Written one- and two-star Google reviews of at least five words, up to 60 per business, from high-review-count property management companies in eight US metros. A language model, with no human-coded check, classified the main reason in 7,554 of them: 82.6 percent how the business is run, 15.6 percent the physical repairs and the property itself, 1.8 percent unclear.
The study covers 7,556 written one- and two-star Google reviews of at least five words, drawn from the up-to-60 lowest-rated reviews retrieved per business, from 261 high-review-count property management companies in Phoenix, Dallas, Atlanta, Charlotte, Columbus, Denver, Tampa and Kansas City. In each metro, up to 200 listings were screened and, after filters including a cap of two locations per brand, the 40 businesses with the most Google reviews were sampled. Exclusions then left 266 businesses, of which 261 have reviews in the corpus. Review dates run from 2009-10-23 to 2026-09-30. A separate multi-label model pass assigned at least one business-process label to 95.1 percent of the 7,556 reviews.
Every figure below states its denominator, and none of them is a share of customers. The study also describes the words in the reviews, not who wrote them. In 59.8 percent of the 7,556 reviews, the text contains tenant vocabulary under a keyword pattern set out in the method section.
The model-classified main reason
The main-reason pass was run by a language model, Claude Haiku 4.5, which chose between how the business is run, the physical repairs and the property itself, and unclear. It classified 7,554 of the 7,556 reviews.
| Model-classified main reason | Share of the 7,554 reviews labelled by the main-reason pass |
|---|---|
| How the business is run | 82.6% |
| The physical repairs and the property itself | 15.6% |
| Unclear | 1.8% |
A second model, Claude Sonnet 4.5, classified 200 of the same reviews. On the binary question of how the business is run against every other main reason, the two models agreed on 91.5 percent of the 200, with a Cohen’s kappa of 0.742 (95 percent confidence interval 0.61 to 0.85). It is not validation against human coding, and no sample was checked against human coding.
Process and the property, counted label by label
A separate pass assigned theme labels, and a review can carry more than one. Some describe the business process, such as communication, billing or leasing. Others describe the physical repairs and the condition of the property. A single review can carry both kinds.
| What the labels cover | Share of the 7,556 reviews |
|---|---|
| A business-process label only | 55.5% |
| Both a process label and a core-service label | 39.6% |
| A core-service label only | 0.9% |
| Neither a process nor a core label | 4.0% |
The model assigned at least one core-service label to 40.5 percent of the 7,556 reviews. The multi-label pass and the main-reason pass answer different questions. One asks what a review mentions, the other asks what the model classified as the main reason, and the two are not reconciled review by review.
The model-assigned themes
The table lists every label in the taxonomy. These are themes a model assigned to what reviewers wrote, not verified events. A review can carry several labels. The shares overlap and are not summed.
| Model-assigned theme | Share of the 7,556 reviews |
|---|---|
| Communication failure | 61.1% |
| Staff conduct | 42.8% |
| Maintenance responsiveness | 38.6% |
| Property condition | 37.2% |
| Leasing and application | 30.6% |
| Billing and fees | 29.0% |
| Deposit and move-out | 18.0% |
| Other | 11.1% |
| Repair quality | 10.7% |
Communication failure
The model assigned the communication failure label to the largest share, 61.1 percent of the 7,556 reviews. It is also one of the two catch-all process labels in the taxonomy. Both quoted excerpts describe repeated contact about an account.
“Multiple messages in person and over the phone asking them to activate my email account so I can pay online led to nothing”
“I shouldn’t have to call In every month to verify that my account statement has a balance of 0 after making a payment”
Maintenance responsiveness
The model assigned the maintenance responsiveness label to 38.6 percent of the 7,556 reviews. The quoted excerpts describe maintenance requests the reviewer says got no response and work orders the reviewer says were closed without resolution.
“we would put in maintenance requests but wouldn’t get a response”
“submitted multiple work orders, which were closed without resolution.”
Maintenance responsiveness is a process label. Repair quality is a separate core-service label, at 10.7 percent of the 7,556 reviews.
Deposit and move-out
The model assigned the deposit and move-out label to 18.0 percent of the 7,556 reviews. One quoted excerpt describes a carpet-cleaning charge at move-out. The other describes a deposit the reviewer says was kept without explanation.
“When I moved out, even though I left the carpet much cleaner than when I moved in, they still charged me for carpet cleaning.”
“they are keeping my deposit with no explanation. I’ve called and of course no one is available”
The study did not check any deposit, inspection or lease, and makes no finding about what any company was entitled to keep.
Leasing and application
The model assigned the leasing and application label to 30.6 percent of the 7,556 reviews. The label describes the leasing process. It does not identify who wrote the review.
“No one offered to walk the unit with her before signing the lease but made her sit through a one hour presentation”
“Only had one showing for an entir week only a 4 hour window”
Disclosure: this study was produced by Sales Roadmaps, which sells operations consulting services. About the operations consulting service
The number that keeps this honest
The 266 businesses sampled after exclusions hold 88,021 lifetime Google reviews between them. Of those, 18,022, or 20.5 percent, are one or two stars, and 74.4 percent are five stars. Of the 266, 261 have at least one review in the corpus studied here.
Percentages in this study use the denominator stated with each figure. Main-reason shares are of the 7,554 reviews the main-reason pass labelled. Label shares and the tenant-vocabulary share are of all 7,556 reviews. The base rate and the five-star share are of all 88,021 lifetime reviews. None of them is a share of customers or of tenants.
Operational questions these reviews raise
These are questions for an owner reviewing their own operation. They are not findings. The study reports what reviewers wrote and how models labelled those reviews. Related reading on Sales Roadmaps covers CRM setup, account ownership and business process management.
How are messages logged and answered?
The model assigned the communication failure label to 61.1 percent of the 7,556 reviews. The quoted examples describe repeated contact about an account, a situation the study did not count separately. An owner can ask how messages are logged and who answers them.
When is a work order closed?
The model assigned the maintenance responsiveness label to 38.6 percent of the 7,556 reviews. One quoted example describes work orders closed without resolution, a situation the study did not count separately. An owner can ask what has to be true before a work order is marked closed.
How are deposit deductions explained?
The model assigned the deposit and move-out label to 18.0 percent of the 7,556 reviews. The quoted examples include a reviewer describing a move-out charge and another saying a deposit was kept without explanation. An owner can ask how each deduction is documented and communicated.
How the study was done
Google reviews were retrieved through the DataForSEO business data API, sorted by lowest rating, up to 60 per business. In each of the eight metros, up to 200 listings were screened. After filters, the 40 businesses with the most reviews per metro were sampled, with at most two locations per brand.
Fifty-four businesses were excluded. Fifty-two were HOA or community-association managers. In each of them, HOA or community-association markers appeared in at least 37.5 percent of its usable reviews and outnumbered tenant markers. Two were vacation-rental managers, identified by name.
The tenant-vocabulary figure uses a case-insensitive pattern matched against review text: \btenants?\b|\brent\b|\blease\b|\blandlord\b|security deposit|move[- ]out. In plain words, it matches tenant, tenants, rent, lease and landlord, and the phrases security deposit, move-out and move out. It counts words, not who wrote the review. In 59.8 percent of the 7,556 reviews, the text matches.
The corpus consists of the retrieved reviews rated one or two stars with at least five words, after exclusions and deduplication. Identical review texts posted under two listings were counted once, which removed 2 reviews. Candidate review-theme codes were proposed by a language model, Google Gemini 3.1 Flash-Lite, on a seeded sample of 200 reviews, and the research team consolidated them into a fixed label set.
Labels were then assigned by the same model. A second model, DeepSeek V3.2, relabelled 200 reviews and agreed on process against not process in 97.5 percent of them, with a kappa of 0.749. Each label was asked to carry a verbatim span from the review, and 95.4 percent of labels carry one. The rest are counted without a span.
As a label-based sensitivity test, which is not a main-reason result, the two catch-all process labels, staff conduct and communication failure, were ignored. On that basis, 81.5 percent of the 7,556 reviews still carry at least one process label. Reweighted by each business’s lifetime one- and two-star count, the any-label process share is 96.6 percent. With equal weight per business it is 93.7 percent.
The stored review data carry a business key, the star rating, the date, the text and an owner-reply flag only. No reviewer names were stored. No reviewer or reviewed business is named in this article.
Limitations
Google reviews are self-selected. The sample covers high-review-count businesses in eight metros: after filters including a cap of two locations per brand, the 40 businesses per metro with the most Google reviews among up to 200 listings screened, before exclusions. Review dates run from 2009-10-23 to 2026-09-30, and the sample is sorted by rating, not by date, so the study makes no claim about trends over time.
All labels and main reasons were assigned by language models, and no sample was checked against human coding.
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Frequently Asked Questions
In this study, which model-assigned themes are most common?
Communication failure, which the model assigned to 61.1 percent of the 7,556 reviews studied. Staff conduct follows at 42.8 percent and maintenance responsiveness at 38.6 percent. A review could receive more than one label, so these shares should not be summed.
In this study, what share of reviews was classified as mainly about repairs and the property?
In the main-reason classification, 15.6 percent of the 7,554 labelled reviews were assigned to the physical repairs and the property itself and 82.6 percent to how the business is run. At least one core-service label was assigned to 40.5 percent of the 7,556.
In this study, were the reviews written by tenants?
The study did not identify reviewers. In 59.8 percent of the 7,556 reviews, the text contains tenant vocabulary under the keyword pattern described in the method.
Among the businesses sampled, what share of all reviews are one or two stars?
Among the 88,021 lifetime reviews held by the 266 businesses sampled, 20.5 percent are one or two stars and 74.4 percent are five stars. That is a share of reviews, not of customers.
How closely did the models agree?
On the binary main-reason question, how the business is run against every other main reason, a second model agreed on 91.5 percent of 200 reviews, kappa 0.742. For the theme labels, process against not process, a separate second model agreed on 97.5 percent of 200 reviews, kappa 0.749. No sample was checked against human coding.
Does the study verify whether what reviewers wrote is true?
No. It reports what reviewers wrote and how often the model assigned each theme. No work order, deposit or lease was checked against company records.
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