From intelligent site inspections and smart blueprint comparison to land‑acquisition analysis, supply‑chain management, and service optimization for homebuyers, artificial intelligence is steadily integrating into every facet of the real estate and construction industries. As large‑scale models, intelligent agents, and big‑data technologies continue to be deployed, the sector is undergoing transformative changes in areas such as smart construction, development decision‑making, and intelligent services, driving the industry forward in a steady, high‑quality transition toward digital and intelligent operations.

　　 Systematic layout

　　AI‑powered construction management has now become a standard feature in projects undertaken by leading companies worldwide. Cameras continuously capture operational details and transmit them in real time to an intelligent management platform, where AI agents can instantly identify safety hazards, dynamically track construction progress, and assess project quality. For a large-scale project with a floor area of 300,000 square meters, the traditional approach would require manual comparison of over 13,000 drawings and verification of 38,000 construction discrepancies; by contrast, leveraging AI‑driven comparison technology boosts overall operational efficiency by more than 50% while significantly enhancing verification accuracy.

　　At present, AI applications in the real estate and construction‑materials sectors have evolved from isolated pilot projects to comprehensive, system‑wide deployments. Companies across various segments are leveraging their unique strengths to identify operational pain points and refine industry‑specific use cases, thereby providing replicable best practices for the sector’s broader drive toward intelligent transformation.

　　In the construction sector, China State Construction International has been deeply engaged in the intelligent transformation of the entire construction process. “Over the past year, we successfully piloted 52 engineering application scenarios and developed 912 in-house intelligent agents,” said Li Yanfei, General Manager of the Information Management Department at China State Construction International Group Co., Ltd. From drafting project bid proposals and generating design plans with precise parameters to identifying safety hazards, supporting project management, and verifying compliance details, the average time required to prepare each proposal has now been reduced to 60 minutes, boosting overall efficiency by 75%. To date, the company has accumulated a high-quality database of more than 4,000 project plans.

　　In the field of front-end development, Beihaojia, a brand under KE Holdings, is leveraging data to reshape real estate development workflows. “As the housing market shifts to a buyer’s market, the core challenge in residential development is evolving from ‘do we have homes to sell?’ to ‘can we better understand customers, define products, set pricing, and drive sales?’” a Beihaojia spokesperson told reporters. According to reports, Beihaojia centers on a C2M (Customer-to-Manufacturer) model, harnessing integrated analytics across multiple data sources—including new‑home and resale‑home listings, customer behavior, and service‑delivery processes—to apply AI and advanced data capabilities to stages such as land‑acquisition assessment, product positioning, planning and design, cost allocation, and proactive marketing. Its lead‑generation analysis model can identify the size and preferences of potential customers within target market segments, while its unit‑type mix model evaluates the balanced sales velocity across different floor‑plan categories by combining historical transaction data with customer‑behavior insights.

　　In the building materials industry, Dongfang Yuhong has established a full‑chain AI‑driven industrial application model, vigorously advancing the deep integration of AI with its core business. In engineering sales scenarios, the “Sales AI Assistant” has reduced the accounts‑receivable reconciliation cycle from 4.98 days to just 1.04 hours, while the “Su Shen Xing” intelligent approval system has cut the processing time for repetitive project approvals from 4.43 days to under 15 minutes. At the retail end, an AI‑powered quality‑inspection system now covers more than 300,000 partner stores nationwide, achieving accuracy rates exceeding 96% in product identification and defect detection.

　　 Ecological Development

　　Industry experts believe that AI applications in the real estate and construction sectors are currently undergoing a critical phase of evolution, transitioning from initial point‑based tool‑like implementations—such as blueprint comparison and safety inspections—to a more advanced, ecosystem‑driven stage characterized by end-to-end data integration and coordinated collaboration among multiple intelligent agents.

　　Experts emphasize that digitizing individual processes is only the foundation; achieving a qualitative leap in workflows requires large-scale data collection and end-to-end digitalization. By leveraging multi-agent collaborative operations, enterprises can break down data silos across design, construction, supply chain, marketing, and operations, driving cost reductions and efficiency gains while continuously strengthening core competitiveness and building distinctive competitive advantages.

　　As the world’s largest market for construction‑industry data and the broadest range of application scenarios, China provides an ideal testing ground for the deployment and iterative advancement of AI technologies. Experts predict that architectural design will undergo transformative breakthroughs: time‑consuming processes such as building and urban planning, which currently take months, could be streamlined to just a few hours with AI support, boosting efficiency by a factor of tens of thousands.

　　Green and low‑carbon transformation has emerged as a key growth area for AI‑driven innovation. According to Academician Ling Wen of the Chinese Academy of Engineering, building‑integrated photovoltaic technology enables solar power generation on all five exterior surfaces of a structure. If deployed at scale in urban renewal and rural development projects, this approach can significantly advance energy efficiency and carbon reduction in buildings, helping to create zero‑carbon and negative‑carbon structures. In practice, China Construction Industry has leveraged the Volta AI energy large model to accurately forecast solar power output and building electricity demand, dynamically optimizing the operation of its solar‑storage‑charging systems and reducing overall project electricity costs by 10%.

　　AI-driven transformation is not only reshaping production and construction models but also redefining the industry’s development logic and market landscape. According to Yan Yuejin, deputy director of the E-House China Research Institute in Shanghai, the core of the real estate sector’s new development paradigm lies in precision in investment decision-making, intelligent project management, personalized customer service, diversified marketing channels, and refined risk control. Meanwhile, Li Yujia, chief researcher at the Guangdong Provincial Housing Policy Research Center, argues that the rise of digital industries such as AI and semiconductors is giving birth to new consumer segments. High-income tech professionals are reconfiguring the luxury housing market, driving a structural “K-shaped” divergence in the property sector. The industry must proactively adapt to these trends by offering smart, high-quality, and differentiated products that precisely meet emerging consumer demands, thereby addressing mismatches between supply and demand.

　　 Steadily advance implementation.

　　AI offers vast potential for upgrading the real estate and construction industries, yet large-scale implementation currently faces multiple practical barriers.

　　From the current state of the industry, most real estate and construction firms face a dual development challenge: general-purpose large models lack domain‑specific corpora and underlying business data for the real estate and construction sectors, resulting in insufficient understanding of core scenarios such as land‑use analysis, project feasibility assessment, engineering operations and maintenance, and property services, and thus poor adaptability. Meanwhile, developing proprietary vertical‑domain large models requires investments on the order of hundreds of millions of yuan and a construction timeline of at least one year—burdens that small and medium‑sized enterprises typically cannot afford. As a result, AI adoption across the industry exhibits an uneven pattern, with leading players taking the lead while smaller firms lag behind.

　　Data shortcomings represent the core bottleneck hindering the full‑scale empowerment of AI. Industry research indicates that over 90% of real estate firms believe that uneven data quality, inconsistent historical data, and pervasive system silos significantly constrain the practical effectiveness of AI models. In the real estate and construction sectors, long value chains, a large number of stakeholders, and complex business scenarios—coupled with decades‑long fragmented IT architectures—have made it difficult to achieve seamless data interoperability and sharing, thereby failing to provide stable, comprehensive, and precise data support for AI model training and intelligent decision‑making.

　　Professor Yao Jianming of Renmin University of China stated that AI applications across various industries are still in the exploratory early stages. When implementing these technologies, it is crucial to avoid blind herd behavior and overemphasis on buzzwords; instead, practitioners should ground their efforts in industry realities, focus on addressing specific business pain points, and advance AI innovation in a steady, well‑planned manner.

　　At the policy level, efforts are steadily being stepped up to ensure the standardized and large-scale development of AI in the industry. In April this year, the Ministry of Industry and Information Technology and the National Data Administration jointly launched the “Model-Data Resonance” initiative, focusing on 20 key sectors within manufacturing to promote the synergistic empowerment of AI models and data resources, with the goal of establishing by year-end a virtuous cycle of mutual reinforcement among data, models, and application scenarios. Meanwhile, provinces such as Jiangsu and Anhui have successively introduced special action plans for “AI + Housing and Urban–Rural Development,” concentrating on critical areas like intelligent design, smart construction, digital operations and maintenance, and smart property management, thereby accelerating the industry’s digital and intelligent transformation.

　　As the policy framework continues to improve, the data infrastructure is steadily strengthened, and technological applications mature, AI will permeate every stage of the real estate value chain—development, construction, operations and maintenance, and green, low‑carbon initiatives—driving the iterative upgrading of traditional industries and paving a new path toward high‑quality growth in the real estate sector.

(Source: Economic Daily; Author: Huang Chunmian, Reporter for China Economic Net)