For Managers Struggling with Property Issue Tracking: How to Visualize Repair Priorities Using Gemini Notebook

“How did we handle the issue with that property last time?” Have you ever been asked this by a tenant or resident and found yourself scrambling through your memory or past emails? Furthermore, when managing multiple properties, determining which one to address first often relies on a manager’s intuition. In this article, I will discuss how to simply upload your property and case-specific issue logs into Gemini Notebook (NotebookLM) to: 1) search for information by asking questions in natural language, and 2) visualize risks across multiple properties to determine repair priorities and even create slide decks for management meetings.

The common field problem: “The response history is only in the manager’s head”

We often hear from the real estate management and building maintenance industries that information regarding property management, schedules, and tenant responses is scattered among different managers, creating risks to business continuity due to over-reliance on specific individuals.

Past issue responses tend to be scattered everywhere—in email and phone exchanges, or in notes kept by individual managers. If a similar inquiry arises, a manager can answer based on memory if they are still there, but if they have transferred or resigned, the context is lost. We also hear that creating monthly reports is time-consuming because it requires recalling and summarizing past response histories one by one. Furthermore, when managing multiple properties, managers often rely on experience and intuition to decide which property to prioritize, making it difficult to provide a well-founded explanation in management meetings.

Actually, you can make your existing records searchable just by uploading them to Gemini Notebook

A precision parts manufacturer (with about 70 employees) has built a system where they centralize 10 years of complaint data into a spreadsheet, connect it to Gemini Notebook, and can extract solutions for similar issues within two minutes simply by entering the conditions of the trouble in natural language. This concept can be applied directly to searching for response histories for properties and cases, not just for handling complaints.

In the case of property management and building maintenance, if you accumulate daily inspection records and issue response notes as text and add them as sources in Gemini Notebook, you can find past context by simply asking, “What issues have occurred at Property XX in the past?” without relying on a manager’s memory. The key point is that you don’t need to sign a new contract for a dedicated property management system; you can start using just the Google account you already use (at no extra cost if you have a Google Workspace contract).

Moreover, Gemini Notebook is useful for more than just searching one item at a time. If you upload records for multiple properties, you can aggregate and visualize trends, such as “which properties have the most issues” or “what types of issues are recurring,” and extract them as reports that can be used for management decisions, such as prioritizing repairs. In this article, we will verify how this single mechanism can cover not only immediate answers to individual inquiries but also the “visualization” of the entire property portfolio.

What we will try this time: A prompt to “upload response history for each property to Gemini Notebook and search using natural language”

Step 1: Summarize issue response records for each property/case in text

Summarize the property name (or case name), date of occurrence, details of the issue, and response details in text format in a Google Doc or similar. If you already have records in emails or reports, you can simply copy and paste that content.

[Replacement Note]: If the data contains unique information such as property names, tenant names, or resident names, please replace them with placeholders like “Property A” or “Resident B” before using.

Step 2: Create a new notebook in Gemini Notebook and add it as a source

https://notebook.google.com/ Log in and create a new notebook. Add the Google Doc prepared in Step 1 as a source. As we have confirmed in previous installments, text (Google Docs) format tends to have more stable reading accuracy than PDF.

Step 3: Ask about your concerns in natural language

① First, use the basic form to ask about the response history of a specific property or case

〔物件名〕で、〔トラブルの種類〕に関するトラブルとその対応内容を教えてください。

② Next, ask about common patterns across multiple properties or cases

複数の物件で共通して発生しているトラブルの種類があれば、物件名とあわせて教えてください。

③ Next, have it propose improvement measures based on the records

〔物件名〕について、過去のトラブル対応記録を踏まえて、
今後同じような問題を防ぐために管理体制の面で改善できる点を3点、提案してください。

④ Finally, aggregate the trends of multiple properties into a report that can be used for management decisions

これまでの物件の対応履歴を踏まえて、物件ごとのトラブル件数・傾向を集計し、
修繕優先度が高い物件はどこか、月次の管理会議やオーナー報告で使えるレポート形式でまとめてください。

[Replacement Area]: Please rewrite the [Property Name] and [Trouble Type] sections to match the specific subject you want to investigate.

Step 4: Use the Studio feature to turn summary results directly into slide materials

From the “Studio” panel on the right side of the Gemini Notebook screen, select “Slides” and provide instructions on the content you want to create, such as “Create slides for management meetings summarizing trouble trends and repair priorities for each property.” A PDF slide deck will be automatically generated in a few minutes. The ability to go beyond just asking questions in a chat to instantly creating materials in a format ready to show others is a unique feature of Gemini Notebook.

I verified its practical utility through 5 experiments

Just because a search works once doesn’t mean it can be used reliably in practice. Therefore, this time, I decided to verify how reliable this method is and where its limits lie through five experiments with different characteristics. I prepared trouble response records for the past 8 months for three properties managed by a fictional building maintenance company (Property A: Office Building, Property B: Commercial Facility, Property C: Rental Apartment. All property names and details are fictional) and had Gemini Notebook read them as sources. I intentionally made the records for Property C more concise and memo-like than the other two, creating a situation close to the actual field where the level of detail varies depending on the person in charge.

Experiment 1: Can it quickly and accurately retrieve response history for a specific property as if asked by a customer on the spot?

Assuming a tenant or resident asks on the spot, “How did you handle that trouble before?”, I asked, “Please tell me about the trouble related to the elevator at Property A and how it was handled.” The following answer was returned in about 3 seconds (actual output).

It returned the information accurately, organized by cause, response, and recurrence prevention measures. Considering the time I would have spent searching through phone calls and emails, I realized the great value of being able to answer immediately on the spot.

Experiment 2: Can it identify common trouble patterns across multiple properties without being given hints?

The records for the three properties include “water leakage/rainwater intrusion” troubles with different timings and causes (Property A: June/pipe deterioration, Property B: September/water supply pipe damage, Property C: September/waterproof sheet deterioration). Without using the word “water leakage” at all, I asked, “If there are types of trouble occurring commonly across multiple properties, please tell me along with the property names.” The following answer was returned in about 13 seconds (excerpt from actual output).

It correctly picked up the water leakage-related records for all three properties and pointed out that they were “occurring commonly.” Moreover, it organized them into five categories—lighting equipment, air conditioning equipment, elevators, security, and bicycle parking—and even pointed out commonalities I hadn’t anticipated. I felt this was a unique strength of Gemini Notebook, which can handle multiple records across the board.

Experiment 3: Does the accuracy of improvement proposals drop even for properties where records are written concisely?

Regarding Property C, which was recorded in a more concise, memo-like style than the other two, I asked, “Based on past trouble response records, please propose three points that can be improved in terms of management systems to prevent similar problems in the future,” and the following proposal was returned (excerpt from actual output).

It was a concrete proposal that didn’t make me feel the conciseness of the records, including “transitioning from reactive to preventive maintenance,” “thorough dissemination of management bylaws and optimization of operational rules,” and “standardization of trouble records (to a format where causes and recurrence prevention measures are recorded as a set, like Property A).” The third point, in particular, was a proposal made after recognizing that the conciseness of Property C’s records was an issue in itself, and it was an interesting discovery that it correctly identified the inconsistency in the recording format.

Experiment 4: Can it aggregate trends for each property and turn them into reports that can be used for management decisions?

When I asked, “Aggregate the number and trends of troubles for each property, and summarize in a report format that can be used for monthly management meetings or owner reports, indicating which properties have high repair priority,” a report was returned that included trend analysis for each property, evaluation of repair priorities, and future action plans (excerpt from actual output).

Property B (commercial facility) was determined to be the top priority, and the reason (the significant impact on operations due to insufficient heat source equipment capacity) was also provided. The ability to not only search for individual cases but also grasp trends and prioritize across multiple properties is a result truly worthy of the name “visualization.”

Experiment 5: Can the Studio slide function automatically generate materials ready for executive meetings?

When I selected “Slides” from the Gemini Notebook “Studio” panel and instructed it to “create slides for an executive meeting that summarize trouble trends and repair priorities for each property,” a 11-page slide deck including a cover page was completed in PDF format about 10 minutes later. Starting with a conclusion slide pointing out major risks common to all properties (water leaks, elevator trouble), it included radar charts showing trends for each property, monthly timelines of trouble occurrences, risk matrices, and even a comparison table of issues by property. I was surprised that materials that would have taken half a day to create individually were completed with a single prompt.

However, there was a discovery here that could not be overlooked. While the report in Experiment 4 determined that “Property B is the top priority,” the “Top Priorities” slide in this presentation stated the following (excerpted from the actual output).

In contrast to Experiment 4, which prioritized Property B, Experiment 5 placed human life risk at Property C as the top priority, resulting in a conclusion where the priorities were swapped. While both correctly based their findings on the record content itself, the difference in evaluation criteria—whether to weigh financial and operational impact more heavily or to weigh human life and safety risks more heavily—influenced the conclusion. This was a very important practical discovery that conclusions can change depending on how you ask or the output format, even from the same data.

Reflections on Using It

Through the five experiments, I felt that Gemini Notebook could handle not only “searching” for response history for each property and case but also “visualizing” it across multiple properties. In particular, the speed and accuracy of Experiments 1 and 2 allowed me to experience the practical value of being able to answer customers on the spot.

On the other hand, the experience of the conflicting priority conclusions in Experiments 4 and 5 was a reminder of the obvious premise that one should not blindly trust the analysis results produced by AI. Whether to prioritize the magnitude of the cost or the risks involving human life is inherently a management decision and not something that should be left to AI. I felt that AI output is merely a tool to “quickly organize materials for judgment,” and that humans must always decide which to prioritize in the end.

Another challenge is that while I prepared well-organized records as dummy data this time, in the actual field, establishing the operation of continuously recording trouble responses in text seems to be the biggest hurdle. The value of this system grows as daily records accumulate, so the first step seems to be deciding on a recording format and creating a way to continue without burden.

Prerequisites for Safe Use

Since you are having it read response history, please observe the following points as prerequisites.

• If unique information such as property names, tenant names, or resident names is included, replace them with pseudonyms like “Property A” or “Resident B” before inputting.

• Use a corporate account contracted by the company (with opt-out settings so that input content is not used for training). Under the terms of service, Gemini Notebook does not use source data for training.

• Treat the answers provided by AI as a “draft” only, and ensure that important decisions (such as whether to charge for restoration costs) are always made after a human has verified the records.

Key Points You Can Copy Starting Tomorrow

• Try “search” and “visualization” as separate experiments: The way you ask and the value you get differ between searching for individual cases (Experiments 1-3) and aggregating/visualizing across multiple properties (Experiments 4-5). It is best to start with searching and challenge yourself with aggregation and slide creation once you get used to it.

• Even if the way records are written varies slightly, the impact on accuracy is limited: Even with concise memo-style notes like those for Property C, it returned specific improvement proposals. It seems better to prioritize starting to record rather than trying to make the format perfect.

• Use the Studio slide function as a “draft”: Slides of a quality that could almost be presented at an executive meeting are completed in minutes, but as seen this time, the conclusion on priorities can change. Always have a human check the content of generated materials and make the final decision.

• Use it with the premise that conclusions can change depending on how you ask or the output format, even with the same data: As the priorities swapped between Experiment 4 and Experiment 5, AI conclusions are influenced by how evaluation criteria are set. I recommend asking from multiple angles before making important decisions.

Conclusion

When you are busy with daily tasks, response histories for properties and cases tend to remain scattered across emails and personal notes. However, you don’t need to sign a contract for a new, dedicated property management system. By using your existing Google account and simply taking the time to compile your daily records into text, you can approach a search mechanism that doesn’t rely on the manager’s memory. Accumulating small efficiencies one by one will eventually become the strength of the entire company—please try this as one step toward that goal.

【Kindle Version Pre-orders Now Open】 Follow-up on the Book

I am writing to provide an update that pre-orders for the Kindle version of the book I mentioned previously, “Turning Individual AI Use into Company Strength” (releasing October 14, 2026), have now begun.

■Kindle Version: Pre-orders open (Launch commemorative price 495 yen. Regular price 990 yen).
■Paperback: Scheduled for simultaneous release on October 14 (Since pre-orders are not possible for this format, I will provide another update on the release date).
■For Kindle Unlimited users: You can read it at no additional cost after the release.

If there is anyone around you who is unsure about how far to expand the operations covered in this series within their company, I would appreciate it if you could let them know as well.

🔗 I have included links to the magazine and each article, so please take a look if you are interested.

1. Where to start with AI? What companies in the early stages of adoption should try first: “Drafting email replies”

2. Creating flyers in minutes with AI—How small shops tried the process from text to final product | Includes copy-paste examples

3. Turning FAX order forms into tables just by taking a photo and giving it to AI—4 steps for sites overwhelmed by paper orders

4. “Dumping” tasks on AI leads to unreadable proposals—How companies with inconsistent proposal quality can create a “template”

5. Childcare workers no longer have to worry about “phrasing for newsletters”—Tips for leaving the writing to AI

6. Generative AI information leakage risks: 3 rules companies should decide on as AI adoption begins to spread

7. How general affairs and HR staff can escape from inquiry handling with internal regulation Q&A—Created with Gemini Notebook

8. For sites struggling with technical succession—How ChatGPT can properly turn even recordings mixed with casual conversation into procedure manuals: How to create an “In-house AI Mentor”

9. For companies struggling with sharing know-how between branches—How to reduce confirmation time from 2-3 days to a few seconds with Gemini Notebook

10. For accounting staff who are always asked about project profit and loss by management—Ask ChatGPT using your current Excel data and get answers in an instant

11. For managers struggling to manage property trouble response history—How to visualize repair priorities with Gemini Notebook (This article)