What Fault Analytics Exposed Across a Multi-Country Tower Portfolio
Lessons from a Large-Scale M&A Due Diligence
Take a Closer Look at Fault Analytics
For TowerCos, MNOs, and financial stakeholders active in telecom infrastructure M&A, the challenge is no longer whether faults exist across a portfolio — it’s whether those faults are visible, quantified, and understood before the deal closes.
Across any large tower portfolio, discrepancies are inevitable: safety components missing or degraded, installations that don’t match design records, undocumented equipment, or inconsistencies between as-planned and as-built conditions. These issues may sit dormant for years until an acquisition forces them into focus.
Fault analytics has therefore evolved from a technical exercise into a transactional necessity. In M&A, what you can’t see, you can’t value, and what you can’t value accurately becomes risk.
Fault Discovery and Bargaining Power in Tower M&A
When TowerCos prepare for mergers or acquisitions, they often discover that many sites have not been inspected recently. Where inspections do exist, data is typically fragmented and spread across spreadsheets, PDFs, and disconnected Asset Management Systems (AMS), a common challenge in telecom asset management, making it difficult to consolidate and analyze in a way that supports transaction-level decision-making.
Valuation decisions depend on verified data, not assumptions. Without a clear understanding of site condition and inventory, uncertainty creeps into pricing, timelines, and risk allocation.
A Multi-Country Portfolio Under the Microscope
In a recent transaction, the international arm of a U.S.-based TowerCo was rapidly expanding through the acquisition of a large portfolio spanning more than ten thousand towers across multiple countries. Accurate valuation was essential, not only to support negotiation but also to provide clarity on potential exposure tied to site condition and undocumented assets.
Years of inconsistent documentation, combined with undiscovered construction and safety issues, created risk. Without proper visibility, the acquiring TowerCo faced the possibility of overpaying for assets or uncovering liabilities only after the deal closed.
Seeing the Portfolio Clearly, at Scale
Given the volume of sites and geographic spread, traditional inspection approaches based on manually flown drones were not sufficient. Manual flight methods introduce variability in coverage and make it difficult to achieve consistent, comparable results across thousands of sites.
To address this, the TowerCo deployed vHive’s autonomous data capture, ensuring each site was captured according to the same predefined methodology. This approach delivered consistent, high-quality imagery across the entire portfolio and enabled the creation of reliable, comparable datasets.
The captured data was processed into high-fidelity digital twins, forming the foundation for portfolio-wide fault and inventory analytics.
What automated Fault Analytics Revealed
Once the portfolio was digitized and analyzed consistently, clear patterns emerged across countries and site types.
Using AI-based fault detection within the digital twin, the analysis identified tens of thousands of faults across the inspected sites, allowing issues to be classified by severity and evaluated at portfolio scale.
Critical Faults Were Widespread
More than one-third of the analyzed sites (37%) contained at least one critical fault, indicating safety or structural issues that could not be ignored during valuation discussions.
This immediately shifted the conversation from “average site condition” to site-level risk concentration within the portfolio.
Safety Compliance Was a Major Exposure
Of the critical faults identified, nearly 90% were related to missing or non-compliant fall-arrest lifelines. These findings exposed potential regulatory and safety liabilities that were not visible in existing documentation but became highly relevant during due diligence.
Security and Access Issues Were Not Isolated
Approximately 6% of the faults were linked to missing or incorrect locking mechanisms, meaning that a portion of the portfolio was not properly secured against unauthorized access or tampering. These issues were largely undocumented prior to inspection.
Severity Was Not Evenly Distributed
Overall, 13% of sites contained either critical or major faults, underscoring that risk was not evenly spread across the portfolio. Some sites showed minimal issues, while others concentrated multiple high-severity findings, a key insight when assessing portfolio-wide valuation and downside exposure.
These findings also informed the TowerCo’s understanding of potential CAPEX exposure tied to remediation, allowing those costs to be reflected directly in valuation discussions rather than discovered post-acquisition.
Turning Fault Insights into Valuation Intelligence
vHive’s fault analytics made it possible to translate site-level findings into valuation-relevant insight, strengthening the overall asset evaluation process during due diligence.
Across the subset of sites analyzed for fault analytics, the identified issues translated into repair exposure in the range of ~$1.5–2 million, based on the severity and type of faults uncovered during due diligence.
This shifted discussions from general uncertainty to evidence-backed valuation considerations.
Inventory Accuracy and Hidden Valuation Gaps
Beyond faults, inventory validation revealed another critical issue: discrepancies between recorded data and actual installations.
As-planned documentation often fails to reflect what is truly installed on towers. Undocumented antennas and equipment are common, and when inventory records are inaccurate, valuation assumptions are impacted.
Baseline inventory analytics, validated through autonomous capture and digital twins, exposed gaps between databases and real-world conditions. These discrepancies affected tenant counts, revenue assumptions, and ultimately, asset valuation.
From Assumptions to Evidence-Based Due Diligence
By leveraging autonomous data acquisition, AI-driven fault analytics, and high fidelity digital twins, the TowerCo replaced samples and assumptions with verified, portfolio-wide intelligence.
With verified fault and inventory data in hand, the acquiring TowerCo was able to approach the transaction with clearer visibility into both near-term CAPEX exposure and longer-term OPEX assumptions, supporting more informed valuation decisions and future OPEX optimization post-acquisition.
In large-scale tower M&A, this shift from estimation to evidence is quickly becoming the standard.
