ORIL

10/28/2026

Launching a software product is just the beginning. Keeping it secure, reliable, and useful as technology and user needs evolve requires ongoing attention. Regular maintenance helps teams address vulnerabilities, maintain compatibility, improve performance, and adapt products without letting technical issues accumulate.

In our article, we explore reasons why continuous app maintenance matters — and how it supports long-term product stability and growth: https://bit.ly/4z3PcEN

10/26/2026

Some of the best product insights don't come from a roadmap meeting.
They come from talking to people solving different problems across the same industry.

Across PropTech, we’re seeing a few themes come up repeatedly:
🔸 More value from existing systems. Companies are looking to make their current technology work better together before adding another tool.
🔸 AI with a purpose. The focus is shifting from experimentation to practical workflows that solve real operational problems.
🔸 Data before intelligence. Fragmented, inconsistent data remains a barrier to reliable analytics and AI.
🔸 Legacy modernization as a business priority. Older platforms can limit how quickly companies adapt, integrate new capabilities, and scale.

These challenges are connected. Better products don’t always require more technology — sometimes they require a stronger foundation and a clearer understanding of what the business actually needs.

That’s what makes exchanging perspectives across the PropTech community so valuable.

Which of these challenges is having the biggest impact on your business right now?

10/19/2026

🏢 Building-performance requirements are making reliable real estate data infrastructure more important than ever.

Property, meter, utility, and energy data often sits across disconnected systems, making accurate reporting and portfolio-level analysis harder than they need to be.

In our latest article, we explore how real estate companies can build a unified, traceable data layer that supports compliance, energy management, and better operational decisions — not just annual reporting.

How 2026 building performance requirements depend on property, utility, and energy data, and how the same data can support reporting, analytics, and energy management.

10/15/2026

🏘️ The real estate market is going through a major shift — with regulation, market conditions, data, and technology increasingly shaping each other.

From planning and rental reforms to building safety, ESG requirements, and pressure on operating costs, property businesses are facing more complexity across both operations and decision-making.

These are also themes we’ve been hearing repeatedly from industry leaders and the wider PropTech community at recent real estate and technology events across Europe and the US — giving us a close view of where the industry is heading.

At the same time, PropTech is moving beyond standalone tools toward connected data, AI-powered workflows, automated compliance, and deeper building intelligence.

👇 Here are some of the key trends we’re watching — and what they mean for the technology behind real estate businesses.

10/13/2026

Expanding into a new real estate market isn’t only a commercial challenge. It’s a data integration challenge.

New sources, schemas, identifiers, and market-specific rules can quickly add complexity to an existing product.

Explore more of the data and integration checklist you need before expanding into a new real estate market in our latest article 👇

A new real estate market brings new MLS feeds, schemas, and business rules. This checklist covers the data and integration work to assess before you launch.

10/12/2026

Real estate data rarely comes from a single source. MLS feeds, property records, PMSs, CRMs, public data, and specialized providers all bring different structures, coverage, identifiers, and update cycles. That complexity makes data evaluation an important part of building reliable real estate products and analytics.

It’s also why we use The Innovation Blueprint Podcast to bring together PropTech practitioners and share practical knowledge around the technology and data challenges shaping the industry.

In this episode, Ivo Draginov from BatchData and Roman Havrylyuk from ORIL discuss what teams should look at when testing a real estate data provider — from quality and coverage to accuracy and consistency.

🎙️ Watch the clip or explore the full episode: https://www.youtube.com/watch?v=H4jd_xJcTmg

What data challenges are you seeing most often across real estate products today?

10/08/2026

📊 Why does the same property have different data across MLS, PMS, CRM, and other real estate systems?

Usually, it’s not because one system is simply “wrong.” Different identifiers, schemas, definitions, formats, and update cycles can make the same property look like different records. The real challenge is building a way to continuously match, normalize, validate, and reconcile those records as the data changes.

We break down the most common causes — and what a reliable reconciliation process looks like — in our latest article.

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MLS, PMS, CRM, and ERP systems might not agree on the same property. See why records conflict and how reconciliation and enrichment build reliable property data.

10/06/2026

What changes when real estate portfolio reporting moves beyond Excel?
It’s not simply a matter of replacing spreadsheets with a dashboard. The real shift is where the reporting work happens.

Before:
🔸 Analysts spend hours collecting and reconciling data
🔸 Reporting depends on individual workbooks and manual processes
🔸 Different teams can work from different versions of the same KPI
🔸 By the time a report is ready, some data may already be outdated

After:
✅ Data preparation happens in automated pipelines
✅ Portfolio metrics are governed centrally
✅ PMS, accounting, CRM, and other sources feed one consistent data layer
✅ Analysts spend more time interpreting performance than rebuilding reports

And this is where the value of a portfolio dashboard really comes from. The dashboard is only the visible layer. Underneath it are the integrations, data normalization, entity matching, KPI definitions, validation, and business logic that make a single portfolio view reliable.

Excel doesn’t have to disappear either. It can still be useful for underwriting, scenario analysis, and exploratory modeling. The goal is to move recurring reporting onto a governed data foundation, while keeping Excel where flexibility matters.

We break down what this architecture looks like and how real estate teams can move from fragmented reporting to a single portfolio view in our latest article. Link in the comments.

10/05/2026

The best technology decisions in real estate don’t start with technology. They start with the business problem.

Before choosing a platform, integration, automation, or AI solution, it’s worth asking:

🔸 What’s slowing the team down?
🔸 Where does information get stuck?
🔸 Which processes still rely on manual work?
🔸 What needs to scale?

The answers often point to very different solutions.

A fragmented workflow might call for system integration and a unified data layer rather than another standalone tool. A growing portfolio might need better data models and scalable architecture before adding new features. A legacy platform may need incremental modernization rather than a complete rebuild. And an AI initiative may need cleaner, connected, governed data before another model is added.

That’s one of the things that makes real estate software development so interesting: the technology has to fit the way properties, listings, transactions, financial data, and operational workflows actually work.

👨‍💻 What technology challenge is creating the most friction in your real estate business right now?

09/30/2026

📊 Your real estate dashboard can tell you what happened. Can it tell you what’s coming next?

Historical reporting is useful — until the business needs to make a decision before the numbers arrive. Predictive analytics can help real estate companies forecast transaction volume, pipeline conversion, revenue, occupancy, pricing, and portfolio performance.

👨‍💻 But the model is only part of the equation. Reliable forecasting starts with connected, well-structured data and the infrastructure to turn predictions into actions inside the systems teams already use.

We break down what it takes to move from historical reporting to production-ready forecasting in our latest article 👇

Learn how real estate teams use predictive analytics to forecast transactions, occupancy, revenue, and pricing, and what data infrastructure makes those forecasts reliable.