Open AccessPredicting the turning points of housing prices by combining the financial model with genetic algorithm
Author(s) -
Shihai Dong,
Yandong Wang,
Yanyan Gu,
Shiwei Shao,
Hui Liu,
Shanmei Wu,
Mengmeng Li
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0232478
Subject(s) - genetic algorithm , real estate , univariate , population , computer science , simulated annealing , economics , algorithm , econometrics , genetic model , multivariate statistics , finance , machine learning , demography , sociology , biochemistry , chemistry , gene
The turning points of housing prices play a significant role in the real estate market and economy. However, because multiple factors impact the market, the prediction of the turning points of housing prices faces significant challenges. To solve this problem, in this study, a historical data-based model that incorporates a multi-population genetic algorithm with elitism into the log-periodic power law model is proposed. This model overcomes the weaknesses of multivariate and univariate methods that it does not require any external factors while achieving excellent interpretations. We applied the model to the case study collected from housing prices in Wuhan, China, from December 2016 to October 2018. To verify its reliability, we compared the results of the proposed model to those of the log-periodic power law model optimized by the standard genetic algorithm and simulated annealing, the results of which indicate that the proposed model performs best in terms of prediction. Efficiently predicting and analyzing the housing prices will help the government promulgate effective policies for regulating the real estate market and protect home buyers.