"The ultimate value of real-estate marketing AI is not to replace people, but to upgrade agents from 'information movers' to 'housing advisors,' finding the perfect balance between technological efficiency and human warmth."
In 2025, as the real-estate industry undergoes deep adjustment, a set of data is rewriting the rules: through its AI system, Lianjia cut per-store customer-acquisition cost by 70%, raised agents' nighttime service response rate from 15% to 42%, and the transaction rate of listings using AI tools is 4 times that of non-AI listings. Behind these numbers lies a business proposition about "trust and efficiency"—in low-frequency, high-ticket property transactions, what role should AI actually play?
01 The First Principles of Real-Estate Brokerage: The Efficiency of Trust Building
The core contradiction of real-estate services has always revolved around the inherent conflict between "low frequency, high ticket" and "high trust cost." You can decide on a coffee on impulse, but buying a home requires an average 90-day consideration cycle, during which more than 7 rounds of information confirmation are needed. This decision-making pattern traps the industry in three major dilemmas:
1. The Conflict Between Offline Dependence and Efficiency Bottlenecks
Although the digital wave has swept every industry, property transactions still heavily depend on offline experience—80% of customers choose a store for their first visit, yet traditional stores operate inefficiently: each store needs about 30 hours of manual traffic tallying per month, and 40% of nighttime organic traffic is wasted due to lack of staffing.
2. The Contradiction Between Service Standardization and Professional Barriers
Property transactions involve specialized knowledge such as school-district policies, floor-plan analysis, and loan calculations, and the 120,000 agents vary widely in expertise. Under the traditional "old-train-new" model, new agents' viewing-to-deal conversion rate is only 15%, while one manual presentation assessment takes 2 hours per person. Moreover, cities change too fast—even veteran agents cannot remember all community-facility changes within half a year. This information asymmetry directly hurts customer trust—when an agent cannot accurately answer "the advancement rate of the designated school," the deal probability drops.
3. The Disconnect Between Dormant Data and Refined Operations
In Lianjia's system, cameras were previously used only for surveillance, and data such as customer movement paths and dwell time was never analyzed. One Lianjia region accumulated 500,000 viewing records in half a year yet could not extract the pattern that "hard-need customers care more about down-payment policies," causing marketing resources to keep flowing into ineffective channels.
The essence of these dilemmas is the excessively low "efficiency of trust building" under the traditional model. The value of AI technology lies precisely in reconstructing the path through which trust is generated, by digital means.
02 AI in Practice Solving Business Pain Points: The Real-Estate Service's "Three-Dimensional Human-Machine Collaboration Model"
Lianjia's AI marketing instead focuses on three core scenarios—"digitalizing offline scenes, standardizing service capabilities, and intelligentizing customer operations"—using technology to compensate for human shortfalls. This pragmatic orientation enabled it to still achieve an impressive RMB 2.25 trillion in existing-home GTV in 2024, a year-on-year increase of 11%, even during the market downturn.
1. Spatial Digitalization: Smart Stores Rebuild Offline Customer-Acquisition Scenarios
Industry-specific logic: Trust building in property transactions relies heavily on offline experience, so AI must become a "digital extension of offline scenes" rather than a replacement. In the first half of 2025, China Telecom Shanghai built a smart-store solution for Lianjia on its Zhiyun network, transforming surveillance cameras from "recording tools" into "intelligent sensing terminals." By integrating existing and new devices and using video AI, it generates orange-and-green customer-flow heat maps: orange high-activity zones prompt account managers to follow up promptly, while green low-density zones trigger automatic product-info updates. A Pudong store used the heat map to discover that "many customers linger in the children's play area between 3 and 5 p.m.," then arranged targeted "family-friendly unit explanations," raising viewing conversion by 22%.
Even more innovative is the business-linkage model—Lianjia opens stores next to a well-known beverage brand, so customers see property ads while buying drinks and drop in to consult. The AI system precisely tracks the "beverage store → Lianjia store" conversion path via Wi-Fi probes and video analysis, lifting joint-marketing ROI by 40%. On cost control, a lightweight monthly-rent model replaces hardware purchases, and combined with an "edge + central" computing layout, Lianjia needs no extra servers, cutting digital-transformation cost by 70%.
2. Capability Standardization: The AI Coach Reshapes Agents' Growth Path
Industry-specific logic: The professionalism of real-estate service directly determines trust, so AI must become a "never-tiring training mentor," accelerating agents' capability iteration. From January to June 2024, the Smart Score AI assessment system completed 50,000 business-presentation assessments for over 20,000 Lianjia Shanghai agents. Built on Beike's Dreamer large model, it simulates 238 typical customer-question scenarios and scores in real time across dimensions such as "information completeness and policy accuracy." Practice data shows AI assessment cut preparation time by 30% and assessment cost by 95%, and doubled the number of agents scoring above 90 from 1,000 to 2,000.
The listing-maintenance assistant launched by Beijing Lianjia in February 2025 further reinforces this logic. Integrating 120-million-scale property-dictionary data, it provides agents with full-process guidance on "price interpretation, viewing feedback, and promotion broadcasting," raising new agents' viewing conversion from 15% to 28%.
3. Service Intelligence: A Full-Chain Tool Matrix Boosts Conversion Efficiency
Property decisions take up to 90 days and need more than 7 touches, so AI must build a full-chain empowerment system of "precise matching—smart follow-up—fulfillment assurance." Beike's "Laike" system, piloted in 2024 and rolled out fully in 2025, became the efficiency engine: an AI home selector generates a report with floor-plan analysis and price trends in 10 seconds, replacing 40 minutes of manual filtering; an intelligent chat assistant delivers second-level nighttime responses, lifting a Dongguan store's dormant-lead activation rate by 40%. By March 2025, 200,000 agents nationwide had completed 2.59 million services via "Laike," with an 82% match between system recommendations and agents' experienced judgment.
On the front-end acquisition side, the AI home-finding assistant "Pudding" precisely matches listing data by parsing vague needs in natural language; on the back-end fulfillment side, the digital companion "Xiaoyi" automatically follows up on contract-quality checks and errands, saving over 30,000 work hours cumulatively. This "precise front, efficient middle, assured back" tool matrix shortened the customer journey from consultation to signing by 22 days, and Lianjia's existing-home GTV hit a record high in 2024.
The synergy of these three dimensions unleashes human work value: AI handles data-intensive work (e.g., listing matching, policy interpretation) while humans focus on emotion-intensive work (e.g., need insight, risk warning). Data shows that after using AI tools, the share of agents' time spent on deep customer communication rose from 35% to 62%—the key to better service quality. In 2024, agents at top-20 developers averaged only 2.8 viewing groups per person, while Lianjia, with AI, reached 4.1; over the same period the industry's average dormant-listing activation rate was below 15%, whereas Lianjia broke through to 40%, fully proving that AI is not "icing on the cake" but a "survival necessity."
Lianjia's AI practice overturns three industry perceptions; these counter-intuitive findings may be more valuable than the data itself.
1. "Online pre-processing of offline scenes" is more effective than pure online innovation.
While many peers invested tens of millions of yuan building metaverse home-viewing systems, Lianjia insisted on deepening its smart-store retrofit. The data proves this choice right: a competitor's metaverse viewing conversion was only 0.3%, while Lianjia's smart stores achieved a 6.8% in-store conversion. The reason is that trust building in property transactions relies heavily on physical-space experience, and AI's role is to make offline scenes "perceptible and interactive"—for example, optimizing listing display positions via heat maps rather than fabricating digital spaces. This pragmatic strategy aligns with the "focus strategy" in Porter's competitive theory: in an era of moderate growth, concentrating resources on high-conversion scenarios matters far more than chasing concepts.
2. Data Compliance Is Not a Cost but a Trust Asset
Lianjia's smart stores strictly follow the principle of "no facial recording, only movement tracking," building a "digital protective shield" through encrypted camera transmission and fine-grained permission control. This compliance design raised customer information authorization to 82%, far above the industry average of 55%. As the algorithmic trust chain grows more important, this becomes a key competitiveness: when AI can accurately recommend listings without invading privacy, customers naturally share more needs, forming a positive cycle of "the more compliant the data, the more precise the experience."
3. The Value of AI in Low-Frequency Transactions Lies in Extending the Service Cycle
Unlike fast-moving consumer goods that pursue instant conversion, property transactions need the "7-touch rule." Lianjia's AI system designed functions such as "48-hour unread private-message second reminder" and "automatic policy-change push," lifting dormant-customer activation by 40%. The payoff of this "slow craft" is: AI leads' deal cycle is 22 days shorter than manual leads, and the down-payment conversion rate reaches 21.3%, above the industry average of 15%. This shows that in low-frequency industries, AI is not an accelerator but a "trust incubator," continuously providing professional support for customer decisions.
These counter-intuitive findings point to a core view: in real-estate services, the ultimate goal of AI is not to raise transaction efficiency but to improve the "efficiency of trust building"—making professional value perceived faster, real needs better satisfied, and long-cycle service warmer.
03 Lessons from the Case: The "Return to Human-Centric Value" in Real-Estate AI
In the low-frequency, high-ticket property industry, the ultimate goal of technology is to "let service return to the human." When AI takes over 40 minutes of listing screening, agents can share life details like "the community supermarket's opening hours"; when the system auto-generates price-trend reports, advisors can focus on interpreting the impact of "school-district policy changes": this division of "machines do efficiency, humans do warmth" redefines the value chain of real-estate services.
Beike Group's 2025 strategy of increasing AI investment is even more revealing: building the housing-domain-specific large model Check Home and the image large model Beike Dreamer is not for technological leadership, but to "understand customers' personalized needs and expand service providers' knowledge base." AI frees property agents from memorizing listings, truly becoming customers' "housing advisors."
The deeper meaning of this transformation is to prove that the digitalization of real-estate services is not "replacing people with machines," but reconstructing the path of "trust building" through technology: when AI solves the industry's chronic "information asymmetry," and when data makes service standards visible and measurable, property transactions can truly move from "low-frequency gaming" to "long-term trust." This is the ultimate revelation that Lianjia's AI case offers all service-heavy industries: the more advanced the technology, the more one must hold fast to the "people-first" essence.
