Empirical analysis of regression techniques by house price and salary prediction

Bansal, U; Narang, Adhyyan; Sachdeva, Akshay; Kashyap, Indu; Panda, S P

doi:10.1088/1757-899x/1022/1/012110

Cited by 11 publications

(9 citation statements)

References 7 publications

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“…The above findings are consistent with economic expectations because an increase in the average gross salary increases purchasing power and the potential rise in residential property prices, whereas a surge in the interest rate on housing loans has the opposite effect. This conclusion is consistent with previously reported findings from other pertinent study in this area (Bansal, Narang, Sachdeva, Kashyap, & Panda, 2021), which revealed a significant relationship between changes in real estate prices and salaries. Serbia DOMex is statistically significant and influences the movement of the mean assessed residential real estate value per square meter in Serbia, according to regression statistics (Table 4).…”

Section: Gapsupporting

confidence: 94%

Residential real estate analysis in Serbia

Martin¹

2023

Anali Ekon fak Subotica

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The analysis of Serbia's residential real estate market is the main goal of this paper. Price movements in that part of the market affect price and financial stability equally, which are thus the main goals of most central banks. Prior to the highly contagious COVID-19 pandemic, there was a gradual increase in the number of transactions involving real estate and prices, with oscillations observed throughout the second quarter of 2020. In this paper, we will present the available databases from the Serbian residential real estate market, as well as regulations that have been in place since the 2000s. By analyzing the trajectory in the long run of the housing credit share to GDP by using a Hodrick-Prescott one-sided filter with the parameter set to 400,000 and correlation and regression analysis, the paper's concluding part will determine whether there is a price bubble in this market segment. According to the analysis, there is currently no price bubble in Serbia's residential real estate market.

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Section: Gapsupporting

confidence: 94%

Residential real estate analysis in Serbia

Martin¹

2023

Anali Ekon fak Subotica

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“…Martin et al analyzed machine learning methods to predict salary of employees that works in the field of information technology in Spain and they showed that the best accuracy, which is 84%, was obtained with decision tree algorithm [11]. Bansal et al compared simple linear regression and multiple linear regression to predict personnel salary and they showed that multiple linear regression was obtained better accuracy results [12]. Das et al designed a system to estimate personnel salary after specific time and they shared the visual results of that system [13].…”

Section: Literature Reviewmentioning

confidence: 99%

SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease

Görmez

Arslan

Sari³

et al. 2022

Advances in Artificial Intelligence Research

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Number of employees are increases with growing in companies. Firms basically make salary increases for their employees in order not to lose their talents and moreover to increase them. Although there is not much problem in how to in-crease the salary in small organizations, this process should be carried out carefully in terms of many parameters in large organizations and should not result in negativities that may disrupt employee motivation. For companies with a large number of employees, creating a model in which the market conditions are determined correctly and all economic parameters are taken into account reveals the need for a process that needs to be worked on for months. In this context, a machine learning-based salary increase prediction system was designed with the study. Specific attributes were determined and a specific scale was developed for performance score for this study.

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“…Bansal et al [2] study the comparison of two regression models in terms of performance. The two models chosen are multiple linear regression and linear regression with two specific scenarios in mind.…”

Section: Related Workmentioning

confidence: 99%

A Case Study Using Machine Learning Techniques for Prediction of House Prices in WP, Malaysia

Bau¹,

Hisham²

2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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The research for this study is to find out performance scores from machine learning techniques in predicting house prices as compared to its actual prices. One of the limitations of existing techniques is predictive models generated do not cover the situation within Malaysia. Therefore, a data collection of Malaysia within Wilayah Persekutuan state, predictive models and data visualizations have been implemented for this case study. The machine learning techniques chosen for the predictive models are multiple linear regression, Bayesian ridge regression, decision tree regression and random forest regression. The house dataset gathered within Wilayah Persekutuan state is from propertyguru.com, a popular property website available in Malaysia. The result concludes that among all the four machine learning techniques chosen, random forest yields the best performance score with a small margin of pricing errors.

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Empirical analysis of regression techniques by house price and salary prediction

Cited by 11 publications

References 7 publications

Residential real estate analysis in Serbia

Residential real estate analysis in Serbia

SALDA-ML: Machine Learning Based System Design to Predict Salary In-crease

A Case Study Using Machine Learning Techniques for Prediction of House Prices in WP, Malaysia

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