26th Annual European Real Estate Society Conference最新文献

{"title":"The link between coworking space demand and venture capital financing: Empirical evidence from European office market","authors":"Felix Gauger, Jan-Oliver Strych, Andreas Pfnür","doi":"10.15396/eres2019_170","DOIUrl":"https://doi.org/10.15396/eres2019_170","url":null,"abstract":"Flexible office space is a game-changer in the real estate industry, forecasting a rapid growth over the next years up to 30.000 flexible offices in 2020 (Gcuc, 2017). Specifically, emerging coworking spaces build a growing field for individuals, entrepreneurs, start-ups, and corporations. The agglomeration of coworking spaces resembles entrepreneurship in incubation centers, but the community aspect among involved firms is strong and invaluable (Bouncken et al. 2018). One of the main reasons to work in coworking spaces is the collaboration and sharing of knowledge, resources, and ideas (Spinuzzi, 2012). This can breed new venture concepts, support the growth in an early start-up stage, and accelerate successful business models. However, entrepreneurs require capital, which is a challenge to obtain. Venture Capital (VC) is considered as a fundamental source of finance for entrepreneurial firms (Colombo et al. 2018). So far, there has been no research on the relationship between venture capital investments in firms using coworking spaces. Previous literature lacks the understanding of how coworking spaces support entrepreneurial activities and how this affects the raise of external equity. Our study aims at understanding the connection between coworking spaces with venture capital investments and spatial founding activities.Our research focuses on addressing the question of whether there is a substitutional or complementary relation between firms’ funding by venture capitalists and their use of coworking spaces?We examine the relationship between the existence of coworking spaces with VC investments and founding activities in the European Union to illuminate the macroeconomic impact of this recently emerged form of business model, transforming the real estate office market. First, we employed a webcrawler identifying coworking spaces throughout the big cities in Europe to generate data for the study. We then determined the size and founding date of each space and aggregated the data per city in order to conduct a panel over 9 years. Furthermore, we compiled VC data via the platform crunchbase. Using the crunchbase API we were able to obtain and compile global investments and funding information. Applying econometric methods, we identified the relation of VC and coworking spaces at a global and country-specific level using a regression analysis and addressing the endogeneity problem. The novelty of our research provides a new spatial component to the venture capital and innovation literature by showing how the physical organization of work affects entrepreneurial activities.The results are threefold. First, VC investors get guidance on where to supply venture capital, as venture capitalists tend to invest within a spatial proximity to the venue. Second, coworking operators can benefit from this analysis in order to attract venture capital and provide an ideal entrepreneurial environment. Third, from an urban perspective, the results influence citie","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133826185","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":"Impacts of urban rail transit accessibility on property prices in Shenzhen, China: Insights for value capture","authors":"Yang Chen, Linchuan Yang","doi":"10.15396/eres2019_89","DOIUrl":"https://doi.org/10.15396/eres2019_89","url":null,"abstract":"Urban rail transit (URT) plays a major role in mitigating numerous contemporary problems (e.g., traffic congestion and greenhouse gas emissions). A multitude of Chinese cities has released URT development plans. However, construction and operation of URT would cause heavy debt burdens for local government. Value capture schemes can be used to finance URT development and its first step is understanding the relationship between URT and property prices. Though numerous studies in the West have focused on this topic, limited studies have been conducted in urban China. Moreover, most studies are silent on 1) whether or not transfer stations provide larger premiums than non-transfer stations, and 2) whether or not URT accessibility benefits are more perceptible in peripheral areas than in central areas. In light of this, based on 722 residential complexes samples in Shenzhen, China, a set of hedonic pricing models is developed to examine the impacts of URT accessibility on property values. The findings are as followings: (1) URT accessibility offers positive externalities; (2) An inverted-U relationship exists between URT accessibility and property prices; (3) Transfer stations provide larger accessibility benefits than non-transfer stations; (4) the URT accessibility benefits are more notable in peripheral areas than in central areas. Though the first two findings are in line with most of the relevant studies, the last two findings have seldom been identified in existing studies, which represent the potential contributions of this work. Practical implications of our findings, such as diversified or location-specific value capture schemes are further discussed.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"187 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115425363","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":"Application of machine learning in real estate transactions – automation of due diligence processes based on digital building documentation","authors":"Philipp Maximilian Müller, Björn-Martin Kurzrock, Felix Meckmann","doi":"10.15396/eres2019_208","DOIUrl":"https://doi.org/10.15396/eres2019_208","url":null,"abstract":"To minimize risks and increase transparency, every company needs reliable information. The quality and completeness of digital building documentation is more and more a factor as “deal maker” and “deal breaker” in real estate transactions. However, there is a fundamental lack of instruments for leveraging internal data and a risk of overlooking the essentials.In real estate transactions, the parties generally have just a few weeks for due diligence (DD). A large variety of Documents needs to be elaborately prepared and make available in data rooms. As a result, gaps in the documentation may remain hidden and can only be identified with great effort. Missing documents may result in high purchase price discounts. Therefore, investors are increasingly using a data-driven approach to gain essential knowledge in transaction processes. Digital technologies in due diligence processes should help to reduce existing information asymmetries and sustain data-supported decisions.The paper describes an approach to automate Due Diligence processes with a focus on Technical Due Diligence (TDD) using Machine Learning (ML), esp. Information Extraction. The overall aim is to extract relevant information from building-related documents to generate a semi-automated report on the structural (and environmental) condition of properties.The contribution examines due diligence reports on more than twenty office and retail properties. More than ten different companies generated the reports between 2006 and 2016. The research work provides a standardized TDD reporting structure which will be of relevance for both research and practice. To define relevant information for the report, document classes are reviewed and contained data prioritized. Based on this, various document classes are analyzed and relevant text passages are segmented. A framework is developed to extract data from the documents, store it and provide it in a standardized form. Moreover the current use of Machine Learning in DD processes, the research method and framework used for the automation of TDD and its potential benefits for transactions and risk management are presented.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"1 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047114","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 the Heathrow Northwest Runway Announcement on Residential Property Prices in Greater London","authors":"L. Papageorgiou","doi":"10.15396/eres2019_97","DOIUrl":"https://doi.org/10.15396/eres2019_97","url":null,"abstract":"As prior research has shown, airport expansions in densely populated urban areas affect homeowners in surrounding areas. The effect on prices is expected to be particularly pronounced in the London urban area due to the high absolute valuations. This study will examine the announcement effects of (a) the release of the planning report for Heathrow’s Northwest Runway on 01.07.15, (b) the acceptance of the plan by the Secretary for Transport on 14.12.2015, and (c) the official government approval for the initiation of Heathrow’s airport expansion on 25.10.2016. The research focus of this paper lies on the extent to which prices of single family, terraced and semi-detached homes in those areas not yet affected by Heathrow’s runway noise changed as a consequence of the announcements. In this context, it is of particular interest whether the observed changes in property prices match the noise contour proposed by the official planners’ report. The novelty of this study lies in the fact that we focus on the home price effects of the announcement of an airport expansion. For airports as opposed to other infrastructure projects, announcement effects have so far not been analyzed in the literature. This is understandable as the area affected by airport expansions tends to be not only far larger than other infrastructure projects, such as sports stadiums, but they are also more uncertain. In fact, the expected future effects can vary significantly with advancing technology and changing mobility patterns.Literature. The related literature reaches back to 1978, when Mieszkowski & Saper estimated the effects of airport noise on residential property values at Toronto’s Malton Airport. In 1990, Pennington et al. measured the same effect at Manchester’s airport. In a similar study in 1998, Tomkins, et. al. found that at Manchester’s airport the benefits of expansion extended beyond the local economy, while the costs were concentrated locally. In 2000, Espey & Lopez found a significant negative price discount for surrounding residential buildings near RenoSparks’ airport. In 2004, Nelson aggregated 20 studies covering 33 estimates of price discounts for 23 airports in Canada and the US to develop a model to explain the percentage drop per decibel increase in airport noise. The first airport expansion case in combination with noise discounts was analyzed by Mcmillen in 2004 on the basis of the 1997 Chicago O’Hare expansion. In 2009, Dekkers & Straaten found that noise discounts for residential property at Amsterdam’s airport surpassed railway and road noise discounts. In 2015, Suksmith & Nitivattananon showed that the noise of aircraft near Bangkok’s airport had an even higher impact on residential house prices than air pollution. The most similar analysis in terms of the data set and the focus on announcement effects is Kavetsos (2012), who measured the impact of the London Olympics announcement on residential prices. He found that properties in host boroughs sold","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116162920","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 role of housing to identify Fuel Poverty","authors":"Paloma Taltavull de La Paz, F. Juárez, P. Monllor","doi":"10.15396/eres2019_292","DOIUrl":"https://doi.org/10.15396/eres2019_292","url":null,"abstract":"The analysis of energy poverty has attracted increasing interest in some countries, including the United Kingdom, Ireland, Austria and New Zealand. Thomson and Snell (2013) examine the EU case as a whole. These studies have provided empirical evidence suggesting that households with some members over 60 years of age, families with children, disabled or chronically ill persons are the most vulnerable groups (ITD, 2001, pp. 8-9, cited in Boardman 2012: p 23) when it comes to energy poverty. The reason for this lies in the fact that their energy costs are higher than other basic needs (O' Neill et al., 2006). Empirical evidence also suggests that energy expenditure is essential; In fact, households could be considered as a \"captive demand\" affected by market control decisions - pricing - and this has severe social effects.The relevance of this problem is twofold. Firstly, because an adequate temperature in the home ensures well-being at any income level. Secondly, because high energy costs could reflect low energy efficiency in buildings, which aggravates poverty situations. Reducing the energy bill does not necessarily imply a cold environment when buildings are energy efficient, a condition that could guarantee both lower energy costs and an adequate temperature if this problem is addressed to eradicate it. The latter relates energy poverty to the energy efficiency of buildings - a key element of EU energy policy to ensure the medium-term sustainability of cities in the European Union. If solutions are found to reduce fuel poverty problems, a twofold objective would be achieved: (a) to reduce energy consumption through a more balanced energy consumption scheme in buildings; and (b) to improve the health and welfare levels of disadvantaged households by reducing energy cost payments based on lower consumption. Incentive policies for investment in rehabilitation are the most widely accepted as they improve energy efficiency and reduce energy poverty.The literature does not contain evidence that measures the sensitivity of energy poverty on changes in poverty levels or that assess the impact of property rates on energy scarcity. Economic logic supports the idea that a sudden fall in income can reduce the purchasing power and could have different effects on energy poverty levels depending on the type of tenure. In this paper, an indicator is calculated that identifies energy poverty in households using the Household Condition Survey (EU-Silk) for Spanish region, combining differently available indicators that allow an approximation to this phenomenon. It takes into account the structure of housing tenure and the level of poverty to explain fuel poverty. The present paper adds empirical evidence of the existence of fuel poverty using Spanish statistics to test two hypotheses. Ho1 is whether and how (housing) deprivation signals are linked to fuel poverty; whereas Ho2 tests the role of fuel poverty as an element directly related to poverty. They both al","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116444614","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":"Prevention of housing stock collapse in Ukraine requires extraordinary strategic decisions","authors":"V. Nikolaiev, O. Kucherenko","doi":"10.15396/eres2019_191","DOIUrl":"https://doi.org/10.15396/eres2019_191","url":null,"abstract":"The whole housing stock in Ukraine is practically private. Social housing fund is almost absent. In the 1990s most of the flats in the multi-apartment buildings have been privatized by the tenants free of charge. About 80% of all buildings in the cities have been built up to 1980, are not yet repaired and require urgent modernization with the cost equal to the actual State budget revenue. By the law of 2015 the responsibility for carrying out capital repairs has been transferred to the flat owners who are co-owners of the buildings. Maintenance management is organized only on the level of separate houses (no-associations) and is unprofessional. The tariff for housing services is enormous low because the component of capital repairs traditionally has been excluded. In the worst homes live exactly the poorest families which do not have any means to maintain and repair their houses because one half of all families in the country receive subsidies to pay their utility bills. Taking into account other urgent and costly needs to maintain public infrastructure the State is also unable to accumulate sufficient funds to renovate the housing stock. At the same time there is a difficult question of the justice of additional state assistance to homeowners for repairing their assets at the expense of all taxpayers. Another question is how to operate this private property on the market. Governments of the country that often replace each other are afraid to raise this problem, which requires extraordinary decisions. It becomes obvious that homeowners are mostly inefficient but the idea of re-privatization can cause social rejection. There are no analogues in the history or in other post-Soviet countries, where either the condition of privatized houses was better, or household incomes were higher, or state aid was regular and where, due to the tariff, funds for capital repairs has been always accumulated and used.Unfortunately, in Ukraine there is no tradition of real estate professional management as a scientific branch, university specialization or profession. Our first attempts to find right decisions which will be described in the paper need approbation. Therefore, we want to draw attention of the best European real estate managers and researchers to the resolution of this problem.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026195","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":"Temporal and Spatial Variations in the Dynamics of US Metropolitan Office Markets","authors":"Angela J. Black, Steven Devaney, P. Hendershott, B. MacGregor","doi":"10.15396/eres2019_173","DOIUrl":"https://doi.org/10.15396/eres2019_173","url":null,"abstract":"A considerable body of research exists on how office rents, vacancy rates and new supply adjust in response to shocks to occupier demand. Research has settled upon an Error Correction Model (ECM) approach for modelling the dynamics. More recent studies have used panel data (such as Hendershott, Jennen and MacGregor, 2013; Adams and Fuss, 2012), but there has been little investigation of either the temporal or the cross sectional variation in the adjustment parameters and why these might vary. Furthermore, the econometric complications from using lagged dependent variables in such models have still to be addressed. We use panel data and dynamic panel estimation techniques for 58 US MSA office markets in this paper and we analyse differences in the parameters found for different locations and time periods, including demand and supply coefficients, implied natural vacancy rates and speeds of adjustment to shocks in fundamental variables. Cross-sectional variations are analysed using variables that depict the characteristics of different locations in terms of their economic activity, urban form and real estate markets.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756177","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 role of property finance in the performance of residential property markets: Evidence from UK House prices: 1967 to 2017","authors":"B. Aha","doi":"10.15396/eres2019_315","DOIUrl":"https://doi.org/10.15396/eres2019_315","url":null,"abstract":"Housing issues have become increasingly important throughout the developed world and have attracted much interest amongst economists, policymakers and the general public, following the catastrophic impacts of the 2007/08 global financial crisis. Globally, housing finance has undergone substantial changes, in terms of structure, composition and regulation, and witnessed considerable expansion within the last three decades. While studies abound seeming to explain national and regional house price movements, the dynamics of the housing market are still not widely understood. Though the importance of finance in the housing industry is generally well appreciated, very little attention has been paid to the drivers of housing finance and their impact on house prices. The determinants of mortgage credit growth and how these interact with macroeconomic, fiscal and regulatory policy variables to shape house prices are still not well understood. This study seeks to make an invaluable contribution to this understanding. by analysing house price dynamics in the UK from 1967 to 2017 and examining the role of mortgage credit in the performance of the housing market. The key drivers underlying housing finance expansion over the last 50 years are also investigated, paying particular attention to the impact of macroeconomic, fiscal and regulatory policy variables.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128162646","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":"Automatic extraction of condition-specific visual characteristics from buildings","authors":"Miroslav Despotovic, David Koch, Sascha Leiber, M. Zeppelzauer","doi":"10.15396/eres2019_284","DOIUrl":"https://doi.org/10.15396/eres2019_284","url":null,"abstract":"The value of a property is influenced by a number of factors such as location, year of construction, area used, etc. In particular, the classification of the condition of a building plays an important role in this context, since each real estate actor (expert, broker, etc.) perceives the condition individually. This paper investigates automatic extraction of condition-specific visual characteristics from buildings using indoor and outdoor images as well as automatic classification of condition classes. This is a complex task because an object of interest can appear at different positions within the image. In addition, an object of interest and/or the building can be captured from different distances and perspectives and under different weather and lighting conditions. Furthermore, the classification method applied with the convolutional neural network, as described in this paper, requires a large amount of input data. The forecast results of the neural network are promising and show accuracy rates between 67 and 81% using various set-up constellations. The described method has a high development potential in the scientific as well as in the practical sense. The results are technically innovative and should, apart from research relevant contribution, make a practical contribution to future automation-supported real estate valuation procedures. The primary aim of this work is to stimulate the development of new scientifically relevant methods and questions in this direction.","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129331373","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":"Challenges in Machine Learning for Document Classification in the Real Estate Industry","authors":"Mario Bodenbender, Björn-Martin Kurzrock","doi":"10.15396/eres2019_370","DOIUrl":"https://doi.org/10.15396/eres2019_370","url":null,"abstract":"Data rooms are becoming more and more important for the real estate industry. They permit the creation of protected areas in which a variety of relevant documents are typically made available to interested parties. In addition to supporting purchase and sales processes, they are used primarily in larger construction projects.The structures and index designations of data rooms have not yet been uniformly regulated on an international basis. Data room indices are created based on different types of approaches and thus the indices also diverge in terms of their depth of detail as well as in the range of topics. In practice, rules already exist for structuring documentation for individual phases, as well as for transferring data between these phases. Since all of the documentation must be transferable when changing to another life cycle phase or participant, the information must always be clearly identified and structured in order to enable the protection, access and administration of this information at all times. This poses a challenge for companies because the documents are subject to several rounds of restructuring during their life cycle, which are not only costly, but also always entail the risk of data loss. The goal of current research is therefore a seamless storage as well as a permanent and unambiguous classification of the documents over the individual life cycle phases.In the field of text classification, machine learning offers considerable potential in the sense of reduced workload, process acceleration and quality improvement. In data rooms, machine learning (in particular document classification) is used to automatically classify the documents contained in the data room or the documents to be imported and assign them to a suitable index point. In this manner, a document is always classified in the class to which it belongs with the greatest probability (ex: due to word frequency). An essential prerequisite for the success of machine learning for document classification is the quality of the document classes as well as the training data. When defining the document classes, it must be guaranteed on the one hand that these do not overlap in terms of their content, so that it is possible to clearly allocate the documents thematically. On the other hand, it must also be possible to consider documents that may appear later and be able to scale the model according to the requirements. For the training and test set, as well as for the documents to be analyzed later, the quality of the respective documents and their readability are also decisive factors. In order to effectively analyze the documents, the content must also be standardized and it must be possible to remove non-relevant content in advance.Based on the empirical analysis of 8,965 digital documents of fourteen properties from eight different owners, the paper presents a model with more than 1,300 document classes as a basis for an automated structuring and migration of documents i","PeriodicalId":152375,"journal":{"name":"26th Annual European Real Estate Society Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127126638","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}