Analysis and prediction of second-hand house price based on random forest

Zhang, Yan; Huang, Jingru; Zhang, Jiahui; Liu, Shuying; Shorman, Samer

doi:10.2478/amns.2022.1.00052

Cited by 10 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing research has examined the role of ML within mass appraisal [18,[25][26][27][28], with early arguments critiquing the 'black-box' nature of the model outputs, and despite improvements in the reporting of 'importance' plots providing information for assessors [35][36][37][38] remains challenging for wholesale uptake within mass appraisal practice given the complexity and repeatability of these types of algorithms. This study shows that the application of Regularized regression incorporating spatial filters is a more obvious choice for the assessment community, and for taxpayers, as the ESF approach provides a foundation for including location providing market professionals and policymakers with a more readily and understandable methodology for applying spatial analysis in a more standardised and explainable hedonic framework for understanding housing markets and for applications seeking to harness such understanding, such as automated valuation modelling for mortgage lending, or mass appraisal of residential values for property taxation purposes.…”

Section: Discussionmentioning

confidence: 99%

“…However, these early forms of ML generated some debate with respect to their predictive capacity [20][21][22] and wider adoption as a consequence of their initial "black box" data-driven nature [10,23] culminating in reduced transparency and opaqueness, both of which are fundamental for defensibility and explainability, particularly within mass appraisal [24,25]. More recent ML approaches have become more prominent due to the increasing availability of open source software packages, codes, digitization and the ability to unearth new pattern recognition which have shown better out-of-sample predictions and valuation accuracy [26][27][28][29][30][31][32][33]. Equally, the "black box" aspect of ML has become less opaque with the augmentation and visibility of (normalized) importance weightings which provide a basis for understanding value significant effects [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

McCord

Davis

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approaches and machine learning (ML) algorithms have been shown to help improve house price prediction and mass appraisal assessment. Nonetheless, the adoption a of ML within mass appraisal has been protracted and subject to scrutiny by assessment jurisdictions due to their failure to account for spatial autocorrelation and limited practicality in terms of value significant estimates needed for tribunal defense and explainability. Existing research comparing traditional regression approaches has tended to examine unsupervised ML methods such as Random Forest (RF) models which remain more esoteric and less transparent in producing value significant estimates necessary for mass appraisal explainability and defense. Therefore, the purpose of this study is to apply the supervised Regularized regression technique which offers a more transparent alternative, and integrate this with a more nuanced geo-statistical technique, the Eigenvector Spatial Filter (ESF) approach, to more accurately account for spatial autocorrelation and enhance prediction accuracy whilst improving explainability needed for mass appraisal exercises. By undertaking such an approach, the research demonstrates the application of this method can be easily adopted for property tax jurisdictions in a framework which is more interpretable, transparent and useable within mass appraisal given its simple and appealing approach. The findings reveal that the integration of the ESFs improves model explainability, prediction accuracy and spatial residual error compared to baseline classical regression and Elastic-net regularized regression architectures, whilst offering the necessary ‘front-facing’ and flexible structure for in-sample and out-of-sample assessment needed by the assessment community for valuing the unsold housing stock. In terms of policy and practice, the study demonstrates some important considerations for mass appraisal tax assessment and for the improvement of taxation assessment and the alleviation of horizontal and vertical inequity.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

McCord

Davis

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The regression analysis of the proportion of the floating population shows that it is negative. The floating population significantly negatively impacts economic growth [12]. There is an positive relationship between the industrial difference and the degree of aggregation of private companies.…”

Section: Research On Multiple Linear Regressionmentioning

confidence: 99%

The Development of Enterprise Management Innovation in the Digital Economy Era Based on the Schumpeter Model

Zhang

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Based on the mechanism of “creation and destruction,” this paper constructs a Schumpeterian economic model of multi-industry dynamic, stochastic population equilibrium, which includes the development elements of digital technology. Then this paper makes an empirical study on the application of digital technology in real industry and finance. The research shows that the integration of digital technology and the manufacturing industry will significantly promote industrial structure optimization. This model enables rapid transformation of the digital economy. Reducing capital restrictions in the era of big data finance has announced the development of the high-tech industry. It has promoted the transformation and upgrading of the industry. This topic provides particular reference significance for discussing digital technology’s application and innovation development in the real economy.

show abstract

“…In accounting, improving the quality of accounting information requires the efficient use of infrastructure consolidation and integration can achieve optimization of accounting accounts in terms of quantity and level [5][6][7]. To achieve the optimization requirements, it is necessary to optimize the system settings with the help of digital algorithmic means and to seek breakthroughs in the project data settings [8][9][10][11][12]. By setting the display information and control information of the project, information on the source channel of funds, nature of funds, budget categories, etc, is obtained [13].…”

Section: Introductionmentioning

confidence: 99%

“…Rui Mi. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] …”

mentioning

confidence: 99%

Integration of government accounting system reform and university infrastructure consolidation based on type two fuzzy sets

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Type II fuzzy set can innovate the accounting method of infrastructure consolidation and integration work. Based on this, this paper constructs a new and feasible accounting system for the integration of government accounting systems and university infrastructure. The specific application is that any point on the financial subordination domain and its corresponding subordination interval constitutes a type two fuzzy set, and the fuzzy set is used as the object for modeling. Using the interval type-two fuzzy set for word calculation, the result of the calculation is output by the decoder and sorted, and the value of the accounting account amount can be derived. The mutual subset measure between the input interval type II fuzzy set and the rule antecedent interval type II fuzzy set is used to check the accounting bills of the rule activation interval and to verify the performance of its use in the capital consolidation work of S universities. The experimental results show that after transferring the balance of $4,982,600 for prepaid construction and $4,956,200 for prepaid provisioning to the prepaid account on the basis of ensuring the consistency of the output KM algorithm, the balance of the account becomes $9,938,800. This result shows that the type II fuzzy set saves 42% of the arithmetic cost for this transfer of university infrastructure accounts, and contributes to the realization of the government accounting system reform and the improvement of financial management of the integration of university infrastructure accounts.

show abstract

Analysis and prediction of second-hand house price based on random forest

Cited by 10 publications

References 15 publications

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

Applying the Geostatistical Eigenvector Spatial Filter Approach into Regularized Regression for Improving Prediction Accuracy for Mass Appraisal

The Development of Enterprise Management Innovation in the Digital Economy Era Based on the Schumpeter Model

Integration of government accounting system reform and university infrastructure consolidation based on type two fuzzy sets

Contact Info

Product

Resources

About