scispace - formally typeset
Open AccessJournal ArticleDOI

Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence

Reads0
Chats0
TLDR
The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.
Abstract
The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.

read more

Content maybe subject to copyright    Report

Citations
More filters

Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils

TL;DR: It was found that the constructed RBF exhibited a high performance than MLP, ANFIS and MR for predicting S%.
Journal ArticleDOI

House price forecasting with neural networks

TL;DR: The usefulness of the machine learning approach to the house price forecasting problem in the Chinese market is demonstrated and could be used on a standalone basis or combined with fundamental forecasting in forming perspectives of house price trends and conducting policy analysis.
Journal ArticleDOI

Intelligent Backpropagation Networks with Bayesian Regularization for Mathematical Models of Environmental Economic Systems

TL;DR: In this investigation, AI-based intelligent backpropagation networks of Bayesian regularization (IBNs-BR) were exploited for the numerical treatment of mathematical models representing environmental economic systems (EESs) in the form of differential models representing their fundamental compartments or indicators for economic and environmental parameters.
Journal ArticleDOI

Second-hand house price index forecasting with neural networks

TL;DR: In this article, the authors proposed a method to forecast the price of a house in China by examining the characteristics of the house market in the past decade, and they applied this method to the current house market.
Journal ArticleDOI

Network analysis of housing price comovements of a hundred chinese cities

TL;DR: In this article , the authors focused on monthly housing prices of 99 major cities in China for the years 2010-2019 by using correlation-based hierarchical analysis and synchronisation analysis, through which one could determine interactions and interdependence among the prices, heterogeneous patterns in price synchronisations and their changing paths over time.
References
More filters
Proceedings ArticleDOI

On genetic algorithms

TL;DR: C Culling is near optimal for this problem, highly noise tolerant, and the best known a~~roach in some regimes, and some new large deviation bounds on this submartingale enable us to determine the running time of the algorithm.
Book

Classical and modern regression with applications

TL;DR: In this article, the authors focus on concepts with a blend between illustrations using real data sets and mathematical and conceptual development and emphasize applications with examples that illustrate nearly all the techniques discussed, including simultaneous influence, maximum likelihood estimation of parameters, and the plotting of residuals.
Journal ArticleDOI

Forecasting with artificial neural networks: the state of the art

TL;DR: In this paper, the authors present a state-of-the-art survey of ANN applications in forecasting and provide a synthesis of published research in this area, insights on ANN modeling issues, and future research directions.
Journal ArticleDOI

Genetic Algorithms and Machine Learning

TL;DR: There is no a priori reason why machine learning must borrow from nature, but many machine learning systems now borrow heavily from current thinking in cognitive science, and rekindled interest in neural networks and connectionism is evidence of serious mechanistic and philosophical currents running through the field.
Journal ArticleDOI

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

TL;DR: This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences.
Related Papers (5)