KE Holdings reveals 36% drop in net income for Q3 2025 while enhancing operational efficiency and share repurchase efforts.

Company.

Please go ahead. Sujing thank you Operator Good evening and good morning everyone. Welcome to KE holdings or KE Holdings third quarter 2025 earnings conference call. The Company's financial and operating results were published in the press release earlier today and are posted on the company's IR website investors.ke.com on today's call we have Mr. Stanley Pence, our co Founder, Chairman and Chief executive officer, and Mr. Tao Xu, our executive director and chief financial officer. Mr. Xu will provide an overview of our business updates and financial performance. Then Mr. Peng will share more on our strategic developments and innovative initiatives. RMBefore we continue, I refer you to our Safe Harbor statement in our earnings press release which applies to this call as we will make forward looking statements. Please also note that KE Holdings earnings press release and this conference call include discussions on unaudited GAAP financial information as well as unaudited non GAAP financial measures. Please refer to the Company's press release which contains a reconciliation of the unaudited non GAAP measures through comparable GAAP measures. Lastly, unless otherwise stated, all figures mentioned during this conference call are in RMB RMB Certain statistical and other information relating to the industry in which the Company is engaged to be mentioned in this call have been obtained from various publicly available official or unofficial sources. Neither the company nor any of its representatives has independently verified such data, which may involve a number of assumptions and limitations, and you are cautioned not to give undue weight to such information and estimates. For today's call, the management will use English as the main language. Please note that the Chinese translation is for convenience purpose only. In the case of any discrepancy, management statements in their original language will prevail. With that, I will now turn the call over to our CFO Mr. Tao Xu. Please go ahead.

Thank you Suqing and thank you everyone for joining our third quarter 2025 earning conference call. In Q3, under the strategy of balancing skill and efficiency, we further optimize our business structure, enhance operational and middle and back office efficiency through AI technology and achieve the city level profitability in both our home renovation and rental business. Before deducting headquarter expenses, their combined contribution profit to companies total gross profit reached record high. The cost and expenses of our core business segment were further optimized. We also significantly enhanced execution of shareholder returns with the single quarter share repurchase spending reaching its highest level in past two years. Regarding our overall financial performance in Q3, our total GTV was RMB 736.7 billion. Remaining flat year over year, total revenues reach RMB 23.1 billion of 2.1% year over year. Gross margin declined by 1.3 percentage points year over year to 21.4%. GAAP net income was RMB 747 million down 36.1% year over year. Non GAAP net income was RMB 1.29 billion down 27.8% year over year. With that overview, I'd like to provide some details on operational and financial performance for each segment. Looking at our Housing Transaction services We have been continuously enhancing the productivity and operational performance through the application of AI and other technologies as well as in depth operational optimization. For our existing home transaction services, we upgraded our AI tool as of end of the third quarter this year. High quality business opportunities identified through Hao Kan account for only single digit percentage of total potential lease yet contribute over 50% of transaction volume on our platform. On the housing supply side, we launched innovations such as Agent specialization model which agents are assigned to specially manage home listing or serve the buyer based on their expertise as well as innovative services including home staging and open house events. These efforts have enhanced the buyer, commercial and the marketing and the sales efficiency of the home listings. For our new home transaction services. We have also continuously iterate our AI agent Tianji system for intelligent operations and marketing as well as AI system Qian Zhi. In terms of the financial performance, revenue from its in home transactions reached RMB 6 billion in Q3 down 3.6 percentage year over year and down 10.8% quarter of quarter. GTV was RMB 505.6 billion up 5.8% year over year and down 13.3% quarter over quarter. The GTV growth outpaced revenue on a yearly basis mainly due to a higher GTV contribution from its in home transaction facilitated by connect agents for which revenue are recorded on net basis while revenue performance outpaced the GTV quarter of the quarter mainly due to the structural shift as revenue contribution from the rental brokerage services increased amid seasonal fluctuations which have a relatively high take rate. The contribution margin of the existing home business was 39% in Q3, a decline of 2 percentage points year over year primarily due to the relatively stable fixed labor cost amid the revenue decline sequentially the contribution margin declined by 1 percentage point due to the decline in revenue exceeding in the fixed labor costs. Our new home GTV reached RMB 196.3 billion in Q3 down 13.7% year over year and 23.1% quarter over quarter. Revenue from the new home transactions was only $6.6 billion in Q3, decreasing by 14.1% year over year and 23% quarter over quarter. Revenue performance was in line with GTV performance both year over year and quarter over quarter, reflecting our steady monetization capability. In new home business, the contribution margin from new home transaction services was 24.1% down by 0.7 percentage points year over year due to an increase in variable cost resulting from our agent benefit improvement. Last year on quarterly basis, the new home contribution margin fell by 0.3 percentage points largely due to a higher variable cost and a smaller decline in fixed labor cost compared with revenue for our home renovation and furniture services, we continued to strengthen our core capability to support long term sustainable growth. On the product side, we successfully replicate our productized showroom model in multiple cities. On supply chain side, we expanded our centralized procurement categories and adapt localized sourcing standards and the selection process, further reducing the overall unit purchase price to enhance delivery quality with focus on improving construction quality standardizing on site management, laying the foundation for a unified system to excise construction site quality. In terms of the financial performance, revenue from our home renovation and furniture business was RMB 4.3 billion remaining relatively flat year over year. Contribution margin for the segment reached 32% up 0.8 percentage point year over year, primarily driven by the reduced procurement cost resulting from a larger proportion of centralized purchasing and the decreased labor cost resulting from enhanced order dispatching efficiency. Sequentially, the contribution margin remained relatively stable for our home rental service business. On product front, our new zero line products have been launched in 10 cities offering property owners diversified service options for unit sales and occupation. Our improved operational efficiency through AI powered housing condition assessment and intelligent pricing while further promoting our quality based traffic allocation rules to achieve faster housing turnover in Q3, the conversion ratio of Carefree Rent business opportunities to rental deals increased by more than 2 percentage points year over year. In terms of the operational management, we enhance the productivity for the property managers and other personnel through the further refinement of the role specialization of labor, the integration of operational process and the empowerment of AI technology. Regarding financial performance, revenue from our home rental services reached a record high of RMB 5.7 billion in Q3 up 45.3% year over year driven by rapid growth due to number of rental units under management. At the end of Q3, we had over 660,000 rental units under management compared with over 370,000 in the same period of 2024. The contribution margin for home rental services was 8.7% up 4.3 percentage points year over year and 0.3 percentage points quarter of quarter, largely driven by improved gross margin from our Carefree Rent business. As we continue to refine the business model, we have adopted a net revenue recognition approach based on service fees for the certain newly signed properties in line with the nature of the underlying service contracts in Q3. Our revenue from emerging and other services decreased by 18.7% year over year and 8.4% cut over quota to RMD 396 million. Now moving to the four other financial metrics in Q3 including other costs and expenses, profitability and cash flow. Our store costs reached RMB663 million in Q3 decreasing by 5.8% year over year and 13% quarter over quarter mainly due to the lower store rental costs. Gross Profit dropped by 3.9% year over year. 2 RMB 4.9 billion gross margin was 21.4% down 1.3 percentage points year over year. The decline was mainly due to the structural impact from a lower revenue proportion of its new home and new home business which have relatively high contribution margins as well as the decrease in contribution margin from the existing home business. This was partially offside by the increase in contribution margin from the home rental services. Gross margin declined by 0.5 percentage points quote unquote in Q3 mainly due to the structural impact as a revenue contribution of new home construction Service declined in Q3. Our GAAP operating expenses totaled RMB 4.3 billion down 1.8% year over year and a 6.7% quarter. Notably GND expenses or RMB 1.9 billion relatively flat year over year and down by 10.3% quarter over quarter primarily attributable to the decrease bad debt provisions and reduced share based compensation expenses. Sales and marketing expenses or RMB 1.7 billion down 10.7% year over year mainly due to the lower personnel expense and the reduced advertising and promotion expenses under the efficiency enhancement strategy on quarterly basis the sales and market expenses were done mainly driven by a reduction in labor related costs. Our RD expenses were RMB648 million up 13.2% year over year and 2.3% sequentially, largely driven by higher personnel expenses in terms of the profitability. GAAP income from the operations totaled RMB 608 million in Q3 down 16.4% year over year and a 42.6% quarter over quarter. GAAP operating margin was 2.6% dropping by 0.6 percentage points from Q3 2024 and 1.4 percentage point quarter over quarter. The non GAAP income from operations totaled RMB 1.17 billion, decreasing 14% year over year and 27% quarter of quarter. Non GAAP operating margin was 5.1%, down 1 percentage point from Q3 2024 mainly due to the decline in gross margin. Non GAAP operating margin was down 1.1 percentage points from the previous quarter mainly due to the increase in operating expenses ratio sequentially. Net income totaled RMB747 million in Q3, down 36.1% year over year and 42.8% quarter over quarter. Non GAAP net income was RMB 1.29 billion, falling 27.8% year over year and 29.4% quarter over quarter. Moving to our cash flow on the balance sheet, we generated net operating cash inflow of RMB851 million in Q3. New home DSO remained at a healthy level with 54 days in Q3. In addition to spending approximately 281 million in share repurchase during Q3, our total cash liquidity excluding customer deposits payable remained at around RMB 70 billion. Facing the short term business challenges brought by external fluctuation and internal strategic transformation, we support and reward our shareholders through consistently active share repurchase to improve the efficiency of the capital operations. From the fourth to third quarter of this year, we spent US$139 million US$254 million and US$281 million on share repurchase respectively with a cumulative amount of approximately 675 million in this year up 15.7% year over year. As of the end of Q3, the number of repurchase share accounts for about 3% of the company's total issued share at the end of 2024. Since the launch of our share repurchase program in September 2022, we had repurchased around US$2.3 billion worth of shares as of the end of September this year, accounting for about 11.5% of our total issued shares before the program began. We have made progress in Q3 this year in proactively optimizing our business structure, strengthening technology empowerment and enhancing shareholder return. Our forward looking layout of the home renovation and furniture services and home rental services have both achieved profitability at the city level before deducting headquarter expenses in third quarter. The AI capabilities have shown initial result in driving the business development and improving the work efficiency of the service provider and the middle and back office personnel. We are also fulfilling our shareholder return commitment with greater intensity repurchasing US$281million in a single quarter, increasing 38.3% year over year as the industry enters a new stage of high quality development while taking initiative in building a residential service ecosystem. With our combination of technological innovation, anti cyclical business portfolio and highly efficient and well structured operation system, we are well positioned to deliver greater value to both customers and investors. Thank you. Next, I would like to turn the call to our Chairman and CEO Stanley.

Thank you Tom for sharing our business and financial developments for the third quarter. We are strategically shifting our growth engine from scale to efficiency. Today I'd like to highlight some initiatives, innovative initiatives we have rolled out across businesses to advance its shift. First, in terms of our core business transaction services, externally we see new demand from both buyers and sellers. Under the new norm for China housing market, home sellers expect stronger marketing capabilities from US Buyers counting on us for customer oriented insights to support their decision making in areas such as timing, asset planning and leasing comparisons. This trend place due requirements on our traditional agent skill model and agents who are great at supporting both buyers and sellers are extremely rare. Since midyear we have been working to restructure our capabilities across both buyer and seller agents. In Shanghai, we piloted a seller and buyer agent specialization mechanism to enhance our marketing and operating excellence on the home sellers agent side. First, the mechanism redefines of organizational roles, commission structures and performance initiatives and offer supporting tech products. This in turn allowed buyer site agents to prioritize quality listings and improve transaction conversion. The underlying logic is that high quality home listings by engineers not ready made. They require skilled agents to master market analytics, pricing, property staging, owner engagement and decision making precision marketing. Second, inventory quality drives customer acquisition. Superior listings inherently attract more serious buyers driving transaction speed and our brand reputation which in turn attracts better talent to join us. Therefore, we did several things to implement this. First, we adjusted our organizational structure and incentive mechanisms. We shifted some senior agents into heartbreak roles that combine management and home seller focused responsibilities, giving them the authorities to to form and lead their own teams dedicated to listing management. Under the ACN commission allocation mechanism, we raised the selling agent share from 40% to over 50%. We are maximizing incentives for top performing agents to focus on marketing high quality home listings. This group of home seller focused agents can earn around 25% more than before assuming our market share remains stable. To mitigate potential pressure on buyer's agents, we reduced the mandatory 10 roll commission split, raised the minimum commission for selling agents and offered offer extra incentives for selling high score listings. Second, we provided agents with systematic sports and digitalized products to help them manage listings. In the past, homeowners relationship management, listing presentation and marketing relied on agents personnel experience that made it hard to replicate and scale. We have built an AI power listing score system that captures and quantify the know how required in six key areas. Home Listing Maintenance completeness Homeowner engagement Depth Property condition for example Renovation Recency Listing cross channel marketing performance AI Power Pricing competitiveness buyers interest for example the listings online offline viewings. These metrics help agents clearly understand what defines a high quality listing and how to better present and market homes. Home buyer agents can also focus on selling nine square listings to drive better sales conversions. In terms of results, in September, high score listings accounted for more than 75% of transactions. Our average average market coverage in Shanghai hit records high in Q3 increased 1.2 percentage points year over year and 2.6 percentage points quarter over quarter. The experience of homeowners looking to sell quickly or improve, Many homeowners reach out to us proactively to learn how to raise their listing scores. Buyers also naturally prefer high scoring listings, creating a positive cycle that benefits everyone involved. The home seller Buyer side agent specialization in Shanghai is an important initiative designed to meet the changing needs of our customers and marks a milestone in our shift from scale to efficiency. We will continue to track its progress and explore new initiatives on the home buyers agent side. In addition, we tried innovative approaches to make our new business more efficient. For example, in our home rental business, Q2 marked the first time we excluded headquarter costs from breakeven at the city level and Q3 is expected to contribute over 100 million yuan in profits. Carefree Rent Our decentralized long term rental business Housing businesses inherently faces challenge challenges including relatively low average selling prices, non standardized products and services, extensive service coverage and high maintenance costs. Traditionally requiring heavy manpower and variable cost investment for scaling and operating. This sector has struggled with economics of scale industry wide with no established best practices in as newcomers, we embrace this as an opportunity to build an AI native operation from inception enabling parallel development of business capabilities, frontline operations and AI intelligence. Through our organizational restructuring, process optimization and AI drilling and products, we are pioneering AI-driven efficiency economically sustainable model. Early results demonstrate significant improvements offering valuable insights for our other platform businesses. I will walk you through three major AI driven blackstores across different dimensions. First, BI has been fully integrated into our rental services business enabling end to end intelligent decision making and business operations for rental unit sign ups. AI now powers critical processes including property lead identification, personnel management and deployment, property evaluation pricing strategies and homeowner communication. For example, previously personnel management and operational relies heavily on experience level with superior supervisors is deciding which agent was responsible for which area. Now, through AI driven grid management supported by our unique dynamic domain data and modeling capabilities, AI can make data driven determinations. It evaluates factors such as a number and quality of property leads, local support, demand relationships and personnel capabilities models. Based on this data set, it determines the optimal personnel assignments, regional coverage and organizational structure. AI can simulate up to 90,000 design scenarios per minute, automatically generating the most efficient staffing and operational strategies. This has greatly improved how we allocate our service personnel deployment configuration and operational scope. We also use AI to guide and execute our core business strategies and daily helping us move forward fully in intelligent operations. For rental unit sign up, we roll out AI Power rental unit sign up assistant that use real time data and algorithms to predict market demand, property inventory and price trends. It generates automated sign up strategies and dynamic pricing recommendations, delivering tailored plans for each property through adaptive decision models. As market conditions change such as customer demands property inventory, enterprise AI can guide our operations team to make timely adjustments. For example, when there is an oversupply or three bedroom units in a certain area, the system automatically triggers price controls and sign up restrictions. When unit types are in short supply, AI reactivates government property lists. Our upcoming AI cloud bot will also automatically contact homeowners of this reactive properties. In Ningbo where we begin pilot operations in August, our workforce decreased by 10% while new rental sign up units grew up 10%. Even in the OPEX for rental unit leasing, our AI inventory management system frequently monitors inventory and checks over managing high risk or low maintenance properties. It dynamically adjusts pricing and targeted discounts while optimizing traffic to speed up Leasing. In Q3 these capabilities accelerated that this out of 350,000 units across 11 cities with 90% price adjustment adoption, this effort generated over RMB 100 million in nationwide cost savings. Second, we used AI and technology to solve the industry's long standing problems with non standardization enable high quality scalable growth the home rental industry has several characteristics. Home listings are scattered and each home has different and complex internal conditions making the products non standard. Service providers are many and their levels vary so the workforce is also non standard. Market price fluctuates on traditional pricing, relies on on frontline staffs, on site judgment leading to non standard pricing. Operational process are mostly offline and complex making sales strategies and service execution non standard as well. There are the traditional constraints of the industry, but with the progress of AI we see changes to achieve both standardization and personalization at the same time. At the property quality and risk assessment stage, we have achieved human AI integration with AI now leading the entire unit sign up workflows. Our AI Property Evaluation Assistant uses visual recognition and multimodal analysis to intelligently capture indoor features, assess property conditions and evaluate potential potential risks. It also incorporates market data to generate intelligent AI driven pricing recommendations. Beyond analyzing photos, the system can interpret property attributes holistically, helping address challenges such as consistent product standards, varying personnel capabilities and pricing accuracy. In the homeowner communication phase, we launched the AI Negotiation Assistant. These two package AI driven property assessments, dynamic pricing and competitive market data into tailored home sign up strategies and negotiation scripts, helping our service providers communicate and negotiate with homeowners more effectively. This provides a more professional and friendly experience for our clients, equipping new service providers with the tools they need to grow quickly and learn how to address non standard sales issues. We piloted this future in nimble and unified. Our productivity rose by over 10 percentage points in Q3 compared with Q2 ranking number one nationwide. Third, we achieved a leap in efficiency by adopting different AI applications during the sign up stage. Our AI reviews system has replaced manual reviews enabling fully automated risk control. As of September, the AI review function cover 11 cities processing each case in just 20 seconds on average making a 60 fold efficiency gain, saving more than 33,000 work hours and intercepting more than 16,000 risky properties. In the leasing stage, we use AI to power content lead marketing, expanding lead generation while reducing labor needs. AI intelligently analyzes and identifies high quality leads, enhancing leasing efficiency. The AI driven operational system in our home rental services has enabled us to see the possibility of scalable yet personalized services for pervious, fragmented, non standardized demands. Demonstrating the potential for traditional industries to overcome these economics of scale through technological innovation. We now integrate AI across our entire home rental services process and and I replicating this system across 13 key cities. Only through continuous innovation can we navigate industry cycle. By implementing home buyer, seller agent specialization and AI driven home rental operations, we have forged a new path that re engineers workflows through technology and fields scale through efficiency. Moving forward, we will deepen AI integration across business scenarios to advance both service providers capabilities and consumer experiences. As China's housing services industry undergoes this next evolution, we afforded a historical opportunity to further its transformation guided by our commitment to technology, power, high quality and its potential to unlock infinity possibilities for modern living sources. This concludes my prepared remarks for today. Operator we are now ready to take questions.

Thank you. As a reminder, we only accept questions on the English language line for the benefit of all participants. On today's call, please limit yourself to one question and if you have additional questions, you can re enter the queue. If you are going to ask a question in Chinese, please follow with an English translation. Your first question comes from John Lin with ubs.

So let me translate my questions so for the new home business. In the past the company has been achieving or exceeding market expectations in terms of the performance. But then it seems that the magnitude. Of the alpha has been diminishing. May I know what's the reason why? And also how should we or how. Should the investor look at the company.

New Home Business Growth Potential thank you. Thank you John. Also, the nearest performance so far of our new home transaction business has been affected by the market volatility. We remain confident in its ability to outperform the market in the long run. China's new home market has gradually matured in past two years with supply side risks steadily easing. Against this backdrop, we have shift from a cautious approach to a more growth driven strategy. Our new home transaction business has significantly outperformed the broader market in past few quarters until this second quarter with a higher brokerage penetration in the industry, our broader housing transaction service network and more collaborative projects. In this Q3 our GTV growth narrowed relative to the market mainly due to several factors. First, customers on our platform often look at both new and easy home before making a purchase decision. Recently, the prices of easing home have been considerably more attractive than the prices of a comparable of new homes, leading both first time buyers and the home upgrader to choose existing homes. Second, this is a basic fact the platform's new home transaction at a relatively higher base in last Q3 as many policy driven new home subscriptions in Q2 were transact in Q3 causing a timing mismatching with the market data. Third, of course it is important to note that in recent years our new home business has grown rapidly from a lower base as we made significant gains in brokerage penetration, the scale of our collaborative projects and our sales network and capabilities. We estimate the brokerage channel penetration ratio in the new home market has grown to over 50% this year from approximately 30% a few years ago. In cities we operate in, the coverage of our collaborative project has expanded to over 70% from roughly 39% in 2023. To achieve further growth in a higher base, we have several key opportunities. First, we plan to expand into more cities and broaden our target market. Second, broken channel penetration in China still lags behind developed markets leaving ample room for growth. Third, we leverage refined operation management to enhance the service capability for the new home customers and sales efficiency as well as improve our coverage and sell through capability for high end products. Now let's take a closer look at the details. First, we are piloting lighter product offerings to tab in some lower tier cities through what we called B. Our platform business still has over 150 prefeature and country level market not yet to be covered. Building on our commitment to authentic listings, the B payload equips local brokerage stores and agents in most cities with system capability, traffic support and commercialization tools. This lighter operation approach enables more flexible collaboration on home listings and sales and the new home sales with our channel partners. As of September 2025, our B business has been piloted in four cities and we plan to expand over 30 cities by end of the year unlocking additional market opportunities. Second, we see the room to grow our sales opportunity with collaborative products. On the two customer end, we will optimize content development and operational strategy for our new home business to reach more buyers and increase the conversion rates. On the to customer end, we will iterate our partnership models and product offerings to developers. Third, both supply and demand in new home markets are increasingly shifting towards the home upgrade projects. On supply side we will more precisely identify these projects and boost their exposure to both agents and customers with a match suitable agent to this upgrade projects and direct more customer traffic to them, creating a closed loop among homes, agents and customers. This approach will also help agents strengthen their sales capability for upgrade products and narrows the price gap between the platform average new home unit and the broader market. Thank you.

Your next question comes from Griffin Chen with Citi.

Yeah, I'm going to translate my question. So this is Griffin from Citi Property Team. So how did the leasing service business manage to turn last year losses into the operating profit by third quarter this year and what opportunity remains further improvement going forward. Thank you. Yes, thank you Griffin. The profitability of our home rental services improved significantly this year. Excluding high court allocations, city level operating profits break even in Q2 and become profitable in fiscal three. First we benefited from economies of skill from rapid growth in both skill and revenue. The total number of match units exceeding 660,000 by end of Q3 up 75% year over year. Revenue from our home rental service business rate RMB 5.7 billion in Q3 up 45.3% year over year. The contribution profit from our home rental services also rose significantly to nearly RMB 500 million in Q3 up 186% year over year with contribution margin of 8.7% up 4.3 percentage point in year. On one hand, the land assets model of our carefree run business has given us a higher margin lower risk rental structure. Starting in this Q3, the revenue from newly added rental unit and the renewed steam unit on the carefree rent has been accounted on a net basis. In Q3, rental units under the net revenue accounting method made up 25% of the total unit under management up 10 percentage points quote over quote contributing approximately RMB470 million in revenue. This structural shift drove RMB130 million increase in cafe rent Q3 contribution profit and lifted its contribution margin by 3 percentage points. At the same time, 2025 has been a year of improving operation efficiency. Streamlines and highly efficient operation have driven the reduction in several cost ratio, adding about RMB170 million to contribution profit and increasing contribution margin by roughly 1.5 percentage points. As coding rental costs recognized on a gross basis, the main cost of carefree rent are labor cost, channel cost, post rental installation and the default costs. The improvement was mainly driven by the optimized operation labor cost. In Q3, the average monthly number of units managed per property per property manager exceeded 130 compared with over 90 in the same period last year. In the first three quarter of this year, average monthly efficiency in unit sales and occupancy rose by approximately 10% and 28% year over year respectively. The default cost ratio declined by 0.1 percentage points. Benefiting from our strong leasing capability in Q3, initial leasing success rate improved by 0.9 percentage points year over year. So far this year, contribution profits from our home rental business segment has grown much faster than operating expenses. These expenses mainly comprise high culture and the city level staff compensation and R and D with a quite low expense ratio. A series of operating management tools have consistently improved the productivity of our middle and back office personnel. The average number of units under management by each middle and back office personnel rose by 7.5% year over year while the overall operating expense ratio declined year over year. In the coming years there is significant room to continuously improve contribution margin in our cavalry run business. The key drivers will be the continuous growth potential of the rental unit scale of the CAFRE ranch and ongoing improvement of our operational efficiency from per EOE optimization perspective. While diversifying our channels for renting out our property to reach broader tenant demographics, increasing the share of our in house rental occupancy team and reducing reliance on the concentrated brokerage channels. This is expected to lower the per unit channel cost ratio. In addition, labor costs remained a large part of per per unit UE and there is still room for further reduction of cost ratio. We see the potential to nearly double the number of units managed per property manager moving towards an average over 200 units per person. Furthermore, we will keep exploring and expanding diverse value added services with the home rental ecosystem. We will continue to invest in AI and online digital capability within our home rental service. While other operating expenses should stay relatively stable as the business continues to scale and we further optimize per unit ue, we expect our home rental service to maintain a strong of weighting leverage in the year ahead. Thank you.

Your next question comes from Jiang Xiao with Barclays.

Thank you very much for taking my questions. Good evening. My question is around your renovation business. You have done very well in cities. Like Beijing and Shanghai and I was.

Just wondering for you to do well in those cities, is that because you have high market share with your Lian Jia brand in those cities? And what sort of do you think that's a key reason? And do you think for cities outside Shanghai and Beijing? How would you kind of motivate your agents to cross sell or to sell the renovation business when you don't have such a high market share? Thank you so much. Thank you Xiaozhou. First of all, it is important to note that the home renovation market in second and third tier cities represent a critical long term growth driver for our future home renovation business carrying irreplaceable strategy value from a market fundamental perspective, compared to the first tier cities, the cost of purchasing a similar size of property is much lower in small cities. Based on the latest data from our platform, the average price of a single home in Beijing and Shanghai is around RMB 4 million versus just over RMB 1 million other cities. This price gap presents a meaningful opportunity as customers in second and 30 cities can allocate a relatively larger budget for the home renovation. In 2024 we recorded approximately 1 million existing home transactions outside Beijing and Shanghai. In the cities home renovation contract order generated through our agent network only accounted for around 30% of overall home renovation contract orders. Our conversion rate from its in home transaction to home renovation contract in these cities was just less than 5% compared to over 20% and 10% in Beijing and Shanghai respectively. Our strategic rationale is clear. Larger scale expansion into additional cities will only begin once the home renovation business underlying operational capability are mature and the module has been fully proven into core cities. Therefore, our resources are highly concentrated in core cities at this moment and we have not yet made a big effort to drive traffic for our home renovation business through Nanlianjiang Agent China in the second and third cities so far. This approach needs to ensure that every step of our goals is solid and sustainable. Meanwhile, we put in place multi dimensional systematic operational framework to engage with and motivate nonlinear agents. It includes three components. First, we aim to deepen our operation team's understanding and expertise in home renovations. Our operation teams have also shared the knowledge and proven operational capability to connect store owners and agents, fostering an ecosystem marked by professional collaboration and the shared competency. Second, we rolled out innovative incentive program to build online brand promotion metrics by offering incentives such as Bitcoins. We encourage more connected store agents to visit our offline home renovation stores and showcase our service through the short video which then will also upload to the leading social media platforms such as TikTok. Since launch of this program in the late April of this year, more than 30,000 agents in over 30 cities have uploaded over 50,000 short videos. This has cultivated a positive environment of full participation and widespread promotion. Lastly, on top of improving agent capability, we are leveraging AI to boost the contract conversion efficiency. Using AI, we access key attributes of the property within the store owner's coverage area such as property age, layout condition and assign quantitative scores. This allows us to accurately identify high scoring homes with a higher likelihood of generating home renovation business Feedback from the pilot cities has been extremely positive. While high scoring homes constitute only low single digit of the total home renovation lease, they contribute to over 20% of preliminary home renovation contracts, underscoring AI's value in boosting our operation efficiency in Q3. This in Q3 this year our home renovation lead from non agent channels achieved year over year growth and the lead to contract conversion rate increased compared with last year's average. In the short term, our approach for the home renovation business remains relatively conservative in the long run. Once our home renovation service meet our established high standards across customer experience, product competitiveness and the delivery quality, we will initiate a more proactive traffic diversion strategy through non-Lianjia agent channels in the cities outside Beijing and Shanghai. Thank you.

Our next question comes from Timothy Zhao with Goldman Sachs.

Great. Good evening management team. Thank you for taking my question. My question is about your cost and expenses. Could you further elaborate what are the measures for the company to control costs and any effect or outcome that you have seen so far and what we should expect from this cost and expenses line going forward? Thank you. Yes, thank you Timothy under the strategic guidance of Operational Efficiency Enhancement, all businesses have already implemented a series of optimization measures and achieved phased results. Now I'd like to elaborate on the cost reduction achievements of each business line and overall operating expenses in the third quarter of 2025 for our existing home transaction services, we continue to boost the productivity of our Lianzhou team and organizational optimization has driven a notable decline in labor cost. Organizational optimization has directly led to a cost reduction with the fixed labor cost in Q3 decreasing by more than 20% compared with the peak in Q4 last year and the labor efficiency has been continuously improved. For new home construction services, we have both streamline the fixed labor costs and the variable cost structure. Through streamlining the organizational structure of new Home Operation Team, we have achieved a reduction of more than 40% irrelevant in relevant fixed labor cost compared to peak in Q4 last year. On the variable cost side, the gross profit margin per project has been steadily increased. By focusing sales strategy to maximize uni sales per single housing project, the commission speed of non-Lianjia channels has decreased by more than 1 percentage point from the peak in Q1 this year. For our home renovation and furniture business, we have effectively lowered the material cost through supply chain integration. By streamlining partner brand selection and SQL counts, we have achieved a significant cost saving. In procurement, our centralized purchasing category has expanded from 4 as of Q2 to 13 as of Q3 Carving core categories such as wooden doors, flooring and tiles. The procurement unit price of some products has decreased by over 20%. The effectiveness of the cost optimization has been reflected in the financial reports, with the proportion of material related costs as a percentage of rent in Q3 decreasing by about 1 percentage point compared to last year average for our home rental services. Cost reduction has been driven by both technology technological impairment and business model refinement. We have improved the efficiency of the rental housing, travel management through AI employment and the task specialization of the service providers. The proportion of operating labor cost to revenue in Q3 decreased by around 1 percentage point year over year. For store cost, we have reduced fixed expenses through the refined management and closed underperforming stores. The number of actively ingested stores has been decreased from around 5600 as of Q4 last year to less than 500200 at the end of this Q3, a decrease of around 8%. Meanwhile, we have actively promoted the rent negotiation with existing industrial owners and achieved average rent reduction of over 10%. Regarding the control of the operating expenses and R and D investments for G and D expenses, we have achieved efficient Cost controls through the organizational optimization on non GAAP basis, the change expenses of the home renovation business have decreased by more than 100 million yuan compared to the peak in Q3 last year. This was mainly due to the adjustments of the organizational structure. The headquarters G and A has also been optimized based on the market conditions for sales and marketing expenses. Both marketing spending optimization and the improvement of the labor efficiency have been implemented on non GAAP basis. The sales and market expenses of the housing transaction business have decreased by around RMB90 million compared to its peak in Q3 last year. Mainly through the optimization of the advertising and the marketing placement. The related advertising and promotion expenses have declined by more than 20% compared to the peak in Q3 last year. The sales and marketing expenses for home renovation business have decreased more significantly by most RMB100 million compared to the peak in Q3 last year. The core driving factors including AI technology, enhancing the operation efficiency of the containers and other front end staff as well as organizational optimization that improved workforce structure for R and D expenses. The non GAAP business. The expenses in Q3 increased by around R&D 79 million year over year as the scale of RD team has expanded steadily. As of Q3, there were more than 2,300 R&D related personnel, an increase of more than 100 compared with Q3 last year, among which the number of AI related R and D personnel exceed 600 doubling compared to the same period last year. R and D resources continue to be tilted towards the core areas with R and D investment related to AI in Q3 exceeding R&B150 million nearly doubling compared to the same period last year. Our operational efficiency enhancement strategy has a clear execution path. We firmly believe that when the market environment stabilize, our continuous operation optimization will fully release the operating leverage effort. Thank you.

We are now approaching the end of the conference call. I will now turn the call over to your speaker today, Mr. Tingley, for closing remarks. Thank you once again for joining us today. If you have any further questions, please feel free to contact Baker's investor relations team through the contact information provided on our website. This concludes today's call and we look forward to speaking with you again next quarter. Thank you and goodbye.