Tài liệu Iccrem 2017 real estate and urbanization

scelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only ICCREM 2017 Real Estate and Urbanization Edited by Yaowu Wang Yongshi Pang Geoffrey Q. P. Shen Yimin Zhu, Ph.D. Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. ICCREM 2017 REAL ESTATE AND URBANIZATION PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON CONSTRUCTION AND REAL ESTATE MANAGEMENT 2017 November 10–12, 2017 Guangzhou, China SPONSORED BY Modernization of Management Committee of the China Construction Industry Association The Construction Institute of the American Society of Civil Engineers EDITORS Yaowu Wang Yongshi Pang Geoffrey Q. P. Shen Yimin Zhu, Ph.D. Published by the American Society of Civil Engineers Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. 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ICCREM 2017 iii Preface Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. We would like to welcome you to the 2017 International Conference on Construction and Real Estate Management (ICCREM 2017). Harbin Institute of Technology, Guangzhou University, Hong Kong Polytechnic University, Louisiana State University, University of Alberta, Luleå University of Technology, Heriot-Watt University, Marquette University, Karlsruhe Institute of Technology. The Conference is a continuation of the ICCREM series which have been held annually since 2003. The theme for this conference is “Prefabricated Construction and Construction Industrialization”. It especially highlights the importance of construction industrialization and prefabricated technology for construction engineering and management. The conference proceedings include 174 peer-review papers covered eleven important subjects. And all papers went through a two-step peer review process. The proceedings of the congress are divided into four parts:     Prefabricated Buildings, Industrialized Construction and PPP Industry Regulation and Sustainable Development Real Estate and Urbanization Project Management and Construction Technology On behalf of the Construction Institute, the American Society of Civil Engineers and the 2017 ICCREM Organizing Committee, we welcome you and wish you leave with a wonderful experience and memory at ICCREM 2017. Professor Yaowu Wang Professor Yongshi Pang Harbin Institute of Technology Guangzhou University P. R. of China P. R. of China Acknowledgments Organized by Harbin Institute of Technology, P.R. China Guangzhou University, P.R. China Hong Kong Polytechnic University, P.R. China Louisiana State University, USA University of Alberta, Canada Luleå University of Technology, Sweden Heriot-Watt University, UK Marquette University, USA Karlsruhe Institute of Technology, Germany © ASCE ICCREM 2017 iv Executive Editors Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Xianfei Yin Xianwei Meng Zhuyue Li Chong Feng Wei Gao Yuru Gao Tingting Chen Jia Ding Xiangkun Qi Yue Cao Zixin Han Tongyao Feng Hongmeng Kang Conference website: http://www.iccrem.com/ Email: [email protected] Conference Committee Committee Chairs Prof. Yaowu Wang, Harbin Institute of Technology, P.R. China Prof. Geoffrey Q.P. Shen, Hong Kong Polytechnic University, P.R. China Conference Executive Chair Prof. Jiyang Fu, Guangzhou University, P.R. China Conference Co-Chairs Prof. Yongshi Pang, Guangzhou University, P.R. China Director Katerina Lachinova, Construction Institute of ASCE.(ASCE members), USA Prof. Yimin Zhu, Louisiana State University, USA Prof. Mohamed Al-Hussein, University of Alberta, Canada Prof. Thomas Olofsson, Luleå University of Technology, Sweden Prof. Ming Sun, Heriot Watt University, UK Prof. Yong Bai, Marquette University, USA Prof. Kunibert Lennerts, Karlsruhe Institute of Technology, German Organizing Committee and Secretariat General Secretariat Prof. Xiaolong Xue, Harbin Institute of Technology, P.R. China Deputy General Secretariat Prof. Xuetong Wang, Guangzhou University, P.R. China © ASCE ICCREM 2017 v Committee Members Asso. Prof. Chengshuang Sun, Harbin Institute of Technology, P.R. China Asso. Prof Qingpeng Man, Harbin Institute of Technology, P.R. China Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Mr. Zhenmin Yuan, Harbin Institute of Technology, P.R. China Mr. Shiwei Chen, Harbin Institute of Technology, P.R. China © ASCE ICCREM 2017 vi Contents Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Economic Evaluation of Variable Office Buildings ................................................. 1 Rainer Schach, Anne Harzdor, and Cornell Weller Maintenance of Large Real Estate Portfolios .......................................................... 9 Alexander Bombeck and Kunibert Lennerts Biophilia as a Factor of Consumer Preferences in Choosing Residential Property Product ...................................................................................................... 15 Ardelia Mandasari and Ahmad Gamal Sharia Housing in the Real Estate Business ........................................................... 27 Nurrul Helen and Ahmad Gamal Effects of Variation on Project Cost of Selected Building Projects in Lagos State ................................................................................................................ 42 Oluwaseun Sunday Dosumu and Clinton O. Aigbavboa Study on the Relationship between the Development of Commercial Real Estate and the Total Retail Sales of Consumer Goods: From Chongqing Case .............................................................................................. 53 Lifang Huang and Lin Wang Influencing Factors on Beijing Real Estate Price .................................................. 62 Chunyu Wang and Jiayi Zhang A Research on the Relationship between Sold Area, Average Sold Price, and Total Retail Sales of Commercial Property: A Case Study of Chongqing ..................................................................................... 68 Lin Wang and Long Yin The Relationship between Management Incentives and Company’s Growth: An Empirical Research on the Chinese Listed Real Estate Companies ................................................................................................................. 75 Yuxin Liu and Yanru Gao The Determinants of Household Housing Affordability in Chengdu, China ...... 86 Yan Liu, Yongxiang Wu, and Xiaoyuan Wang Empirical Study on Influencing Factors of Audit Opinions Based on China’s Real Estate Listed Companies ................................................................... 98 Xiuhua Li and Lin Qu © ASCE ICCREM 2017 A Research on the Influence of Real Estate Listed Companies’ Social Responsibility on Financial Performance ............................................................. 107 Xuejun Hou and Bo Chen Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. 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The Impacts of Primary Schools and Spatial Heterogeneity on Housing Prices: A Case Study of Shenzhen ......................................................... 116 Botong Song and Jie Zhao Financial Efficiency of China’s Listing Real Estate Developers Based on Malmquist Index Method ...................................................................... 125 Xu Han, Shen Zhong, and Yuqi Liu Research on Affordable Apartment Demand Forecasting Based on the Apartment Affordability: A Case Study of Tianjin Xiqing District .................. 132 Lipeng Wei, Xueshan Sun, and Xin Yang Challenges and Way Forward in Chinese Real Estate Market: From a Public Resource Management Perspective ........................................................... 140 Yu Ding and Xinyi Liu Analysis of Commodity Housing Price Factors from the Perspective of System Dynamics .................................................................................................... 148 Xingwu Du and Junwu Wang An Empirical Study on Listed Real Estate Companies: The Relationship between Cash Flow and Dividend Policy .............................................................. 157 Siyang Li and Ying Chang The Research on Site Selection Factors of Old-Age Real Estate ........................ 166 Dan Dong, Gang Wang, Huabo Duan, and Hui Zeng Study of System Dynamics for Health Care Housing Development in Panzhihua ................................................................................................................ 175 Daowu Dong and Yucun Hu Study on the Price and Economic Impact of Residential Land Based on System Dynamics .................................................................................................... 187 Liangbao Li, Yumei Chen, and Can Yu The Empirical Study on the Relationship between the Stock Market and the Real Estate Market in China...................................................... 197 Ling Chen, Huijing Huang, and Wei Xu Research on the Systematic Risk of Real Estate Listed Companies in China: Based on Financial Perspective................................................................. 210 Peinan Ji, Guang Yu, and Xiangbin Yan © ASCE vii ICCREM 2017 The Regional Real Estate Investment Environment Research Based on Prime Component Analysis: The Case of Shandong ........................................... 217 Wei Wang, Jiaomin Yang, and Xixi Gong Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. 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Empirical Analysis of Influencing Factors of Real Estate Development: A Case Study of Harbin ......................................................................................... 225 Qun Cao Research on Social Responsibility of Major Infrastructure Projects Based on Spiritual Leadership .............................................................................. 232 Xuetong Wang, Jiaxuan Zhao, Weirui Xue, and Chen Lu Assessing the Disaster Resistance Ability of Road Infrastructure in Village and Town Regions...................................................................................... 244 Shu Shang and Xiaolong Xue A Research on Influence Factors of Migrant Workers Housing Satisfaction Based on Logistic Model: Empirical Analysis from Guangzhou............................................................................................................... 253 Wenying Zhang, Lin Chen, and Jianhui Tan An Empirical Study on Development Evaluation of the Key Development Zones Based on the Perspective of Major Function-Oriented Zoning .............. 262 Hui Ma and Min Li Framework of Life-Cycle Intellectual Management Platform for Infrastructure Projects ........................................................................................... 270 Wei Zhang and Xueping Luo Impact of the Chinese Sponge City and Underground Utility Tunnel Construction on the Infrastructure Development in Developing Countries ..... 288 Jian Liu and Wei Zhou Research on Property Management and Community Governance of Old Community in Guangzhou ..................................................................................... 298 Ping Chen and Dehao Chen “People, Land, and Money” Collaborative Flow System of a New Urbanization Construction: New Thinking Based on the Supply-Side Structural Reform ............................................................................. 306 Quan Liu and Junzhi Liu Study on the Impact of Listed Property Corporate Social Responsibility on Its Financial Performance ....................................................... 318 JiaYing Bai and Ying Chang © ASCE viii ICCREM 2017 A Study on Optimizing the Allocation of Urban Residential Land Resources in China ................................................................................................. 328 Chao Wang and Xiao Fu Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. 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Economic Revitalization in the Developing Regions of Guangdong by Innovating the High Speed Railway Plan and Construction Mode ................... 337 Jian Liu and Ru Liu Research on New-Type Smart City in China Based on Smart Construction Theory .............................................................................................. 347 Yudi Wu and Jinxiu Cai Critical Factors for the Resilience of Complex Urban Public Spaces................ 355 Hui Xu, Bin Xue, and Yongtao Tan The Research of Green Infrastructure Implementation System and Stakeholders Participation Mechanism ........................................... 364 Yan Wang Analysis on the Influencing Factors of Colleges and Universities to the Surrounding House Price: Taking Harbin as an Example ................................. 370 Wei Gao, Lixin Sun, and Hongmeng Kang Research on the Evaluation of Smart City Development Level Based on “Galaxy” Model ...................................................................................................... 380 Sichen Pan, Yikun Su, and Weiyi Cong A Model of Micro-Environmental Healthy Vulnerability in Urban Subway Systems .......................................................................................... 392 Jiao Qi, Peng Mao, Yongtao Tan, and Liyan Jin The Feasibility of Central Bank’s Monetary Policy Tool to Regulate the Price of Real Estate ................................................................................................. 400 Ping Wang, Haijun Shi, Xu Hu, and Lu He The Estimation and Regional Comparison of the Price Elasticity of Housing Supply in China: Based on the Panel Data in 34 Cities ....................... 407 Zijing Wu and Lin Zhu Study on the Evaluation Model and Path for Smart City ................................... 415 Fengping Xue © ASCE ix ICCREM 2017 1 Economic Evaluation of Variable Office Buildings Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Rainer Schach1; Anne Harzdorf2; and Cornell Weller3 1 Professor, Dept. of Civil Engineering, Institute of Construction Management, Dresden Univ. of Technology, Dresden, Germany. E-mail: [email protected] 2 Ph.D. Candidate, Dept. of Civil Engineering, Institute of Construction Management, Dresden Univ. of Technology, Dresden, Germany. E-mail: [email protected] 3 Ph.D. Candidate, Dept. of Civil Engineering, Institute of Construction Management, Dresden Univ. of Technology, Dresden, Germany. E-mail: [email protected] Abstract Generally, buildings have to be adapted to vary user requirements. In addition to shorter utilization cycles, also the types of use vary. The relatively high vacancy rate of office buildings in Germany and many other European countries show the necessity of suitable concepts for several uses. The purpose is to plan and realize buildings, which can be transformed to different use types with limited resources. Thus, the risk of potential vacancy rates can be minimized and the value retention can be ensured. In the present paper, variable office buildings will be analyzed and presented from the economic point of view. The modeling and simulation of construction costs, taking into account the risks, will be specifically discussed. These studies build the basis for the economic evaluation of different scenarios for the conversion of office buildings. The presented results were developed in the research project P1118 of the research association FOSTA AIF. INTRODUCTION Until the end of the 20th century, the demand predominates the supply in the German real estate market. This fact led regularly to value retention and continuous increase of the market value of buildings. Due to dynamic market developments and increasing user requirements, it is not possible to expect nowadays a principally lasting value retention or value enhancement of buildings (Brauer 2013). Currently, there can be observed a relatively high vacancy rate of office buildings in Germany and other European countries. The average vacancy rate for major European office real estate markets was around 10% in the second quarter of 2016 (CommerzReal 2016). To counteract this development, it is necessary to involve concepts for variable office buildings during the planning period. Within the scope of the research project P1118 of the research association FOSTA AIF, a guideline for the optimization of office buildings in consideration of steel and steel composite construction methods will be compiled. The focus is on the conception strategy © ASCE ICCREM 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. of variable buildings from the architectural, structural and economic point of view. The aim is to reduce the risk of vacancy rates, to ensure the value retention and to realize necessary adaptions with limited resources. This article deals with the methodical approaches for the economic evaluation of office buildings. In addition to the presentation of main standards, calculation methods and in particular full financial plans will be explained in detail. Furthermore, the possibilities for the integration of cost risks will be shown. These studies form the basis for a scenario-specific evaluation of variable office buildings in further work steps. BASIC INFORMATIONS FOR THE ECONOMIC EVALUATION Overview. A comprehensive economic life cycle analysis is to be carried out with taking into account all relevant payment flows and potential risks (Viering et al. 2015). Thus, it is not sufficient to consider only the costs in the realization phase (Lennerts and Schneider 2011). In the following sections, the normative and methodical conditions will be explained in detail. Basic conditions. For the life cycle analysis of buildings, the international standard ISO 15686 (International Organization for standardization 2008) has been developed on the basis of the national standards ASTM E917-02 (ASTM International 2002) from USA, NS 3454 (Standards Norway 2000) from Norway and AS/NZS 4536 (Standards Australia and Standards New Zealand 1999) from Australia/New Zealand (Pelzeter 2015). The international standard generally distinguished between life cycle costing and whole life costing. The life cycle costing covers only the costs. This includes for example the costs for realization, use, management, maintenance and demolition of buildings. The whole life costing covers additionally the proceeds. This includes the proceeds from rental or sale. Moreover, the whole life costing comprises externalities and non-construction costs (Lützkendorf 2011). The ISO 15686 (International Organization for standardization 2008) does not contain obligatory guidelines for the calculation method of life cycle costs (Preuß and Schöne 2016). Calculation methods. The guideline GEFMA/IFMA 220-1 (German Facility Management 2010) can be used for gaining information about suitable calculation methods. As shown in Table 1, there are several ways to carry out a capital budgeting. Basically, it can be differentiate between classic and modern methods. Classic methods can be additionally divided into static and dynamic methods. All procedures of the modern methods are based on full financial plans (Schulte et al. 2016). The core component of the dynamic methods, which are integrated also in full financial plans, is the discounting of all payments with the calculation of compound interests. Thus, the nominal lower value of future payments will be considered. Real estate investments can be represented realistically using the full financial plans (Schulte et al. 2016). In particular, this applies to the economic evaluation of variable office buildings. © ASCE 2 ICCREM 2017 3 Table 1.Methods of Capital Budgeting. Classic Methods Static Methods Dynamic Methods Modern Methods Full Financial Plans Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Investigation period. The forecast uncertainty increases exponential to the chronological distance in relation to the starting point of view. Therefore, a suitable period under review has to be selected. The period should be a maximum of 25 years for the resilience of the results (Pelzeter 2015). DEVELOPMENT OF A CALCULATION MODEL Basic facts of full financial plans. The concept of full financial plans differs from classic methods in that way that all investment related payments can be shown directly and with the costs-by-cause principle (Schulte et al. 2016; Grob 2006). Full financial plans represent in general original and derivative payments. These were gathered over a specified period of time in a chronological sequence (Gürtler 2007). Original payments include all payments, which are directly related to the investment. Derivative payments include all financing and tax payments (Grob 2006). Based on these payments, important additional information for the calculation of financial target values can be derived. Also, the structure of full financial plans gives the possibility to add further calculations. Table 2 summarizes the several components of full financial plans. Table 2.Parts of Full Financial Plans. Plan Original Payments Derivative Payments Additional Information Additional Calculation Cash Outflow, Cash Inflow Financing Payment, Tax Payment, Use of Cash Inflow Surpluses, Compensation of Cash Outflow Surpluses Balance of Financing, Credit Score, Account Balance, Aggregated Balance Depreciation Plan, Financial Plan, Tax Calculation, Further Calculations In order to assess an investment regarding the profitability, full financial plans offer the possibility to determine different target values. This includes for example the final assets, the amortization period as well as the return on equity and investment. An additional advantage of full financial plans is the stochastic transformation of payment risks. By integrating realistic intervals for the input variables, different target values can be outputted as probability distributions in the model. On this basis, the investment risks can be calculated and an assessment of the input variables can be made. A more detailed description will not be given here and can be read in appropriate literature (Grob 2006; Gürtler 2007; Schulte et al. 2016). In the following section the costs to be determined will be described more detailed. These are required for the original payments in terms of cash outflow. © ASCE ICCREM 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Determination of costs. In Germany, the standards DIN 276-1 (Beuth 2008a) and DIN 18960 (Beuth 2008b) are used for the calculation of construction and usage costs (Lützkendorf 2011). The DIN 276-1 (Beuth 2008a) specifies the different levels of cost calculation and defines the classification of costs in connection with the construction and refurbishment as well as the conversion and deconstruction of buildings. The DIN 18960 (Beuth 2008b) identifies the different levels of usage cost calculation and defines the classification of costs in connection with the operation of buildings (Möller and Kalusche 2013). Table 3 shows the different elements of the first classification level for the standards DIN 276-1 (Beuth 2008a) and DIN 18960 (Beuth 2008b). Depending on the specification level, the costs can be divided into a second and third level of detail. Table 3.Cost Classification of Construction and Operational Costs. Construction Costs: DIN 276-1 (Beuth 2008a) Usage Costs: DIN 18960 (Beuth 2008b) No. Description No. Description 100 Property 100 Capital Costs 200 Preparation and Development 200 Management Costs 300 Building – Construction 300 Operating Costs 400 Building – Technical Facilities 400 Maintenance Costs 500 Appurtenant Structures 600 Furnishings and Artwork 700 Additional Building Costs For the calculation of construction and usage costs, reliable data will be needed. In Germany, there can be used different databases. For the construction costs can be used for example the database of ‘Baukosteninformationszentrum’ (BKI 2016) and ‘SIRADOS Baupreishandbuch’ (SIRADOS 2017). The ‘Office Service Charge Analysis Report’ (Janssen et al. 2016) and the ‘FM.Benchmarking Report’ (Rotermund 2016) can be used to determine the usage costs. STOCHASTIC MODELING OF COSTS General information. The cost analysis comprises the stochastic modelling of costs. All risky input variables will be described with probability distributions. Thereby, cost fluctuations can be taken into account adequately (Gürtler 2007). Approach. First of all, the risky cost elements have to be determined. Afterwards, a distribution function has to be assigned to each input variable. The selected function has to be chosen in that way, that the respective probability of occurrence will be represented at its best. The choice based on statistical evaluations or experience of specialists. To represent the cost fluctuations, continuous distribution functions should be used. Because of the limited investigation period of full financial plans, the costs of an income variable x has to be defined in a finite interval [a, b] (Beichelt 1995). For the period a ≤ x ≤ b rectangular, triangular or pert distribution (PERT = Program Evaluation and Review Technique) should be used preferably. Figure 1 shows the mentioned probability distributions schematically. The designation of the axes was omitted (x-axis = costs, y-axis = probability of occurrence). © ASCE 4 ICCREM 2017 5 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. In the rectangular distribution, all values occur with the same probability. Therefore, only the parameters for the minimum value (cmin) and for the maximum value (cmax) are required. The triangular distribution has additionally a most probable value (ce) within the defined interval of the minimum value (cmin) and maximum value (cmax). The pert distribution can be defined with the same three parameters. Rectangular Distribution cmin cmax Triangular Distribution cmin ce cmax Pert Distribution cmin ce cmax Figure 1.Schematic diagrams of relevant probability distributions. After the determination of the probability distributions of the costs in detail, a probability distribution of the total costs have to be accumulated. In multiple simulation runs, the randomized taking of input variables (artificial sampling) represented the selected distribution frequency (Mun 2006). For example @Risk from Palisade or Crystal Ball from Oracle can be used as simulation software. The selection of an appropriate simulation method will be explained in the following section. Preliminary study. In a preliminary study, appropriate distributions for the detailed costs have to be selected, the simulation method has to be predefined and the number of iterations has to be determined. In a specific example, costs for ten different cost items were calculated and represented by rectangular, triangular and pert distributions. Subsequently, the distribution for the total cost of the ten different cost items was determined with the help of a stochastic simulation with the program @Risk. The comparison of the total cost distributions shows, that rectangular distributions generate rather high costs, triangular distributions mean costs and pert distributions rather low costs. The influence of the selected probability distributions on the specific results of the full financial plan has to be checked with the help of sensitivity analyzes. Another test criterion is the number of iterations in a simulation run. Common methods are the Monte Carlo Simulation (MCS) and the Latin Hypercube Simulation (LHS) (Busch 2003). In the MCS, samples are drawn from the entire probability interval in each simulation run. In the LHS, samples are drawn from interval sections in each simulation run (Fang et al. 2005). In comparison of both methods, the LHS requires significantly less iterations to represent a stable distribution. Figure 2 (Kautt and Wieland 2001) shows the described methods. The designation of the axes was omitted (x-axis = costs, y-axis = probability of occurrence). © ASCE ICCREM 2017 6 Monte Carlo Simulation Latin Hypercube Simulation Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Random Values Random Values Figure 2.Comparison of MCS and LHS. With the help of an iteration example, the necessary simulation runs were determined for both methods. The simulation was carried out by using a rectangular distribution with the lower limit value -10,000, the upper limit value +10,000 and the mean value ±0 (Matijevic 2008). In the simulation, random values are generated between the defined interval limits. With exactly one iteration, deviations in the entire selected interval are possible. With the increasing number of iterations, the result approaches the calculated mean value. Table 4 shows the simulation results of the presented example for MCS and LHS depending on the number of iterations. Already after 100 to 500 iterations an acceptable mean value arises with the LHS. Comparable deviations with MCS can only be achieved after 100,000 simulation runs. Thus, the LHS will be preferred for the cost analysis with the number of 1,000 iterations. Table 4.Determination of Required Iteration Levels for MCS and LHS. Monte-Carlo-Simulation (MCS) Latin-Hypercube-Simulation (LHS) Iterations Mean Value Deviation [%] Iterations Mean Value Deviation [%] 1 -3,036.843 -30.3684 1 4,797.938 47.9794 10 -1,664.270 -16.6427 10 273.810 2.7381 50 -1,428.880 -14.2888 50 13,200 0.1320 100 665.760 6.6576 100 -6.510 -0.0651 500 -278.100 -2.7810 500 0.294 0.0029 1,000 96.450 0.9645 1,000 -0.064 -0.0006 5,000 -76.130 -0.7613 5,000 0.016 0.0002 10,000 33.170 0.3317 50,000 -19.440 -0.1944 100,000 3.550 0.0355 Further worksteps. After the mentioned work steps, the stochastic costs have to be integrated into the full financial plan and the further income parameters also have to be determined and modeled. SUMMARY AND OUTLOOK In this article, the methodical approach for the economic evaluation of buildings within the life cycle analysis has been presented. Especially, calculation methods with full financial plans were discussed and the determination and stochastic modeling of costs were © ASCE ICCREM 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. represented. Selected examples were used to choose suitable probability distributions, simulation method and necessary iteration steps for the simulation. The presented approach built the basis for the economic evaluation of variable office buildings. Different alternatives can be compared and evaluated with the help of stochastic scenario analyzes. REFERENCES ASTM International. (2002). ASTM E917-02: Standard practice for measuring life-cycle costs of buildings and building systems, ASTM International, West Conshohocken. Beichelt, F. (1995). Stochastics for engineers: an introduction to probability theory and mathematical statistics, Teubner Verlag, Stuttgart. (in German). Beuth. (2008a). DIN 276-1: Costs in construction: part 1: building construction, Beuth, Berlin. (in German). Beuth. (2008b). DIN 18960: Usage costs in building construction, Beuth, Berlin. (in German). Brauer, K.U. (2013). Basics of real estate management, Springer Gabler, Wiesbaden. (in German). Busch, T.A. (2003). Risk management in general contracting company, In-House Publication, Zurich. (in German). CommerzReal. (2016). The european office property market: review and outlook, In-House Publication, Dusseldorf. (in German). Fang, K.T., Li, F. and Sudjianto, A. (2005). Design and modeling or computer experiments, Chapman & Hall, CRC Verlag, New York. German Facility Management (GEFMA). (2010). GEFMA /IFMA 220-1: Life cycle cost assessment in FM: introduction and fundamentals, In-House Publication, Bonn. Grob, H.L. (2006). Introduction to investment: a case study history, Franz Vahlen, Munich. (in German). Gürtler, V. (2007). Stochastic risk analysis in PPP projects, Expert Verlag, Renningen. (in German). International Organization for standardization (ISO). (2008). ISO 15686-1: Buildings and constructed assets: service-life planning: part 5: life-cycle costing, ISO, Geneva. Janssen, U., Prokot, A. and Barthauer, M. (2016). OSCAR: Büronebenkostenanalyse, In-House Publication, Dusseldorf. Kautt, G. and Wieland, F. (2001). “Modeling the future: the full monte, the latin hypercube and other curiosities.” Journal of Financial Planning, 14(12), 78-88. Lennerts, K. and Schneider, D. (2011). Life Cycle Considerations for Sustainable Real Estate, Perspectives Of Construction, Real Estate and Infrastructure Management. Bauhaus University of Weimar, Weimar, 103-113. (in German). Lützkendorf, T. (2011). Standards as a basis for understanding and instructions for sustainable building, DIN German institute for standardization e. V.: sustainable building: future-oriented concepts for planners and decision-makers, Beuth, Berlin. (in German). © ASCE 7 ICCREM 2017 Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Matijevic, D. (2008). Disturbed construction processes: aspects for avoiding or minimizing a construction time extension, University publishing house of the Technical University of Berlin, Berlin. (in German). Möller, D.A. and Kalusche, W. (2013). Planning and construction economics: economic theory for builders and architects, Oldenbourg Verlag, Munich. (in German). Mun, J. (2006). Modeling risk: applying monte carlo simulation, real options analysis, forecasting and optimization techniques, Jon Wiley & Sons, Hoboken. Pelzeter, A. (2015) Design Variables and Whole-Life Cost Modelling, In: Robinson, H.; Symonds, B., Gilbertson, B. and Ilozor, B.: Design Economics for the Built Environment: Impact of Sustainability on Project Evaluation, Wiley Blackwell, Chichester, 107-120. Preuß, N. and Schöne, L.B. (2016). 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Viering, M., Rodde, N. and Zanner, C. (2015). Real estate and construction industry: developments and trends, Springer Vieweg, Wiesbaden. (in German). © ASCE 8 ICCREM 2017 9 Maintenance of Large Real Estate Portfolios Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. Alexander Bombeck1 and Kunibert Lennerts2 1 Research Associate, Institute of Technology and Management in Construction, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany. E-mail: [email protected] 2 Professor, Institute of Technology and Management in Construction, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany. E-mail: [email protected] Abstract Maintenance of large real estate portfolios constitutes a significant challenge for their owners. Different corporate goals create a tension field that needs systematic approaches to be resolved. An owner might strive for a given level of maintenance quality to satisfy the occupiers’ needs or to uphold the value of their assets. Then again real estate is very capital intensive, so that cash strapped owners might opt for forgoing maintenance actions that seem not to be necessary. To decide which of the above stated goals is to be pursued information needs to be gathered and processed. We analyze different methods for maintenance planning and budgeting from before advent of readily available computer systems and modern solutions and discuss their implications. Finally, we will deduce which methods are suited for which type of owner and will especially showcase the need for simple solutions for specific organizations. INTRODUCTION Many organizations, for-profit and non-profit, own large real estate portfolios. These are often a legacy of a long and complex past, meaning ownership structures and managing processes are as byzantine as the portfolio itself. In such organization maintenance backlogs often foreshadow upcoming financial strains or might even be reason for security concerns. Every few years researchers and innovators claim to have developed new methods for maintenance management, that make it fast, easy, reliable and cheap to gather and process all necessary information to perform best practice maintenance strategies. In this paper we will discuss different approaches to systematic maintenance budgeting as proposed by researchers. On the basis of simple thought experiments we will show the main weaknesses of each of these approaches. The conclusion will show, why and how these approaches work in practice and what they are useful for. © ASCE ICCREM 2017 MAINTENANCE OBJECTIVES Downloaded from ascelibrary.org by RMIT UNIVERSITY LIBRARY on 01/03/19. Copyright ASCE. For personal use only; all rights reserved. The real estate strategy of an organization reflects the organizations strategy. In corporate real estate management, the support of the corporate strategy is even the ultimate goal of real estate management. Therefore, the maintenance strategy may differ wildly depending on the organization at hand. With maintenance there are some boundaries to be drawn. Aside from special cases as investors holding protected buildings in the hope of replacing them with more lucrative developments, every real estate holder has an interest in conserving structural integrity and guaranteeing safety of his assets. Not least because he is legally obliged to do so. Following up from here the real estate strategy might dictate different financial, business and social targets, e.g. (Hens 1999): Minimizing cost, Flexibility, Embodying a marketing message, Support of the sales process, Efficient service, Support of management processes, Utilizing value potential. From these targets the required level of maintenance can be derived. As with corporate real estate strategies though, maintenance objectives and strategies are seldom formulated. MAINTENANCE BUDGETING METHODS Bahr (2008) classifies maintenance budgeting methods into four different categories: Key figure oriented budgeting methods, Value-oriented budgeting methods, Analytic budgeting methods, Maintenance budgeting by description of condition. Key figure oriented budgeting methods, also called history-based budgeting, and is defined by Bahr (2008) as the budgeting of future maintenance costs by historical accounts. Future budgets are calculated on the basis of historic costs from maintenance. In this calculation historical costs are mapped to a key variable such as gross square footage. The resulting indicator is then applied to the current portfolio. The second approach is defined as allocating a maintenance budget on the basis of the buildings value e.g., a certain percentage of the buildings value is set aside as a maintenance budget every year. Both methods are criticized for being imprecise and only producing indicative values. By Bahr's (2008) definition analytic budgeting methods differ from key figure oriented and value-oriented budgeting methods by including correction factors for variables, which have a significant impact on maintenance cost. Those significant factors may be, but are not limited to, building age, geometry, building services share of construction costs. © ASCE 10