Artificial Neural Networks and the Mass Appraisal of Real Estate

Zhou, Gang; Ji, Yicheng; Chen, Xiding; Zhang, Fangfang

doi:10.3991/ijoe.v14i03.8420

Cited by 35 publications

(27 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The writers considered exogenous variables and circumstances, such as neighbouring structures and the surrounding area of a home. Zhou et al [22] [25] attempted to develop a forecasting model for real estate evaluation prices using ANN regression and compared the model to ANN and multiple regressions. The study is essential since it tried to forecast a time series index for the African real estate industry using a macroeconomic indicator and machine learning techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

Ge¹,

Ishaku²,

Lewu³

2021

JCC

View full text Add to dashboard Cite

To transition from conventional to intelligent real estate, the real estate industry must enhance its embrace of disruptive technology. Even though the real estate auction market has grown in importance in the financial, economic, and investment sectors, few artificial intelligence-based research has tried to predict the auction values of real estate in the past. According to the objectives of this research, artificial intelligence and statistical methods will be used to create forecasting models for real estate auction prices. A multiple regression model and an artificial neural network are used in conjunction with one another to build the forecasting models. For the empirical study, the study utilizes data from Ghana apartment auctions from 2016 to 2020 to anticipate auction prices and evaluate the forecasting accuracy of the various models available at the time. Compared to the conventional Multiple Regression Analysis, using artificial intelligence systems for real estate appraisal is becoming a more viable option (MRA). The Artificial Neural network model exhibits the most outstanding performance, and efficient zonal segmentation based on the auction evaluation price enhances the model's prediction accuracy even more. There is a statistically significant difference between the two models when it comes to forecasting the values of real estate auctions.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

Ge¹,

Ishaku²,

Lewu³

2021

JCC

View full text Add to dashboard Cite

show abstract

“…Mass appraisal, also called automatic valuation of real estate assets, is the introduction of mathematical statistics, computer technology, and geographic information technology to establish a mathematical model that serves as a systematic appraisal of a group of real estate properties and reveals its market value (Zhou, Ji, Chen, & Zhang, 2018). The Basel II Accord, issued by the Basel Committee on Banking Supervision (BCBS) in 2008, states that "the bank is expected to monitor the value of the collateral on a frequent basis and at a minimum once every year.…”

Section: Introductionmentioning

confidence: 99%

A House Price Valuation Based on the Random Forest Approach: The Mass Appraisal of Residential Property in South Korea

Hong

Choi

Kim

2020

International Journal of Strategic Property Management

113

View full text Add to dashboard Cite

Mass appraisal is the standardized procedure of valuing a large number of properties at the same time and is commonly used to compute real estate tax. While a hedonic pricing model based on the ordinary least squares (OLS) linear regression has been employed as the traditional method in this process, the stability and accuracy of the model remain questionable. This paper investigates the features of a house price predictor based on the Random Forest (RF) method by comparing it with that of a conventional hedonic pricing model. We used apartment transaction data from the period of 2006 to 2017 in the district of Gangnam, one of the most developed areas in South Korea. Using a data set covering 40% of all transactions in the sample area, we demonstrate that the accuracy of a machine learning-based predictor can be surprisingly high. The average of percentage deviations between the predicted and the actual market price was found to be only around 5.5% in the RF predictor, whereas it was almost 20% in the OLS-based predictor. With the RF predictor, the probability of the predicted price being within 5% of its actual market price was 72%, while only about 17.5% of the regression-based predictions fell within the same range. These results show that, in the practice of mass appraisal, the RF method may be a useful complement to the hedonic models, as it more adequately captures the complexity or non-linearity of actual housing markets.

show abstract

“…The application of ANN in the mass appraisal of properties has improved the credibility and efficiency of the evaluation system [55]. Unarguably, this area remains a fertile ground for more research activities as revealed by [56].…”

Section: Mass Appraisalmentioning

confidence: 99%

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

Nkolika¹

2020

IJATCSE

View full text Add to dashboard Cite

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.

show abstract

Artificial Neural Networks and the Mass Appraisal of Real Estate

Cited by 35 publications

References 24 publications

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

Research on the Effect of Artificial Intelligence Real Estate Forecasting Using Multiple Regression Analysis and Artificial Neural Network: A Case Study of Ghana

A House Price Valuation Based on the Random Forest Approach: The Mass Appraisal of Residential Property in South Korea

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

Contact Info

Product

Resources

About