Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process

Hamzaoui, Youness El; Perez, Jose Alfredo Hernandez

doi:10.1109/micai.2011.14

Cited by 21 publications

(12 citation statements)

References 10 publications

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“…The number of variables highly depends on the completeness of data. The model architecture plays an important role in any ANN design: in different approaches, different architectures have been investigated such as 8-13-1 (Morano, Tajani & Torre, 2015), 40-10-1 (Ahmed, Rahman & Sabirah, 2014), and 6-6-1 (Hamzaoui & Perez, 2011) (where a-b-c represents numbers of inputs, hidden layers, and outputs, respectively). The more numbers of hidden layers the ANN architecture has, the more complex it is.…”

Section: Introductionmentioning

confidence: 99%

Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Kalliola

Kapočiūtė-Dzikienė

Damaševičius

2021

PeerJ Computer Science

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Accurate price evaluation of real estate is beneficial for many parties involved in real estate business such as real estate companies, property owners, investors, banks, and financial institutes. Artificial Neural Networks (ANNs) have shown promising results in real estate price evaluation. However, the performance of ANNs greatly depends upon the settings of their hyperparameters. In this paper, we apply and optimize an ANN model for real estate price prediction in Helsinki, Finland. Optimization of the model is performed by fine-tuning hyper-parameters (such as activation functions, optimization algorithms, etc.) of the ANN architecture for higher accuracy using the Bayesian optimization algorithm. The results are evaluated using a variety of metrics (RMSE, MAE, R2) as well as illustrated graphically. The empirical analysis of the results shows that model optimization improved the performance on all metrics (reaching the relative mean error of 8.3%).

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Section: Introductionmentioning

confidence: 99%

Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Kalliola

Kapočiūtė-Dzikienė

Damaševičius

2021

PeerJ Computer Science

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“…The prediction has to be accurate and a pointer to the success or failure of real estate [68]. High positive correlation coefficient is desirable to indicate that the prediction is accurate [69]. Different residential real property variables can be used [70].…”

Section: Price Prediction and Forecasting Of Real Estatementioning

confidence: 99%

Review on the Application of Artificial Neural Networks in Real Estate Valuation

Nkolika¹

2020

IJATCSE

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Real estate appraisal is needed in assessment of the value of properties and contribute the regional economy of any country. Real estate valuation is thus an important subject, which has to be studied carefully as there are many individual subjective criteria, which often results in variations, which makes the traditional valuation methods to be inadequate. This work aimed at coping with the complexity of real estate valuation, by putting forth the advantages of artificial neural network, one of the novel model of real estate appraisal. The review of literature has helped to establish that the neural network model is better than the traditional model previously. This work opens up a new and better understanding to the principle of real estate evaluation.

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“…This means that in the field of real estate valuation, this capability can be very useful in complex systems found in this field where motivations, tastes and budget availability often do not follow rational behaviors. The ANN has demonstrated its robustness as a real estate valuation model comparing to the hedonic models in many cases [35], [48], [55], [71], [74]. Moreover, due to its theory of universal approximation, the ANN is capable of fitting any continuous function, allowing them to capture complex trends, and working with extrapolated data [14].…”

Section: Introductionmentioning

confidence: 99%

A Real Estate Valuation Model Using Boosted Feature Selection

et al. 2021

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To estimate real estate values, a complex valuation model based on artificial neural network (ANN) has been established as a successful means in modern machine learning research, specifically when high-dimensional data are available. Unfortunately, the real estate data in many locations, such as Thailand, are quite limited in terms of features. Hence, it becomes mandatory to reduce the complexity using feature selection techniques. These techniques aim to improve performance by identifying significant factors and help decrease the computational overload and model construction. However, due to the lack of explicability and interpretability in ANNs, the analysis of input factors cannot be explained directly by model composition. In this research, we apply a combination of a boosting strategy and input sensitivity analysis in an improved Garson's algorithm to perform feature selection that can adjust its selection criteria through each iteration on an ANN model. This proposed technique is then compared with other traditional feature selection techniques using synthetic data and real-world house valuation data. The results show that our model can maintain the sensitivity coefficient for every informative feature. The technique of this study provides a set of features that influences the house price and implies the character of each specific area. It also contributes to statistical improvement in most valuation cases, with a lower error than that obtained without a selected feature dataset, and it placed in the top 24% of the public leaderboard in the Zillow Prize competition.

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Application of Artificial Neural Networks to Predict the Selling Price in the Real Estate Valuation Process

Cited by 21 publications

References 10 publications

Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Neural network hyperparameter optimization for prediction of real estate prices in Helsinki

Review on the Application of Artificial Neural Networks in Real Estate Valuation

A Real Estate Valuation Model Using Boosted Feature Selection

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