Our work speaks for itself

Situation. A multifamily owner-operator believed occupancy could rise and rents could increase if they became efficient at every step of the leasing funnel.

Solution. We analyzed ads, leads, showings, and signed leases — uncovering delays in agent follow-up, appointment scheduling, and lease processing. The findings drove new SOPs, call-center training, and better lead tracking in AppFolio.

Situation. A multifamily operator set the same market rent for all similar unit types from external market data, suspecting their 3,000 units were underperforming.

Solution. We created refined "Enriched Unit Type" classifications — balconies, views, floor, renovation date, amenities — so each unit could be priced to its true potential, helping their team upsell and report realistic economic occupancy.

Situation. A manufactured housing operator manually ran Rent Roll and A/R Aging reports for every property to build a morning call list — taking hours, or not getting done at all.

Solution. We automated the task list by pulling reports from their PMS and highlighting delinquent accounts. It can be run on demand to reflect payments received in the last five minutes.

Situation. A multifamily operator wanted regional managers to see daily occupancy, revenue, and collections, but did not want to pay for expensive Yardi Business Intelligence add-ons.

Solution. We built a Power BI + Excel-export solution that refreshes daily and cycles through each regional manager’s metrics on large TVs in the corporate office.

Situation. A mostly single-family management company asked us to analyze every maintenance work order to find improvement opportunities.

Solution. By studying close times by property, request type, technician, and season, we uncovered chances to change SOPs, improve office-to-tech communication, and target technician training.

Situation. A multifamily owner-operator wanted to forecast vacant units over the next 90 days to right-size advertising and call-center staffing.

Solution. We calculated leasing-funnel conversion ratios from historical data and combined them with supply signals (lease endings, notices to vacate, units coming back online) to project demand and optimize lead-generation spend.

Situation. A multifamily operator had no visibility into how many days units sat vacant after move-out, or what contributed to the delay.

Solution. Using process analysis on work-order data, we built an improvement plan around the key events that drive days vacant — inspections, supply ordering, tech scheduling, and leasing-team coordination.

Situation. A client wanted to know how long newly developed properties took to reach 85% occupancy — with no PMS report and inconsistent lease entry.

Solution. We analyzed GL transactions to track the first rent payment per unit, estimating occupancy and days since operations began — revealing how long each property took and why some were faster.

Situation. A manufactured housing operator wanted analysts to explain budget variance in detail — by GL account, property, and month — and tell the story of what moved NOI up and down.

Solution. We loaded budget and P&L data into the warehouse and built an interactive Power BI report to drill from overall NOI into positive and negative variances, then further by GL account and property.

Situation. An operator manually updated a weekly metrics scorecard from Entrata, running multiple reports across 15 properties and typing numbers into Excel.

Solution. We connected their Excel scorecard to the Entrata API with scheduled refreshes — surfacing P&L, resident retention, and box-score metrics without manual exports.

Situation. A manufactured housing operator kept deal data in Excel, making it hard to compare actual community performance to proformas.

Solution. We loaded proforma spreadsheets into their warehouse so actuals could be compared to proformas and budgets — letting them show investors what was pitched versus delivered.

Situation. A franchisor received monthly P&Ls from over 100 franchisees, each with their own chart of accounts — making roll-ups difficult.

Solution. We automated pulling all P&L data from the Rent Manager API, built translation tables to roll up GL accounts, and generated consolidated P&Ls automatically for realistic system-wide reporting.

Situation. An investment company was acquiring manufactured housing parks across many states with limited knowledge of local communities.

Solution. We built maps overlaying school locations, upcoming Amazon distribution centers, and demographic and income data so they could value assets more intelligently than seller-provided data alone.

Situation. An owner-operator wanted to separate their best-performing properties from the rest using more than the usual NOI metrics.

Solution. Using a BCG-style quadrant analysis on ratios like utility and marketing expense, lead conversion, and work-order duration, we helped them build an action plan to coach property managers and lift NOI.