AI‑led Digital Upgrading Gains Ground Across China’s Real Estate and Construction Sectors

Artificial intelligence technologies including large language models and intelligent agents are being deployed throughout real‑estate and construction workflows, covering on‑site inspection, drawing comparison, land‑acquisition assessment, supply‑chain management and home‑buyer‑focussed services. The adoption is reshaping working practices for construction, property development and customer‑oriented operations across the domestic industry.

For major construction projects, AI‑powered site supervision has become standard practice at leading enterprises. Cameras operating round‑the‑clock feed live footage back to central management platforms. Intelligent agents identify safety hazards instantly while calculating construction progress and assessing build quality. For a large‑scale development spanning 300,000 square metres, conventional workflows required staff to manually check more than 13,000 drawings and verify 38,000 construction discrepancies. Automated AI drawing comparison has lifted overall operational efficiency by over 50 per cent and sharpened verification accuracy. Instead of scattered one‑off trials, businesses are rolling out systematic AI deployment, tailoring technical solutions for distinct operational segments.

China State Construction International Holdings Limited has carried out intelligent upgrades across its whole construction workflow. Within the past twelve months, the business ran trials across 52 engineering scenarios and built 912 proprietary business‑oriented intelligent agents. Preparation times for tender documents and technical design outputs have been reduced to an average of 60 minutes per file, delivering an efficiency gain of 75 per cent, while an internal database has accumulated more than 4,000 high‑quality engineering documents.

Founded under KE Holdings, Beihaojia reconstructs property development decisions with data‑driven analysis. Working on the C2M operating framework, it integrates datasets from existing home transactions, new‑property sales, user behaviour records and service logs. AI tools support land appraisal, product positioning, layout planning, cost allocation and early‑stage marketing. Its latent‑customer evaluation model evaluates the scale and preferences of prospective residents within target zones, whilst housing‑mix modelling gauges sales performance for units of varying sizes against historical transaction records.

Oriental Yuhong has built end‑to‑end AI‑driven industrial workflows for building‑materials operations. Its AI assistant for field sales representatives has compressed ledger reconciliation cycles from 4.98 days down to 1.04 hours. The internal “Quick‑Review Star” automated approval platform cuts repetitive project review work from 4.43 days to less than 15 minutes. At retail outlets spread nationwide across more than 300,000 partner stores, an AI visual inspection system runs product quality checks with a detection accuracy exceeding 96 per cent.

Industry‑wide deployment is evolving towards an ecosystem‑oriented pattern. Rather than isolated tools for drawing validation or worksite patrols, multi‑agent collaboration now seeks to break down data silos separating design, on‑site construction, supply‑chain coordination, marketing and facility management. Green and low‑carbon development represents one expanding field for AI applications. China State Construction Far East Holdings applies the Volta AI energy large‑scale model to forecast photovoltaic power generation together with building power consumption profiles. Dynamic tuning of the integrated photovoltaic‑storage‑charging system has brought a 10 per‑cent reduction in overall electricity costs for relevant projects.

Structural shifts are also visible within property consumption patterns. Digital‑sector growth has brought changes to high‑end residential market demand, creating divergent trends across housing segments. Industry participants adjust product planning with smarter, higher‑quality and differentiated housing offerings to align with shifting consumer requirements.

Significant practical barriers still slow the large‑scale roll‑out of AI solutions across the sector. Generic large‑scale models lack specialised datasets tailored for property and construction use‑cases. Training bespoke vertical‑industry AI systems demands heavy capital outlay of several hundred million yuan alongside development cycles lasting no less than twelve months, placing such investment out of reach for most small‑and‑medium‑sized firms. An uneven adoption pattern persists, with leading corporations advancing rapidly while smaller market participants lag behind. Surveys of property‑sector operators indicate that more than 90 per cent of surveyed businesses identify inconsistent historical records, fragmented legacy IT architecture and poor cross‑system data sharing as major constraints holding back AI model performance.

Policy frameworks are being put in place to back the industry’s technical transformation. In April, the Ministry of Industry and Information Technology and the National Data Administration jointly launched the “Data‑Model Resonance” initiative targeting twenty priority manufacturing sectors. The programme aims to foster a mutually reinforcing loop linking datasets, AI models and real‑world application scenarios by the end of the calendar year. Regional administrative authorities in Jiangsu, Anhui and other provinces have separately released dedicated action plans for “AI plus housing‑urban‑rural development”, prioritising intelligent design, automated construction, smart facility maintenance and digitalised property management.