GENERATIVE BI · SELF-HOSTED · OPEN SOURCE
Real Estate Analytics: Self-Hosted, AI-Powered BI for CRE and Proptech
Real estate analytics that unifies property management, financial, leasing, and maintenance data in one self-hosted platform. AI text-to-SQL for NOI, occupancy, and portfolio dashboards, no per-seat pricing. Book a demo.
Business Intelligence Built for the Whole Property Portfolio
Real estate analytics is business intelligence for owners and operators, where the numbers live in a dozen systems and the answer to one question often sits in four of them at once. Analytify gives CRE firms, REITs, and property managers an AI-powered platform that unifies property management, financial, leasing, and maintenance data in one place, runs entirely inside their own environment, and lets a portfolio manager ask a question in plain English. It is the rare combination of generative analytics and a single source of truth that a real estate organization can actually deploy.
Most real estate teams do not have a reporting problem so much as a plumbing problem. Rent roll lives in the property management system, valuations sit in a financial model, work orders are in a maintenance app, and lease terms are buried in PDFs. Pulling a clean portfolio number means stitching all of it together by hand. That gap, between the data a firm owns and the answers it can get quickly, is exactly what a unified real estate analytics platform closes.
See real estate analytics running on your own portfolio data, self-hosted.
What Is Real Estate Analytics?
The distinction from ordinary financial analytics software is the shape of the data. Real estate is not one ledger; it is hundreds of assets, thousands of leases, and a constant flow of maintenance and capital events, each with its own system of record. Proptech BI is the category that treats unifying those sources as the first job, not an afterthought, so a portfolio dashboard reflects reality instead of last quarter’s manual export.
Why Real Estate Data Needs a Different Kind of BI
Three forces make property analytics its own discipline. The data is structurally fragmented: a Vistra and Funds Global Intelligence survey of 166 real estate investment professionals found that 64% of GPs have abandoned strategies or halted fundraising because of poor data quality, a figure that rises to 82% in some segments. Assembling reports eats the day: industry research shows 37% of investment teams cite consolidating data into reports as their most time-consuming task. And the AI everyone is piloting only works when the underlying data is clean and connected.
The takeaway for a CRE or REIT leader is blunt: the appetite for AI is enormous, but the payoff depends on whether the data is unified first. According to JLL, around 90% of real estate companies are piloting AI while only 5% have achieved most of their goals, and the single most-cited use case is simply sorting and organizing fragmented information. A platform that unifies the data is the prerequisite, not the nice-to-have.
What Can Real Estate Analytics Do? Core Use Cases
Portfolio performance
Roll every asset up into one view: net operating income, occupancy, cap rate, and total portfolio value across markets and property types, with the ability to drill from the portfolio dashboard down to a single building. This is the headline use case, and it benefits directly from predictive analytics for forecasting income and value.
Leasing and occupancy
Track occupancy and vacancy rates, leasing velocity, renewal pipeline, and tenant mix across the portfolio. Spotting a softening submarket or a cluster of upcoming expirations early is the difference between a proactive plan and a scramble.
NOI and financial analytics
Net operating income by asset and portfolio, expense ratios, rent collection, and budget-versus-actual, all tied back to the general ledger. This is the financial heart of real estate analytics and the number every investor and lender watches.
Maintenance and capex
Work-order volume, response times, maintenance cost ratio, and capital expenditure tracking against budget. Connecting maintenance data to financial data is where most teams find avoidable cost leaking out of the portfolio.
Tenant and lease lifecycle
Lease expiry exposure, escalations, tenant retention, and lease-level profitability across the book. Knowing which leases roll when, and at what risk, is the core of asset-management strategy.
Market and comps
Benchmark rents, cap rates, and absorption against the submarket, and bring external comps alongside internal performance so acquisition and disposition decisions rest on the same governed numbers as everything else.
Essential Real Estate Dashboards and KPIs
A strong portfolio dashboard tracks the metrics owners, lenders, and asset managers all watch. These are the core ones.
| KPI | What it measures | Why it matters |
|---|---|---|
| Net operating income (NOI) | Property income minus operating expenses | Core profitability of an asset |
| Occupancy and vacancy rate | Leased space versus total space | Income stability and leasing health |
| Capitalization rate | NOI relative to asset value | Valuation and return benchmark |
| Lease expiry exposure | Share of income from soon-expiring leases | Renewal and rollover risk |
| Rent collection | Billed rent actually collected | Cash flow and tenant health |
| Maintenance cost ratio | Maintenance spend as a share of revenue | Operating efficiency |
| Portfolio value | Total market value across assets | Equity and reporting to investors |
| Tenant retention | Share of tenants renewing | Long-term income durability |
Build these portfolio dashboards on data that never leaves your environment.
Unifying Property, Financial, Leasing, and Maintenance Data
This is where real estate analytics earns its budget. Instead of analysts exporting from the property management system, the accounting package, and the maintenance app and reconciling them in a spreadsheet every month, a unified platform connects to all of them and computes portfolio numbers from source data. Two capabilities make that work at scale. A semantic layer defines NOI, occupancy, and cap rate once, so every report uses the same logic instead of each analyst’s version. And a real data warehouse underneath keeps property, lease, financial, and maintenance records joined and queryable, which is what turns a pile of disconnected systems into a portfolio dashboard you can trust.
Analytify connects to the systems real estate teams already run. Pull operational and financial data from PostgreSQL and SQL Server, scale on Snowflake or BigQuery, and bring in the rent rolls and budgets that still live in Excel, all into one governed model.
Deployment: Where Your Portfolio Data Lives
This is the section most proptech BI pages skip, and for real estate it matters more than people expect. Rent rolls, tenant records, and financials are commercially sensitive, and many institutional owners and their investors have firm requirements about where that data sits. Analytify is a self-hosted BI tool. It runs on-premises or in your own private cloud account, so portfolio data stays in your environment and never transits a vendor’s cloud.
Because it is an open-source BI tool, your own team can inspect exactly what the software does, and because it is self-hosted, you are not handing your entire rent roll to a third party to get a dashboard. The licensing model matters just as much. There is no per-seat pricing, so every property manager, analyst, and asset manager can use the platform without the cost climbing each time you add a user. That is a meaningful difference from most embedded options and from cloud BI tools priced per head. The same approach underpins our work across financial services and adjacent verticals like insurance analytics.
Ask Your Portfolio in Plain English: AI Text-to-SQL
Self-hosting does not mean giving up modern AI. Analytify brings generative BI inside your environment, so a leasing or asset-management user can ask a question in plain English and get a governed SQL query in return, all without the data leaving your infrastructure. This is AI-powered business intelligence that a non-technical property team can actually use.
→ Analytify writes the SQL across your property, lease, and financial tables, returns the breakdown, highlights the expiry exposure, and keeps the query visible for review.
Pairing AI text-to-SQL with unified, self-hosted data is the combination most real estate analytics tools do not offer. It is the most useful thing a property team can put in front of people who know the assets but do not write SQL, and it works as a self-service analytics platform the whole firm can share.
How Does Analytify Compare to Power BI, Tableau, and Proptech BI Tools?
The incumbents and the niche proptech tools are capable, but they tend to be cloud-first and priced per seat. For a real estate firm, the deciding factors are whether the platform unifies your data, where it runs, and what it costs as the team grows.
For specific side-by-sides, see Analytify vs Power BI, Analytify vs Tableau, and Analytify vs Looker, or review pricing.
| Capability | Power BI / Tableau / Proptech BI | Analytify |
|---|---|---|
| Unifies property, financial, leasing, and maintenance data | Varies, often single-source | Yes, one governed model |
| Self-hosted, data stays in your environment | Limited or cloud-first | Yes, by default |
| Open source and inspectable | No | Yes |
| AI text-to-SQL on your own portfolio | Cloud-based AI | Runs in your environment |
| Embedded and white-label | Add-on or limited | Built in |
| Pricing | Per seat, scales with headcount | Platform license, no per-seat pricing |
Frequently Asked Questions
It is business intelligence software property owners, investors, and managers use to track portfolio performance, occupancy, net operating income, and asset value. It must unify fragmented data from property management, accounting, leasing, and maintenance systems into one governed view.
Because it is structurally fragmented across separate systems. Industry research found 37% of investment teams cite consolidating data into reports as their most time-consuming task, and 64% of GPs have abandoned strategies or halted fundraising due to poor data quality. Unifying the data first is what makes analytics reliable.
Yes. Analytify is self-hosted and runs on-premises or in your own private cloud, so rent rolls, financials, and tenant records stay in your environment and never transit a vendor’s cloud.
Net operating income, occupancy and vacancy rate, capitalization rate, lease expiry exposure, rent collection, maintenance cost ratio, portfolio value, and tenant retention.
Through text-to-SQL. A leasing or asset-management user asks a question in plain English and the platform writes a governed SQL query against the property, lease, and financial data, so people who know the assets but do not write SQL can still get answers.
Yes. Analytify connects to the property management system, accounting package, leasing records, and maintenance app, then defines metrics like NOI and occupancy once in a semantic layer so every report uses the same logic.
The appetite is real but readiness lags. Deloitte found 76% of CRE firms are exploring or implementing AI, while JLL reports about 90% are piloting and only 5% have achieved most of their goals, largely because the underlying data is not unified. Connecting the data first is the prerequisite.
Power BI, Tableau, and most proptech tools price per seat, so cost climbs as the team grows. Analytify uses a platform license with no per-seat pricing, so every property manager, analyst, and asset manager can use it on infrastructure you already run.
See Analytify running on your own data
Book a walkthrough and we will show Analytify against a stack like yours, self-hosted, with no per-seat pricing.
