Method and system for Multi-Attribute Market Value Assessment in analysis of construction and retrofit projects

Multi-Attribute Decision-Making (MADM) methods prioritize the alternatives of comparative projects quite accurately. Problems arise when there is a need to determine the utility degrees and market values of the project alternatives. This becomes especially important for establishing the market value of real estate property in tender offers. However, the available MADM methods cannot accomplish this. Thus the authors of this article developed the MAMVA method, which permits determining the utility degrees and market values of project alternatives, and also developed a system on the basis of this developed method. This article presents the proposed Multi-Attribute Market Value Assessment (MAMVA) Method and the Decision Support System for Construction and Retrofit Projects (DSS-CRP). It also presents a case study to demonstrate the effectiveness of this method and system. The application of the MAMVA Method and DSS-CRP System for prioritizing and for determining the utility degrees and market values of construction and retrofit projects under consideration for financing by the European Economic Area (EEA) and Norway Financial Mechanism Grant made it possible to decrease the amount of requested support.This article also presents the analysis and comprehensive assessment of the noted construction and retrofit projects. These were performed in consideration of the entire life cycle of a project and of needs satisfaction relevant to all the groups interested in a project. The developed MAMVA Method and DSS-CRP System permit assessing the appropriateness of projects under analysis in conceptual and qualitative forms. This method and system automatically submit the values of the project alternatives.     

房地产交易核价系统价格评估方法分析

对目前我国在房地产交易价格评估时常用的三种评估方法进行了分析研究,提出了一种基于市场法的房地产交易价格评估循环算法：该算法计算结果可靠,已应用于房地产交易税收核价系统中。     

The mass appraisal of the real estate by computational intelligence

Mass appraisal is the systematic appraisal of groups of properties as of a given date using standardized procedures and statistical testing. Mass appraisal is commonly used to compute real estate tax. There are three traditional real estate valuation methods: the sales comparison approach, income approach, and the cost approach. Mass appraisal models are commonly based on the sales comparison approach. The ordinary least squares (OLS) linear regression is the classical method used to build models in this approach. The method is compared with computational intelligence approaches – support vector machine (SVM) regression, multilayer perceptron (MLP), and a committee of predictors in this paper. All the three predictors are used to build a weighted data-depended committee. A self-organizing map (SOM) generating clusters of value zones is used to obtain the data-dependent aggregation weights. The experimental investigations performed using data cordially provided by the Register center of Lithuania have shown very promising results. The performance of the computational intelligence-based techniques was considerably higher than that obtained using the official real estate models of the Register center. The performance of the committee using the weights based on zones obtained from the SOM was also higher than of that exploiting the real estate value zones provided by the Register center.     

An integrated cost-based approach for real estate appraisals

Real estate appraisal information systems have been studied by many researchers in the past including those systems that have integrated geographic information systems, artificial neural networks, etc. This paper proposes a new integrated approach for real estate appraisals which can be used in real estate appraisal systems to improve efficiency and accuracy. Motivated by the identified limitations of existing cost approaches for real estate appraisals, we integrate some elements from sales comparison approach and income approach into the cost approach to improve the accuracy of the valuation of real estate appropriately. As a result, the new integrated cost-based approach is capable of taking all of the major factors into accounts; these factors are closely related to the assets of real estate in one way or another. In the implementation of the new approach: (1) the concept of replacements cost is revisited and expanded to consider dynamic, environmental, and cultural factors in real estate appraisals; (2) the conventional depreciation values and depreciation rates are replaced by adjustment values and coefficients to include both the positive and negative impact on the changes of real estate value; (3) the theory of technology economics is applied, six forces have been systematically analyzed to determine replacement costs; and finally, (4) different methods for value adjustments, including the algorithm based on artificial neural network, have been utilized to deal with the randomness and uncertainties of mass data for the determination of adjustment values and coefficients.     

A case-based reasoning approach to the selection of comparable evidence for retail rent determination

Case-based reasoning is an artificial intelligence technique which utilizes past experience to solve current problems and in this respect it mirrors the process involved in real estate appraisal. This paper investigates its application as a computer-assisted valuation tool to the specific domain of retail rent determination. As property appraisal is goal orientated, it is essential that the most appropriate examples of previous rent determinations are selected. 1n exploring a case-based reasoning approach to the retail real estate domain five models are built namely; pure inductive, inductive (Q-model), inductive (prototype), inductive (Q-model and prototype) and nearest neighbour.     

A fuzzy linguistic ontology payoff method for aerospace real options valuation

In this paper, we present a fuzzy linguistic ontology payoff method for the valuation of real options in the aerospace industry. Using real data, we apply the fuzzy linguistic approach to determine the credibility measures and the credibilistic expected value for the fuzzy real options valuation payoff method. This approach is used to obtain a multi-scenario modeling process by envisioning three scenarios: optimistic, most likely, and pessimistic. In addition, our experience with the scenario estimates is premised on results in an operating profit forecast. This forecast corresponds to a plausible outcome within the aerospace licensing maintenance, repair, and overhaul market and provides a decision-making tool. This tool can be utilized for determining real options for project valuation of aerospace licensing revenues based on unit costs, recurring costs, and quantity of units sold.     

High-accuracy finite-difference methods for the valuation of options

A finite-difference scheme often employed for the valuation of options from the Black–Scholes equation is the Crank–Nicolson (CN) scheme. The CN scheme is second order in both time and asset. For a rapid valuation with a reasonable resolution of the option price curve, it requires extremely small steps in both time and asset. In this paper, we present high-accuracy finite-difference methods for the Black–Scholes equation in which we employ the fourth-order L-stable Simpson-type (LSIMP) time integration schemes developed earlier and the well-known Numerov method for discretization in the asset direction. The resulting schemes, called LSIMP–NUM, are fourth order in both time and asset. The LSIMP–NUM schemes obtained can provide a rapid, stable and accurate resolution of option prices, allowing for relatively large steps in both time and asset. We compare the computational efficiency of the LSIMP–NUM schemes with the CN and Douglas schemes by considering valuation of European options and American options via the linear complementarity approach.     

Valuation by using a fuzzy discounted cash flow model

The objective of this paper is to extend the classical discounted cash flow (DCF) model by developing a fuzzy logic system that takes vague cash flow and imprecise discount rate into account. In order to explicitly discuss a more appropriate valuation model, uncertain information will be fuzzified as triangular fuzzy numbers to quantify and evaluate the intrinsic value of a company's financial asset under the framework of DCF approach. We will find that the fuzzy discounted cash flow (FDCF) model proposed in this paper is one extension of classical (crisp) model and should be more suitable to capture the elements of valuation than non-fuzzy models.     

Dynamic Model for the Simulation of Equilibrium Status in the Land Use Market

This paper presents a dynamic equilibrium model for the real estate market. Households have stochastic behavior and compete for quasi-unique locations (real estate goods), which are assigned to the best bidder through an auction-type mechanism. The producers are modeled as maximizers of their profits over the long-term through the production of real estate assets, represented by the present value of future sales. It is assumed that the producers do not possess complete information about future levels of demand or prices. Rather, it is assumed that producers are myopic, meaning that they take the actual and historic prices in each period as the relevant information for their decision-making. A notion of equilibrium is used that adjusts prices given two situations: supply and demand surplus. In the supply surplus case, the prices are diminished and supply in the market is reduced until supply equals demand. In the case of demand surplus, the prices rise and demand diminishes (homeless households) until demand equals supply. This equilibrium condition yields prices that are jumpy over time, resembling observations of inventories in the real estate market and the manufacture industry.     

改进的ANFIS在房产评估中的应用

房产评估是个较为复杂的非线性过程,目前的方法存在房产近邻难以定义等问题。为解决这一问题,提出基于k近邻的自适应神经模糊推理方法,并应用于房产评估。该方法通过定义不同意义的全变量、部分变量、空间、时空的k近邻,计算k近邻均价,将k近邻均价加入模型。实验结果表明,使用基于空间k近邻和时空k近邻改进自适应神经模糊推理方法对房产价格进行预测,准确性显著提高。     

A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach

This research uses a solution based on a combined grey-fuzzy DEMATEL method to deal with the objective of the study. This study is aimed to present a perception approach to deal with real estate agent service quality expectation ranking with uncertainty. The ranking of best top five real estate agents might be a key strategic direction of other real estate agents prior to service quality expectation. The solving procedure is as follows: (i) the weights of criteria and alternatives are described in triangular fuzzy numbers; (ii) a grey possibility degree is used to result the ranking order for all alternatives; (iii) DEMATEL is used to resolve interdependency relationships among the criteria and (iv) an empirical example of real estate agent service quality ranking problem in customer expectation is used to resolve with this proposed method approach indicating that real estate agent R₁ (CY real estate agent) is the best selection in terms of service quality in customer expectation.     

基于洛伦兹变换和PageRank算法的数据资产估值

数据资源是组成数字社会的重要生产资料, 数据资源价值的评估是数据交易、数据流通和数据升值变现的重要基础. 以洛伦兹变换和PageRank算法为理论基础依据, 首先利用PageRank算法计算其在数据资产参价体系中的权重系数, 进而得到数据资产初始估值. 然后再利用质速关系映射的数据资产估值模型对给定的数据资产进行估值. 实验结果表明, 提出的数据资产估值方法, 具有一定的高效性和市场参照效用.     

Vision-based real estate price estimation

Since the advent of online real estate database companies like Zillow, Trulia and Redfin, the problem of automatic estimation of market values for houses has received considerable attention. Several real estate websites provide such estimates using a proprietary formula. Although these estimates are often close to the actual sale prices, in some cases they are highly inaccurate. One of the key factors that affects the value of a house is its interior and exterior appearance, which is not considered in calculating automatic value estimates. In this paper, we evaluate the impact of visual characteristics of a house on its market value. Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos. We also develop a novel framework for automated value assessment using the above photos in addition to home characteristics including size, offered price and number of bedrooms. Finally, by applying our proposed method for price estimation to a new dataset of real estate photos and metadata, we show that it outperforms Zillow’s estimates.     

Valuation of expansion flexibility in flexible manufacturing system investments using sequential exchange options

In this paper, a valuation approach that modifies traditional discounted cash flow (DCF) methodology is presented to incorporate the option premium of expansion flexibility while evaluating a flexible manufacturing system investment. Expansion flexibility allows for changing the production capacity in response to deviations in demand, while disregarding the assumption that management makes an irrevocable decision based on its future market expectations. Expansion flexibility provides a key strategic advantage by avoiding the large financial commitment at the initial investment stage and enabling investment in a phased manner according to the changes in market conditions. The aim of this paper is to present a thorough valuation methodology that accounts for both the benefits of keeping the option to expand alive and the loss of market share to competing firms if the expansion investment were delayed. In this paper, the value of expansion flexibility is computed using sequential exchange options. The proposed method employs an analytic approximation scheme for valuing American exchange options on dividend-paying assets. A numerical example demonstrates the application of the valuation framework. The options approach that incorporates the expansion flexibility option into the analysis results in a higher value than the standard DCF approach, which ignores the value of the option to expand. We also perform sensitivity analyses to see whether the expansion option increases in value in cases of high uncertainty where management can respond flexibly to new market information, and where the investment analysis ignoring flexibility yields a marginally positive net present value. The results of the proposed valuation framework are presented in comparison with a previous model that ignores the opportunity cost of delaying expansion investment.     

Simulation–optimization mechanism for expansion strategy using real option theory

A right expansion strategy can bring a company more market shares and profits, and hence increase shareholders’ equities. However, limited financial resources and various uncertainties require business practitioners to achieve their goals while controlling the risks incurred at an acceptable level. Therefore, justification of expansion investments is an important and complex topic in industry. The traditional investment analysis tools such as net present value (NPV) often tend to undervalue investment decisions. We formulate the expansion investments using real options, and develop a financial model to assess the option value. Monte Carlo simulation is considered a good way to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate expansion strategy based on an integrated view of the market dynamics, but optimization is still a difficult problem to resolve. This paper presents a model of optimization under uncertainty combining system simulation with GA-based optimization to resolve the expansion problem. An industry case is used to demonstrate the application of real options to value expansion investment by using simulation–optimization. This approach also provides some new insights for the real options theory.     

数据挖掘技术在房地产预警系统中的应用

分析了各种房地产应用软件,将数据挖掘技术引入房地产预警系统设计中;结合数据仓库和决策支持系统,建立了基于粗糙集数据预处理的支持向量机预警模型,运用3σ警界控制原理确定各状态区间,划分出不同的警度,并提出了房地产市场预警预报系统的完整解决方案;最后,结合银川市的实际情况,构建了银川市房地产预警系统指标体系。     

房地产经济投资决策仿真分析研究

该文以房地产市场需求众多影响因素，用线性多元回归数理统计作仿真分析研究，为房地产经济投资决策做量化参考，文章引用了一组实际调查的数据。     

Sustainability assessment in the presence of undesirable factors over time: A case on gas companies

Sustainability is an essential ingredient for long-term success of firms, and its assessment has a significant impact on decision making and sustainability management. In the current paper, a data envelopment analysis-based approach is proposed to assess the sustainability of systems over several periods when undesirable outputs are present in the process. Indeed, sustainability is assessed in each period and, as a whole, simultaneously. Furthermore, social, economic, and environmental efficiency scores of multiperiod systems are evaluated in each period. The weak disposability assumption is used to handle undesirable outputs. Also, the current study concerns socio-economic, socioenvironmental, and eco-environmental factors in addition to economic, social, and environmental aspects. A real-world application on Iranian gas companies is used to illustrate the applicability of the proposed approach.     

A fuzzy real option approach for investment project valuation

The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project.