression analysis includes a multivariate analysis of 8,928 dwellings in the Munich residential real estate market.

This study’s framework disentangles the total retrofit value effect into three components: the capitalisation of energy savings, the exposure to the value discount because of stricter standards and the value uplift because of indirect benefits (health, employee satisfaction, marketing etc.). The regression results indicate that the value gains because of energy efficiency improvements are in the range of 2.4–7.4%, while the indirect benefits and reduced exposure to stricter standards amount to another 3%.

While numerous studies have investigated the upside value effects of energy efficiency in the real estate sector, there is scant academic research which has sought to evidence the value of green retrofit solutions and the extent to which this can be capitalised. Instrumentalising the various value effects of energetic retrofit that have been identified is not straightforward. At the same time, inadequate value capture of energetic retrofit effects could delay intervention timelines or aborting of proposed retrofit actions which should be of primary concern to policymakers and stakeholders tasked with the decarbonisation of real estate assets.

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.

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.

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.

The purpose of this paper is to test the extent to which company-specific attributes including market capitalisation, capital structure and investment focus impact upon the performance of European listed real estate companies. Enhanced understanding of firm-level performance drivers is important for investors in order to diversify their investment portfolios and to mitigate company-specific risks at different points in the real estate cycle.

The study centres on six key listed European real estate markets selected on the basis of market capitalisation, diversity, transparency and maturity. A series of statistical tests are undertaken using EPRA and Bloomberg data for the period of 2007–2017 using 113 listed property companies, all of whom were contemporaneous constituents of EPRA indices in this period. A series of customised performance indices were constructed to evaluate firm-level performance attributes.

Firm-level attributes collectively account for more variation of risk-adjusted return than sector-level attributes over the investigation period. The impact of firm-specific attributes on performance varies significantly from country to country attributable to the contrasting cyclical property market trends in the pre– and post–Global Financial Crisis period. REITs outperformed non-REITs on a risk-adjusted basis attributed to the strong performance of “niche” market entrants allied with stronger regulatory structure. Finally, the findings showcase that sector specialist firms outperform diversified companies inferring that investors should seek to attain diversification through portfolio-based approaches rather than firm-level strategies.

The results have implications for real estate companies aiming to raise capital internally for growth as higher return on equity in general signals reduced cost of capital. Secondly, the findings should be of practical use to multinationals specialising in international real estate trading in designing their business plans in general and formulating cross-country investment strategies in particular. Last but not least, a more refined conceptualisation of corporate-level performance drivers should complement existing professional practices in relation to business/company appraisal.

The research integrates EPRA and Bloomberg data sets to create a series of bespoke index constructs to measure the impact of firm-specific attributes on European listed real estate companies. Additionally, the authors construct a Herfindahl Index (H.I.) to further the debate on the impacts of diversification within the listed real estate sector. This serves to further heighten investor understanding of investment allocation and portfolio optimisation strategies for the listed real estate sector given the increasingly diverse range of investment opportunities within emerging sub-markets.

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.

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.

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.

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.