Our Work Speaks for Itself
Here are examples of real estate analytics success stories that we have created with real estate owners and management companies across multifamily, manufactured housing, single family and commercial.
The RentViewer team brings process engineering and data analytics knowhow to solve real-world challenges that yield hundreds of thousands in NOI for our clients.
Let’s brainstorm together how we can help your company uncover value by analyzing your data and identifying process improvement opportunities that will drive NOI growth.
List of Success Stories
Lead Conversion Stories
Customer Service Stories
Economic Occupancy Stories
Income, Expense, Cashflow Stories
- Faster Corrective Actions With Automated Metrics Scorecards
- Better Accountability with Proforma vs Actual
- Tracking Forecast Changes Leads to Sales Forecasting Discipline
- Regional Metrics on a Wall TV for all to See
- Bills in One System Become Journal Entries in Another
- Accelerating Unit Turns Leads to More Rental Income
- Drilling into Work Orders Leads to Savings, Resident Satisfaction
- Better Offers When the Acquisition Pipeline is on a Map
- Cash Cows and Dogs of the Portfolio
- A Clearer Financial Picture with a Consolidated
- Show Stabilized Assets Only, Please!
- Ratios, Ratios, and More Ratios
- All Budget Variances Are Not Created Equal
Customer Lifetime Value Stories
Faster Corrective Actions With Automated Metrics Scorecards
A multifamily operator had to manually update their metrics scorecard each week from Entrata. They had to run multiple reports for 15 properties, and type numbers into a formatted Excel report.
We connected their Excel scorecard to the Entrata API and scheduled automatic refreshes. Now they can look at P&L data, resident retention, box score and more metrics without exporting to Excel.
Variances to targets can be identified faster, and corrective actions taken before it’s too late.
Forecasting Apartment Inventory
A multifamily owner operator wanted to forecast the number of vacant units over the next 90 days. They wanted to use this information to decide how much to spend on advertising and call center staffing for managing leads. The number of vacant units could not be determined by simply tallying leases signed and move in / move out dates.
Additional information that also had to be looked at included the ads already placed, the leads already captured, showings scheduled and lease applications not yet approved.
We helped our client calculate leasing funnel conversion ratios by analyzing historical data, and applied these to make projections for upcoming demand. We figured out how many leads were generated from ads on apartments.com, craigslist and more, and how many leads got converted into showings and leases. We used this to estimate demand. We calculated the supply based on lease ending dates, notices to vacate and renovated units becoming rentable.
The company was able to spend the optimal amount on lead generation in order to achieve its occupancy targets, and also developed a repeatable process to make rolling forecasts for supply and demand.
Better Accountability with Proforma vs Actual
A manufactured housing owner operator-maintained deal data in Excel files. It was difficult to compare the actual performance of the communities compared to the deal assumptions and proformas.
We loaded data from the proforma spreadsheets into their data warehouse. Now, actuals can be compared to proformas as well as budgets. The company can show investors what it pitched and what it delivered
Tracking Forecast Changes Leads to Sales Forecasting Discipline
A company that uses Salesforce noticed that their revenue forecast changed significantly from month to month. They needed a way to trace the changes for better accountability and planning.
We solved the mystery using Salesforce’s reporting snapshot feature and Tableau. Now, any sales rep who modifies a deal knows that the prior values will be preserved for comparison. And they will be asked to explain why their forecast changed so drastically.
Regional Metrics on a Wall TV for All to See
A multifamily operator wanted its regional managers to know their daily metrics for occupancy, revenue and collections. The company used Yardi but did not want to pay for expensive Yardi Business Intelligence add-ons.
We built a simple solution with Power BI and Excel exports that refreshes daily. The latest metrics are visible on large TVs in the corporate office. The TV cycles through every regional manager’s metrics throughout the day.
Bills in One System Become Journal Entries in Another
A manufactured housing operator with over 10,000 units uses separate systems for billing and accounting. They wanted to post transactions from the billing system into the General Ledger of their accounting system. The volume was hundreds of transactions per day. The manual process with Excel was error prone and labor-intensive.
We automated the process and reduced errors by writing an API that transfers bills from the billing system and posts them as journal entries in the accounting system. The API is “always on” and bills can be submitted at any time.
Accelerating Unit Turns Leads to More Rental Income
Unrented units cost money. A multifamily operator did not have visibility into how many days their units were vacant after a move out. Reports didn’t tell them how long it took to turn the units, or what else contributed to the vacancy.
By applying process analysis methods and analyzing existing data, we created a process improvement plan for having better visibility into key events that drive “days vacant”, such as timing of inspections, ordering of supplies, scheduling of techs and coordinating progress with the leasing team. This would not have been possible without analyzing data on work orders and uncovering process gaps.
Accelerating “Lead-to-Lease” Means More NOI
A multifamily owner operator came to us with this hypothesis: Occupancy can be increased and rents can be raised if they could become really efficient at every step of the leasing funnel (the “lead-to-lease” cycle).
We analyzed data on their ads, their leads (incoming phone calls, web inquiries), showings and the volume of leases signed. We uncovered delays in follow up by leasing agents, in scheduling appointments, as well as timing for processing lease documents.
These findings led to new SOPs, training for their call center team, better tracking of leads in Appfolio, and overall coordination between teams placing the ads, taking the phone calls, showing the units and processing the leases.
Drilling into Work Orders Leads to Savings, Resident Satisfaction
Maintenance work orders cost money, take time, and impact resident satisfaction. A management company with mostly single-family homes asked us to analyze every work order and identify opportunities for improvement.
By looking at work order close times by property, request type, technician and time of year, we uncovered opportunities for changing SOPs, improving communication between the office and maintenance techs, and training of the techs.
Better Offer Price When the Acquisition Pipeline Can Be Seen on a Map
A real estate investment company was acquiring manufactured housing parks across many states, but had limited knowledge of the local communities. We built maps that displayed the locations of the properties in their deal pipeline.
By overlaying additional information on the maps, such as school locations, upcoming Amazon distribution centers, demographic and household income data, our client was able to place a value on the assets more intelligently than by simply looking at the data provided by the sellers during due diligence. They used this enriched information preparing their offers to the sellers.
Cash Cows and Dogs of the Portfolio
Using a framework similar to the Boston Consulting Group quadrant analysis, we helped an owner operator separate the best performing properties from the others. Instead of looking at usual metrics such as NOI, we looked at ratios such as Utility Expenses, Marketing Expenses, Lead Conversion, Work Order Durations etc.
With an understanding of the differences between properties, the company created an action plan for prioritizing improvements and coaching their property managers. This helped the customer take control of the underlying drivers of NOI and overall asset performance.
A Clearer Financial Picture with a Consolidated Chart of Accounts
A franchisor received monthly P&L statements from over 100 franchisees. Since each franchisee had their own chart of accounts, it was not simple to roll up the numbers into the overall system profile.
RentViewer automated the pulling of all the P&L data from the Rent Manager API, created translation tables for rolling up the GL accounts into reporting headings, and generated consolidated P&Ls automatically. The result was much more realistic reporting of the performance of the franchises, and the ability to analyze differences across geographies and franchisees.
Days to Reach 85% Occupancy of a New Property
A client wanted to understand how long it was taking newly developed properties to reach 85% occupancy. There’s no report in their property management system that gives them that information. Also, leases weren’t being uniformly entered into the system, so that also created some challenges
Our solution was to analyze GL transactions and track the date of the first rent payment for each unit in each property. This enabled us to estimate occupancy in each property and the days since the start of operations. The customer is now able to see how long each property took to reach 85% occupancy and has insights into why some were faster than others.
Show Stabilized Assets Only, Please!
A multifamily owner operator with around 30 buildings was acquiring and disposing a few assets each year. When they displayed their systemwide NOI, occupancy and other performance metrics, the charts weren’t smooth. This was because newly developed assets that weren’t collecting rent but were incurring expenses were also included in the charts.
Excluding these properties from the reports wasn’t simple. We worked with the client to design a flexible, interactive solution for excluding selected properties for specific time periods in order to display metrics more realistically.
Collections Doesn’t Have to be Complicated
A manufactured housing operator wanted a simple way to create a task list each morning for their community managers. The task list needed to tell each community manager how far behind they were in collections, how many delinquent accounts there were, and which residents they needed to call or not call. Before speaking with us, this company ran the Rent Roll and A/R Aging report for each property, examined the data, and manually compiled the task list. That took hours, or didn’t get done at all, resulting in a ballooning A/R picture.
We automated the compilation of the task list by pulling reports from their property management system and highlighting the delinquent accounts. This report can be run on demand in case there were any payments received in the prior five minutes.
Ratios, Ratios and More Ratios
Do you know how many leads an ad on apartments.com generates for you? A client that does third-party management of Single-Family Homes gave us a list of ratios they wanted at their fingertips. These included leads generated per dollar spent on marketing, lead conversion rates by lead source, work orders per year by age of property, cost per work order, days vacant per property and more.
By building out these ratios based on years of historical data, and nuanced by variables such as property age, property type, geography and time of year, we provided our client with a powerful framework for making spending decisions. As a result, they are able to set realistic expectations with property owners who sign up with them, and can make projections for commissions, management fees and rental income when spending money on marketing and major improvements.
All Budget Variances Are Not Created Equal
Companies that look only at overall budget variance for NOI could be missing huge opportunities for brining income and expenses under control. A manufactured housing owner operator wanted their asset management analysts to be able to explain the causes of budget variance in great detail: By GL account, by property, by month, by impact on NOI. And then to tell the overall story of which variances caused the NOI to go up and which variances caused NOI to go down.
We pulled their budget and P&L data into the data warehouse and built an interactive report in Power BI that enabled them to drill down from overall NOI to positive and negative variances, and then drill down further by GL account and property. Being able to examine variances in this way leads to more realistic budgets and greater accountability. This enables smarter capital allocation and higher long-term NOI.
Rent Optimization, Unit by Unit
When it comes to setting market rents for your units, there’s an easy way and a right way. A multifamily operator had set the same market rent for all similar unit types (1Bed/1Bath, 2Bed/1Bath etc.) from externally acquired market data. The client believed that economic occupancy and rental income did not reflect the true potential of their 3,000 units.
We worked with this client to create more refined classifications of their Unit Types, by taking into account factors such as balconies, views, floor, last renovation date, amenities etc. in each specific unit.
By adding these “Enriched Unit Types” in their data warehouse, the client was able set market rent for each unit that reflected the true potential. Now the ads for their units reflect the true value of each unit, their leasing team can upsell prospects, and their economic occupancy numbers are more realistic.
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