Predicting property price index using artificial intelligence techniques

Abidoye, Rotimi Boluwatife; Chan, Albert P.C.; Abidoye, Funmilayo Adenike; Oshodi, Olalekan Shamsideen

doi:10.1108/ijhma-11-2018-0095

Cited by 45 publications

(25 citation statements)

References 82 publications

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“…It always depends on the nature of the data available. For example, in their comparison with ANNs, some authors identify them as more effective (Lam et al, 2009), others as less effective (Abidoye et al, 2019).…”

Section: General Literature Reviewmentioning

confidence: 99%

Who performs better? AVMs vs hedonic models

Valier

2020

JPIF

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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine learning models are more accurate than traditional regression analysis in their ability to predict value. Nevertheless, many authors point out as their limit their black box nature and their poor inferential abilities.Practical implicationsAVMs machine learning offers a huge advantage for all real estate operators who know and can use them. Their use in public policy or litigation can be critical.Originality/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.

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Section: General Literature Reviewmentioning

confidence: 99%

Who performs better? AVMs vs hedonic models

Valier

2020

JPIF

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“…The authors Rohmah et al [14] observed that the Gaussian-RBF outperformed other kernels and found it more suitable for forecasting the CPI . In forecasting Hong Kong's property price index, the authors Abidoye et al [10] contrasted the ARIMA methodology to two well-known AI approaches: (i) SVM and (ii) Artificial Neural Network (ANN) [10]. They employed the "backpropagation multilayer perceptron ensemble" algorithm to train the ANN.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

Das¹,

Chakrabarti²

2021

IJATEE

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Forecasting predicts/ estimates the future by taking into account historical data. It's an instrument of decision-making that assists companies/ enterprises/ institutions in managing uncertainties. It allows the business to set up goals and create a budget. Forecasting helps us to anticipate changes and therefore guide us towards data-driven strategies and reasoned decisions/ choices. It serves as a proactive rather than a reactive measure. There are several forecasting approaches for the univariate time series (TS) data, e.g. *Author for correspondence Auto-Regressive Integrated Moving Average (ARIMA) [1-3], exponential smoothing (ES) [4-6], Support Vector Regression (SVR) [7, 8, 9], neural approaches [10-12]. The recent studies show that the researchers employed these approaches for forecasting various TS data, e.g., stock prices [1, 2, 13], prices of agricultural products [4, 5, 6], price index [10, 14].India's chemical industry is highly diverse, covers a wide range of products, and has a large import and export base. More than two million people are working here. On the economic growth of India, this sector's contribution is notable. In the future, there exists a significant opportunity for the industry's business/ revenue growth.

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“…Overfitting occurs when the model, over-trained on the training set data, is unable to predict effectively when it is provided with the testing set data. Some techniques can be used to prevent the phenomenon, the best known is cross-validation [ 29 , 30 ].…”

Section: Frameworkmentioning

confidence: 99%

The Cross Validation in Automated Valuation Models: A Proposal for Use

Valier

2020

Lecture Notes in Computer Science

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The appraisal of large amounts of properties is often entrusted to Automated Valuation Models (AVM). At one time, only econometric models were used for this purpose. More recently, also machine learning models are used in mass appraisal techniques. The literature has devoted much attention to assessing the performance capabilities of these models. Verification tests first train a model on a training set, then measure the prediction error of the model on a set of data not met before: the testing set. The prediction error is measured with an accuracy indicator. However, verification on the testing set alone may be insufficient to describe the model’s performance. In addition, it may not detect the existence of model bias such as overfitting. This research proposes the use of cross validation to provide a more complete and effective evaluation of models. Ten-fold cross validation is used within 5 models (linear regression, regression tree, random forest, nearest neighbors, multilayer perception) in the assessment of 1,400 properties in the city of Turin. The results obtained during validation provide additional information for the evaluation of the models. This information cannot be provided by the accuracy measurement when considered alone.

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Predicting property price index using artificial intelligence techniques

Cited by 45 publications

References 82 publications

Who performs better? AVMs vs hedonic models

Who performs better? AVMs vs hedonic models

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

The Cross Validation in Automated Valuation Models: A Proposal for Use

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