How we researched this.
ClearLogic’s property maintenance research draws on publicly posted customer reviews from NoCowboys, the same major New Zealand tradesperson-review platform used in our trades research — a separate corpus, crawled the same way. This page states exactly how we collected, scored, and filtered that data, so any figure we publish can be checked rather than taken on faith. (Our trades research methodology is a separate page, covering a separate corpus.)
Publicly posted reviews, crawled politely.
We crawled NoCowboys respecting the site’s published robots.txt,
including its explicit allowance for AI agents and its 5-second crawl delay. We did not access anything
not publicly visible on the platform, and we did not create an account or interact with the site beyond
reading published pages.
The corpus covers three property maintenance service categories, national scope, no geographic pre-filtering:
| Category | Businesses | Reviews |
|---|---|---|
| Cleaning services | 47 | 4,483 |
| Garden & outdoor | 126 | 6,211 |
| Pest control | 13 | 1,876 |
| Total | 186 | 12,570 |
Category construction, stated plainly: garden & outdoor is a merged category — the platform’s “garden-services” and “landscaping-services” listings overlapped 46 businesses (60%/48% of each list), so they were deduped by business and combined into one category rather than double-counted or reported as two separate, overlapping corpora. “Housekeeping” (4 businesses, 270 reviews) was dropped entirely as a 100% subset of cleaning-services.
This is a cross-sectional snapshot of each business’s full public review history as it stood at crawl time (12 August 2026). It is not a rolling monitor and does not update automatically — if we republish an updated version later, we’ll date it separately from this one.
Same crawl method as our trades research.
Consistent with our trades research: a cross-sectional snapshot of each business’s full public review history as it stood at crawl time, not a rolling monitor.
How reviews are scored, and how we coded them.
The scoring system
Each review on the platform carries four sub-scores — communication, quality, reliability, and value — plus an overall score, all on a 0–100 scale, self-reported by the reviewer at the time they left it. We didn’t create or adjust this scoring system; we report it as published. Same system, same method as our trades research.
Complaint & praise coding
We defined a negative review as one scoring below 50/100 on any dimension, and a positive review as one scoring 80/100 or higher overall. Within those groups, we coded the reason for the score against a fixed taxonomy — 5 complaint categories, 13 praise categories — using an LLM classifier (Claude Haiku 4.5, run in batches) rather than keyword matching. The taxonomy is fixed in advance of coding, not built from the results.
A statistic only gets published if it clears two thresholds.
- 01At least 30 reviews behind any single figure (a category × region, category × business-size, or similar breakdown), so a figure isn’t resting on a handful of opinions.
- 02At least 5 different businesses contributing to that figure — our own addition, on top of the review-count threshold, because a stat built from 2–4 businesses’ reviews can be traced back to roughly which businesses it describes even with no name attached. Breakdowns that don’t clear 5 businesses are dropped from what we publish, not estimated or smoothed over to fill the gap.
This corpus is smaller and more regionally concentrated than our trades research (186 businesses vs. 369, 69–78% Auckland-based in every category here vs. a more even national spread there), so more breakdowns fail the second threshold — 32% of candidate figures here, against 13% in our trades research. Eight specific breakdowns were dropped for this reason:
| Category & breakdown | Businesses | Reviews |
|---|---|---|
| Cleaning services, Canterbury region | 1 | 41 |
| Cleaning services, Taranaki region | 1 | 48 |
| Cleaning services, Waikato region | 1 | 34 |
| Garden & outdoor, Bay of Plenty region | 2 | 84 |
| Garden & outdoor, Canterbury region | 1 | 56 |
| Pest control, Wellington region | 2 | 292 |
| Pest control, mid-sized businesses (review-count tercile) | 4 | 376 |
| Pest control, large-sized businesses (review-count tercile) | 4 | 1,391 |
Pest control’s size-tier comparison is not published at all as a result — two of its three tiers (mid, large) fail the business-count threshold, leaving only “small” able to be reported alone, which isn’t a comparison. Each of the rows above clears the 30-review threshold, in some cases by a wide margin (1,391 reviews behind 4 businesses) — exactly the failure mode the business-count threshold exists to catch: review volume can look robust while the business count behind it is small enough to identify.
No names, ever.
No business name, owner name, staff name, or individual review appears in anything we publish. Every figure we report is an aggregate across many reviews and many businesses. No verbatim review text is published or linkable from our property maintenance research.
Same platform, same method, one genuine difference.
Our trades research (electricians, plumbers, painters) found a small negative correlation between business size and approval rating for two of the three trades (r = −0.32 plumbers, r = −0.23 electricians). This property maintenance corpus shows no equivalent pattern in any of its three categories (r = 0.04 cleaning, −0.02 garden & outdoor, 0.15 pest control). We flag the comparison here rather than in the study itself, because the two datasets describe different service models — largely one-off callouts in the trades corpus, largely recurring client relationships in this one — and reading one as a rebuttal of the other would overstate what either corpus can show.
Read the numbers with these in mind.
- —This is public reviews, not a random sample of every job done. People with an unremarkable, fine experience are less likely to leave a review at all than people who were thrilled or angry, so the corpus skews toward strong opinions in both directions.
- —Correlation isn’t causation. The “no size penalty” finding is an association across a fairly small business count (as low as 13 for pest control), not a proven mechanism — pest control’s correlation is the least reliable of the three given its size.
- —Small figures on small bases are directional, not precise. Only cleaning-services had enough negative reviews (55) to break into complaint categories at all; garden & outdoor and pest control’s negative samples were both too thin to categorise, stated plainly rather than estimated around.
Figures drawn from this corpus are treated internally as candidate findings until reviewed and approved for publication. This page will be updated if the underlying data is refreshed or re-crawled. Last updated: 12 August 2026.
