96% of proptechs fail to get to series A funding – here is why
Thought Leadership by Andrew Stanton CEO Proptech-PR
The proptech sector has never been short of ideas. From AI-driven valuations and digital conveyancing to smart buildings and tokenised real estate, innovation in property technology continues at pace. Yet despite the apparent appetite for disruption, a significant number of proptech startups struggle and ultimately fail to raise external funding.
This is not simply a function of market cycles or investor caution. In many cases, proptechs fail to secure funding because of structural issues in how they are conceived, positioned, and executed. Understanding these pitfalls is critical for founders, investors, and industry stakeholders alike.
As I enter my ninth year helping Proptech founders get from MVP to exit, I thought it useful to explore from my expereience the most common reasons proptechs fail to raise funding, and what can be learned from them.
Solving a problem that doesn’t really exist – a great idea in the bath should stay there
One of the most frequent issues investors encounter is solution-first thinking. Many proptech founders build impressive technology without validating whether the problem is sufficiently painful, widespread, or urgent. Property is an inherently conservative industry, and if a startup is addressing a nice-to-have rather than a must-have, adoption will be slow, which investors recognise quickly.
Vanity projects, or ideas dreamed up in the shower, or ‘personal stories’ of woe around pain points in the plan, build, sale, lease or management of buildings, should come with an investor health warning. The amount of multi-million failure proptech guzzlers who produce a stubborn £15,000/£30,000 a month of revenue, against costs 50 or a hundred times that size is considerable.
Typical warning signs include weak evidence of genuine customer pain, early traction driven by curiosity rather than repeat usage, and buyers who express interest in the product but are unwilling or unable to allocate budget to it. If customers are not actively searching for a solution, investors are unlikely to fund one.
Long sales cycles and slow adoption
Proptech companies often sell into estate agencies, developers, housing associations, and large corporate or institutional organisations. These buyers are typically risk-averse, budget-constrained, and highly bureaucratic, which leads to long and unpredictable sales cycles that can range from six to eighteen months.
Many startups underestimate the complexity of procurement processes, the impact of compliance and legal requirements, and the number of stakeholders required to sign off on purchasing decisions. When revenue growth is slow and difficult to forecast, even technically strong products can become unattractive to investors.
Founders who don’t understand the property Industry
A recurring issue in proptech fundraising is a disconnect between founders and the realities of the property industry. Founders from purely technical backgrounds often underestimate the complexity of regulation, the reliance on legacy systems, the fragmented nature of the market, and the deeply ingrained behaviours that define how property businesses operate.
Investors in all sectors, commercial realestate through to residential, office and home assets, look closely at whether the founding team has direct experience in property, access to credible industry advisors, and a clear understanding of how purchasing and decision-making processes actually work. Without this credibility, startups struggle to sell effectively, and investors struggle to believe the growth narrative.

Overestimating market size – and the cost to penetrate a possibly saturated market
Many proptech pitch decks attempt to justify large funding rounds by referencing the trillions of pounds or dollars associated with the global property market. Investors tend to view this sceptically, as they know that most proptech businesses address only a narrow function, operate within a specific geography, and serve a limited customer segment.
If the realistic serviceable market is too small to support venture-scale returns, funding becomes difficult to secure, regardless of how large property is as an overall asset class.
Also many times I have seen zero thought on the GTM, and cost of acquisition of new clients, all of which are the beating heart of scaling a company.

Andrew Stanton CEO Proptech-PR
Week 38: What CRE Can Learn from Hospitality’s Approach to Tech-Enabled Experience
In this weekly series, we explore how the commercial real estate industry is being transformed by data and digital infrastructure. Guided by the principles in Peak Property Performance, we unpack a new idea every week to help owners unlock value, reduce risk, and future-proof their portfolios. Learn more about OpticWise and Bill Douglas, the authors of this series.
CRE hasn’t always been a Hotel—But It’s Closer Than You Think
Owners of office, multifamily, and mixed-use properties often look at hospitality as a completely different category. But in today’s experience-driven real estate economy, the lines are blurring fast.
Tenants now expect seamless, personalized, and responsive environments—the same way guests expect high-touch service at a hotel.
And there’s one industry that’s already mastered the intersection of space, service, and technology: hospitality.
What CRE Can Borrow from Hospitality
Hospitality isn’t just about beds and breakfasts. It’s about designing every detail of the occupant journey—and delivering it through connected systems.
Here’s what commercial real estate can—and should—learn:
- Personalized Experience
Just like hotels remember your preferences, buildings can use data to adapt to tenant routines, climate comfort, access, and services. - Unified Tech Stack
Hotels run everything—from check-in to housekeeping to energy systems—on one digital platform. CRE often runs on disconnected silos. - Proactive Service Models
When a room’s air conditioner breaks, hotels know before the guest complains. In CRE, tenants file tickets. That’s backward. - Revenue Per Square Foot Mentality
Hospitality has long optimized space usage and monetized amenities. CRE can replicate this with tech-enabled shared spaces, digital services, and tenant offerings. - Brand Through Experience
In hospitality, the guest experience is the brand. CRE can adopt this mindset by building tech-enabled experiences into every touchpoint—from access to amenity booking to issue resolution.

Andrew Stanton CEO Proptech-PR
Something Big Is Happening – by Matt Shumer (AI is building the next AI)
‘This is so important that I reproduce in full Matt’s take on where we are and where technology and AI has placed us and possible outcomes coming quickly down the road’. Andrew Stanton CEO Proptech-X.
By Matt Shumer (Picture) • Feb 9, 2026

Think back to February 2020.
‘If you were paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren’t paying close attention. The stock market was doing great, your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone told you they were stockpiling toilet paper you would have thought they’d been spending too much time on a weird corner of the internet. Then, over the course of about three weeks, the entire world changed. Your office closed, your kids came home, and life rearranged itself into something you wouldn’t have believed if you’d described it to yourself a month earlier.
I think we’re in the “this seems overblown” phase of something much, much bigger than Covid.
I’ve spent six years building an AI startup and investing in the space. I live in this world. And I’m writing this for the people in my life who don’t… my family, my friends, the people I care about who keep asking me “so what’s the deal with AI?” and getting an answer that doesn’t do justice to what’s actually happening. I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I’ve lost my mind. And for a while, I told myself that was a good enough reason to keep what’s truly happening to myself. But the gap between what I’ve been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy.
I should be clear about something up front: even though I work in AI, I have almost no influence over what’s about to happen, and neither does the vast majority of the industry. The future is being shaped by a remarkably small number of people: a few hundred researchers at a handful of companies… OpenAI, Anthropic, Google DeepMind, and a few others. A single training run, managed by a small team over a few months, can produce an AI system that shifts the entire trajectory of the technology. Most of us who work in AI are building on top of foundations we didn’t lay. We’re watching this unfold the same as you… we just happen to be close enough to feel the ground shake first.
But it’s time now. Not in an “eventually we should talk about this” way. In a “this is happening right now and I need you to understand it” way.
I know this is real because it happened to me first
Here’s the thing nobody outside of tech quite understands yet: the reason so many people in the industry are sounding the alarm right now is because this already happened to us. We’re not making predictions. We’re telling you what already occurred in our own jobs, and warning you that you’re next.
For years, AI had been improving steadily. Big jumps here and there, but each big jump was spaced out enough that you could absorb them as they came. Then in 2025, new techniques for building these models unlocked a much faster pace of progress. And then it got even faster. And then faster again. Each new model wasn’t just better than the last… it was better by a wider margin, and the time between new model releases was shorter. I was using AI more and more, going back and forth with it less and less, watching it handle things I used to think required my expertise.
Then, on February 5th, two major AI labs released new models on the same day: GPT-5.3 Codex from OpenAI, and Opus 4.6 from Anthropic (the makers of Claude, one of the main competitors to ChatGPT). And something clicked. Not like a light switch… more like the moment you realize the water has been rising around you and is now at your chest.
I am no longer needed for the actual technical work of my job. I describe what I want built, in plain English, and it just… appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done. Done well, done better than I would have done it myself, with no corrections needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits. Now I just describe the outcome and leave.
Let me give you an example so you can understand what this actually looks like in practice. I’ll tell the AI: “I want to build this app. Here’s what it should do, here’s roughly what it should look like. Figure out the user flow, the design, all of it.” And it does. It writes tens of thousands of lines of code. Then, and this is the part that would have been unthinkable a year ago, it opens the app itself. It clicks through the buttons. It tests the features. It uses the app the way a person would. If it doesn’t like how something looks or feels, it goes back and changes it, on its own. It iterates, like a developer would, fixing and refining until it’s satisfied. Only once it has decided the app meets its own standards does it come back to me and say: “It’s ready for you to test.” And when I test it, it’s usually perfect.
I’m not exaggerating. That is what my Monday looked like this week.
But it was the model that was released last week (GPT-5.3 Codex) that shook me the most. It wasn’t just executing my instructions. It was making intelligent decisions. It had something that felt, for the first time, like judgment. Like taste. The inexplicable sense of knowing what the right call is that people always said AI would never have. This model has it, or something close enough that the distinction is starting not to matter.
I’ve always been early to adopt AI tools. But the last few months have shocked me. These new AI models aren’t incremental improvements. This is a different thing entirely.
And here’s why this matters to you, even if you don’t work in tech.
The AI labs made a deliberate choice. They focused on making AI great at writing code first… because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That’s why they did it first. My job started changing before yours not because they were targeting software engineers… it was just a side effect of where they chose to aim first.
They’ve now done it. And they’re moving on to everything else.
The experience that tech workers have had over the past year, of watching AI go from “helpful tool” to “does my job better than I do”, is the experience everyone else is about to have. Law, finance, medicine, accounting, consulting, writing, design, analysis, customer service. Not in ten years. The people building these systems say one to five years. Some say less. And given what I’ve seen in just the last couple of months, I think “less” is more likely.

Andrew Stanton CEO Proptech-PR

