While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.

While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.

Main variables of GA-PSO-BP neural network model and their meanings.

The United States wall coverings market is projected to exhibit a CAGR of 3.41% during the forecast period of 2025-2033, reaching a value of $XX million by 2033. The growth of the market is attributed to factors such as the increasing demand for aesthetic and functional wall coverings, rising construction activities, and the growing popularity of home renovations and decor. Wallpaper is a popular segment in the market, accounting for a significant share of the revenue, driven by its aesthetic appeal and variety of designs. The residential sector is a major end-user of wall coverings, as homeowners seek to enhance the interiors of their homes. Key trends in the market include the increasing adoption of sustainable and eco-friendly wall coverings, the emergence of smart wall coverings with interactive and functional features, and the growing popularity of online sales channels. Major companies in the market include Crossville Inc, The Valspar Company, Len-Tex Corporation, Brewster Home Fashion, Ahlstrom-Munksjo Oyj, Rust-Oleum Corporation, Sherwin-Williams Company, Johns Manville Corporation, York Wall Coverings, Benjamin Moore & Co, Georgia-Pacific, F Schumacher, Koroseal Wall Protection, Mohawk Industries Inc, and Wallquest Inc., among others. Recent developments include: April 2022 - Georgia-Pacific lumber business is collaborating with leaders in the mass timber industry on a new office building in Atlanta's famous Ponce City Market development to support the construction of a four-story mass timber loft office building. The building will have 90,000 square feet of office space and 23,000 square feet of ground-level retail space, and it will be LEED-certified and run on net-zero carbon., May 2021 - Crossville Brings Large Format Tile Panels to the United States Market - "Laminam by Crossville" Launches Nationwide Through Exclusive Distribution Agreement with Laminam. Under the agreement, Crossville is the sole source for Laminam's 3+ products-innovative, 3mm-thick surfacing tiles-throughout the United States. The distribution agreement is effective immediately, with product availability.. Key drivers for this market are: Rebounding Residential Construction Activity, Recovery in Wall Panel Sales Aided by Higher Awareness; Increasing Demand for Digitally Printed Solutions; Growth in Non-woven and Paper-based Wallpapers. Potential restraints include: Strong Competition from the Paints Segment, Recent Changes in Macro-environment Expected to Impact Customer Spending. Notable trends are: Rebounding Residential Construction Activity in the United States is Boosting the Market.