Property Valuation Using Linear Regression and Random Forest Algorithm

Goundar, Sam; Bhardwaj, Akashdeep

doi:10.4018/ijsda.20211001.oa13

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…, 2020), real estate websites (Sangha, 2021), public domain (Ho et al. , 2021), field work (Yilmazer and Kocaman, 2020) and social networks like Facebook (Goundar and Bhardwaj, 2021) to prepare the required data sets. As already mentioned, there are some critical issues concerning these sources of data.…”

Section: Discussion and Future Applicationsmentioning

confidence: 99%

“…Data recorded are used for training and testing ML-based models. Data collection is a complex and time-consuming process, and researchers draw on various sources such as governmental organizations (Hong et al, 2020), real estate websites (Sangha, 2021), public domain (Ho et al, 2021), field work (Yilmazer and Kocaman, 2020) and social networks like Facebook (Goundar and Bhardwaj, 2021) to prepare the required data sets. As already mentioned, there are some critical issues concerning these sources of data.…”

Section: Bim and Real Estate Valuationmentioning

confidence: 99%

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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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Section: Discussion and Future Applicationsmentioning

confidence: 99%

Section: Bim and Real Estate Valuationmentioning

confidence: 99%

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

Jafary¹,

Shojaei

Rajabifard

et al. 2022

ECAM

View full text Add to dashboard Cite

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“…i.e. there is a list of slices, similar to MapReduce, which also requires distributed data to perform the relevant computations [13][14].…”

Section: ) Spark Ecosystemmentioning

confidence: 99%

“…A specific set of DAG operations is generated after certain arithmetic operations are performed on an RDD, which also gives the notion of a loop that essentially stores the arithmetic operations between RDDs. When an error occurs somewhere, it recovers the RDD according to Lineage [13][14].…”

Section: ) Spark Ecosystemmentioning

confidence: 99%

An Improved Random Forest Algorithm Based on Spark

2022

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Classification algorithms are an important branch of data mining, and are also of great importance in the era of BD. The random forest algorithm (RFA) is one of the classification algorithms and is widely used in various industries for its good classification performance. However, the performance of RFA is not so good when dealing with high-dimensional data and unbalanced data. The main objective of this paper is to improve the RFA based on Spark. In this paper, we read a large amount of relevant algorithm literature in terms of algorithm research, and gain a comprehensive understanding of what feature selection and unbalanced classification are, as well as what characteristics they have and how these problems should be solved. It then focuses on how some domestic and international scholars have solved these problems. This paper focuses on studying and analysing the strengths and weaknesses of the RFA, and makes relevant improvements to address the two weaknesses of the RFA. In order to solve the problems of the RFA in the field of feature selection and the field of unbalanced classification, the optimization is improved respectively, and the parallelized design of the optimized algorithm is finally implemented on Spark.

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