Journal of European Real Estate Research最新文献

{"title":"Automatic energy demand assessment in low-carbon investments: a neural network approach for building portfolios","authors":"L. Gabrielli, A. Ruggeri, M. Scarpa","doi":"10.1108/jerer-12-2019-0054","DOIUrl":"https://doi.org/10.1108/jerer-12-2019-0054","url":null,"abstract":"\u0000Purpose\u0000This paper aims to develop a forecasting tool for the automatic assessment of both environmental and economic benefits resulting from low-carbon investments in the real estate sector, especially when applied in large building stocks. A set of four artificial neural networks (NNs) is created to provide a fast and reliable estimate of the energy consumption in buildings due to heating, hot water, cooling and electricity, depending on some specific buildings’ characteristics, such as geometry, orientation, climate or technologies.\u0000\u0000\u0000Design/methodology/approach\u0000The assessment of the building’s energy demand is performed comparing the as-is status (pre-retrofit) against the design option (post-retrofit). The authors associate with the retrofit investment the energy saved per year, and the net monetary saving obtained over the whole cost after a predetermined timeframe. The authors used a NN approach, which is able to forecast the buildings’ energy demand due to heating, hot water, cooling and electricity, both in the as-is and in the design stages. The design stage is the result of a multiple attribute optimization process.\u0000\u0000\u0000Findings\u0000The approach here developed offers the opportunity to manage energy retrofit interventions on wide property portfolios, where it is necessary to handle simultaneously a large number of buildings without it being technically feasible to achieve a very detailed level of analysis for every property of a large portfolio.\u0000\u0000\u0000Originality/value\u0000Among the major accomplishments of this research, there is the creation of a methodology that is not excessively data demanding: the collection of data for building energy simulations is, in fact, extremely time-consuming and expensive, and this NN model may help in overcoming this problem. Another important result achieved in this study is the flexibility of the model developed. The case study the authors analysed was referred to one specific stock, but the results obtained have a more widespread importance because it ends up being only a matter of input-data entering, while the model is perfectly exportable in other contexts.\u0000","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74415679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investing in gold or REIT index in Turkey: evidence from global financial crisis, 2018 Turkish currency crisis and COVID-19 crisis","authors":"Levent Sumer, B. Ozorhon","doi":"10.1108/jerer-04-2020-0023","DOIUrl":"https://doi.org/10.1108/jerer-04-2020-0023","url":null,"abstract":"Purpose Under the current Coronavirus Disease 2019 (COVID-19) pandemic circumstances where the gold prices are increasing and the stocks are in free fall, this research aims to compare the returns of gold prices and Turkish real estate investment trust (T-REIT) index by covering the 2008 global financial crisis, 2018 Turkish currency crisis and 2020 COVID-19 pandemic-based economic crisis periods and examine the effects of the returns of gold and the T-REIT index on each other, a research area that has been limited in the literature Design/methodology/approach For the empirical analysis, vector auto regression model was used, and Augmented Dickey-Fuller and Granger causality tests were also conducted The average returns were compared with the coefficient of variation analysis Findings The results of the study exhibited that except for the 2008 global financial crisis period, 2018 Turkish currency crisis and 2020 COVID-19 pandemic-based economic crisis, the T-REIT index performs better than gold prices, but it is a riskier instrument, and both investment instruments do not affect the returns of each other The segmentation of both instruments recommends the fund managers including both tools for diversification of a portfolio Research limitations/implications In Turkey, gold prices are valued based on the fluctuations of the global gold prices, as well as the Turkish Lira/US Dollar currency exchange rates The effect of the exchange rates may be considered in future studies, and the study may be conducted based on the USD values of the T-REIT index and global gold prices Further studies may also include the comparison between the T-REIT index returns and a set of commodities such as the Goldman Sachs Commodity Index This study covered only the first five months of 2020 to analyze the COVID-19 pandemic-based economic crisis initial effects, and a successor study is also recommended by including more new data of the post-COVID-19 pandemic and comparing both results Practical implications The results of the research are expected to contribute to the REIT literature and give insight to investors about their investment choices while including both investment tools in their portfolio, especially for the future conditions of the new COVID-19 pandemic-based economic crisis Social implications The study may provide insight for individuals, especially those who are considering possible investment options in the Turkish real estate market in the post-COVID-19 pandemic crisis Originality/value Gold and real estate have always been considered as important investment instruments Gold is commonly accepted as a safe haven in the literature, and the REITs are considered as long-term investment instruments by many scholars While gold prices increase in the windy periods, the returns of real estate investments have more cyclical movements based on mostly the macroeconomic conditions and its integration with stock markets, yet the real estate is a common long-term inve","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80840378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glocal real estate market: evidence from European Countries","authors":"R. Wiśniewski, Justyna Brzezicka","doi":"10.1108/jerer-09-2019-0031","DOIUrl":"https://doi.org/10.1108/jerer-09-2019-0031","url":null,"abstract":"\u0000Purpose\u0000This paper aims to analyse globalisation, localisation and glocalisation on the real estate market and define the characteristic features of a glocal real estate market (GREM). The GREM involves real estate properties and real estate products, as well as linking the local and global dimensions of real estate market. Further aims of the study were to provide a methodology for developing the glocal real estate market index (GREMI), and compare selected European markets by analysing their glocalisation potential.\u0000\u0000\u0000Design/methodology/approach\u0000A novel method of identifying and assessing the GREM was prepared in the work. The methodology provides tools for calculating the GREMI. This is an index based on a few dozen variables from various thematic scopes, describing the glocalisation potential of a selected market, calibrated to a range <0, 1>. GREMI values were calculated for 12 countries, which accessed European Union (EU) in 2004. The sample covers period from 2004 to 2017.\u0000\u0000\u0000Findings\u0000The study shows that the GREMI continues to increase in all countries over time and the results are becoming synchronised. Romania is a country with the highest number of minimum GREMI values in all years (2004–2017). The highest values of the GREMI were determined in Estonia over the period of nine years (2004–2006, 2008 and 2013–2017).\u0000\u0000\u0000Research limitations/implications\u0000The prepared index may be applied to analyse different real estate markets, though the necessity to select an identical set of variables for analysis to allow for comparing between markets is a limitation for applying the method. The actual selection of variables is also a study limitation, which was of an opening nature to research in this scope and may be disputable.\u0000\u0000\u0000Originality/value\u0000This paper provides the original methodology of the GREMI index for countries joining the EU from 2004 onwards.\u0000","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76759044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The future of sustainable real estate investments in a post-COVID-19 world","authors":"J. Pike","doi":"10.1108/jerer-07-2020-0042","DOIUrl":"https://doi.org/10.1108/jerer-07-2020-0042","url":null,"abstract":"The purpose of this paper is to suggest that property investors should engage with governments to influence outcomes. Global collaboration is required from the real estate investment community, working closely with governments and legislators, to provide a clear road map to zero carbon emissions. Covid-19 has shown how quickly governments around the world can react with draconian responses, including widespread lockdowns, when faced with an existential threat. What bigger existential threat is there than climate change?,Personal viewpoint from general research.,Three pillars of likely government and legislative interventions are identified; namely, increased and enhanced energy regulation and carbon pricing to force a rapid switch to green energy sources for buildings; an enhanced role for Energy Performance Certificates, standardised methodologies and strict enforcement; and mandatory reporting of financial and physical climate risks based on the Financial Stability Board’s Task Force on Climate-related Financial Disclosures. It is suggested that property investors should now engage with governments to influence outcomes.,Personal viewpoint to encourage greater involvement of the real estate investment community in governmental and regulatory decision making.","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82618337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon risk real estate monitor: making decarbonisation in the real estate sector measurable","authors":"M. Spanner, J. Wein","doi":"10.1108/jerer-05-2020-0031","DOIUrl":"https://doi.org/10.1108/jerer-05-2020-0031","url":null,"abstract":"The purpose of this paper is to investigate the functionality and effectiveness of the Carbon Risk Real Estate Monitor (CRREM tool). The aim of the project, supported by the European Union’s Horizon 2020 research and innovation program, was to develop a broadly accepted tool that provides investors and other stakeholders with a sound basis for the assessment of stranding risks.,The tool calculates the annual carbon emissions (baseline emissions) of a given asset or portfolio and assesses the stranding risks, by making use of science-based decarbonisation pathways. To account for ongoing climate change, the tool considers the effects of grid decarbonisation, as well as the development of heating and cooling-degree days.,The paper provides property-specific carbon emission pathways, as well as valuable insight into state-of-the-art carbon risk assessment and management measures and thereby paves the way towards a low-carbon building stock. Further selected risk indicators at the asset (e.g. costs of greenhouse gas emissions) and aggregated levels (e.g. Carbon Value at Risk) are considered.,The approach described in this paper can serve as a model for the realisation of an enhanced tool with respect to other countries, leading to a globally applicable instrument for assessing stranding risks in the commercial real estate sector.,The real estate industry is endangered by the downside risks of climate change, leading to potential monetary losses and write-downs. Accordingly, this approach enables stakeholders to assess the exposure of their assets to stranding risks, based on energy and emission data.,The CRREM tool reduces investor uncertainty and offers a viable basis for investment decision-making with regard to stranding risks and retrofit planning.,The approach pioneers a way to provide investors with a profound stranding risk assessment based on science-based decarbonisation pathways.","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76865977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reframing the properties, places and crime paradigm: exploring spatiotemporal regime shifts","authors":"J. Delisle, T. Grissom, Brent Never","doi":"10.1108/jerer-12-2019-0059","DOIUrl":"https://doi.org/10.1108/jerer-12-2019-0059","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to explore spatiotemporal factors that affect the empirical analysis of whether crime rates in buffer areas surrounding abandoned properties transferred to a Land Bank that differed among three regimes: before transfer, during Land Bank stewardship and after disposition and whether those differences were associated with differences in relative crime activity in the neighborhoods in which they were located.\u0000\u0000\u0000Design/methodology/approach\u0000This study analyzed crime incidents occurring between 2010 and 2018 in 0.1-mile buffer areas surrounding 31 abandoned properties sold by the Land Bank and their neighborhoods in which those properties were located. Using Copulas, researchers compared concordance/discordance in the buffer areas across the three regime states for each property and approximately matched time periods for associated neighborhoods.\u0000\u0000\u0000Findings\u0000In a substantial number of cases, the relative crime activity levels for buffer areas surrounding individual sold properties as measured by the Copulas shifted from concordant to discordant states and vice versa. Similarly, relative crime activity levels for neighborhoods shifted from concordant to discordant states across three matched regimes. In some cases, the property and neighborhood states matched, while in other cases they diverged. These cross-level interactions indicate that criminal behavioral patterns and target selection change over time and relative criminal activity. The introduction of Copulas can improve the reliability of such models over time and when and where they should be customized to add more granular insights needed by law enforcement agencies.\u0000\u0000\u0000Research limitations/implications\u0000The introduction of Copulas can improve the spatiotemporal reliability of the analysis of criminal activity over space and time.\u0000\u0000\u0000Practical implications\u0000Spatiotemporal considerations should be incorporated in setting interventions to manage criminal activity.\u0000\u0000\u0000Social implications\u0000This study provides support for policies supporting renovation of abandoned properties.\u0000\u0000\u0000Originality/value\u0000To the best of authors’ knowledge, this research is the first application of Copulas to crime impact studies. As noted, Copulas can help reduce the risk of applying intervention or enforcement programs that are no longer reliable or lack the precision provided by insights into convergent/divergent patterns of criminal activity.\u0000","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84817808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of crime on apartment prices in Hamburg, Germany","authors":"J. Graaff, J. Zietz","doi":"10.1108/jerer-11-2019-0047","DOIUrl":"https://doi.org/10.1108/jerer-11-2019-0047","url":null,"abstract":"\u0000Purpose\u0000The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.\u0000\u0000\u0000Design/methodology/approach\u0000The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.\u0000\u0000\u0000Findings\u0000The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.\u0000\u0000\u0000Social implications\u0000The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.\u0000\u0000\u0000Originality/value\u0000The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.\u0000","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79767948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rent dynamics in France between 1970 and 2013","authors":"Alexis Pourcelot, A. Coën, Richard Malle, Arnaud Simon","doi":"10.1108/jerer-12-2019-0057","DOIUrl":"https://doi.org/10.1108/jerer-12-2019-0057","url":null,"abstract":"The purpose of this study is to highlight the determinants of market rents and to build a hedonic market rent index for each urban area and rental sector in France for the period 1970–2013. The authors also analyse the market rent dynamics over this period, with a special attention to the turning points in the French housing policy.,For this purpose, the authors implement a hedonic model, called stratified time dummy variable, using the Box–Cox transformation as a functional form.,The contribution of this study to the housing research is threefold: First, the study improves our understanding of the French’s rental submarket specificities and their valuation. It sheds new light on the determinants of rents. Second, this study builds a hedonic market rent index over the period 1970–2013 for each geographical and sectoral segment (Paris urban area, urban areas of more and less than 100,000 inhabitants and private and public rental sectors). Third, this study explains rent dynamics focusing on the turning points in the French housing policy.,Finally, the authors provide the first long-term market rent index in France by submarket (geographical and sectoral). In the case of the French market, no long-term market rent exists. The only long series available is an indexed rent.","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86490166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Dutch housing cycle and near-term market prospects","authors":"Arvydas Jadevicius, P. V. Gool","doi":"10.1108/jerer-07-2019-0020","DOIUrl":"https://doi.org/10.1108/jerer-07-2019-0020","url":null,"abstract":"This study is a practice undertaking examining three main concerns that currently dominate Dutch housing market debate: how long is the cycle, will the current house price inflation continue and is housing market in a bubble. With national house prices reaching record highs across all major cities, future market prospects became a topic of significant debate among policymakers, investors and the populace.,A triangulation of well-established academic methods is used to perform investigation. The models include Hodrick-Prescott (HP) filter, volatility autoregressive conditional heteroskedasticity (ARCH approximation) and right tail augmented Dickey–Fuller (Rtadf) test (bubble screening technique).,Interestingly, over the years from 1985 to 2019 research period, filtering extracts only one Dutch national housing cycle. This is a somewhat distinct characteristic compared to other advanced Western economies (inter alia the UK and the USA) where markets tend to experience 8- to 10-year gyrations. Volatility and Rtadf test suggest that current house prices in most Dutch cities are in excess of historical averages and statistical thresholds. House price levels in Almere, Amsterdam, The Hague, Groningen, Rotterdam and Utrecht are of particular concern.,Retail investors should therefore be cautious as they are entering the market at the time of elevated housing values. For institutional investors, those investing in long-term, housing in key Dutch metropolitan areas, even if values decline, is still an attractive investment conduit.","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89219326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combining realized measures to forecast REIT volatility","authors":"Jian Zhou","doi":"10.1108/jerer-03-2020-0021","DOIUrl":"https://doi.org/10.1108/jerer-03-2020-0021","url":null,"abstract":"\u0000Purpose\u0000This study aims to show that the best-performing realized measures vary across markets when it comes to forecast real estate investment trust (REIT) volatility. This finding provides little guidance for practitioners on which one to use when facing a new market. The authors attempt to fill the hole by seeking a common estimator, which can study for different markets.\u0000\u0000\u0000Design/methodology/approach\u0000The authors do so by drawing upon the general forecasting literature, which finds that combinations of individual forecasts often outperform even the best individual forecast. The authors carry out the study by first introducing a number of commonly used realized measures and then considering several different combination strategies. The authors apply all of the individual measures and their different combinations to three major global REIT markets (Australia, UK and US).\u0000\u0000\u0000Findings\u0000The findings show that both unconstrained and constrained versions of the regression-based combinations consistently rank among the group of best forecasters across the three markets under study. None of their peers can do it including the three simple combinations and all of the individual measures. The conclusions are robust to the choice of evaluation metrics and of the out-of-sample evaluation periods.\u0000\u0000\u0000Originality/value\u0000The study provides practitioners with easy-to-follow insights on how to forecast REIT volatility, that is, use a regression-based combination of individual realized measures. The study has also extended the thin real estate literature on using high-frequency data to examine REIT volatility.\u0000","PeriodicalId":44570,"journal":{"name":"Journal of European Real Estate Research","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2020-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72549637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}