Service Innovation Co., Ltd.: Changing the Decision-Making Process in the Custom Home Industry with "Housing AI Concierge"

The Massive Untapped Custom Home Builder Market in Regional Areas

Hello, everyone. My name is Masatoshi Umemura, CEO of Service Innovation Co., Ltd. Thank you for having me today.

We develop and operate "Housing AI Concierge," a vertical platform and generative AI agent specialized in the custom housing industry.

First, let me share the current state of the custom housing market. Due to the aging population and declining birthrate, the custom housing market size itself is shrinking. However, the market size remains extremely massive, and building a home is still the single biggest purchase in most people's lives.

It is commonly believed that major house builders and housing exhibition sites dominate this industry. In reality, however, regional "home builders (local contractors)" make up the vast majority of the market. Furthermore, while building a house in metropolitan areas like Tokyo can easily cost 100 million to 200 million yen including land, it is completely normal in regional areas for young families in their 20s or early 30s to build their own homes. We are convinced that the true opportunity for innovation lies within this regional home builder and young family market.

"Four Major Changes" and the New Pain Points for Users and Housing Companies

For over a decade, users trying to build a home lacked information, so they had to visit multiple exhibition sites in person. As a result, they visited many housing companies but struggled to make a decision. The standard response from housing companies was pushy sales tactics aimed at capturing customers through framing and differentiation.

However, four major changes are now reshaping the industry.

First, with the rise of Instagram and social media, users' "ideals" and "anxieties" have ballooned simultaneously. While their visual concepts expand, their actual quotes exceed budgets. This triggers endless loops of design revisions and budget adjustments, leaving users exhausted by the decision-making process.

Second, home building involves highly sensitive personal information, such as annual income and financial status for mortgage loans. This makes it difficult to consult friends or relatives, while users simultaneously want to avoid aggressive sales representatives.

Third, the criteria for home-building decisions have shifted from professionals (housing company sales reps) to reviews from amateurs or influencers (general home buyers) on the internet.

Fourth, "digital selection" on the internet to narrow down candidates before visiting exhibition sites has become the default behavior.

These changes have created new pain points. Users struggle to choose a company from the flood of information and cannot compare quotes on their own. Housing companies suffer from a sharp decline in customer acquisition because they get filtered out digitally before they can communicate their value, and they get dragged into price wars where their true strengths go unrecognized.

"Housing AI Concierge" Enables an Informed and Confident Decision-Making Process

The problems we solve are simple. We help users organize their ideals and budgets to find the best housing company for them—all without receiving a single pushy sales call. We also eliminate anxiety in quote comparisons, enabling them to proceed to contracts with confidence. For housing companies, we prevent them from being filtered out early, connect them with "hot leads" who understand their strengths, and improve closing rates by helping them get selected based on compatibility and value rather than discount competition.

The solution is "Housing AI Concierge," an AI agent that supports family demographics in their 30s and 40s.

As a concrete step, the AI, embedded with the expertise of top salespeople, interviews users to organize their requirements. It then automatically generates a personalized "My Home Chart."

Equally critical is the financial planning aspect. By inputting current rent and annual income, the AI calculates a reasonable mortgage limit. It automatically factors in complex tax deductions and local government subsidy data to present a realistic financial plan instantly. Building a home requires a balance of "image," "lifestyle," and "budget." The AI bridges the information gap by providing detailed specifications and price data from housing companies, which users usually lack. In quote comparisons, the AI standardizes differing calculation criteria and items to support confident decision-making.

A Hybrid Model Starting at 10,000 Yen per Month and Future Strategy

Our business model charges housing companies a monthly system fee of 10,000 yen, combined with a success fee upon closing a contract. Since the average contract value for a custom home is around 30 million yen, our success fee (approx. 5%) equates to about 1.5 million yen per contract.

Unlike existing portals that serve as simple "information comparison sites," we assist the user's decision-making process as an active agent, giving us a completely different market positioning. For housing companies, because the AI sends hot leads with completed charts—detailing requirements, income, and budgets—they can cut their marketing and sales costs by nearly half.

In terms of traction, we have already released the beta version and partnered with 16 housing companies. User click-through rates and partnership requests from housing companies are exceptionally high. Moving forward, we plan to expand our coverage areas and enter other housing sectors beyond custom homes, eventually expanding nationwide. We also aim to evolve beyond a matchmaking platform into a sales enablement (SFA) system that automates marketing and sales operations for housing companies using AI.

Building a home is the biggest event in a lifetime. We will bring transparency to this opaque process and establish a new standard. I look forward to discussions and meeting potential partners today. Thank you for your time.

Q&A and Feedback

Mr. Nakazawa (Commentator): Thank you very much. The real estate and home-buying sector is one of the slowest to digitize. Therefore, an independent AI-based decision support system is highly innovative. While we will see a shift toward AI agents committing directly to contracts in the future, how do you ensure the freshness and reliability of the pricing and proposal data? What is the underlying database structure or algorithm? I am also curious about the actual improvement in consultation and closing rates since launching the beta version with 16 companies, and how you define the division of labor between AI and human sales reps.

Mr. Umemura: Thank you for your questions. Regarding the first point about pricing proposals and the algorithm, to be honest, it is still difficult for AI to handle everything perfectly at this stage. Therefore, we utilize a hybrid model: the AI handles initial requirement filtering and financial planning, and our human customer success members step in to support the detailed matching and coordination process. For pricing data, we receive detailed costs and specifications directly from our partner companies and store them in our database. In the housing industry, actual budgets and real square-footage costs are almost never published online. By leveraging this closed, primary data, we can deliver highly accurate budget proposals even at the early planning stages. This is our greatest competitive advantage.

Mr. Nakazawa: I see. Because you have direct access to primary data that isn't publicly available, you can compare budgets with high reliability.

Mr. Umemura: Yes, exactly. Regarding the second point about consultation/closing rates and the division of labor, we just launched the beta version in October and are running rapid agile testing. Thus, we are still accumulating finalized contract data. However, unlike traditional lead-generation services that send cold leads who are only vaguely interested, our AI pre-fills a "My Home Chart" containing crucial details that top salespeople want to know—such as annual income, current rent, requirements, and desired number of rooms—before sending the lead. From the housing company's perspective, they receive a highly qualified lead with all necessary details already prepared. This dramatically improves the quality of the initial consultation, driving higher consultation and closing rates, as well as higher average contract values. The AI automates preliminary interviews and customer profiling, allowing human sales representatives to focus on high-value tasks like custom design proposals and building face-to-face trust.

Mr. Nakazawa: Building a home is a life-defining decision. Turning what was once an opaque, gamble-like process into a reliable, data-backed choice will have a massive social impact. I look forward to your future progress. Thank you very much.

Mr. Umemura: Thank you. We will continue to increase transparency and build a world where users and high-quality local builders are matched optimally. Thank you.