We mapped 117 Georgia vacation rental management companies from the VRMA directory: the largest operators, top-5 concentration (42%), dominant software, and what the data means. Primary-source figures, cleaned.

# Georgia Vacation Rental Management Companies: The 2026 Market in Numbers

We pulled every Georgia member of the national vacation rental managers’ association and ranked the market by the numbers: how many companies, who manages the most units, how concentrated it is, and what software runs it.

**Georgia is home to 117 VRMA-member vacation rental management companies**, of which 56 disclosed a unit count totaling 3,663 managed units. The market is best described as a moderately concentrated market.

The largest reporting operator is **Vector Travel** at roughly 400 units. The top five reporting operators control about 42 percent of all units disclosed, the most common property management system among members is Streamline, and the median member company was founded around 2014.

## 01Georgia at a glance

VRMA member companies headquartered in Georgia, March 2026.

## 02The largest operators

Ranked by self-reported units managed. Figures are disclosed by the operators themselves.

| # | Operator | Units (self-reported) |
| --- | --- | --- |
| 1 | Vector TravelAtlanta | 400 |
| 2 | Escape to Blue RidgeAlpharetta | 325 |
| 3 | Tybee Vacation RentalsTybee Island | 300 |
| 4 | Lucky Savannah Vacation RentalsSavannah | 300 |
| 5 | Coastal Vibe VacationsSmyrna | 212 |
| 6 | Tybee Beach Vacation Rentals & Property Management LLCTybee Island | 180 |
| 7 | Jekyll Island Property Sales & Mgmt., Inc.Jekyll Island | 175 |
| 8 | Minty LivingAtlanta | 170 |
| 9 | Southern Comfort Cabin RentalsBlue Rdge | 151 |
| 10 | Blue Sky Cabin RentalsEllijay | 145 |

## 03How concentrated is the market?

The five largest reporting operators control about **42 percent** of every unit disclosed by Georgia members. That is a middle-of-the-road split typical of established markets.

## 04What software runs the market

Most-named property management systems among Georgia members.

According to the VRMA directory, the unit-heavy incumbents in Georgia still lean on Streamline and legacy HomeAway/Escapia systems, the same generational pattern we documented across the whole industry in [the PMS market analysis](https://rapideyeinspections.com/blog/vacation-rental-pms-generational-shift/).

## 05What the data says

According to the VRMA public member directory (compiled March 2026), Georgia has 117 member vacation rental management companies, of which 56 disclosed a unit count totaling 3,663 managed units. The largest reporting operator is Vector Travel at roughly 400 units, and the top five reporting operators hold about 42 percent of disclosed units, making Georgia a middle-of-the-road split.

Most Georgia members are small independents rather than large portfolio operators; the median member company was founded around 2014. Among members that named a property management system, Streamline is the most common, consistent with the broader industry pattern in which Streamline and legacy HomeAway and Escapia systems still run most unit-heavy incumbents.

## 06Compare other states

Figures come from RapidEye's analysis of the **VRMA (Vacation Rental Management Association) public member directory**, compiled March 2026. Counts cover companies that list a Georgia headquarters. Unit counts are **self-reported**; only 56 of 117 Georgia members disclosed one, so the 3,663-unit total is a floor, not a census of every short-term rental in the state.

We de-duplicated companies that appear under multiple brand spellings (keeping the higher figure) and excluded entries whose self-reported totals reflect national franchise networks, timeshare or HOA portfolios, or brands since acquired and folded into a national operator. Georgia required only standard de-duplication; no national-franchise, timeshare, or HOA totals needed to be removed.

## 07Sources

- VRMA (Vacation Rental Management Association) — Public Member Directory, compiled March 2026 (primary data set).https://www.vrma.org/search/newsearch.asp

- RapidEye — The Largest Short-Term Rental Management Companies (2026).https://rapideyeinspections.com/blog/largest-short-term-rental-management-companies/

- RapidEye — The Vacation Rental PMS Market Just Flipped: 40 Years in One Chart.https://rapideyeinspections.com/blog/vacation-rental-pms-generational-shift/

## 08Frequently asked

### How many vacation rental management companies are in Georgia?

According to the VRMA public member directory compiled in March 2026, Georgia has 117 member vacation rental management companies. Of those, 56 disclosed a unit count, together reporting 3,663 managed units.

### Who is the largest vacation rental management company in Georgia?

Based on self-reported figures in the VRMA directory, the largest reporting operator in Georgia is Vector Travel with approximately 400 units. The five largest reporting operators together account for about 42 percent of all units disclosed by Georgia members.

### What property management software do Georgia vacation rental companies use?

Among Georgia members that named a property management system, Streamline is the most common, consistent with the broader pattern in which Streamline and legacy HomeAway/Escapia systems still run most unit-heavy incumbents.


Haitian Creole Cleaning Vocabulary for Vacation Rental Turnovers

Haitian Creole is Florida's third most-spoken language and the first language of much of the South Florida cleaning workforce. The essential turnover vocabulary, verified, for managers who don't speak it.

# Haitian Creole cleaning vocabulary for turnovers

In much of Florida, your cleaning team's first language is Haitian Creole, yet almost no operational resources exist for it. Here is the essential, verified turnover vocabulary for managers who don't speak it.

Haitian Creole speakers in Florida, making it the state's third most-spoken language after English and Spanish. According to [Axios Tampa Bay](https://www.axios.com/local/tampa-bay/2025/06/06/haitian-third-most-spoken-language-florida-english-and-spanish) (June 2025), citing Census data.

### Greetings and courtesy

### The cleaning core

### Rooms and the house

### Things in the property

### Problems and timing

### Use this as a verified starting point

Every term above was checked against published Haitian Creole dictionaries and phrasebooks, but this is a core set, not an exhaustive SOP. Haitian Creole is a full language with French and West African roots, written in a standardized phonetic spelling, and regional usage varies. For full sentences and property-specific instructions, confirm the exact wording with your own team members, who will appreciate the effort either way. For the broader system this fits into, see the [guide to managing a team you don't share a language with](https://rapideyeinspections.com/blog/how-to-manage-spanish-speaking-cleaning-team/).

### When the vocabulary runs out, use photos

Here is the honest part: there is far less ready-made bilingual turnover material in Haitian Creole than in Spanish, so you will hit the edge of any word list quickly. That is exactly why the most reliable tool for a Haitian Creole-speaking team is not text at all, it is a picture of the finished result. A reference photo of a correctly staged bed or a stocked bathroom communicates the standard with zero translation, in any language. RapidEye is built on that idea: cleaners document each turnover with photos, and the AI compares against a baseline, so quality control never depends on a shared language. See [how to build photo-based instructions](https://rapideyeinspections.com/blog/photo-cleaning-instructions-non-english-speakers/).

## Frequently asked questions

Because in much of Florida, your cleaning team speaks it. According to Axios, citing Census data, Haitian Creole is Florida's third most-spoken language after English and Spanish, with roughly 426,000 speakers statewide, concentrated in Miami, Fort Lauderdale, Orlando, and Tampa. For vacation rental operators in those markets, a Haitian Creole-speaking cleaning team is common, yet almost no operational resources exist for it. A small, respectful working vocabulary goes a long way.

No. The same playbook that works for a Spanish-speaking team works here: a handful of core words, a translation-friendly channel like WhatsApp, and above all photo-based standards that need no translation at all. Haitian Creole has fewer ready-made bilingual turnover tools than Spanish, which makes visual instructions and reference photos even more valuable. Learn the essential vocabulary, then let pictures carry the rest.

Haitian Creole uses a standardized phonetic spelling: letters consistently represent the same sounds, so once you learn the system, words are read much as they are written. It is a distinct language with French and West African roots, not a dialect of French. Because regional and individual usage varies, the safest approach is to confirm exact wording with your own team members, who will appreciate the effort regardless.

### Sources

### Related reading

#### How to Manage a Spanish-Speaking Cleaning Team

The full playbook for running a team you don't share a language with.

#### Cleaning Instructions With Photos Instead of Words

The visual approach that matters most where bilingual tools are scarce.

#### Spanish Cleaning Vocabulary for Turnovers

The same reference for the other major cleaning-workforce language.

#### How to Communicate With Cleaners Who Don't Speak English

WhatsApp translation, voice notes, and photo confirmation.


We mapped 106 Hawaii vacation rental management companies from the VRMA directory: the largest operators, top-5 concentration (51%), dominant software, and what the data means. Primary-source figures, cleaned.

# Hawaii Vacation Rental Management Companies: The 2026 Market in Numbers

We pulled every Hawaii member of the national vacation rental managers’ association and ranked the market by the numbers: how many companies, who manages the most units, how concentrated it is, and what software runs it.

**Hawaii is home to 106 VRMA-member vacation rental management companies**, of which 45 disclosed a unit count totaling 6,077 managed units. The market is best described as an island-by-island, resort-condo market.

The largest reporting operator is **Castle Resorts & Hotels** at roughly 1,000 units. The top five reporting operators control about 51 percent of all units disclosed, the most common property management system among members is HomeAway / Escapia (legacy), and the median member company was founded around 2008.

## 01Hawaii at a glance

VRMA member companies headquartered in Hawaii, March 2026.

## 02The largest operators

Ranked by self-reported units managed. Figures are disclosed by the operators themselves.

| # | Operator | Units (self-reported) |
| --- | --- | --- |
| 1 | Castle Resorts & HotelsHonolulu | 1,000 |
| 2 | Outrigger Resorts & HotelsHonolulu | 880 |
| 3 | AA Oceanfront Rentals and SalesKihei | 500 |
| 4 | CONDOMINIUM RENTALS HAWAIIKIHEI | 375 |
| 5 | The Parrish Collection LLCKoloa | 350 |
| 6 | My Perfect Stays LLCLahaina | 330 |
| 7 | Rentals Maui IncKihei | 320 |
| 8 | Kona Coast VacationsKailua Kona | 280 |
| 9 | Elite Pacific PropertiesHonolulu | 205 |
| 10 | Chase N Rainbows Real Estate Inc.Lahaina | 205 |

## 03How concentrated is the market?

The five largest reporting operators control about **51 percent** of every unit disclosed by Hawaii members. That is a middle-of-the-road split typical of established markets.

## 04What software runs the market

Most-named property management systems among Hawaii members.

According to the VRMA directory, the unit-heavy incumbents in Hawaii still lean on Streamline and legacy HomeAway/Escapia systems, the same generational pattern we documented across the whole industry in [the PMS market analysis](https://rapideyeinspections.com/blog/vacation-rental-pms-generational-shift/).

## 05What the data says

Hawaii is an island-by-island market dominated by resort-condominium operators. According to the VRMA public member directory (compiled March 2026), 106 Hawaii companies are members. After excluding Marina Hawaii Vacations (see below), the largest reporting short-term rental operator is Castle Resorts & Hotels at roughly 1,000 units, followed by Outrigger Resorts & Hotels.

The inventory is concentrated on Oahu's Waikiki, Maui, Kauai, and the Big Island, and it skews toward condo-resort buildings rather than detached homes. That shows up in the software: legacy HomeAway and Escapia systems, built for large repeatable condo inventories, are the most common here, unlike the Streamline-led mainland markets. The median Hawaii member was founded around 2008, the oldest cohort of any state we mapped.

## 06Compare other states

Figures come from RapidEye's analysis of the **VRMA (Vacation Rental Management Association) public member directory**, compiled March 2026. Counts cover companies that list a Hawaii headquarters. Unit counts are **self-reported**; only 45 of 106 Hawaii members disclosed one, so the 6,077-unit total is a floor, not a census of every short-term rental in the state.

We de-duplicated companies that appear under multiple brand spellings (keeping the higher figure) and excluded entries whose self-reported totals reflect national franchise networks, timeshare or HOA portfolios, or brands since acquired and folded into a national operator. In Hawaii, the largest excluded entry was Hawaiian Properties, Ltd. dba Marina Hawaii Vacations (35,840 self-reported units), a residential and HOA portfolio, not short-term rentals.

## 07Sources

- VRMA (Vacation Rental Management Association) — Public Member Directory, compiled March 2026 (primary data set).https://www.vrma.org/search/newsearch.asp

- RapidEye — The Largest Short-Term Rental Management Companies (2026).https://rapideyeinspections.com/blog/largest-short-term-rental-management-companies/

- RapidEye — The Vacation Rental PMS Market Just Flipped: 40 Years in One Chart.https://rapideyeinspections.com/blog/vacation-rental-pms-generational-shift/

## 08Frequently asked

### How many vacation rental management companies are in Hawaii?

According to the VRMA public member directory compiled in March 2026, Hawaii has 106 member vacation rental management companies. Of those, 45 disclosed a unit count, together reporting 6,077 managed units.

### Who is the largest vacation rental management company in Hawaii?

Based on self-reported figures in the VRMA directory, the largest reporting operator in Hawaii is Castle Resorts & Hotels with approximately 1,000 units. The five largest reporting operators together account for about 51 percent of all units disclosed by Hawaii members.

### What property management software do Hawaii vacation rental companies use?

Among Hawaii members that named a property management system, HomeAway / Escapia (legacy) is the most common, consistent with the broader pattern in which Streamline and legacy HomeAway/Escapia systems still run most unit-heavy incumbents.


Property managers lose thousands to undetected damage every year. Learn why manual inspections miss so much and how baseline comparison changes the game.

# The Hidden Cost of Missed Damage in Short-Term Rentals

**The short version:** Damage claims occur in roughly 0.4% to 1.5% of short-term rental stays, but property managers only recover 56% to 68% of what they claim. The biggest reason claims get denied is missing baseline documentation: cleaners working under 20 to 30 minute turnover windows cannot reliably distinguish new damage from existing wear, and platforms like Airbnb and Vrbo will not pay out claims that lack timestamped before-and-after evidence.

Baseline comparison, creating a visual record of each property and comparing new inspections against it, is the only systematic way to prove damage attribution and recover otherwise-lost claim revenue.

Most property managers I talk to know damage happens. What they don’t know is how much of it slips through.

Let me share some numbers that surprised me. An analysis of [20,000+ bookings in the Smoky Mountains](https://avadaproperties.com/airbnb-vrbo-damage-claims-statistics-and-assumptions/) found damage claims occurred in about 0.7% of Airbnb stays and 0.4% of Vrbo stays. Another dataset of [nearly 19,000 reservations](https://myangelhost.com/debunking-the-myth-lower-prices-and-property-damage-in-vacation-rentals/) showed a 1.48% claim rate. So roughly 1 in 100 to 1 in 200 stays results in a damage claim.

That sounds manageable until you think about scale. If you’re running 50 units with decent occupancy, you’re looking at several damage incidents per year. And here’s the thing: those are just the claims that actually get filed. The damage that gets caught, documented, and submitted. What about everything else?

## Why Manual Inspections Miss So Much

I’ve spent a lot of time talking to property managers about their turnover process. The pattern is pretty consistent.

Cleaners have maybe [20 to 30 minutes](https://www.doorloop.com/blog/rental-property-inspection) per unit. They’re focused on getting the place guest ready, not forensically documenting every surface. They’re working in whatever lighting happens to be there. They’re often rushing between properties.

Under those conditions, here’s what gets missed:

- **Small scratches and scuffs** on walls, floors, and furniture

- **Stains** that blend into patterns or show up differently under certain light

- **Missing items** like remotes, kitchen tools, or decor pieces

- **Damage that looks like existing wear** because no one remembers what it looked like before

That last one is the killer. If you can’t prove something is new damage versus pre-existing wear, you can’t claim it. And most property managers don’t have a reliable visual baseline to compare against.

## The Downstream Pain

Missed damage doesn’t just mean repair costs. It creates a whole cascade of problems.

### Claims Get Denied

Both Airbnb and Vrbo give you [14 days after checkout](https://help.vrbo.com/articles/How-do-I-file-a-damage-deposit-claim) to file a damage claim. That sounds reasonable until you realize you need to:

- Discover the damage

- Document it properly

- Gather evidence that it wasn’t pre-existing

- Submit everything through the platform

If your cleaner doesn’t catch a scratch until the next guest has already checked in, you’ve now got an attribution problem. Was it Guest A or Guest B? Platforms won’t pay out if you can’t prove it.

Even when you file on time, [Airbnb’s Host Damage Protection](https://www.airbnb.com/help/article/2869) explicitly requires “evidence” including photos, videos, receipts, and repair estimates. That same Smoky Mountains analysis found hosts only get [56 to 68% of claimed amounts approved](https://avadaproperties.com/airbnb-vrbo-damage-claims-statistics-and-assumptions/). Nearly half of what you claim just disappears.

### The Wear vs Damage Problem

Platforms [don’t cover normal wear and tear](https://www.airbnb.com/help/article/279). Makes sense. But in practice, this creates a gray zone that works against you.

Is that couch cushion stain from this guest or has it been slowly accumulating? Is that scuff on the hardwood from someone dragging luggage or just years of foot traffic? Without before and after documentation, you’re guessing. And when you’re guessing, the platform defaults to “wear and tear” and denies the claim.

### Guest Disputes Turn Ugly

When you do try to charge a guest for damage, they often push back. Understandably. They don’t remember causing it, or they think it was already there.

Without timestamped visual evidence from before their stay, these disputes become he said she said. You might be right, but you can’t prove it. So you either eat the cost or damage your relationship with the guest and potentially your reviews.

## What Actually Works: Baseline Comparison

The core problem isn’t that cleaners are bad at their jobs. It’s that humans aren’t great at remembering exactly what 50 different properties looked like last week.

The solution is pretty simple in concept: create a visual baseline of each property, then compare new inspection footage against it to spot changes.

This is what we’re building at RapidEye. The system works with video or existing photos. You don’t have to change your workflow. Your cleaners do their normal walkthrough, we process the footage, and our AI flags anything that looks different from the baseline.

New scratch on the coffee table? Flagged. Stain on the carpet that wasn’t there before? Flagged. Missing throw pillow? Flagged.

Each detection comes with timestamps and visual evidence. So when you need to file a claim or respond to a guest dispute, you have actual proof that the damage is new and attributable to a specific stay.

## If You’re Not Ready for Automation

Look, not everyone is ready to bring in new software. I get it. Here’s what you can do right now to reduce missed damage:

### Standardize Your Photo Protocol

Create a shot list for each property. Same angles, same sequence, every turnover. This at least gives you something to compare against manually.

### Improve Lighting During Inspections

Damage hides in shadows. If your cleaners are inspecting in dim conditions, they’re going to miss things. Consider adding “turn on all lights and open blinds” to your checklist.

### Build in Dedicated Inspection Time

Separate cleaning from inspecting. Even 5 minutes of focused damage checking after the clean is done will catch more than trying to do both simultaneously.

### Document Known Wear Proactively

Take photos of existing wear items and keep them on file. When a guest claims “that scratch was already there,” you can actually verify whether they’re right.

### Set Calendar Reminders for Claim Deadlines

You’ve got 14 days. Set an alert at checkout plus 7 days to review the property condition before you run out of time.

## The Bottom Line

The [vacation rental market](https://www.fortunebusinessinsights.com/vacation-rentals-market-113271) is projected to nearly triple to $481 billion by 2034. That’s a lot of properties, a lot of turnovers, and a lot of damage slipping through the cracks.

Property managers who figure out damage detection now are going to have a real advantage. Not just in recovered costs, but in cleaner operations and fewer disputes.

Whether you use RapidEye or build your own process, the principle is the same: you can’t catch what you can’t see, and you can’t claim what you can’t prove. Baseline comparison solves both problems.

If you want to see how automated detection works in practice, I’m happy to show you. But honestly, even just tightening up your manual documentation will put you ahead of most operators. Start there.

## Sources

- [Avada Properties: Airbnb and Vrbo Damage Claims Statistics and Assumptions](https://avadaproperties.com/airbnb-vrbo-damage-claims-statistics-and-assumptions/)
https://avadaproperties.com/airbnb-vrbo-damage-claims-statistics-and-assumptions/

- [MyAngelHost: Debunking the Myth, Lower Prices and Property Damage in Vacation Rentals](https://myangelhost.com/debunking-the-myth-lower-prices-and-property-damage-in-vacation-rentals/)
https://myangelhost.com/debunking-the-myth-lower-prices-and-property-damage-in-vacation-rentals/

- [Airbnb Help Center: Host Damage Protection (Article 2869)](https://www.airbnb.com/help/article/2869)
https://www.airbnb.com/help/article/2869

- [Airbnb Help Center: Wear and Tear Policy (Article 279)](https://www.airbnb.com/help/article/279)
https://www.airbnb.com/help/article/279

- [Vrbo Help Center: How Do I File a Damage Deposit Claim?](https://help.vrbo.com/articles/How-do-I-file-a-damage-deposit-claim)
https://help.vrbo.com/articles/How-do-I-file-a-damage-deposit-claim

- [DoorLoop: Rental Property Inspection Guide](https://www.doorloop.com/blog/rental-property-inspection)
https://www.doorloop.com/blog/rental-property-inspection

- [Fortune Business Insights: Vacation Rentals Market Size, Share & Forecast](https://www.fortunebusinessinsights.com/vacation-rentals-market-113271)
https://www.fortunebusinessinsights.com/vacation-rentals-market-113271

## Related Reading

- [How to Actually Get Reimbursed Through Airbnb AirCover in 2026](https://rapideyeinspections.com/blog/airbnb-aircover-damage-claim-guide/)

- [Best AI Turnover Inspection Tools for Short-Term Rentals in 2026](https://rapideyeinspections.com/blog/best-ai-turnover-inspection-short-term-rentals-2026/)

- [The 5-Layer AI Stack for Short-Term Rental Operations](https://rapideyeinspections.com/blog/best-ai-short-term-rental-operations-2026/)

- [Does Paraspot Work for Short-Term Rentals?](https://rapideyeinspections.com/blog/does-paraspot-work-for-short-term-rentals/)

- [Why STR Damage Claims Get Denied](https://rapideyeinspections.com/blog/why-str-damage-claims-get-denied/)


AI Automations You Can Build on Hostaway (2026)

A builder-grade map of the AI automations the Hostaway API actually supports, ranked by how well it supports them: guest messaging and pricing (full read/write), task and review automation, owner reporting, plus the one thing the API can't do alone, turnover photo inspection. Every endpoint verified against Hostaway's own API source.

# The AI automations you can actually build on Hostaway

Hostaway has a genuinely open API, which makes it one of the better property management systems to wire AI into. But the docs do not tell you which automations the API truly supports and which it only half-supports. We read the API source and mapped it, ranked by how far you can actually take each one.

Hostaway exposes a well-documented REST API (OAuth2, 24-month tokens) covering listings, calendar and pricing, reservations, guest messaging, tasks, reviews, financials, and webhooks. The AI automations it supports **fully**, meaning read and write, are **guest messaging** (you can send messages back through the API) and **dynamic pricing** (you can push rates to the calendar). It also supports **task creation**, **review analysis**, and **owner-reporting** automations. The one thing you cannot build on the API alone is **visual turnover inspection**, because the photos your cleaners take never cross the API boundary. Here is the grounded map, capability by capability.

## 01What the Hostaway API actually gives you

According to Hostaway's public API reference, these are the building blocks an AI automation can read from and write to. Authentication is OAuth2 client-credentials: trade your account ID and API key for a bearer token that is valid for 24 months.

That last row is the one to read carefully. The API exposes the staged marketing gallery you show guests, not the condition photos a cleaner shoots on turnover. That distinction is what decides which automations below are fully buildable and which are not.

## 02The automations, ranked by how far the API takes you

Each one shows what the API hands you, the AI layer you add, and how completely the API supports it end to end.

### 1. AI guest messaging and inbox triage

What the API gives you

Full read and write on guest conversations. You can pull every message thread and post a reply straight back to the guest through the channel.POST /v1/conversations/{conversationId}/messages

The AI layer you add

A model that drafts or auto-sends answers to common questions (check-in, wifi, parking, local tips), triages by urgency, and escalates anything sensitive to a human. Fire it on the conversation-message webhook so it reacts in real time.

### 2. AI-assisted dynamic pricing

What the API gives you

Read and write access to each listing's calendar, including price, availability, and minimum stay.PUT /v1/listings/{listingId}/calendar

The AI layer you add

A model that weighs demand signals, events, and pacing, then writes rates back to the calendar. Most operators use PriceLabs or Beyond here, but the API lets you run your own logic if you want control over the model.

### 3. Automated task and cleaning coordination

What the API gives you

Create, read, and update tasks, with reservation webhooks to trigger them on booking, arrival, or departure.POST /v1/tasks · reservation webhook

The AI layer you add

Logic that spins up the right turnover task on checkout, assigns the nearest available cleaner, and reshuffles when a same-day booking lands or a cleaner cancels. The task lifecycle is fully scriptable.

### 4. Review intelligence

What the API gives you

Read access to your reviews across channels.GET /v1/reviews

The AI layer you add

Sentiment and theme analysis that surfaces recurring complaints (a noisy AC, a tricky lock) and drafts responses for approval. Pulling and analyzing is fully API-driven; posting public responses back to a channel is usually handled in-platform rather than through this endpoint.

### 5. Owner reporting and expense summaries

What the API gives you

Expense records, financial reporting, and owner-statement data.expenses · financialReporting · ownerStatement

The AI layer you add

A monthly job that turns raw financials into a plain-language owner statement, flags unusual costs, and answers owner questions in natural language. All the underlying numbers are retrievable.

### 6. Booking ops and guest screening

What the API gives you

Read and create reservations, plus reservation and unified webhooks that fire on every booking change.GET/POST /v1/reservations · unified webhook

The AI layer you add

A workflow that scores new bookings for risk, sends a tailored pre-arrival sequence, and routes one-off cases to a human. The booking lifecycle is the API's strongest surface.

## 03The one you cannot build on the API alone

There is exactly one high-value automation where the Hostaway API stops short, and it happens to be the one most exposed to real money: turnover damage detection.

### AI turnover inspection & damage detection

You can automate the trigger and the status. A reservation webhook tells you a checkout happened; the task object tells you the clean is marked complete, who did it, and when. What the API will not give you is the thing an inspection AI needs: the actual before/after photos. The task object carries no photo or attachment field, and there is no endpoint to pull a task's images. The only photos the API serves are the guest-facing marketing gallery.

So the visual half of the automation, the part that decides whether a guest gets charged for damage, cannot run off the Hostaway API by itself. We traced exactly where the photos do and do not live in [can you pull turnover photos from the Hostaway API](https://rapideyeinspections.com/blog/hostaway-api-turnover-photos/). The practical pattern: capture condition photos in a system built for inspection (Breezeway and Turno both structure required-photo tasks), trigger off the Hostaway reservation event, and route the images to a tool built to read them.

### That last automation is the one RapidEye exists for

RapidEye is AI inspection intelligence for vacation rental managers. It ingests the turnover documentation your cleaners already capture and runs computer-vision damage detection over it, comparing each turnover against the property's own baseline to catch new scratches, stains, and missing items. It is the piece that the messaging, pricing, and task automations above cannot reach, because the evidence lives in photos the PMS API does not serve.

If you are building out your Hostaway automation stack, this is the gap to plan for. Start with [is there AI that reviews turnover photos](https://rapideyeinspections.com/blog/is-there-ai-that-reviews-airbnb-turnover-photos/) or [automating inspections at 200+ units](https://rapideyeinspections.com/blog/how-to-automate-property-inspections-200-vacation-rentals/).

## 04A note on Hostaway's own AI

Worth saying plainly: Hostaway is not standing still. It ships native AI features of its own and runs a marketplace of AI partners, so for some of the automations above the build-versus-buy answer is genuinely a toss-up. The point of this page is not that you must build everything yourself; it is that the API is open enough to build on where you want control, and knowing exactly which capabilities it exposes is what lets you decide build versus buy with your eyes open. Everything here reflects Hostaway's published API reference as of June 14, 2026, and APIs change; confirm current behavior before you ship.

### Related reading

- [Can you pull turnover photos from the Hostaway API?](https://rapideyeinspections.com/blog/hostaway-api-turnover-photos/)

- [Connect Hostaway to Claude (MCP)](https://rapideyeinspections.com/claude-guides/hostaway/)

- [Best AI guest messaging software](https://rapideyeinspections.com/blog/best-ai-guest-messaging-software-short-term-rentals/)

- [AI that reviews turnover photos](https://rapideyeinspections.com/blog/is-there-ai-that-reviews-airbnb-turnover-photos/)

- [Automate inspections at 200+ units](https://rapideyeinspections.com/blog/how-to-automate-property-inspections-200-vacation-rentals/)

### Sources

- [Hostaway Public API Reference](https://api.hostaway.com/documentation) – authentication, conversations and messages, calendar, reservations, tasks, reviews, financial reporting, owner statements, and webhooks.https://api.hostaway.com/documentation

- [Hostaway/api](https://github.com/Hostaway/api) – the open-source repository behind the API reference. Every endpoint path and capability on this page (including POST /conversations/{id}/messages, PUT /listings/{id}/calendar, the tasks endpoints, GET /reviews, and the OAuth2 24-month token flow) was read directly from this source.https://github.com/Hostaway/api

- [What is Hostaway AI?](https://support.hostaway.com/hc/en-us/articles/37265258021787-What-is-Hostaway-AI) – Hostaway Support, on the platform's native AI features.https://support.hostaway.com/hc/en-us/articles/37265258021787-What-is-Hostaway-AI

Last verified: June 14, 2026, against Hostaway's published public API reference and its open-source documentation repository. Support level reflects what the documented API exposes; some automations are also available as native Hostaway features or marketplace apps. This is an independent technical reference and is not affiliated with or endorsed by Hostaway.


Can You Pull Turnover Photos From the Hostaway API?

A verified developer reference: the Hostaway API does not expose cleaning or turnover task photos. The task object has no attachment field, there is no task-photo endpoint, and no task webhook. Here is exactly what is and isn't exposed, and how to get turnover photos into an AI review step.

# Can you pull turnover photos from the Hostaway API?

If you are wiring AI damage detection onto your turnovers, the first question is whether you can even get the cleaner's photos out of Hostaway programmatically. We read Hostaway's own API source to find out. The answer is more specific than a yes or no.

**No, not through the documented public API.** As of June 2026, Hostaway does not give you a way to download the before/after photos a cleaner attaches to a turnover task. The task object at GET /v1/tasks contains only metadata: status, assignee, timing, checklist items, cost. There is no attachments, photos, files, or images field, and no separate task-attachment endpoint. File attachments do exist in the API, but only on conversation messages, where each carries a URL that expires after seven days. The result: there is no supported way to bulk-export turnover photos from Hostaway for AI review. Operators who run automated photo inspection capture the images in a system built for it and feed an AI layer directly, rather than scraping the PMS.

## 01What the API actually returns for a task

According to Hostaway's public API reference, the platform shipped task endpoints in August 2023. You authenticate, then call GET /v1/tasks for a list or GET /v1/tasks/{taskId} for one task. Here is the full task object as published in that reference, with one thing worth noticing.

That is the whole shape (trimmed for length; the real object adds more timing and ranking fields, none of them media). According to Hostaway's published task schema, a task tells you *that* a clean happened, who did it, and when, but never *what it looked like*. The checklistItems array holds checklist line items, not photos, and in the documented examples it is empty. There is no GET /v1/tasks/{id}/attachments route anywhere in the reference.

## 02The capability matrix

Here is what the Hostaway API does and does not hand you, mapped to what an operator building an inspection pipeline actually wants.

| What you want | Exposed via API? | Where it lives |
| --- | --- | --- |
| Turnover / cleaning task metadata (status, assignee, timing) | Yes | GET /v1/tasks |
| Task completion status and timestamp | Yes | task.status, task.completedAt |
| Checklist line items on a task | Yes | task.checklistItems[] |
| Before/after photos a cleaner uploaded to a task | No | not in task object; no task-attachment route |
| A webhook when a task is completed | No | only reservation + message webhooks |
| File attachments, generally | Partial | conversation messages only, includeResources=1 |
| Listing photos (the marketing gallery) | Yes | listing image object (guest-facing, not condition) |

The last row is the trap worth naming. Hostaway absolutely exposes photos through its listing image endpoints, so a quick skim of the docs can make it look solved. But those are the staged marketing photos you show guests on Airbnb, with caption, vrboCaption, and sortOrder fields. They have nothing to do with the condition of the unit after a checkout.

## 03The one indirect path, and why it is not an export

The API does model file attachments. According to Hostaway's published attachment-object schema, they appear in the attachments array on conversation messages, the guest-messaging surface. To populate it you must add ?includeResources=1 to the request, otherwise nested resources come back empty. Each attachment looks like this:

That taskId field is genuinely interesting: it proves Hostaway's data model can associate a file with a task. But notice what is missing. There is no endpoint that says "give me every attachment for task 1234." You would have to walk conversations, pull messages with includeResources=1, and filter attachments by taskId yourself, and only the attachments that happen to ride along a conversation message would surface at all. That is a messaging feature, not a turnover-photo pipeline.

The seven-day clock

There is one more catch for anyone who thinks they can cache their way around this. For private attachments, Hostaway's documentation states the url is a temporary link that **expires seven days after it is generated**. You cannot store the URL; once it lapses you have to re-fetch the message to mint a fresh one. If you want to keep the file, you have to download the bytes and store them on your side before the link dies.

## 04For completeness: auth and the task list call

None of the above means the API is weak. It is well-built for what it covers. Authentication is OAuth2 client-credentials: you exchange your Hostaway account ID and API key for a bearer token that is valid for 24 months.

So a realistic, fully-supported Hostaway integration can tell you that the departure clean for a reservation is marked complete, who completed it, and when. It simply cannot tell you whether the unit is actually in good condition, because the photographic evidence never crosses the API boundary.

## 05So how do operators get turnover photos into AI?

If the goal is automated damage detection on every turnover, the practical answer is to stop treating the PMS as the photo source and capture the images where they can actually flow into an AI step.

#### Capture condition photos in a system built for inspection

Breezeway and Turno are designed around required-photo turnover and inspection tasks, so the documentation is structured and consistent rather than scattered across guest threads. Breezeway in particular has become the system of record for turnover photos at many mid-to-large operations, which is exactly why a consistent capture standard matters. See our [turnover inspection and documentation checklist](https://rapideyeinspections.com/blog/str-turnover-inspection-checklist-damage-documentation-photos/) for what to shoot.

#### Trigger your pipeline off the reservation lifecycle

Hostaway does expose reservation and unified webhooks, and every reservation carries its departure date. Use those to time your review around checkout, then collect the photos from wherever they were captured. You are using Hostaway for the signal it is good at (the booking lifecycle), not for media it does not serve.

#### Send the photos to a tool that ingests turnover documentation directly

Rather than rebuilding photo extraction, deduplication, and baseline comparison from scratch against a PMS that was never meant to export condition media, route the captured photos to a layer purpose-built to read them. That is the job an inspection-intelligence tool does.

### This is exactly the gap RapidEye was built to close

RapidEye is AI inspection intelligence for vacation rental managers. It ingests the turnover documentation your cleaners already capture and runs computer-vision damage detection over it, comparing each turnover against the property's own baseline to surface new scratches, stains, and missing items. The point is that you do not have to solve the messy photo-plumbing problem, including the parts a PMS API was never designed to serve, to get AI eyes on every checkout.

If you are evaluating whether this fits your operation, start with [is there AI that reviews Airbnb turnover photos](https://rapideyeinspections.com/blog/is-there-ai-that-reviews-airbnb-turnover-photos/) or [how to review turnover photos at scale](https://rapideyeinspections.com/blog/how-to-review-turnover-photos-at-scale/).

## 06Will this change?

Possibly, and the API itself hints at it. The presence of a taskId on the attachment object means Hostaway's data model already links files to tasks; exposing a clean task-attachment endpoint would be a natural extension. Until Hostaway publishes one, treat turnover task photos as not retrievable through the public API. If you are scoping a build that depends on it, confirm current behavior with Hostaway's API or partnerships team rather than assuming, because undocumented behavior can change without notice. Everything on this page reflects Hostaway's published API reference as of June 14, 2026.

### Related reading

- [AI automations you can build on Hostaway](https://rapideyeinspections.com/blog/hostaway-ai-automations/)

- [Connect Hostaway to Claude (MCP)](https://rapideyeinspections.com/claude-guides/hostaway/)

- [Is there AI that reviews turnover photos?](https://rapideyeinspections.com/blog/is-there-ai-that-reviews-airbnb-turnover-photos/)

- [Review turnover photos at scale](https://rapideyeinspections.com/blog/how-to-review-turnover-photos-at-scale/)

- [Can AI detect damage from photos?](https://rapideyeinspections.com/blog/ai-property-damage-detection-from-photos/)

- [Automate inspections at 200+ units](https://rapideyeinspections.com/blog/how-to-automate-property-inspections-200-vacation-rentals/)

### Sources

- [Hostaway Public API Reference](https://api.hostaway.com/documentation) – the live, authoritative documentation for authentication, the task object, the attachment object, webhooks, and listing images.https://api.hostaway.com/documentation

- [Hostaway/api](https://github.com/Hostaway/api) – the open-source repository that generates the API reference. Task object, attachment object (including the taskId field and seven-day private-URL expiry), and the OAuth2 client-credentials flow with 24-month token TTL were read directly from this source.https://github.com/Hostaway/api

- [Tasks: Create, Manage & Assign Tasks](https://support.hostaway.com/hc/en-us/articles/360036506093-Tasks-Create-Manage-Assign-Tasks) – Hostaway Support (covers in-app task photo uploads by cleaners).https://support.hostaway.com/hc/en-us/articles/360036506093-Tasks-Create-Manage-Assign-Tasks

- [Using Hostaway Webhooks](https://support.hostaway.com/hc/en-us/articles/360009022653-Using-Hostaway-Webhooks) – Hostaway Support (reservation and conversation-message webhook events).https://support.hostaway.com/hc/en-us/articles/360009022653-Using-Hostaway-Webhooks

Last verified: June 14, 2026, against Hostaway's published public API reference and its open-source documentation repository. APIs change; if a task-photo capability ships after this date, this page will be out of date until updated. This is an independent technical reference and is not affiliated with or endorsed by Hostaway.


Which Hotel PMS Platforms Have Open APIs (and Which Are AI-Ready)? 2026 Scorecard

A neutral, sourced scorecard of API access for 13 major hotel and hospitality PMS platforms: Mews, Apaleo, Cloudbeds, Oracle OPERA Cloud, Stayntouch, RMS Cloud, SiteMinder, Guesty and more. Which have open APIs, what they cost, and which are ready for AI agents and MCP.

# Which hotel PMS platforms have open APIs (and which are AI-ready)?

A neutral, primary-source scorecard of 13 major hotel and hospitality property management systems: how you get API access, what it costs, how it authenticates, and whether it is ready for AI agents and the Model Context Protocol.

Most hotel property management systems are not yet built for AI agents. Of the 13 major hotel and hospitality PMS platforms reviewed here, exactly one ships an official Model Context Protocol (MCP) server that lets an assistant like Claude connect to live property data, and it is Guesty, a vacation-rental-first platform. Three platforms (Guesty, Cloudbeds, and SiteMinder) publish an llms.txt index for AI agents.

The most genuinely open, self-serve developer experience among hotel-native systems belongs to **Apaleo**, where any developer can register a free account and start building. The industry's largest system, **Oracle OPERA Cloud**, exposes more than 3,100 REST operations through its integration platform but bills every call. And nine of the thirteen platforms gate API access behind a partner application, NDA, or certification.

## Hotel PMS API access, side by side

Sorted from most open to least. Every verdict below is drawn from the platform's own developer documentation or official site; see the methodology and sources for the exact wording behind each call.

| Platform | API access | Auth | AI-ready | Cost to build | Marketplace |
| --- | --- | --- | --- | --- | --- |
| ApaleoCloud, API-first hotel PMS | Open | OAuth 2.0 | None | Free to start | apaleo Store |
| GuestyVacation-rental-first; serviced apts, hostels | Open | OAuth 2.0 | MCP server | Guesty plan | Guesty Marketplace |
| Oracle OPERA CloudVia OHIP · the enterprise giant | Open · paid | OAuth 2.0 + key | None | ~$10 / 10k calls | Oracle Cloud Mktpl. |
| CloudbedsCloud PMS for independents | Partner-gated | API key / OAuth 2.0 | llms.txt | Free dev account | Yes |
| SiteMinderDistribution platform + Little Hotelier PMS | Partner-gated | WSSE / Bearer | llms.txt | Not disclosed | Hotel App Store |
| MewsCloud PMS, open docs + demo | Partner-gated | Token pair | None | Partner program | Mews Marketplace |
| StayntouchCloud, mobile-first PMS | Partner-gated | OAuth 2.0 | None | Not disclosed | Integration Hub |
| RMS CloudCloud PMS, broad property types | Partner-gated | API token | None | AUD $550 + module | Module market |
| Protel / PlanetCloud PMS (protel I/O) | Partner-gated | Bearer / SOAP | None | Not disclosed | planet Marketplace |
| HotelogixCloud PMS for small/mid hotels | Partner-gated | HMAC-SHA1 | None | Not disclosed | Partner directory |
| Maestro PMSIndependent + hotel groups | Partner-gated | Not disclosed | None | Not disclosed | No app store |
| WebRezProCloud PMS, independents | Partner-gated | Not disclosed | None | Not disclosed | Partner directory |
| innRoadCloud PMS, small hotels | No public API | n/a | None | n/a | No |

"Cost to build" is the cost to a developer for access and testing, not the hotel's PMS subscription. "Not disclosed" means the platform does not publish a figure on its developer or pricing pages; contact the vendor's partner team. AI-ready reflects officially documented support only, not third-party connectors or marketing intent.

## Two questions, honestly answered

Builders care about two things before they commit: can I get in, and is it ready for the way I want to build now. So every platform gets one verdict on each.

#### Self-serve access

You can register for credentials yourself, against published docs, without a sales conversation. "Open · paid" means self-serve but billed per use.

#### Approval required

Production credentials require a partner application, NDA, mutual customer, or formal certification first. Docs may still be public to read.

#### AI-readiness

"MCP" means an official Model Context Protocol server. "llms.txt" means a published machine index for AI agents. "None" means neither is documented.

## The six that matter most

Short, sourced profiles of the platforms a builder is most likely to start with, with the exact access and authentication facts behind each scorecard verdict.

### Apaleo

Apaleo is the cleanest answer to "which hotel PMS is genuinely open." It is built API-first, meaning the API is the product rather than an afterthought. According to [Apaleo's developer documentation](https://apaleo.dev/), getting started is as simple as "sign up for your apaleo developer account. It's free!" Access uses OAuth 2.0 (authorization code, client credentials, and refresh-token grants), and the core API spans "rates and reservations, accounting, housekeeping, inventory management, and payment."

Public integrations are published through the **apaleo Store** after a certification review, while custom client apps can be built for a single hotel without listing. The trade-off for that openness is scale: Apaleo is strongest with independents and boutique groups rather than large chains.

### Oracle OPERA Cloud

OPERA is the system most large and chain hotels actually run, and it is more open than its reputation suggests, with one catch: cost. Integration happens through the Oracle Hospitality Integration Platform (OHIP). According to [Oracle](https://www.oracle.com/hospitality/integration-platform/), OHIP exposes "over 3,100 available operations" across reservations, front desk, guest profiles, distribution, housekeeping, and back-office cashiering, secured with "OAuth 2.0 based security plus individual application-key based access."

Partners self-register online rather than waiting for approval, but every call is metered. According to Oracle's OHIP datasheet, pricing "starts at $10 for up to 10,000 REST API transactions per month," billed monthly in arrears, with even sandbox usage billed. Publishing to the Oracle Hospitality Cloud Marketplace requires an Oracle PartnerNetwork membership; formal certification is optional.

### Guesty

Guesty is vacation-rental-first, but it earns its place here because it is used by serviced apartments, aparthotels, and hostels, and because it is the only platform in this scorecard with an official MCP server. According to [Guesty's Open API documentation](https://open-api-docs.guesty.com/), access is self-serve: an account holder creates an application in the dashboard and authenticates with OAuth 2.0 client credentials. The API covers reservations, listings, calendars, guests, payments, financials, tasks, and messaging.

According to Guesty's MCP documentation, its **MCP server** "is currently in an early-stage beta release" and "exposes read endpoints only," connecting AI assistants including Claude Desktop, Cursor, and VS Code to live Guesty data. Guesty also publishes an llms.txt index. No hotel-native PMS in this review has matched that yet.

### Cloudbeds

Cloudbeds pairs a broad API with the clearest AI-agent signal of any hotel-focused PMS short of a full MCP server. According to [Cloudbeds' developer documentation](https://developers.cloudbeds.com/), third-party apps need a Partner Dev Account that the Cloudbeds team enables, after which "API Keys Authentication is the recommended authentication method for all Marketplace partners," with OAuth 2.0 available as an alternative. A self-service API also lets an individual property generate its own credentials.

For AI builders, Cloudbeds' docs include a direct instruction: "For AI agents: visit https://developers.cloudbeds.com/llms.txt for an index of all pages formatted in Markdown and endpoints in OpenAPI." That is a deliberate, low-effort step toward agent-readiness that most peers have not taken.

### Mews

Mews has one of the most approachable developer experiences in hotel tech, even though production is gated. Its Connector API documentation is fully public and ships with a shared demo environment, so you can make real calls before any agreement. According to [Mews' Connector API documentation](https://docs.mews.com/connector-api/getting-started), authentication uses a token pair: a ClientToken identifies your application and an AccessToken identifies the property you are connecting to.

The API covers reservations, customers, rates, spaces, orders, and accounting. To move past the demo and connect real hotels, you complete the Mews certification process and list in the Mews Marketplace. No official MCP server or llms.txt is published yet, despite Mews' strong open-platform positioning, an obvious gap for them to close.

### SiteMinder

SiteMinder is a distribution platform (channel manager and booking engine) rather than a classic PMS, but it is core hotel infrastructure and it owns the small-property PMS Little Hotelier, so builders meet it constantly. Access is strictly partner-gated. According to [SiteMinder's developer guide](https://developer.siteminder.com/), "Access to SMX requires an active partnership agreement with SiteMinder," and "each API requires its own certification process," with most integrations completed within 60 days.

It is unusually forward on AI tooling for documentation: SiteMinder publishes an official llms.txt, a dedicated AI Tools page, and an agentic MCP path for its Channels Plus API. Note the scope, though, that tooling helps an AI read and reason over the docs and one distribution API, not operate a full PMS. Little Hotelier has no separate developer API of its own.

## Hotel tech is barely AI-ready, and you can name the exceptions

The signal is not subtle. Across 13 platforms that collectively run a large share of the world's hotel rooms, **exactly one ships an official MCP server**, and it is a vacation-rental company. **Three publish an llms.txt.** The rest, including category-defining systems, have shipped nothing AI-specific at all.

That is the opposite of where short-term-rental tech sits, where open APIs and self-serve access are the norm. For developers and tech-forward operators, it means the agentic hotel stack is wide open: the platforms that wire up cleanly to AI assistants first will define how the next wave of hotel software gets built. Right now, almost nobody has planted a flag.

## What the API actually exposes is the real question

Access is the first gate; data is the second. Once you are in, what an API exposes determines what you can build on top of it. The richest operational surfaces here are Apaleo, Oracle OPERA Cloud, and RMS Cloud, all of which expose housekeeping and room-status data alongside reservations and folios, and Guesty, whose API includes tasks and housekeeping. Distribution-layer APIs like SiteMinder's deliberately do not: they move reservations, rates, and availability, not housekeeping.

That distinction matters for anyone building condition, quality, or inspection workflows. RapidEye works the same way on the short-term-rental side: it plugs into the platform an operator already uses, reads the photos and operational data that platform already stores, and turns them into damage detection and cleaning verification. If you want to see how that integration pattern is built in practice, our [Claude integration guides](https://rapideyeinspections.com/claude-guides/) walk through connecting property platforms to AI step by step, and our breakdown of the [vacation-rental PMS generational shift](https://rapideyeinspections.com/blog/vacation-rental-pms-generational-shift/) shows how openness reshaped that market.

## Common questions

Among hotel-native systems, Apaleo. It is API-first, lets any developer register a free account and build immediately, and exposes rates, reservations, accounting, housekeeping, inventory, and payments over OAuth 2.0. Oracle OPERA Cloud is also self-register but charges per transaction, and Guesty is fully self-serve but is vacation-rental-first.

Yes, through the Oracle Hospitality Integration Platform (OHIP), which Oracle says offers more than 3,100 operations. Partners self-register online, but usage is billed pay-as-you-go, starting at $10 for up to 10,000 REST transactions per month per Oracle's datasheet. Even sandbox calls are metered.

As of June 2026, Guesty is the only platform here with an official MCP server, which connects assistants like Claude to live data and is currently a read-only beta. Cloudbeds and SiteMinder publish an llms.txt index for AI agents but do not yet offer a full operating MCP server. Most hotel-native PMS platforms have shipped nothing AI-specific.

MCP (Model Context Protocol) is the emerging standard for connecting AI assistants to live business systems. An MCP server for a PMS exposes its API as tools an assistant can call in plain language, so an agent can look up reservations, availability, or housekeeping status, and sometimes write changes back, without custom integration code.

Usually. Nine of the 13 platforms reviewed gate production credentials behind a partner application, NDA, or certification. The self-serve exceptions are Apaleo, Guesty, and Oracle OPERA Cloud (self-register but paid). innRoad has no public developer API at all; its integrations are pre-built partner connections enabled inside the app.

## Every verdict, traced

Each platform's scorecard call is drawn from its official developer documentation or website, reviewed in June 2026.

- [Apaleo Developer Documentation](https://apaleo.dev/) – free self-serve dev account, OAuth 2.0, API scope, apaleo Store.
 https://apaleo.dev/

- [Oracle Hospitality Integration Platform (OHIP)](https://www.oracle.com/hospitality/integration-platform/) – open REST APIs, 3,100+ operations, OAuth 2.0 + app key.
 https://www.oracle.com/hospitality/integration-platform/

- [Oracle OHIP Datasheet (PDF)](https://www.oracle.com/a/ocom/docs/industries/hospitality/hosp-integration-platform-ds.pdf) – pay-as-you-go pricing from $10 per 10,000 REST transactions, self-registration.
 https://www.oracle.com/a/ocom/docs/industries/hospitality/hosp-integration-platform-ds.pdf

- [Guesty Open API Documentation](https://open-api-docs.guesty.com/) – self-serve OAuth 2.0 access, API scope, official MCP server (beta, read-only), llms.txt.
 https://open-api-docs.guesty.com/docs/guesty-mcp-server-beta

- [Cloudbeds Developer Documentation](https://developers.cloudbeds.com/) – Partner Dev Account, API Keys / OAuth 2.0, llms.txt for AI agents.
 https://developers.cloudbeds.com/

- [Mews Connector API Documentation](https://docs.mews.com/connector-api/getting-started) – public docs, demo environment, ClientToken + AccessToken model, partner certification.
 https://docs.mews.com/connector-api/getting-started

- [SiteMinder Developer Guide](https://developer.siteminder.com/) – partnership agreement and per-API certification, WSSE / Bearer auth, Hotel App Store, llms.txt and AI Tools.
 https://developer.siteminder.com/

- [Stayntouch Developers](https://www.stayntouch.com/developers/) – open APIs marketed, NDA required before sandbox, OAuth 2.0.
 https://www.stayntouch.com/developers/

- [RMS Cloud REST API Documentation](https://restapidocs.rmscloud.com/) – partner onboarding with mutual customer, API token auth, AUD $550 developer kit, housekeeping endpoints.
 https://restapidocs.rmscloud.com/

- [Planet (Protel) Open API Framework](https://www.weareplanet.com/news/planet-enhances-protel-cloud-pms-empower-hoteliers-greater-integration-efficiency-and-control) – open API framework, partner-gated developer portal, dedicated sandbox.
 https://hub.protel.io/

- [Hotelogix Developer API](https://www.hotelogix.com/developer-api) – keys issued on request, HMAC-SHA1 request signing, XML over HTTP, demo endpoints.
 https://www.hotelogix.com/developer-api

- [Maestro PMS Open APIs & Integration Partners](https://www.maestropms.com/interfaces-open-apis-integration-pms-partners.html) – "Genomi" open API, partner application, no public spec.
 https://www.maestropms.com/interfaces-open-apis-integration-pms-partners.html

- [WebRezPro Integrations](https://webrezpro.com/integration-partners/) – partner-based integrations (AppConnect / Hapi), no public self-serve developer portal.
 https://webrezpro.com/integration-partners/

- [innRoad](https://www.innroad.com/) – no public developer API; integrations are pre-built partner connections enabled in-app.
 https://www.innroad.com/

RapidEye is AI inspection intelligence for property operators. It plugs into the platform a team already uses, reads the turnover photos and operational data already being captured, and flags damage and cleaning issues people miss. Built by two Carnegie Mellon researchers on patented inspection technology. [See what it can find →](https://rapideyeinspections.com/#contact)


From Blueprint to Booking: How AI Inspects Every Stage of a Property

AI now inspects properties at every stage of their lifecycle. From catching drawing errors before construction starts to detecting damage after guests leave, here's how the technology works at each phase.

## Preconstruction: Catching errors in the drawings

Before a single shovel hits dirt, someone has to review the construction drawings. A typical commercial project generates hundreds of pages of plans across architectural, structural, mechanical, electrical, and plumbing disciplines. These drawings need to be coordinated: the ductwork can't run through the structural beams, the fire sprinklers need clearance from the ceiling grid, the plumbing stacks need to align across floors. When these conflicts slip through review, they surface during construction as RFIs, change orders, and rework.

According to [Helonic's research](https://helonic.com/blog/construction-rework-costs), the US construction industry loses $31 billion annually to rework, and the root cause is usually errors in the drawings that nobody caught. Manual review of a 50-sheet set takes 8 to 12 hours and catches 60 to 80% of issues. The other 20 to 40% become problems in the field.

**[Helonic](https://helonic.com)**, a Y Combinator-backed startup, built a proprietary AI model trained specifically on construction drawings to close that gap. The platform cross-references every page in a drawing set simultaneously, checking for coordination conflicts between disciplines, code compliance gaps against 380+ building codes (IBC, IRC, NEC, IPC, NFPA, ADA), missing information, dimension errors, and constructability issues. Every finding includes a precise page location and severity rating. The AI works directly with 2D PDFs, no BIM required, and integrates with Procore and Autodesk Construction Cloud so detected issues can be pushed as RFIs with one click.

According to [Helonic's website](https://helonic.com), the platform has analyzed over 100,000 drawing pages, caught over 150,000 issues before construction started, and prevented an estimated $30 million in rework. The detection accuracy is 95%+ across 10 issue categories. For context, catching a single major coordination conflict before construction can save $50,000 to $500,000+ depending on the trade and project scale.

The preconstruction AI category also includes [Helonic's knowledge base](https://helonic.com/knowledge-base), which houses 100+ free guides on reading construction drawings, from MEP coordination to fire protection plans. It's one of the most comprehensive construction reference libraries available online.

## Construction: Documenting progress and catching deviations

Once construction starts, the inspection challenge shifts. The drawings are (mostly) finalized. Now the question is whether what's being built matches what was designed. Walls go up in the wrong location. Ductwork gets rerouted to avoid an unforeseen conflict. Rebar spacing doesn't match the structural drawings. These deviations happen daily, and the traditional way to catch them is periodic site walks where a superintendent or owner's rep compares what they see to what the drawings say.

AI-powered site capture platforms like OpenSpace have automated this. Teams wear 360-degree cameras on hardhat clips during regular site walks. The platform stitches the footage into a navigable, time-stamped record of the jobsite and layers AI analysis on top. The AI tracks installation progress against the schedule, compares as-built conditions against BIM models, and flags deviations before they compound. According to Commercial Observer, OpenSpace covers 75,000+ construction projects across 124 countries.

The value here is documentation as much as detection. Construction disputes cost the industry over $15 billion annually, and the most common defense is photographic evidence of what was built and when. AI site capture creates that evidence automatically, at every stage of construction, without anyone having to remember to take photos.

## Transaction: Scoring condition from photos

Before a property changes hands, someone assesses its condition. Appraisers walk the property. Lenders need condition scores for underwriting. Insurance companies need to understand risk. Investors need to price in deferred maintenance. Historically, all of this has been manual: a human visits the property, takes photos, and writes a subjective assessment.

Computer vision companies like FoxyAI are automating the assessment step. Their AI analyzes standard property photos and extracts structured condition data: water damage indicators, mold, standing water, structural issues, deferred maintenance signals. In a pilot with an iBuyer, FoxyAI scored 19,105 properties and brought each one an average of $3,000 closer to the actual sales price. That's $57 million in aggregate valuation improvement from better condition assessment.

The shift here isn't just speed. It's consistency. A human appraiser's condition assessment varies based on experience, attention, and how many properties they've seen that day. AI applies the same criteria to every photo, every time. For lenders underwriting thousands of properties, that consistency directly reduces risk.

## Operations: Detecting damage between guests

Once a property enters service as a vacation rental, the inspection cycle accelerates dramatically. Every guest checkout triggers a cleaning turnover, and every turnover generates photos. A property manager running 200 units might process 5,000+ photos per week. These photos are uploaded to operations platforms like Breezeway, Guesty, and Track. In theory, someone reviews them. In practice, the volume makes thorough review impossible.

**RapidEye** connects to those operations platforms, ingests every turnover photo, and builds a visual baseline for each property. When new images come in after a turnover, the AI compares them against the baseline and flags changes: wall damage, broken glass, stains, missing items, furniture damage, staging inconsistencies, cleanliness failures. The comparison approach is what makes it work at scale. The AI doesn't need to understand every possible type of damage. It just needs to know what the property looked like before and what it looks like now.

In a trial with one 500+ unit property manager, RapidEye analyzed over 1.5 million turnover photos and surfaced an average of 4 missed damages per property that the cleaning team and inspectors had overlooked. The cost of those missed damages compounds: unrecovered security deposit claims, accelerated wear that becomes capital expenditure, guest complaints about pre-existing issues, and the operational time spent investigating after the fact instead of catching it in real-time.

## Sources

- [The Real Cost of Construction Rework in 2025](https://helonic.com/blog/construction-rework-costs) - Helonic
 https://helonic.com/blog/construction-rework-costs

- [Helonic - AI-Powered Construction Drawing Analysis](https://helonic.com) - Helonic
 https://helonic.com

- [Helonic Knowledge Base - Construction Drawing Guides](https://helonic.com/knowledge-base) - Helonic
 https://helonic.com/knowledge-base

- [How Visual AI Is Reshaping Value and Risk in Commercial Real Estate](https://commercialobserver.com/2025/12/visual-ai-value-risk-commercial-real-estate/) - Commercial Observer
 https://commercialobserver.com/2025/12/visual-ai-value-risk-commercial-real-estate/


A clear breakdown of how AI damage detection uses baseline comparison to find new scratches, stains, and damage in rental properties. No marketing fluff.

# How Automated Damage Detection Actually Works

“AI-powered damage detection” sounds like something a marketing team made up. I get it. When I first started digging into this space, I had the same reaction. But the technology behind it is actually pretty straightforward once you understand the core concept.

Let me break down how this stuff actually works.

## The Baseline Concept

Every automated damage detection system starts with the same idea: you need a “before” to compare against an “after.”

This isn’t new. Construction companies have been doing this for years. [OpenSpace](https://www.openspace.ai/products/track/) uses cameras to capture job sites and track hundreds of visual components against project schedules, flagging unfinished work or blockers. Auto insurance companies like [Tractable](https://tractable.ai/) analyze vehicle photos at pixel level to assess damage. The rental car industry uses guided capture systems to document vehicle condition at pickup and dropoff.

For property inspections, the baseline is a complete visual record of your unit in good condition. Every wall, every appliance, every surface. This becomes the reference point that all future inspections get compared against.

The key insight from [computer vision research](https://www.sciencedirect.com/topics/computer-science/detection-pipeline) is that comparing two states actually reduces false positives. A scuff mark that looks like damage might have been there all along. Without a baseline, you can’t tell. With one, the system only flags what’s actually new.

## How the Comparison Works

Once you have a baseline, the system needs to compare new footage against it. This happens in a few steps.

**Alignment comes first.** This is the part most people don’t think about. Your cleaner isn’t going to film from the exact same angle every time. The camera might be higher, lower, tilted differently. Before comparing anything, the system has to figure out what it’s looking at and match it to the corresponding baseline footage.

[OpenSpace describes](https://www.openspace.ai/the-tech/) using computer vision combined with 3D reconstruction to estimate camera position and map images to floor plans. Academic researchers are working on [change detection in unaligned images](https://arxiv.org/abs/2412.07612) specifically because this alignment problem is so common in real world applications.

**Then comes the actual comparison.** The classic approach is [image differencing](https://solutions.arcgis.com/defense/help/image-change-detection/workflows/image-differencing/): subtract one image from another and look at what’s left. If the pixel values are similar, you get close to zero (no change). If something’s different, it shows up.

Modern systems use deep learning instead of simple subtraction. You train a model on pairs of images with labeled changes, and it learns to [identify what counts as meaningful change](https://pro.arcgis.com/en/pro-app/latest/help/analysis/image-analyst/detect-change.htm) versus normal variation.

**The output is a set of flagged areas** where the system detected something new. A scratch on the countertop. A stain on the carpet. A dent in the wall. Each gets highlighted with its location and timestamp.

## What It Catches

The types of damage these systems detect pretty well:

- Surface damage: scratches, scuffs, chips on walls, floors, countertops

- Stains: carpet stains, fabric stains, water marks

- Structural changes: dents, cracks, holes

- Missing items: if something that was in the baseline isn’t there anymore

- Broken items: visible damage to fixtures, appliances, furniture

What’s harder to catch:

- Damage that requires testing (a broken dishwasher that looks fine)

- Subtle wear that accumulates over many stays

- Anything hidden from camera view

One thing worth noting: [environmental factors affect accuracy](https://link.springer.com/article/10.1007/s00477-024-02898-7). Different lighting, different times of day, different weather visible through windows. Good systems account for this, but it’s a real challenge. Shadows can look like stains. Sunlight can wash out scratches.

## How Reports Get Generated

This is where the technology becomes actually useful for property managers.

When the system flags potential damage, it generates a report with:

- **Timestamped evidence**: exactly when the damage was first detected

- **Visual comparison**: the baseline image next to the current image

- **Location context**: where in the property the damage is

- **Severity assessment**: how significant the change appears

Why does this matter? Because [Airbnb’s Host Damage Protection](https://www.airbnb.com/help/article/2869) explicitly requires “receipts, photographs, videos” as proof when filing claims. You have 14 days from checkout to initiate, and the quality of your documentation determines whether you get reimbursed.

Data from UK deposit schemes shows the difference documentation makes. According to [industry analysis](https://thenegotiator.co.uk/features/safe-as-houses-2/), only 14 to 20% of landlords receive their full claimed amount, and it comes down to “producing evidence of sufficient quality.” The majority of disputes end in split decisions or landlord claims getting rejected entirely.

Timestamped, automatically generated reports change that equation.

## Two Ways This Works in Practice

Most systems offer two modes:

**Real time guidance during walkthroughs.** The cleaner or inspector opens an app, starts filming, and gets prompts to capture specific areas. The system confirms each checklist item is visible on camera. If something looks off, it flags it immediately. [Paraspot AI](https://www.paraspot.ai/) and [similar tools](https://propvu.ai/) describe this as “AI-guided workflows” that detect issues in real time.

**Post inspection analysis.** You upload existing photos or video after the fact, and the system processes them against the baseline. This works for property managers who already have cleaners taking photos but aren’t doing anything systematic with them.

The second mode is important because it means you don’t need to change your workflow. If your cleaners already snap photos on their phones, those photos can be analyzed. No new equipment. No retraining.

## The Adoption Curve

This category is still early but moving fast. [Hostaway’s surveys](https://www.hostaway.com/blog/vacation-rental-trends-2025/) show AI tool adoption among short term rental operators jumped from 60% to 84% between summer 2024 and summer 2025. Most of that is dynamic pricing and guest messaging, but inspection automation is growing.

The [NAA/AppFolio research](https://www.globenewswire.com/news-release/2025/12/10/3203354/0/en/Technology-Implementation-Now-a-Top-Challenge-for-Real-Estate-Leaders-NAA-Research-in-Partnership-with-AppFolio-Finds.html) found 43% of property managers are already using AI features embedded in their management software, and 77% report performance improvements.

The main concerns holding people back, according to [Ferguson Partners surveys](https://www.mannpublications.com/mannreport/2025/01/08/ferguson-partners-survey-real-estate-is-leaning-into-ai/): lack of understanding of how AI actually works (67%), difficulty seeing clear ROI (58%), and data quality issues (58%).

Hopefully this breakdown helps with that first one.

## Quick FAQ

**Does this replace human inspections entirely?**

Not really. It catches visual damage automatically, but someone still needs to check things like whether the AC works or the smoke detectors have batteries.

**What about false positives?**

They happen. Lighting changes, moved furniture, even shadows can trigger flags. Good systems let you review and dismiss false positives, and they learn from that feedback.

**Do I need special cameras?**

No. Most systems work with smartphone cameras. [Multiple vendors](https://www.paraspot.ai/) explicitly market “from any mobile device” as a feature.

**How long does setup take?**

Creating the initial baseline is the main time investment. After that, ongoing inspections integrate into normal turnover workflows.


How Do Hotels Use AI for Quality Control and Brand Standards? (2026)

How hotels use AI for quality control and brand-standard inspections in 2026: the shift from twice-a-year anonymous audits (LQA, Forbes Travel Guide) to continuous AI photo verification of every room against standard.

# How do hotels use AI for quality control and brand standards?

Hotel quality has always been enforced by an expensive ritual: an anonymous inspector who shows up once or twice a year and scores the property against hundreds of standards. AI is changing the cadence, from a snapshot to something continuous. Here is how, and where it stops.

Hotels use AI to turn brand-standard quality control from a periodic event into a continuous one. Traditional auditing sends anonymous human inspectors who visit roughly twice a year and score a property against hundreds of standards. AI reads the room and area photos a team already captures during everyday checklists, checks them against those same standards, and flags deviations for a human to confirm, so the standard is verified on every room and every turnover rather than only on audit day. It does not replace the human judgment of service quality; it makes the physical and visual half of the standard continuous, and it does so consistently across every property in a portfolio.

## The old way: the audit that happens a couple of times a year

To understand what AI changes, start with how hotel quality has been enforced for decades. It is rigorous, respected, and very infrequent.

The gold standard is the anonymous brand audit. According to [Leading Quality Assurance (LQA)](https://lqagroup.com/hotel-assessments/), one of the main luxury hospitality auditors, an assessment covers "over 1,000 luxury standards" across departments, with 179 standards in front of house, 379 in food and beverage, and 117 in housekeeping, and is conducted entirely incognito, "under the guise of a regular guest and without any staff knowing who the assessor is." The catch is frequency: LQA recommends "a minimum of 2 assessments annually." Forbes Travel Guide runs a similar model, sending anonymous inspectors to score properties against proprietary standards for its star ratings, also on roughly an annual cycle. These audits are the backbone of hotel quality, and they happen on a handful of days a year.

#### LQA (Leading Quality Assurance)

Incognito luxury assessments against 1,000-plus standards, recommended a minimum of twice a year. Source of the departmental standard counts above.

#### Forbes Travel Guide

Anonymous, paid inspectors evaluate properties against proprietary standards for Five-Star, Four-Star, and Recommended ratings, on roughly an annual cycle.

## The new way: continuous AI verification

AI does not replace the audit. It fills the long gap between audits with the one thing a once-a-year inspector cannot provide: coverage.

The mechanism is photo verification. The 117 housekeeping standards an LQA assessor scores by hand are exactly the kind of thing a model can now read from a photo: AI checks the room and area photos a team already captures against the brand's own standards, then flags anything off to a human. The housekeeping-audit platform [OpsAnalitica](https://www.opsanalitica.com/industries/hotel) is built on this idea, with a system that "automatically audits every photo submitted during a room cleaning checklist" to verify "that beds are made to brand standards, amenities are stocked, and safety protocols are followed." Applied continuously, it turns the physical half of a brand standard from an annual sample into an every-room check, with people still making the judgment calls.

### Periodic human inspection

- About once or twice a year

- Anonymous inspector, hundreds of standards

- Scores service and emotional experience

- Comprehensive but expensive

- A snapshot of one stay

### Continuous photo checking

- Every room, every turnover

- Reads existing checklist photos against standard

- Checks the physical and visual standard

- Cheap to run at scale

- A continuous record, with humans on each flag

The two are complementary, not competing. The audit sets and certifies the standard a few times a year, including the human service dimension a machine cannot judge. AI holds the physical side of that standard in place every day in between, so a property is not only compliant on inspection day.

## What AI can check, and what stays human

Being honest about the boundary is what makes AI quality control credible. It is very good at one half of a standard and cannot touch the other.

- Beds made to brand standard

- Amenities and brand-mandated items present and placed

- Room and public-area cleanliness

- Damage, stains, and missing items

- Safety and setup details visible in a photo

- Warmth, attentiveness, and anticipation

- The emotional impact LQA and Forbes weight most

- Judgment and recovery when something goes wrong

- Whether a flagged issue actually matters

- The final pass or fail on every flag

This is why AI quality control reads as augmentation, not replacement. The anonymous-inspector audits weight the human service experience most heavily, and no model judges genuine hospitality. What AI does is take the repetitive, visual, high-volume half of the standard, the part a supervisor cannot possibly check on every room, and make it continuous and consistent.

## RapidEye is the continuous brand-standard check

RapidEye is AI inspection for hotels and short-term rental operators, built to hold the physical standard in place between audits. It reads the housekeeping and turnover photos a team already captures, checks every room against standard, and flags missed cleaning, damage, missing items, and setup that is off, then routes each flag to a human for the final call. For a brand or a management group, that turns brand-standard compliance from a once-a-year score into a daily, comparable measure across every property, which is exactly the consistency a portfolio struggles to get from human spot-checks that vary by inspector and property.

Built by two Carnegie Mellon researchers on patented inspection technology, it plugs into the photo workflow a team already runs. The audit still certifies the standard a couple of times a year; RapidEye keeps it true the other 363 days.

## How to put AI into your QA program

**Keep the audit, fill the gap.** Do not drop LQA, Forbes, or your brand audit. Add AI to verify the physical standard on the days the auditor is not there.

**Point it at the photos you already take.** The fastest path is AI over your existing housekeeping and inspection photos, no new hardware and no new step for the floor.

**Use it for portfolio consistency.** If you run more than one property, the real prize is one comparable standard across all of them, every day, not a stack of annual reports that arrive months apart.

**Keep a human on every flag.** AI proposes, a person disposes. That is what keeps the program trusted and keeps service judgment where it belongs.

## Frequently asked questions

Hotels use AI to turn quality control from a periodic event into a continuous one. Traditional brand-standard auditing relies on anonymous human inspectors who visit roughly twice a year and score a property against hundreds of standards. AI reads the room and area photos a team captures during everyday checklists, checks them against those same standards, and flags deviations for a human, so the standard is verified on every room and every turnover rather than only on audit day. It does not replace the human judgment of service quality; it makes the physical and visual half of the standard continuous.

Far less often than guests assume. Leading Quality Assurance (LQA), one of the main luxury hospitality auditors, recommends a minimum of two anonymous assessments per year, and external mystery audits typically happen about once a year because they are expensive. That means a hotel's brand standards are independently verified across only a handful of inspector-nights a year, and self-policed the rest of the time. Closing that gap between audits is the main thing AI quality control does.

AI is strong on the physical and visual half of a standard: whether a bed is made to brand standard, amenities are placed correctly, brand-mandated items are present, the room is clean, and there is no damage. It reads this from photos in seconds, on every room. What it cannot judge is the human service experience, the warmth, anticipation, and emotional impact that audits like LQA and Forbes Travel Guide weight most heavily. The practical model is AI for the continuous physical check, humans for service and the final call.

A brand audit is an infrequent, comprehensive snapshot by an anonymous human inspector scoring hundreds of standards, including service. AI quality control is a continuous, narrower check of the physical and visual standards on every room and turnover, with a human reviewing each flag. They are complementary: the audit sets and certifies the standard a few times a year, and AI holds the physical side of that standard in place between audits, so the property is not only compliant on inspection day.

Yes, and consistency across properties is one of its strongest use cases. Because the AI applies the same standard to every room's photos regardless of which property or inspector captured them, it gives a brand or management group a single, comparable measure of physical-standard compliance across the portfolio, every day, rather than waiting for each property's annual audit. That is difficult to achieve with human spot-checks that vary by inspector and property.

## Sources

- [Leading Quality Assurance (LQA): Hotel Assessments](https://lqagroup.com/hotel-assessments/). Source for "over 1,000 luxury standards" (front of house 179, food and beverage 379, housekeeping 117), incognito assessment "under the guise of a regular guest," and the "minimum of 2 assessments annually" recommendation.
 https://lqagroup.com/hotel-assessments/

- [Forbes Travel Guide (overview)](https://en.wikipedia.org/wiki/Forbes_Travel_Guide). Source for anonymous, paid inspectors evaluating properties against proprietary standards for Five-Star, Four-Star, and Recommended ratings.
 https://en.wikipedia.org/wiki/Forbes_Travel_Guide

- [OpsAnalitica: Hotel Operations & AI Housekeeping Audit Software](https://www.opsanalitica.com/industries/hotel). Source for AI that "automatically audits every photo submitted during a room cleaning checklist" against brand standards (beds, amenities, safety) in sub-second time.
 https://www.opsanalitica.com/industries/hotel

- [GoAudits: Hotel Quality Assurance, A Comprehensive Guide](https://goaudits.com/blog/which-software-hotel-quality-assurance/). Context on brand-standard auditing (LQA, Forbes) and the move to digital, photo-based quality inspections.
 https://goaudits.com/blog/which-software-hotel-quality-assurance/