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GOGOUP · REAL CASE LIBRARY

Real Cases

Learn from real decisions, success, failure and turnaround.

Search cases or enter through seven simple categories: Fashion, Food, Living, Mobility, Services, Interests and Other. Failure reasons are secondary filters only for failure cases.

Real Cases

2 cases
HousingReal Estate / iBuying / Home Buying and Resale / PropTechOngoingAs of 2026, Opendoor cannot yet be classified simply as Success or Failure. After the 2022 housing and interest-rate shock, the company did not exit iBuying. Instead, it continued trying to prove an unresolved question: can stricter pricing, faster inventory turnover, and stronger capital discipline make direct home buying sustainably profitable across different housing cycles?The central risk is timing.Opendoor must decide what a home is worth today and commit real capital to buy it, while the eventual resale price will only be known later. During that period, repair, maintenance, financing, and other holding costs continue to accumulate.If the purchase price is disciplined and the home sells quickly, the spread may cover those costs.If the initial pricing is wrong or the home takes longer to sell, a thin expected margin can disappear quickly.Revenue was roughly $4.4 billion in 2025. Q2 2026 revenue was approximately $883 million, down materially year over year, while net loss remained significant.Those figures alone do not establish Success or Failure. The real test is whether contribution per home, inventory turnover, and returns on capital can improve sustainably.Therefore, as of September 12, 2026, the Outcome is Ongoing.

Opendoor Still Believes in iBuying: If a Home Sells for $420,000, How Much Did It Actually Earn?

The easiest Opendoor number to misunderstand is revenue. If a software company generates $420,000 of revenue, customers may have paid $420,000 for software and services. If Opendoor generates $420,000 of revenue, it may simply have sold one home for $420,000. If that home originally cost $400,000, the initial spread is only $20,000. Then additional costs must be deducted. Repairs. Maintenance. Financing. Property-related expenses. Transaction costs. Operating expenses. And the cost of holding the home longer than expected. So the correct question is not: How many billions of dollars of homes did Opendoor sell? It is: **How much money was left from each home after all costs?** There is a second question: **How long did it take to convert that home back into cash?** That is inventory turnover. If a home was expected to sell in 30 days but actually took 120 days, Opendoor did not simply receive its money 90 days later. Capital remained tied up for another 90 days. Financing costs continued. Maintenance continued. And housing prices could continue changing. In iBuying, time itself is a cost. During 2020–2021, rising home prices could hide some of this risk. If prices continued increasing after Opendoor purchased a home, holding the property longer could sometimes be partially offset by market appreciation. After 2022, the environment changed. Interest rates rose. Mortgage affordability weakened. Housing transactions came under pressure. Opendoor had to answer more difficult questions. What price should we pay for this home today? What can it realistically sell for later? How long will that take? What will happen to repair and financing costs during that period? And if the market declines another 5%, is there still enough margin of safety? Those questions define the real capability required for iBuying. This is where Opendoor and Zillow provide a useful contrast. Zillow encountered Principal Risk and decided in 2021 to exit. Its answer was: **This is not the risk we should be carrying.** Opendoor made a different choice: **This is our core business. We will continue, but we must manage it more rigorously.** That makes Opendoor's test more direct. It cannot prove strategic correction by exiting. It must prove that iBuying itself can produce sustainable unit economics. Opendoor needs to buy at the right price. Leave enough margin of safety. Complete repairs efficiently. Turn inventory quickly. Control financing costs. And avoid allowing old inventory to accumulate. A systematic problem in any one of these areas can consume a thin expected spread. That is why $4.4 billion of annual revenue does not by itself prove that the model works. If revenue is large but contribution per home remains insufficient, scale may simply increase capital requirements. Conversely, if Opendoor deliberately buys fewer homes and reported revenue declines while the remaining inventory has better economics, lower revenue does not automatically mean weaker operations. Revenue was approximately $4.4 billion in 2025. Q2 2026 revenue was approximately $883 million, down materially year over year, while net loss remained significant. As of September 12, 2026, there is not enough evidence to declare the model a Success. But Opendoor continues operating and has not exited iBuying as Zillow did. The Outcome therefore remains Ongoing. The real question still waiting for an answer is: **Can Opendoor make each home consistently profitable after fully accounting for capital and time?**

CASE 038United StatesOpendoor buys homes directly from owners, performs necessary inspections, repairs, or preparation, and then resells those homes to new buyers.Reported revenue is naturally large because the full selling price of a home becomes revenue.But high revenue is not the same as high profit.The basic economics of one home can be expressed as:**Resale price − purchase price − repairs − holding costs − financing costs − transaction and operating costs = true per-home economics.**For example, if Opendoor buys a home for $400,000 and sells it for $420,000, reported revenue may be $420,000.But the initial spread is only $20,000.If repairs, financing, holding, and transaction costs exceed $20,000, the home can generate $420,000 of revenue and still lose money.The two critical operating questions are therefore:**How much contribution does each home ultimately generate?**and**How quickly can inventory be converted back into cash?**
LivingAI Architectural Design & Visualization SoftwareSuccessful TurnaroundExcessive Product Complexity / Long Path to Customer Value

From About $150/Month to $8.6K MRR: How Visualizee Found Growth Again

Piotr Obidowski built Visualizee, an AI architectural visualization product for architects and interior designers. The market need was real and the AI could generate architectural images, yet after nearly two years the product was still producing only about $100–$150 per month, mainly from one-time payments. The core problem was not that the AI failed to work. The first node-based version was too complex for its actual target users. Piotr later rebuilt the core experience around a simpler chat-based interaction, moved more prompt and technical complexity behind the product, shifted the business model toward subscriptions, and invested systematically in high-intent SEO. Piotr later publicly reported that Visualizee reached $8.6K MRR. The case shows that technical capability creates business value only when customers can reach the desired result easily.

CASE 101PolandAI architectural visualization SaaS for architects and interior designers; shifted from primarily one-time payments toward subscription revenue while retaining some one-time payments