AppWork Expands Top Maintenance Rating Program to 200 Winners Per Quarter
Summary
AppWork, a maintenance operations platform, announced on July 1, 2026, a significant expansion of its Top Maintenance Rating Program, co-presented with Multifamily Media Network (MMN). Beginning with the Summer 2026 season, the program will recognize 200 winning properties quarterly, quadrupling the previous 50 winners. This expansion introduces two new size-based award groups: properties with fewer than 100 units and those with 100 or more units, each producing 100 winners. The program automatically enters active AppWork clients and evaluates properties based on the highest percentage of 4 and 5-star work order ratings from verified residents. This restructuring reflects AppWork's platform growth and client base diversity.
Key takeaway
For property managers and multifamily operators aiming to enhance their community's reputation, focus on consistently achieving high resident satisfaction with maintenance services. Your team's performance, reflected in 4 and 5-star work order ratings, can lead to recognition through AppWork's expanded Top Maintenance Rating Program. Earning these awards provides valuable marketing assets and validates your operational excellence.
Key insights
AppWork expanded its maintenance rating program to recognize more properties and ensure fair competition.
Principles
- Program growth reflects platform scaling and client diversity.
- Recognizing more maintenance teams is beneficial.
Method
Properties are automatically entered and evaluated quarterly based on resident 4 and 5-star work order ratings, with new size-based groups for fair competition.
In practice
- Winners receive digital badges and physical crystal trophies.
- Size-based grouping ensures fair competition for smaller communities.
Topics
- AppWork
- Property Management
- Maintenance Operations
- Resident Satisfaction
- Multifamily Housing
- Performance Recognition
Best for: Operations Professional, Domain Expert
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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI Journal.