Spatial Dependency and Hedonic Housing Regression Model

Oladunni, Timothy; Sharma, Sharad

doi:10.1109/icmla.2016.0097

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…House is profoundly sound among the economic, financial, then political structure regarding every country. Nevertheless, [3] suggested as the fluctuation over residence fees has always been a problem for residence owners, structures yet real estate, without [4] stated as house has end up unaffordable so like is sizeable virtue boom among a number of international locations of the housing sector. Residents' exorcism concerning existence as much properly so national financial system relies upon on the potential residence charge increase.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For gradient boosting models, it makes use of grid search in accordance with fine-tuning their model's hyperparameters. Oladunni et al [3] reduce mistakes in the hedonic housing regression mannequin by means of investigating spatial dependence substitutability regarding submarkets and geospatial attributes. The model is educated using superior subsets of linear regression or regression creeper algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

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Machine learning based prediction of house price

Suganya¹,

Gowtham²,

Raja³

2022

ijhs

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Getting a house of our wishes within our budget in a residential area of our customization is quite a tedious process. In order to overcome this, we have developed a model to get a houses of our interest with religious belief and budget this Implemented model is of linear regression and k nearest neighbor’s algorithm with gradient descent optimization to make an optimal model for predicting house prices using the dataset. Performed feature engineering and selection using lasso and ridge penalties to eliminate features which had little or no impact on the residual sum of squares error. Then exposes Jupyter notebook cells as REST Endpoints to make prediction with new information. Finally, we are trying to send email alert to the concern user to give alert of the house price

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%