ORIL

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.

hashtag hashtag hashtag hashtag hashtag hashtag hashtag hashtag

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.

09/28/2026

The deal closes. The data integration problem begins.
🏘 A real estate acquisition can bring a second CRM, PMS, ERP, MLS feeds, property IDs, client records, and reporting logic — all describing the same business differently.
Connecting those systems isn’t enough; the combined business needs a shared data model, resolved entities, normalized data, and governed reporting to create one reliable view of the portfolio.

In our latest article, we break down how real estate companies can approach post-M&A data integration — and build a foundation that can support the next acquisition too.

&A

Learn how real estate companies consolidate CRM, PMS, ERP, and MLS data after an acquisition using data mapping, canonical models, entity resolution, and governed analytics.

09/25/2026

The ORIL team is back from Blueprint Vegas 🚀
A few days of conversations across the built world — from new products and startups to property operations, homebuilding, multifamily, and companies figuring out what to do with systems that have been around for years.

One takeaway we’re leaving with: technology conversations are becoming much more practical. The interesting questions weren’t only “What can this technology do?”
They were:
🔸 Where does it actually fit into the business?
🔸 What needs to change underneath it?
🔸 Can the team support it after launch?
🔸 What happens when you need to scale it across more properties, users, or markets?

That applies whether you’re starting something from scratch or trying to modernize a platform that has been running for years.

Thanks to everyone who stopped by, shared what you’re working on, and had the honest conversations that rarely make it into a pitch deck.

09/23/2026

Your listings can be everywhere — and still lose customer trust.

📊 🏢 When the same property shows different prices, availability, or fees across channels, the problem isn’t just data quality; it can affect inquiries, operations, and how customers trust the platform.

The competitive advantage is not simply distributing listings faster — it’s building the data architecture that keeps them consistent across every channel and ready for analytics and AI.

👨‍💻 We break down what that takes in our latest article — link in the comments.