Abstract
JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2021, VOL. 20, NO. 5, 546–555 https://doi.org/10.1080/13467581.2020.1800474 Development of an income and cost simulation model for studio apartment using probabilistic estimation a b b c Ji-Myong Kim , Kiyoung Son , Junho Jang and Seunghyun Son a b Department of Architectural Engineering, Mokpo National University, Mokpo, Republic of Korea; School of Architectural Engineering, University of Ulsan, Ulsan, Republic of Korea; Department of Architectural Engineering, Kyung Hee University, Suwon, Republic of Korea ABSTRACT ARTICLE HISTORY Received 10 January 2020 The Korean construction industry has attracted interest and investment demand for lease- Accepted 16 July 2020 oriented investment products, such as shopping malls and studio apartments, as a substitute for financial products because of the low interest rates of the banks that resulted from the KEYWORDS economic recession after the global financial crisis in 2008. However, there have been huge Studio apartment; economic damages because of problems such as the oversupply, the increase in the unsold probabilistic estimation; presale rate, and the decrease in rental profit. For studio-apartment development projects, system dynamics; risk dynamic analysis should be applied considering the correlation of variables in business analysis analysis, which is complicated by such factors as profit structure and money flow. Therefore, we aim in this study to develop a statistical analysis model of studio apartments using probabilistic estimation. For this purpose, we developed a causal-loop diagram and established a simulation and optimization model. The developed model was verified by applying it to actual cases. Our results can be used as a reference for the optimization and risk management of studio-apartment business analysis in academia. In addition, from a practical point of view, this model can be used to develop a forecasting feasibility study based on risk and for business feasibility analysis. 1. Introduction studio apartments has steadily increased since 2010, exceeding the first 30,000 in 2011, 48,000 in 2014, and The Korean construction industry has had increased the highest in 2017 at 68,233 (Jang et al. 2019). interest and investment in lease-based investment However, according to Real Estate 114, Korea’s lar- products, such as shopping malls and studio apart- gest provider of real-estate information, 31,943 rooms ments, as a substitute for financial products because (46%) out of the total 68,823 office buildings sold of the low interest rates of banks that resulted from the nationwide in 2017 were not sold until early economic downturn after the global financial crisis in March 2018. Studio apartments’ rental yield has also 2008 (Cho 2015). In particular, studio apartments are been declining. It declined to 6.01% in 2011, 5.95% in free from regulations, such as strengthening loan 2012, 5.87% in 2013, 5.81% in 2014, 5.70% in 2015, screening and resale that are applied to larger apart- 5.45% in 2016, and 5.22% in 2017, and plunged to ments, and leasing demand is also relatively high 0.59% (Ministry of Land, Infrastructure and Transport, because of the rapid increase in one-person house- Republic of Korea). holds (Jang et al. 2018). As a result, the supply of new CONTACT Seunghyun Son seunghyun@khu.ac.kr Kyung Hee University, Gyeonggi-do 17104, Republic of Korea © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the Architectural Institute of Japan, Architectural Institute of Korea and Architectural Society of China. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 547 This decline in rental yields has had a negative effect 2. Methodology on the studio-apartment supply, which has increased The methodology of this study is as shown in Figure 1. until recently, and has caused massive damage to First, the risk factors directly affecting profits on the society, including the bankruptcy of large-scale con- studio apartment development project are examined struction companies and economic damage to persons and the methodologies such as system dynamics and who had been given a studio apartment. Monte Carlo simulation are investigated. For example, the continuous decline of studio apart- Second, the casual loop diagrams are created by ments is related to decrease occupancy rate as well as analyzing causal relationships among the major risk new project promotion. In this respect, these can be factors defined above. Third, based on the created caused severe financial stress to the construction com- casual loop diagram, an income and cost simulation panies and ultimately decrease of housing supply. and an optimization model sequentially are con- Especially, since current occupancy rate of presold structed. Then, the Monte Carlo simulation is used to studio apartment is low, the private construction com- set the control limits for each factor. Finally, the simu- panies suffered from the difficulty of balance recovery lation results are obtained through this support the by increased default rate. Therefore, accurately predict- decision-making of the owner in the studio- ing business risks early in development is critical to the apartment development project. success of the project (Isaac and Navon 2009). Park and Choi (Park and Choi 2013) carried out a feasibility analysis of existing studio-apartment 3. Literature reviews development projects by means of deterministic ana- lysis, which relies on one representative value. 3.1. Analysis of factors that influence risk However, it can be considerably changed between There have been many studies on the factors affecting initial plan and final result since it takes a long time the building development business. However, most for the land development (Yu et al. 2017). In this studies have shown that factors influencing the suc- respect, the deterministic method is difficult to analyze cess or failure of a business are macro level factors, the impact of influential factors quantitatively such as changes in political environment and govern- (Zavadskas, Turskis, and Tamošaitiene 2010). ment policy (Rachmawati et al. 2018; Shi Ming and In addition, for successful project, the developer Chee Hian 2005) and changes in market demand should decide whether or not the project progress by (Caldera and Johansson 2013; Michael, Vicky, and predicting cash flow of investment cost and establish- Michael 2002), and project-level factors, such as market ing the control range of influential factors. In this level (Go et al. 2005) and appropriateness of business respect, the profit is directly related to manage the method and fundraising ability (Ferreira and Jalali range of the factors such as land costs, construction 2015; Koo and Jung 2007), and listed only the effect costs, sales price, financial costs. If the factors can be of factors on business performance (Rachmawati et al. simulated with actual price of surrounding area, the 2018; Shi Ming and Chee Hian 2005; Michael, Vicky, and control range of these factors can be identified then Michael 2002; Go et al. 2005; Koo and Jung 2007). In the developer can predict various project costs effec - other words, they failed to quantitatively and clearly tively by simulating influential factors. calculate the relationship between these qualitative Therefore, for studio-apartment development pro- factors and profit as a business performance. jects, dynamic analysis is required because it has Therefore, it is very difficult to use them for practical a complex system dealing with, e.g., profit structure purposes. In order to solve this problem, we developed and flow of funds. In particular, among the probabil- istic analysis methods, the system dynamics techni- que can analyze the dynamic relationship between influential factors and make a dynamic analysis according to the change of variables (Park et al. 2008; Son 2007; Sin 2012; Park 2018; Son 2018). In this respect, the objective of this study is to develop a financial analysis model for studio apartments using stochastic estimation such system dynamics technique. The findings of this study suggest that it is possible to create a forecasting feasibility study of risk based on the practical application of studio apartments, which provides a basis for optimizing the risk management of studio apartments. Figure 1. Methodology. 548 J.-M. KIM ET AL. Table 1. Previous studies about influence factors. 3.3. Monte Carlo simulation Risk influence factors Monte Carlo simulation is a probabilistic analysis Sales Sales Building Land Finance method used to obtain the solution of the input vari- Author Period ratio price costs costs costs Jang ● ● ● able by means of a probability model and random (2019) numbers (Lee 2017). The Monte Carlo technique allows Sin (2012) ● ● ● ● ● ● precise simulation, because the distribution of input Park ● ● ● ● ● (2018) values is constant. In addition, for applying Son ● ● ● ● ● ● a probability distribution suitable for a given problem, (2018) Sayegh ● ● ● an algorithm for generating a random number follow- (2015) ing the probability distribution is important (Jeon Lee (2017) ● ● ● ● ● ● 2015). Therefore, Monte Carlo simulation can be used to study the prediction of the appropriate time and a model which is limited to factors that have a more cost range considering the effects of risk (Lee 2017; direct effect on the profit of the project. As shown in Jeon 2015; Peleskei et al. 2015). We construct a range Table 1, there are six main influencing factors (Jang of variance and an optimization model based on major et al. 2019; Sin 2012; Park 2018; Son 2018; El-Sayegh uncertainties arising from studio-apartment business and Mansour 2015; Lee 2017). balance analysis through Monte Carlo simulation. In terms of risk management, the qualitative factors mentioned above are reflected in the surrounding market prices, the nearby land prices, and the nearby 4. Model development prevalence rate, and as a result, they are expressed as The developed model in this study is divided into t sales price, land cost, construction cost, sales ratio, and -t as shown in Figure 2. The t proceeds the eco- 3 0 so on (Sin 2012; Park 2018; Son 2018; Lee 2017). These nomic feasibility analysis of the project as the initial studies are limited to the key factors that directly affect project review. In the t , if the feasibility is confirmed the project profit and define the variables used in the by the simulation model, the target land is pur- overall profitability and risk analysis of the project. chased. The t is the acquisition process regarding Therefore, we consider s seven factors, which are direct building permit. In this phase, the project contents selling price, presale rate, presale period, construction such as the number of buildings, housing units, cost, land cost, and financial cost. building heights, building coverage and volume can be changed unlike initial planning. As a result, the sales price should be reviewed again by the sug- 3.2. System dynamics gested simulation model in this phase since profit System dynamics is based on a causal-loop diagram can be changed. (Sterman 2001), which is a structural reflection of the The t is the phase of building construction and problems as shown in Table 2. starting sales at the same time. The final sales price Based on the causal-loop diagram, it is possible that has to be decided in this phase after simulating the forecast how a specific variable changes dynamically sales price by suggested model. If the feasibility is not over time (Son 2007). In other words, unlike the existing good, the developer can be made decisions such as the deterministic method of deriving an estimated value of return rate change and taking risks. an event or a variable, system dynamics can identify the Therefore, the objective of the suggested model is dynamic change of the variable over time (Son 2018). to manage the profit by using stochastic method such Therefore, we intend to develop a causal model for the dynamic simulation. The model can set the control analysis of the major balance-sheet items affecting stu- range regarding influence factors and support the dio-apartment development projects, and to develop decision of the developer from the results of simula- a balance-sheet analysis model based on this tion model. Table 2. Comparison between existing deterministic analysis and system dynamics methodology (Son 2007, 2018; Sterman 2001). Characteristics Traditional deterministic analysis System dynamics Static form (point estimation) Dynamic form type (1) Object of analysis (2) Inference method Causal relationship of existing empirical data Causal relationship of variables (3) Analysis focus The relationship between two variables Circular relations between variables (4) Goal of analysis Pursuit of numerical accuracy Pursuing structural accuracy Observable objective phenomena Feedback structure (5) Object of knowledge (6) Definitions of Understanding Difficulty (numerical expression) Easy (using graphs and diagrams) JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 549 Figure 2. Simulation phases. 4.1. Casual-loop diagram way, it is the range of the various influencing fac- tors (variables) related to these that determine the Using the system dynamics method, we analyzed the change in sales revenue or business expense. causal relationship based on the factors selected in Therefore, we used the probabilistic estimation Table 1. The causal-loop diagram of risk factors is technique to simulate presale income and business shown in Figure 3. expenditure within the range of influence variables To explain the causal relationship in Figure 3, the and to confirm the business profit. business success of the studio-apartment develop- ment project is determined by the profit of the project. Business profits are determined by the level of income 4.2. Income and cost simulation model (income) and expenditure (cost). At this time, the level of income is determined by the sales price and the 4.2.1. Income simulation model sales ratio, and the expenditure is determined by the The income simulation model consists of variables, such as sales income, sales ratio, and interim payment level of building costs, land costs, and financial costs (Choi et al. 2017). schedule, as shown in Figure 4. For income, there is a negative correlation with The import model in Figure 4 is described in more detail. First, the monthly sales ratio, the interim pay- the subsequent variables in the process leading to the presale rate and presale income. This structure ment condition, and the interim payment condition should adjust the selling price by adjusting the are input. The total sales income is then calculated by target rate of return when the presale rate is low automatically calculating the cumulative sales ratio, and profit is decreased. Decreasing presale prices apartment and studio-apartment sales income, and will increase the willingness of potential buyers to commercial sales income, which reflects the ainterim buy, which may lead to an increase in the presale payment schedule and the interim payment condition rate, which means an increase in total presale rev- in the calculated total sale revenue. As a result, we can enue. For expenditure, there is a positive correlation identify the structure of cash flows for income over with construction cost, land cost, and finance cost. time. At this time, the apartment sales, studio-apartment In addition, since the construction cost and the land cost are expenditures in the financial cost, the two sales, and commercial variables and the financial cost have a positive (+) sales income are modeled automatically, as shown in Figure 5. In Figure 5(a), the apartment and studio- relationship. In particular, if uncertain risks such as land costs and lending rates rise, land and finance apartment sales model is designed so that it can input costs will increase and the total business costs will equilibrium (A ), household number (N ), and unit sale i i increase. Increased business costs are directly linked price (P ) for each type. Then, the total presale income to business profits, forming a feedback loop. In this of apartments and studio apartments is automatically Figure 3. Causal-loop diagram. 550 J.-M. KIM ET AL. Figure 4. Income simulation model. calculated through Equation (1). The calculated results 4.2.2. Cost simulation model are interrelated with the income simulation model in The cost simulation model of this study consists of Figure 4. financial costs, land costs, construction costs, and inci- dental costs, as shown in Figures 6 and 7. The core of these is the financial costs model in Figure 6, because, I ¼ P � N � A (1) s i i i if the initial presale rate increases after the start of the sale, no trouble will arise if it is possible to obtain Here, I : apartment and studio-apartment sales a smooth fund (Huh, Hwang, and Lee 2012). In other income, P : unit sale price, N : the number of units, A : i i i words, it is possible to increase the profits by repaying the sale area of each unit, i: the number of unit types. the initial costs for project finance and other financial Figure 5(b) shows the number of floors (j), balances costs. However, if not, it is difficult to predict the (A ), and units sold (P ). Then, the total sales revenue of j j circulation of funds. A major cause of business failure the shopping mall is automatically calculated through is their not being able to redeem the financial Equation (2). The calculated results are connected with expenses (Warszawski 2003). Most of the recent real- the import simulation model in Figure 4. estate development business failures can be found in financial cost forecast failure (Lee, Lee, and Kim 2016; I ¼ P � A (2) c j j Warszawski 2003). Therefore, we constructed the simu- lation model by linking the financial cost generated during the project promotion with the import model. Here, I : commercial sales income, P : the sales price c j The financial cost model in Figure 6 consists of per m in each floor, A sale area of each floor, j: the project financing cost (F ), loan amount ratio (A ), number of floors. proj loan principal of loan (L ), and interest on loans (I ). The prin loan project financing cost (F ) is calculated as the pro- proj duct of land purchase cost (C ), direct construction land cost (C ), and loan interest rate (R ), as shown in dire loan Equation (3). The ratio of the loan (A ) can be loan obtained by subtracting the capital ratio (R ) from capi 1, as shown in Equation (4). Also, the project financing cost (F ) is a component of the subsequent variable, proj loan cost (C ), and the loan amount (C ) is loan loan a component of the principal repayment amount (L ). At this time, the principal repayment amount prin (L ) is found by comparing it with monthly sales prin income (S ), as shown in Equation (5). The loan mont interest rate (R ) is applied to the period during loan which the project financing cost (F ) is repaid, as proj shown in Equation (6), and the deposit interest rate (R ) is applied to the sale income after the principal depo repayment is completed. Consequently, the financial- cost model of this study can estimate the financial cost over time by automatically calculating the principal repayment amount according to the presale income level. Figure 5. Sales income model. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 551 Figure 6. Financial cost model. F ¼ ðC þ C Þ� R (3) proj land dire loan Profit ¼ ðIncome Cost Þ� Ur (7) k k k A ¼ 1 R � 100 (4) loan capi L ¼ ifðC < S ; C ; S Þ (5) prin loan mont loan mont Income ¼ ðI þ I Þ� C � R � Ur (8) k s c interim interim k I ¼ if C > L ; C � R ; S � R loan loan prin loan loan mont depo (6) Here, F : financial cost, C : land cost, C : direct proj land dire construction cost, R : loan interest rate, A : loan loan loan amount ratio, R : capital ratio, L : principal repay- capi prin ment amount, C : loan cost, S : monthly sales loan mont income, I : loan interest. loan The land cost, construction cost, and incidental cost models are shown in Figure 7. The land cost in Figure 7 (a) is calculated by multiplying the land cost for each lot by the land area for each lot and the other land acquisition costs. The construction cost in Figure 7(b) is calculated as the sum of direct construction cost and indirect con- struction cost. Direct construction costs consist of unit construction cost and total floor area. Indirect con- struction costs include design and supervising. The incidental costs of Figure 7(c) include property tax, education tax, rural special tax, housing bonds, and registration fees. 5. Profit simulation model As seen in Figure 8, the optimization model of this study can feed back the presale revenue and business costs by uncertain risks. In addition, this model can set a control limit for each factor to achieve the target return. The business entity can manage the business so as not to escape. Moreover, the business entity can select an optimal solution that is suitable for the situation and conditions among the alternatives through the simulation. In this case, the formula for calculating profit, income, and cost of the analytical model is Equations (7), (8), (9). In order to simulate changes in funding over time, the number of months (k) from the start of the project to the end of the project is set as a variable. Figure 7. Land, construction, and incidental cost model. 552 J.-M. KIM ET AL. Table 3. Overview of a case project. Description Contents Project title DWC studio apartment development Location Goyang, Gyeonggi, Korea Site area 12,919 m Total floor are 172,240 m Building area 6,699 m Number of units Apartment: 298 units (2 types) Studio apartment: 430 units (2 types) Shopping mall: 28,050 m (area supplied) Construction period 28 months Table 3 shows the overview of a case project. The project consists of 298 households, 430 studio apartment, shopping malls of 28,050 m . The land was purchased by Figure 8. Income and cost optimization model. 1,460 USD/m in the case project and households, studio apartment and shopping mall were sold by 3,291 USD/ 2 2 2 m , 2,278 USD/m , and 5,063 USD/m respectively. The Cost ¼ C þ C þ C þ F � Ur (9) project was predictable that it is sold out within 6 months k land cons inde proj k after selling starts. However, the return rate (10.3%) could not be achieved by low sales ratio. Here, Income : monthly sales income, Cost : monthly k k The conventional deterministic feasibility analysis project cost, I : apartment and studio-apartment sales cannot be predictable the dynamic variation due to income, I : commercial sales income, C : intermedi- c interim the change of sales ratio and interests. In other words, ate payment schedule, R : intermediate payment interim it is difficult to response risks before identifying the rate, C : land cost, C : construction costs,C : inci- land cons inci results of the project. However, in the developed dental costs, F : financial costs, Ur : uncertain risks, k: proj k model, the profit can be predictable by simulating the number of months from project commencement the influential factors according to the cash flow to completion As shown in Equation (7), profit is calculated as the difference between total business costs and income. 6.1. Initial review phase The income of Equation (7) is calculated as the sum of Table 4 shows the initial input value of influential the sales income of each item (i.e., studio apartment, factors in the initial project review stage. These values apartment, and shop), as shown in Equation (8). At this were applied in the developed model and 100,000 time, the flow of funds is determined by the intermedi- random numbers that follows the lognormal distribu- ate payment schedule (C ) and the intermediate interim tion were generated. These values were used to decide payment rate (R ), and is changed by uncertain interim the range of the profit variation. risks (Ur ) such as low presale rate, schedule change As a result, as shown in Table 5 and Figure 9, the of intermediate payment rate, and ratio. In addition, average of apartment was 3,291 USD/m , the maximum the total project cost can be calculated as the sum of 2 2 was 4,607 USD/m , the minimum was 1,810 USD/m . The land costs (C ), construction costs (C ), incidental land cons average of studio apartment and shopping mall was costs (C ), and financial costs (F ), as shown in inci proj 2 2 2,278 USD/m , and 5,063 USD/m , respectively. In addi- Equation (9). However, it is subject to uncertain risks tion, the average of land cost was 1,460 USD/m , the (Ur ), such as land expense exceeding the projected maximum was 2,044 USD/m , the minimum was 803 amount, or excessive financial costs. USD/m and the average of construction cost was 1,316 USD/m . Finally, due to the variation of these influence 6. Case study Table 4. Initial input values. The case study is conducted in this section for the valida- Item Unit Initial value tion of the developed model. The model can be applied in Unit sale price USD/m 3,291 each phase from t to t . The developer sets the risk 0 3 1. Apartment 2,278 management limit regarding influential factors such as 2. Studio apartment 5,063 3. Shopping mall presale price, sale period, land cost, construction cost, Land cost USD/m 1,460 finance cost, and other expenses to achieve the target Construction cost USD/m 1,316 Incidental cost USD/m 222 profit. If the planned sales ratio is appropriate before t Loan interest rates % 4.3 phase, the target profit can be achieved. However, the Deposit interest rates % 2.7 Project period Months 28 sales ratio is not fit, the sales price can be changed by KRW 1,197.00 = USD 1.00 as of 2019/10/06 (Bank of Korea) conducting the simulation of income and cost. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 553 Table 5. Result of simulation. 6.2. Review phase after identifying low sales ratio Item Average Maximum Minimum If there is lower sale ratio than initial plan, the devel- Unit sale price (USD/m ) 3,291 4,607 1,810 1. Apartment 2,278 3,189 1,253 oper should make a decision regarding project pro- 2. Studio apartment 5,063 7,088 2,785 gress. In the t phase, the land cost and construction 3. Shopping mall cost were determined. In this case, the key factors are Land cost (USD/m ) 1,460 2,044 803 Construction cost (USD/m ) 1,316 1,842 724 residual sales ratio and financial cost. Profit (1,000 USD) 34,042 120,153 −49,477 For example, as shown in Table 3, the sales ratio KRW 1,197.00 = USD 1.00 as of 2019/10/06 (Bank of Korea). of the case project was 20% (10%, 6%, and 4%) in early 3 months. This result is one-third level of the factors, the profit variation was identified from predicted rate of t (60%). Accordingly, the financial −49,477,000 USD to 120,153,000 USD. cost will be increased because of low sales ratio. As As shown in Table 6, if the target return rate by the a result, it was failed to achieve the target return developer sets from 10% to 12%, the sales prices of rate. In this respect, this study conducted the simu- apartment, studio apartment and shopping mall lation the financial cost and the profit using the should be decided to the range such as 2,534–3077 assumption deducting rate by 5% in the original 3 3 USD/m , 1,754–2,130 USD/m , and 3,899–4,734 USD/ sales price. The simulation result represents Figure m respectively. In addition, land cost and construction 10 and Table 7. cost have to decide in the range such as 924–1,128 As shown in Table 7, the simulation repeated by 3 3 USD/m , and 842–1,027 USD/m . If the profit is out of 1,000 and average financial cost was 9,993,000 USD, the control limit, the new plan such the change of maximum and minimum were 11,884,000 USD and return rate should be established. 8,398,000 USD. In addition, the average profit was Therefore, the developed model can be predictable 30,783,000 USD, maximum and minimum were the return rate according to the change of influential 35,063,000 USD and 24,375,000 USD. Therefore, to factors. In addition, the upper and lower range limit achieve the maximum profit, the developer has to can set by conducting simulation. In this respect, the sell them within 4 months. If the planned rate could developer can achieve the target profit if the factors not be achieved in next month, the developer should can manage within the limit. establish new plan by using the model. In this method, Figure 9. Random variable generation. Table 6. Setting the control limit of risk factors. Unit sale price (USD/m ) Land cost Construction cost 2 2 No. of trials Earnings rate (%) Apartment Studio apartment Shopping mall (USD/m ) (USD/m ) 1 −29.70 1,810 1,253 2,785 2,044 1,842 2 −29.69 1,932 1,260 2,793 2,003 1,820 . . . . . . . . . . . . . . . . . . . . . 52,101 10.02 2,534 1,754 3,899 1,128 1,027 52,102 10.04 2,541 1,762 3,891 1,124 1,013 . . . . . . . . . . . . . . . . . . . . . 99,999 39.68 4,600 2,992 6,998 868 759 100,000 39.69 4,607 3,189 7,088 803 724 KRW 1,197.00 = USD 1.00 as of 2019/10/06 (Bank of Korea). 554 J.-M. KIM ET AL. Figure 10. Simulation results of financial cost and profit. Table 7. Simulation results of financial cost and profit for changes in residual sales ratio. Unit: 1,000 USD No. of trials M + 3 M + 4 M + 5 M + 6 . . . M + 11 M + 12 Total Financial cost Profit 1 12% 11% 10% 10% . . . 3% 2% 100% 11,884 24,375 2 12% 10% 11% 8% . . . 5% 5% 100% 10,456 25,287 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 999 24% 18% 17% 16% . . . 0% 0% 100% 8,415 34,983 1000 25% 23% 17% 15% . . . 0% 0% 100% 8,398 35,063 according to time flow, the developed model can be by using simulation results. For the case project, if the repeated. target rate of return is 10% ~ 12%, the sales price of apartments, studio apartments, and shopping malls should be managed within 2,534–3,077 USD/m , 2 2 7. Conclusion 1,754 ~ 2,130 USD/m , and 3,899 ~ 4,734 USD/m , respectively. In this study, the income and cost simulation, and Third, the developed model can support the devel- profit model of studio-apartment development pro- oper’s decision when there is low sales ratio in early jects was developed. This allows a developer to phase. For the case project, if the 5% deductible sales control sales income and business costs within the price is applied when the appropriate sales ratio can- range of variables that affect cost and income, so not be achieved within 3 months after starting sales, that the profits remain positive. In addition, the the predicted profit can be changed from 24,375,000 developed model proved its effectiveness through USD to 35,063,000 USD. case study. Therefore, the developed model can be predictable As a result, first, the simulation model can predict the influential factors and conducted simulation results the range limit of influential factors by analyzing the in advance. The findings of this study can be utilized causal loop diagram between project costs (financial the fundamental materials for developing optimization costs, construction costs, land costs, and other costs) and risk management model to reduce economic and factors influencing earnings (presale prices, pre- losses in studio apartment project. sale period, presale rate). In the case project, the simu- lation result shows that the average of profit was 34,042,000 USD, maximum and minimum were Disclosure statement 120,153,000 USD and 49,477,000 USD. Second, it is possible to derive the management No potential conflict of interest was reported by the scope for each factor to achieve the target return rate authors. JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 555 Funding Lee, G. 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Journal
Journal of Asian Architecture and Building Engineering
– Taylor & Francis
Published: Sep 3, 2021
Keywords: Studio apartment; probabilistic estimation; system dynamics; risk analysis
