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

Abstract

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.

Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina

“…Leveraging insights from the literature review, we found it particularly compelling that the ANN model showcased the highest R 2 value, indicating the strongest fit among the array of models tested. This empirical observation is substantiated by previous work from scholars such as Mc Cluskey et al (2012), Renigier-Biłozor and Wi sniewski (2013) and Ge et al (2021), who extolled the virtues of ANN models particularly when grappling with large and intricate datasets. Thus, our findings echo the sentiment prevalent in the literature, underscoring the potential for machine learning techniques, such as ANN, to drive breakthroughs in modeling complexity.…”

“…The potential of ANNs in real estate appraisal has been extensively investigated in various studies (McCluskey and Borst, 1997; Nguyen and Cripps, 2001; Limsombunchai et al ., 2004; McCluskey et al ., 2013; Nguyen et al ., 2015; Abidoye and Chan, 2016; Demetriou, 2017; Abidoye and Chan, 2018; Yacim and Bosho, 2018a, b; Zhou et al ., 2018; Wang and Li, 2019; Horvath et al ., 2021). Among these studies, it was observed that ANNs demonstrated superior performance compared to MRA when utilizing moderate to large data sample sizes (Nguyen and Cripps, 2001; Ge et al ., 2021).…”

“…Contrary to earlier studies conducted by James and Lam (1996) and Rossini (1997), the research indicates that ANNs outperform MRA when a moderate to large dataset is utilized (Mc Cluskey et al ., 2012; Renigier-Biłozor and Wiśniewski, 2013a, b; Ge et al ., 2021). These results provide a plausible explanation for the varying outcomes observed in previous comparisons of MRA and ANN in predicting housing values.…”

Prediction of Anti-Inflammatory Activity of a Series of Pyrimidine Derivatives, by Multiple Linear Regression and Artificial Neural Networks

“…An artificial neural network (ANN) is a biologically inspired computer algorithm designed to work in the same way as the human brain processes information [37]. It consists of a number of processing elements (or cells) which represent artificial neurons.…”

Evaluation Model of Mathematics Teaching Quality Based on Recurrent Neural Network

“…Evaluation Index Data Fusion. Through the feature extraction method of association rules, the association rules are extracted according to the evaluation index data of mathematics teaching quality [22], and the phase space distribution W of big data is established, which can be regarded as a judgment matrix n × m. ρ q and Pðn i Þ are used to represent the characteristic distribution vector and probability distribution function, respectively, so as to realize the effective fusion of evaluation index data of mathematics teaching quality [23,24]. When constructing the regression analysis model of data, it is necessary to make statistical analysis on the data related to the evaluation index of mathematics teaching quality under the method of regression analysis and clarify the data association rules.…”