Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Ja’afar, Nur Shahirah; Mohamad, Junainah

doi:10.21837/pm.v19i16.953

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…In another work, the property prices in China is modelled using linear regression, SVM regression, neural network regression and the k-nearest neighbour approach and it is shown that the SVM regression has the best performance indicators [18]. Similarly, the housing prices in Malaysia is modelled using random forest, decision tree, lasso regression, ridge regression and linear regression and it is concluded that the random forest approach has the best accuracy [19]. Similarly, the property values in Kota Bharu, Malaysia are modelled using various regression models where it is demonstrated that the rank transformation regression model has better performance compared to the ordinary least squares model [20].…”

Section: Literature Surveymentioning

confidence: 99%

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Tuncsiper

2024

CJMS

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The real estate market in Türkiye has witnessed significant growth and complexity in recent years necessitating advanced modelling techniques for accurate property price predictions. This paper presents a comprehensive study on the application of deep learning networks to model unit property prices across Türkiye, with a specific emphasis on its three major cities: Istanbul, Ankara, and Izmir. The unit property prices in the overall country and its three major cities are taken from the official resources and then the square meter unit property prices in USD are obtained. Then, a deep learning network is developed in Python programming language which accepts the lagged values of the unit property prices as input values. Then, the deep learning network is trained using the datasets separately for Türkiye and the three major cities. The loss curves of the deep learning network show a rapid convergence indicating the suitability of the developed deep learning network for the unit property price modelling. The actual and the modelled data are plotted indicating the accuracy of the developed model. Finally, the performance indicators of the developed deep learning model namely the coefficient of determination, mean absolute error, root mean square error and the mean absolute percentage error are computed verifying the high accuracy with R2 values greater than 0.95 for all four modelling cases. The alternative utilization opportunities of the proposed deep learning model are also discussed in the conclusions section.

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Section: Literature Surveymentioning

confidence: 99%

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Tuncsiper

2024

CJMS

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Uncertainty in Business Valuation for Tax Purposes

Prasetyo¹,

Swasito²,

Safitra³

2021

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An important but often neglected aspect in business valuation for tax purposes is uncertainty. It is known in the literature that changes in economic structure, people’s behavior, business risk, or political leader are expected. Unfortunately, these factors cannot be effectively measured and reported in business valuation for tax purposes. As such, most business valuations for tax purposes are unable to capture uncertainty adequately. Valuation reports under a tax investigation process only present an estimated value in the form of a single value, which is unable to represent uncertainty sufficiently. This paper aims to demonstrate a method to quantify uncertain variables into the business valuation for tax purposes. The research engaged scenario analysis to arrive in three different possible value estimates, and Monte Carlo simulation to take uncertainties into accountto produce a frequency distribution containing all possible values between predetermined limits. Using the range of value produced in the simulation, a taxpayer will have more complete information to decide whether taxpayers submit an undervalue report or not.

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Machine Learning for Property Price Prediction and Price Valuation: A Systematic Literature Review

Ja’afar

Mohamad

Ismail

2021

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Machine learning is a branch of artificial intelligence that allows software applications to be more accurate in its data predicting, as well as to predict current performance and improve for future data. This study reviews published articles with the application of machine learning techniques for price prediction and valuation. Authors seek to explore optimal solutions in predicting the property price indices, that will be beneficial to the policymakers in assessing the overall economic situation. This study also looks into the use of machine learning in property valuation towards identifying the best model in predicting property values based on its characteristics such as location, land size, number of rooms and others. A systematic review was conducted to review previous studies that imposed machine learning as statistical tool in predicting and valuing property prices. Articles on real estate price prediction and price valuation using machine learning techniques were observed using electronics database. The finding shows the increasing use of this method in the real estate field. The most successful machine learning algorithms used is the Random Forest that has better compatibility to the data situation.

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Application of Machine Learning in Analysing Historical and Non-Historical Characteristics of Heritage Pre-War Shophouses

Cited by 5 publications

References 23 publications

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Modelling the Unit Property Prices in Türkiye Employing Deep Learning Methods

Uncertainty in Business Valuation for Tax Purposes

Machine Learning for Property Price Prediction and Price Valuation: A Systematic Literature Review

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