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.
Highlights
The mean value of the forecast auction sale price rate obtained from the artificial neural networks (ANN) model (0.8407) is closer to the market auction sale price rate than the mean values obtained from the other models (0.8209)
The research examines how artificial intelligence may aid in our comprehension of real estate market movements
Because artificial intelligence and machine learning techniques may be utilized to gain a competitive edge in real estate markets in various ways, the research focused on one of them—using them as a valuation assistance tool
Summary
Real estate valuation techniques that have been around for a while, such as a cost and a sale comparison, are lacking in a standard and certification process [1]. In this way, the availability of a real estate value prediction model helps close a crucial information gap while improving the efficiency of the real estate market [2]
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