A Bayesian Approach to Residential Property Valuation Based on Built Environment and House Characteristics

Liu, Zhicheng; Yan, Shuai; Jun, Cao; Jin, Tanhua; Tang, Jiabo; Yang, Junyan; Wang, Qiao

doi:10.1109/bigdata.2018.8622422

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…Considering economic-and management criteria, the use of AI in general allows to improve the value proposition of the firm, through customization, value co-creation, search optimization or optimization of processes (Moreno-Izquierdo et al, 2018), which allows to enhance the customer value, understood from the customer perspective, as well as from the firm perspective on customer value. Furthermore, the accuracy of forecasts is a critical element for decision making of investment and using AI can significantly improve the valuation accuracy, but often needs to be traded off with interpretability and does not always return profitable covering the realized investment (Sakri and Ali, 2022;Liu et al, 2018;Moreno-Izquierdo et al, 2018). The cost saving of predictions using AI algorithms comes especially in terms of time savings in the price prediction, which happens fast or even in real-time (Agrawal et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

Erdmann,

Yazdani,

Mas Iglesias

et al. 2024

Informatica

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Artificial Intelligence (AI) in the price management process is being applied in business practice and research to a variety of pricing use cases that can be augmented or automated, providing opportunities as a forecasting tool or for price optimization. However, the complexity of evaluating the technology to prioritize implementation is challenging, especially for small and medium enterprises (SMEs), and guidance is sparse. Which are the relevant stakeholder criteria for a sustainable implementation of AI for pricing purpose? Which type of AI supported price functions meet these criteria best? Theoretically motivated by the hedonic price theory and advances in AI research, we identify nine criteria and eight AI supported price functions (AISPF). A multiple attribute decision model (MADM) using the fuzzy Best Worst Method (BWM) and fuzzy combined compromise solution (CoCoSo) is set up and evaluated by pricing experts from Germany and Spain. To validate our results and model stability, we carried out several random sensitivity analyses based on the weight of criteria exchange. The results suggest accuracy and reliability as the most prominent attribute to evaluate AISPF, while ethical and sustainable criteria are sorted as least important. The AISPF which best meet the criteria are financial prices followed by procurement prices.

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Section: Literature Reviewmentioning

confidence: 99%

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

Erdmann,

Yazdani,

Mas Iglesias

et al. 2024

Informatica

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“…, 2008; Belniak and Wieczorek, 2017; Watkins, 1999; Arribas et al. , 2016; Wyatt, 1997; Yalpir and Ozkan, 2018; Liu et al. , 2018b; Abidoye and Chan, 2018a, b; Francke and Vos, 2004).…”

Section: Existing Studies and Challengesmentioning

confidence: 99%

“…Although the reviewed papers presented some 3D elements extractable from BIM and other 3D models as potential factors for real estate valuation, the main issue here is the level of efficiency of these 3D variables in reducing the uncertainty of the current valuation methods. Area, age, floor, number of bedrooms and bathrooms, location, access to public transportation, access to urban facilities and services, design quality, parking, date of transaction and current market prices have been raised as the most important factors affecting the value of houses (Aladwan and Ahamad, 2019;Rubio et al, 2008;Belniak and Wieczorek, 2017;Watkins, 1999;Arribas et al, 2016;Wyatt, 1997;Yalpir and Ozkan, 2018;Liu et al, 2018b;Abidoye and Chan, 2018a, b;Francke and Vos, 2004). Thus, it is essential to evaluate the exact effect of the introduced 3D factors in improving the accuracy of the current real estate valuation models in comparison to the primary valuation factors.…”

Section: Introduction Of Some New 3d Factors Extractable From Bim And...mentioning

confidence: 99%

BIM and real estate valuation: challenges, potentials and lessons for future directions

Jafary¹,

Shojaei

Rajabifard

et al. 2022

ECAM

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PurposeBuilding information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.Design/methodology/approachThis paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.FindingsFour domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.Originality/valueThis paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

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“…The traditional ML approaches are Support Vector Machines (SVM), Decision Tree, and Nearest Neighbor classifier. For example, [14] used Bayesian approach to residential property valuation, [15]…”

Section: Fig 1 Relationship Between Emerging Technologies In Smart Ci...mentioning

confidence: 99%

Investigating Emerging Technologies Role in Smart Cities’ Solutions

Al‐Badi¹,

Sharma²,

Jain³

et al. 2020

IFIP Advances in Information and Communication Technology

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A smart city is defined as a one that provides solutions to rapid urbanization, exploding population, scarce resources, congested traffic, and energy management through the effective and integrated use of information and communication technology. The conceptualization, integration, and implementation of smart cities have been recognized and seen as a means to optimize the limited resources and improve the quality of human lives. The smart cities planning, designing, and development have been affected due to big data storage, big data governance, Internet of Things (IoT), and artificial intelligence (AI) techniques. The smart cities' solutions cover different themes of varying importance such as smart health, smart education, intelligent transportation, smart energy, smart governance, etc. The emerging technologies are the one which are presently under development or might be developed in the future, and which can have a wide impact on research, business, and social lives. The emerging technologies are the groups of technologies that have been partially explored, continuously evolving, and under development such as, IoT, big data, machine learning (ML), social network, and cloud computing. The emerging technologies have created renewed interest in smart cities' solutions. The smart cities' progress and advancement are the results of the successful exploitation of emerging technologies.This paper aims to investigate and discuss the success stories of emerging technologies in smart cities' solutions. The emerging technologies included in the study are the IoT, big data, and AI. The paper further summarizes a process of applying tools and techniques for the successful initiative of transforming a traditional city into a smart one using emerging technologies.

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A Bayesian Approach to Residential Property Valuation Based on Built Environment and House Characteristics

Cited by 12 publications

References 15 publications

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

Pricing Powered by Artificial Intelligence: An Assessment Model for the Sustainable Implementation of AI Supported Price Functions

BIM and real estate valuation: challenges, potentials and lessons for future directions

Investigating Emerging Technologies Role in Smart Cities’ Solutions

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