From fragmented Excel
to a live portfolio view.
How a residential developer with 22 apartments and 23 parking spaces gained a consolidated financial picture of their project — including cost tracking, unit-level sales analysis, rental yield, and market-benchmarked pricing recommendations — from raw Excel data alone.
across 2 blocks
extracted & cleaned
consolidated
fully remote
The Situation
A residential developer was managing a two-block, 22-apartment project with project finances spread across multiple disconnected Excel workbooks — one for cost cashflows, one for unit sales tracking, one for rental income projections. None of them talked to each other.
There was no consolidated view of total costs paid versus outstanding, no unit-level breakdown of what had been sold at what price, no visibility into rental yield by apartment, and no structured comparison of the portfolio’s pricing against current market rates. Decisions on pricing, sales strategy, and cash management were being made without a clear financial picture of the project as a whole.
What We Found
A thorough review of the source data revealed four categories of error — each with material consequences for how the project’s financial position was being understood.
Construction, specialist works, and management costs were grouped in the same buckets, making it impossible to benchmark any category individually or identify where overruns were occurring.
Scheduled future payments were being treated as pending liabilities rather than committed cashflows. This overstated outstanding project costs by approximately €2.5M against actual contract obligations.
On duplex mansardă units, price per square metre was calculated against interior area only — omitting the sous-toiture space included in the contracted pricing. This inflated the apparent €/m² by over 90% on the affected units.
Cost data was denominated in RON across multiple sheets with no consistent EUR conversion framework, making it impossible to read the project in a single currency without manual recalculation.
The most consequential errors were not in the large numbers. They were in the structural assumptions — how costs were categorised, how square metres were defined, how currency was handled. Each one was invisible until someone looked for it systematically.
What Was Delivered
A consolidated, standalone project finance dashboard delivered as a single file — no software licences, no external dependencies, fully operational on any device. All values displayed in EUR, with RON equivalents shown in parentheses throughout.
Total project cost, sales revenue collected, unsold portfolio value, monthly rental income, and gross yield — all derived live from the underlying data. Cost breakdown by category with percentage share.
All 22 apartments with status (sold, for rent, available), price per m², advance received, amount outstanding, buyer reference, rental income, renovation budget, and gross yield per unit. Filterable by block and status.
All 23 spaces with status, sale price, monthly rental income, and annualised yield at 90% occupancy.
69 cost line items categorised across Land, Management, Construction, Specialist, Facade, Lifts, Doors, Insurance, Bank, Security, Sales, and Adjustment. Paid-to-date and scheduled amounts shown separately with EUR and RON columns.
Per-unit evaluation benchmarked against current local market data (sourced from public databases), including sale price vs market range, unrealised gain on sold units, and specific value optimisation actions for each apartment.
The Outcome
The developer moved from five disconnected Excel files to a single consolidated view of the project’s financial position. Specific capabilities gained:
€8.16M in project costs consolidated across 69 line items, split between amounts paid to date and amounts scheduled — with contract totals shown for reference where applicable.
Per-unit view of contracted sale price, advance received, and outstanding balance for each of the 7 sold apartments — with percentage collected and buyer reference.
Monthly and annualised income for all 13 rented apartments and 14 parking spaces, with gross yield calculated against each unit’s contracted value.
Each unsold and rented unit compared against current local market pricing, with specific repricing targets and rental uplift recommendations — including identification of two significantly underrented units generating 20–25% below market rates.
All data unified in EUR with RON equivalents throughout, eliminating the need for manual conversion across sheets.
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Start a conversationAll project details have been anonymised with client permission. Market benchmark data is sourced from public databases and is indicative only. This document does not constitute regulated financial advice.